An intelligent mine management system based on the Internet of Things

Abstract

The present invention relates to the field of Internet of Things data processing, and particularly to an intelligent mine management system based on the Internet of Things, including: a data acquisition module for collecting the power supply parameters of each characteristic device; a data analysis module connected to the data acquisition module for determining the category of the characteristic fluctuation of the device when initially determining that a single fluctuation conforms to a preset characteristic fluctuation standard according to the fluctuation characteristic value; a data storage module connected to the data analysis module; and a control module respectively connected to the data analysis module and the data storage module for determining a storage strategy for the power supply parameters according to the category of the characteristic fluctuation of the device, determining an initial optimization strategy for the category of the characteristic fluctuation according to the device correlation degree, and determining an iterative strategy for the category of the characteristic fluctuation according to the retrieval frequency of the characteristic fluctuation data, thereby improving work efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data processing, and particularly to an intelligent mine management system based on the Internet of Things. Background Art

[0002] A smart mine is premised on and based on mine digitization and informatization, and comprehensively applies advanced technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to actively sense, automatically analyze, and quickly process mine production, occupational health and safety, technical support, and logistics support. The Internet of Things technology collects data such as mine environment, equipment status, and production progress in real time by deploying various sensors and intelligent terminals, constructs a digital twin of mine operation, and provides a basis for intelligent decision-making.

[0003] Chinese Patent Publication No.: CN117319957B discloses a remote management system for mine equipment, including an acquisition device, a wireless relay node, a data aggregation unmanned vehicle, and a remote management center; the acquisition device is used to acquire the monitoring data of mine equipment and send the monitoring data of mine equipment to the wireless relay node; the wireless relay node is used to send the monitoring data of mine equipment to the data aggregation unmanned vehicle; the data aggregation unmanned vehicle is used to change its position with an adaptive moving period and send the received monitoring data to the remote management center by satellite communication; the remote management center is used to remotely manage mine equipment according to the monitoring data. It can be seen that the above technical solution lacks the analysis and processing of equipment data, resulting in technicians being difficult to quickly determine the cause of the problem due to the lack of historical data records and analysis when the equipment fails. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent mine management system based on the Internet of Things to overcome the problems of low mining efficiency and insufficient safety caused by the lack of processing of voltage fluctuation data of mining equipment in the prior art.

[0005] To achieve the above object, the present invention provides an intelligent mine management system based on the Internet of Things, including:

[0006] A data acquisition module, which is used to acquire the power supply parameters of each characteristic device, the characteristic device is a mine equipment with an emergency power supply, and the power supply parameters include the parameters of grid power supply and the parameters of emergency power supply;

[0007] A data analysis module, which is connected to the data acquisition module, is used to determine the fluctuation characteristic value of the device according to the parameters of the power grid power supply. When it is initially determined that a single fluctuation conforms to the preset characteristic fluctuation standard according to the fluctuation characteristic value, the category of the characteristic fluctuation of the device is determined according to the frequency of the characteristic fluctuation within the first preset duration, or, the category of the characteristic fluctuation of the device is determined according to the heating rate of the emergency power supply within the second preset duration under a single power supply switch, where the category of the characteristic fluctuation of the device includes the first type of characteristic fluctuation to the fourth type of characteristic fluctuation;

[0008] A data storage module, which is connected to the data analysis module, and the data storage module includes a local storage unit and a cloud storage unit;

[0009] A control module, which is respectively connected to the data analysis module and the data storage module, is used to determine the storage strategy of the power supply parameters according to the category of the characteristic fluctuation of the device, determine the initial optimization strategy of the category of the characteristic fluctuation according to the device correlation degree, and determine the iterative strategy of the category of the characteristic fluctuation according to the retrieval frequency of the characteristic fluctuation data.

[0010] Further, the data analysis module initially determines that a single fluctuation conforms to the preset characteristic fluctuation standard under the condition that the fluctuation characteristic value of the device is greater than or equal to the first preset fluctuation threshold; when the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, the category of the characteristic fluctuation is determined according to the frequency of the characteristic fluctuation within the first preset duration, and, when the fluctuation characteristic value is greater than or equal to the second preset fluctuation threshold, the category of the characteristic fluctuation is determined according to the heating rate of the emergency power supply within the second preset duration under a single power supply switch;

[0011] The fluctuation characteristic value is jointly determined according to the duration and amplitude of a single fluctuation.

[0012] Further, the data analysis module determines the category of the characteristic fluctuation according to the characteristic fluctuation frequency value within the first preset duration, where,

[0013] if the characteristic fluctuation frequency value is greater than or equal to the first preset characteristic fluctuation frequency value and less than the second preset characteristic fluctuation frequency value, the characteristic fluctuation is marked as the first type of characteristic fluctuation, and the first type of characteristic fluctuation data is stored in the local storage unit, and the first type of characteristic fluctuation data is the fluctuating power supply data under the first preset duration;

[0014] if the characteristic fluctuation frequency value is greater than or equal to the second preset characteristic fluctuation frequency value, the characteristic fluctuation is marked as the second type of characteristic fluctuation, and the second type of characteristic fluctuation data is stored in the local storage unit, and the second type of characteristic fluctuation data is all the power supply data under the first preset duration.

[0015] Further, the data analysis module determines the category of characteristic fluctuations according to the heating rate of the emergency power supply during the second preset duration under a single power supply switch, where,

[0016] If the heating rate is less than the preset heating rate, the characteristic fluctuations are marked as the third category of characteristic fluctuations, and the data of the third category of characteristic fluctuations are stored in the local storage unit. The data of the third category of characteristic fluctuations are the power supply data with fluctuations under the second preset duration;

[0017] If the heating rate is greater than or equal to the preset heating rate, the characteristic fluctuations are marked as the fourth category of characteristic fluctuations, and the data of the fourth category of characteristic fluctuations are stored in the local storage unit. The data of the fourth category of characteristic fluctuations are all the power supply data under the second preset duration.

[0018] Further, the control module determines the storage strategy of the power supply parameters based on the category of the characteristic fluctuations under the first preset condition, where,

[0019] If the characteristic fluctuations are the first category of characteristic fluctuations, the storage strategy of the power supply parameters for deleting the data of the characteristic fluctuations in the local storage unit is executed;

[0020] If the characteristic fluctuations are the second category of characteristic fluctuations, the storage strategy of compressing the data of the characteristic fluctuations in the local storage unit and storing them in the cloud storage unit and deleting the data of the characteristic fluctuations in the local storage unit is executed;

[0021] If the characteristic fluctuations are the third category of characteristic fluctuations, the storage strategy of storing the data of the characteristic fluctuations in the local storage unit in the cloud storage unit and deleting the data of the characteristic fluctuations in the local storage unit is executed;

[0022] If the characteristic fluctuations are the fourth category of characteristic fluctuations, the storage strategy of backing up the data of the characteristic fluctuations in the local storage unit to the cloud storage unit is executed;

[0023] The first preset condition is that the power supply parameters are stored in the local storage unit and the storage duration reaches the preset iteration duration.

[0024] Further, the control module upgrades the category of the characteristic fluctuations in response to the condition that the device correlation degree is greater than or equal to the preset correlation degree threshold.

[0025] Further, the device correlation degree of a single device is jointly determined according to the data exchange volume of the associated devices and the number of associated devices.

[0026] Further, when the storage duration of the feature fluctuation data is less than the preset iteration duration and in response to the condition that the first retrieval frequency is greater than or equal to the first preset retrieval frequency value, the control module upgrades the category of the feature fluctuation in the local storage unit.

[0027] Further, when the storage duration of the feature fluctuation data exceeds the preset iteration duration and in response to the condition that the second retrieval frequency is less than the second preset retrieval frequency value, the control module iterates the feature fluctuation data in the local storage unit and downgrades the category of the feature fluctuation.

[0028] Further, when the storage duration of the feature fluctuation data reaches the third preset duration, the control module deletes the feature fluctuation data of the four types of feature fluctuations in the local storage unit.

[0029] Compared with the prior art, the beneficial effect of the present invention is that the present invention determines whether a single fluctuation meets the preset feature fluctuation standard according to the fluctuation feature value of the device. Among them, if the fluctuation feature value is less than the first preset fluctuation threshold, it is determined that the single fluctuation does not meet the preset feature fluctuation standard, and the voltage fluctuation parameter of the device is continuously monitored; if the fluctuation feature value is greater than or equal to the first preset fluctuation threshold, it is determined that the single fluctuation meets the preset feature fluctuation standard. By setting the preset fluctuation threshold and determining the feature fluctuation by judging the single fluctuation, the effective data can be analyzed targeted.

[0030] Further, the present invention determines the category of the feature fluctuation according to the feature fluctuation frequency value within the first preset duration, classifies the feature fluctuation into the first-class feature fluctuation and the second-class feature fluctuation, and determines the category of the feature fluctuation according to the heating rate of the emergency power supply during the second preset duration under a single power supply switch, classifies the feature fluctuation into the third-class feature fluctuation and the fourth-class feature fluctuation, and improves the efficiency of data management by classifying the feature fluctuation.

[0031] Further, the present invention determines the storage strategy of the power supply parameter based on the category of the feature fluctuation under the first preset condition. Among them, the storage strategy of the first-class feature fluctuation data is to delete the feature fluctuation data in the local storage unit; the storage strategy of the second-class feature fluctuation data is to compress the feature fluctuation data in the local storage unit and store it in the cloud storage unit, and delete the feature fluctuation data in the local storage unit; the storage strategy of the third-class feature fluctuation data is to store the data in the local storage unit in the cloud storage unit, and delete the feature fluctuation data in the local storage unit; the storage strategy of the fourth-class feature fluctuation data is to back up the feature fluctuation data in the local storage unit to the cloud storage unit, and reduces the cost of data storage by adopting different storage strategies for different categories.

[0032] Further, the present invention determines whether to upgrade the category of feature fluctuations based on the device correlation. Specifically, if the device correlation is less than a preset correlation threshold, the category of feature fluctuations will not be upgraded; if the device correlation is greater than or equal to the preset correlation threshold, the category of feature fluctuations will be upgraded. The present invention introduces the concept of device correlation and determines the correlation between devices based on the data exchange volume and data transmission distance between devices, improving the accuracy and efficiency of data processing.

[0033] Further, the process of determining whether to upgrade the category of feature fluctuations in the local storage unit according to the first retrieval frequency under the condition that the storage duration of the feature fluctuation data is less than the preset iteration duration includes: comparing the first retrieval frequency with the first preset retrieval frequency value; if the first retrieval frequency is less than the first preset retrieval frequency value, the category of feature fluctuations in the local storage unit will not be upgraded; if the first retrieval frequency is greater than or equal to the first preset retrieval frequency value, the category of feature fluctuations in the local storage unit will be upgraded, maintaining the timeliness and accuracy of the data while reducing the cost of data storage and maintenance.

[0034] Further, under the condition that the storage duration of the feature fluctuation data reaches the preset iteration duration, the present invention determines whether to iterate the feature fluctuation data in the local storage unit according to the second retrieval frequency. Specifically, if the second retrieval frequency is less than the second preset retrieval frequency value, the feature fluctuation data in the local storage unit will be iterated, and the category of feature fluctuations will be downgraded; if the second retrieval frequency is greater than or equal to the second preset retrieval frequency value, the feature fluctuation data in the local storage unit will not be iterated, which can efficiently utilize local storage and cloud storage resources to ensure the timely storage and backup of key data. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of module connections of an intelligent mine management system based on the Internet of Things according to an embodiment of the present invention;

[0036] Figure 2 It is a flowchart for determining whether a single fluctuation meets the preset feature fluctuation standard according to an embodiment of the present invention;

[0037] Figure 3 It is a flowchart for determining the category of feature fluctuations according to the feature fluctuation frequency value according to an embodiment of the present invention;

[0038] Figure 4 It is a flowchart for determining the storage strategy of power supply parameters according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to make the objectives and advantages of the present invention more clear and understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0041] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 as shown, which are respectively the schematic diagram of module connection of an intelligent mine management system based on the Internet of Things according to an embodiment of the present invention; the flowchart for determining whether a single fluctuation conforms to a preset characteristic fluctuation standard according to an embodiment of the present invention; the flowchart for determining the category of characteristic fluctuations according to the characteristic fluctuation frequency value according to an embodiment of the present invention; the flowchart for determining the storage strategy of power supply parameters according to an embodiment of the present invention.

[0042] An intelligent mine management system based on the Internet of Things according to an embodiment of the present invention includes:

[0043] A data acquisition module, which is used to acquire the power supply parameters of each characteristic device, the characteristic device is a mine device with an emergency power supply, and the power supply parameters include the parameters of grid power supply and the parameters of emergency power supply;

[0044] A data analysis module, which is connected to the data acquisition module, and is used to determine the fluctuation characteristic value of the device according to the parameters of grid power supply, and when it is initially determined that a single fluctuation conforms to the preset characteristic fluctuation standard according to the fluctuation characteristic value, determine the category of the characteristic fluctuation of the device according to the frequency of characteristic fluctuations within a first preset time period, or determine the category of the characteristic fluctuation of the device according to the heating rate of the emergency power supply within a second preset time period under a single power supply switch, wherein the category of the characteristic fluctuation of the device includes the first type of characteristic fluctuation to the fourth type of characteristic fluctuation;

[0045] A data storage module, which is connected to the data analysis module, and the data storage module includes a local storage unit and a cloud storage unit;

[0046] A control module, which is respectively connected to the data analysis module and the data storage module, and is used to determine the storage strategy of power supply parameters according to the category of the characteristic fluctuation of the device, determine the initial optimization strategy of the category of the characteristic fluctuation according to the device correlation degree, and determine the iterative strategy of the category of the characteristic fluctuation according to the retrieval frequency of the characteristic fluctuation data.

[0047] Specifically, the data analysis module determines whether a single fluctuation conforms to the preset characteristic fluctuation standard according to the fluctuation characteristic value of the device, wherein,

[0048] If the fluctuation characteristic value is less than the first preset fluctuation threshold of 0.56, it is determined that a single fluctuation does not meet the preset characteristic fluctuation standard, and the power supply parameters of the device are continuously monitored;

[0049] If the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold of 0.70, it is preliminarily determined that a single fluctuation meets the preset characteristic fluctuation standard, and the category of the characteristic fluctuation is determined according to the frequency of the characteristic fluctuation within the first preset duration of 1 h;

[0050] If the fluctuation characteristic value is greater than or equal to the second preset fluctuation threshold, it is determined that a single fluctuation meets the preset characteristic fluctuation standard, and the category of the characteristic fluctuation is determined according to the heating rate of the emergency power supply during the second preset duration of 30 min under a single power supply switch;

[0051] The process of jointly determining the fluctuation characteristic value according to the duration and amplitude of a single fluctuation includes:

[0052] Square the ratio of the duration of a single fluctuation to the duration threshold and then multiply it by the first evaluation coefficient to obtain the duration evaluation value, where the first evaluation coefficient is 0.52 and the duration threshold is 2 s;

[0053] Square the ratio of the absolute value of the amplitude of a single fluctuation to the amplitude threshold and then multiply it by the second evaluation coefficient to obtain the amplitude evaluation value, where the second evaluation coefficient is 0.45 and the amplitude threshold is 10 V;

[0054] Add the duration evaluation value and the amplitude evaluation value and record it as the fluctuation characteristic value.

[0055] Specifically, the duration and amplitude of the single fluctuation can be measured by a digital multimeter, an oscilloscope or a voltage monitor, and no specific limitation is made.

[0056] Specifically, the data analysis module determines the category of the characteristic fluctuation according to the characteristic fluctuation frequency value within the first preset duration, where,

[0057] If the characteristic fluctuation frequency value is less than the first preset characteristic fluctuation frequency value of 2, no mark is made on the characteristic fluctuation and it is not stored in the local storage unit;

[0058] If the characteristic fluctuation frequency value is greater than or equal to the first preset characteristic fluctuation frequency value and less than the second preset characteristic fluctuation frequency value of 5, the characteristic fluctuation is marked as a first-class characteristic fluctuation, and the first-class characteristic fluctuation data is stored in the local storage unit, and the first-class characteristic fluctuation data is the fluctuation power supply data under the first preset duration;

[0059] If the characteristic fluctuation frequency value is greater than or equal to the second preset characteristic fluctuation frequency value, mark the characteristic fluctuation as a type-two characteristic fluctuation, and store the type-two characteristic fluctuation data in the local storage unit. The type-two characteristic fluctuation data is all the power supply data under the first preset duration.

[0060] Specifically, the characteristic fluctuation frequency value can be measured by a voltage fluctuation meter or a handheld power quality analyzer, and no specific limitation is made.

[0061] Specifically, the data analysis module determines the category of the characteristic fluctuation according to the heating rate of the emergency power supply during the second preset duration under a single power supply switch. Among them,

[0062] If the heating rate is less than the preset heating rate of 5 °C / h, mark the characteristic fluctuation as a type-three characteristic fluctuation, and store the type-three characteristic fluctuation data in the local storage unit. The type-three characteristic fluctuation data is the fluctuating power supply data under the second preset duration;

[0063] If the heating rate is greater than or equal to the preset heating rate, mark the characteristic fluctuation as a type-four characteristic fluctuation, and store the type-four characteristic fluctuation data in the local storage unit. The type-four characteristic fluctuation data is all the power supply data under the second preset duration.

[0064] Specifically, the control module determines the storage strategy of the power supply parameters based on the category of the characteristic fluctuation under the first preset condition. Among them,

[0065] If the characteristic fluctuation is a type-one characteristic fluctuation, execute the storage strategy of deleting the power supply parameters of the characteristic fluctuation data in the local storage unit;

[0066] If the characteristic fluctuation is a type-two characteristic fluctuation, execute the storage strategy of compressing the characteristic fluctuation data in the local storage unit and storing it in the cloud storage unit, and deleting the power supply parameters of the characteristic fluctuation data in the local storage unit;

[0067] If the characteristic fluctuation is a type-three characteristic fluctuation, execute the storage strategy of storing the characteristic fluctuation data in the local storage unit in the cloud storage unit, and deleting the power supply parameters of the characteristic fluctuation data in the local storage unit;

[0068] If the characteristic fluctuation is a type-four characteristic fluctuation, execute the storage strategy of backing up the characteristic fluctuation data in the local storage unit to the cloud storage unit;

[0069] The first preset condition is that the power supply parameters are stored in the local storage unit and the storage duration reaches the preset iteration duration of 168 h.

[0070] Specifically, the control module determines whether to upgrade the category of feature fluctuations based on the device association degree, where

[0071] if the device association degree is less than the preset association degree threshold of 0.85, the category of feature fluctuations is not upgraded;

[0072] if the device association degree is greater than or equal to the preset association degree threshold, the category of feature fluctuations is upgraded.

[0073] Specifically, the process of determining the device association degree of a single device based on the data exchange volume of associated devices and the number of associated devices includes:

[0074] Taking the square root of the ratio of the data exchange volume to the data exchange volume threshold and then multiplying by the third evaluation coefficient to obtain the data exchange volume evaluation value, where the third evaluation coefficient is 0.55 and the data exchange volume threshold is 1×10 5 GB;

[0075] Taking the square root of the ratio of the number of associated devices to the number of associated device thresholds and then multiplying by the fourth evaluation coefficient to obtain the number of associated device evaluation values, where the fourth evaluation coefficient is 0.42 and the number of associated device thresholds is 3;

[0076] Adding the data exchange volume evaluation value and the number of associated device evaluation values and recording it as the device association degree.

[0077] Specifically, the process by which the control module determines whether to upgrade the category of feature fluctuations in the local storage unit according to the first retrieval frequency under the condition that the storage duration of the feature fluctuation data is less than the preset iteration duration includes:

[0078] Comparing the first retrieval frequency with the first preset retrieval frequency value of 5;

[0079] If the first retrieval frequency is less than the first preset retrieval frequency value, the category of feature fluctuations in the local storage unit is not upgraded;

[0080] If the first retrieval frequency is greater than or equal to the first preset retrieval frequency value, the category of feature fluctuations in the local storage unit is upgraded.

[0081] Specifically, the upgrade process is to raise the category of feature fluctuations in the local storage unit by one level. Through the upgrade process, the storage strategy of the power supply parameters can be changed to facilitate the retrieval of this type of feature fluctuation data.

[0082] Specifically, the control module determines whether to iterate the feature fluctuation data in the local storage unit according to the second retrieval frequency under the condition that the storage duration of the feature fluctuation data reaches the preset iteration duration, where

[0083] If the second retrieval frequency is less than the second preset retrieval frequency value of 10, the characteristic fluctuation data in the local storage unit is iterated, and the category of the characteristic fluctuation is downgraded.

[0084] If the second retrieval frequency is greater than or equal to the second preset retrieval frequency value, the characteristic fluctuation data in the local storage unit is not iterated.

[0085] Specifically, the downgrading process is to lower the category of the characteristic fluctuation in the local storage unit by one level.

[0086] Specifically, when the storage duration of the characteristic fluctuation data reaches the third preset duration of 240h, the control module deletes the characteristic fluctuation data of the four types of characteristic fluctuations in the local storage unit.

[0087] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or replacements to the relevant technical features, and the technical solutions after these changes or replacements will all fall within the protection scope of the present invention.

[0088] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent mine management system based on the Internet of Things, characterized in that: include: A data acquisition module, which is used to collect power supply parameters of each characteristic device, wherein the characteristic device is a mining device with an emergency power supply, and the power supply parameters include parameters of power supply of the power grid and parameters of power supply of the emergency power supply; A data analysis module, which is connected to the data acquisition module, is used to determine the fluctuation characteristic value of the equipment according to the parameters of the power supply of the power grid, and when it is preliminarily determined that a single fluctuation meets the preset characteristic fluctuation standard according to the fluctuation characteristic value, the category of the characteristic fluctuation of the equipment is determined according to the frequency of the characteristic fluctuation within a first preset time period, or, after completing the switching of the emergency power supply and determining the category of the characteristic fluctuation of the equipment according to the heating rate of the emergency power supply within a second preset time period under the single switching of the power supply, wherein the categories of the characteristic fluctuation of the equipment include one category of characteristic fluctuation to four categories of characteristic fluctuation; A data storage module, which is connected to the data analysis module, and the data storage module includes a local storage unit and a cloud storage unit; A control module, which is connected to the data analysis module and the data storage module respectively, and is used to determine a storage strategy of power supply parameters according to the category of characteristic fluctuations of the device, determine an initial optimization strategy of the category of characteristic fluctuations according to the device association degree, and determine an iterative strategy of the category of characteristic fluctuations according to the frequency of retrieval of characteristic fluctuation data; The control module determines a storage strategy for power supply parameters based on the category of characteristic fluctuation under a first preset condition, wherein: If the characteristic fluctuation is a type of characteristic fluctuation, executing a storage strategy of the power supply parameter of deleting the characteristic fluctuation data in the local storage unit; If the characteristic fluctuation is a second-class characteristic fluctuation, a storage strategy of compressing the characteristic fluctuation data in the local storage unit and storing it in the cloud storage unit, and deleting the storage strategy of the power supply parameters of the characteristic fluctuation data in the local storage unit is executed; If the characteristic fluctuation is a third type of characteristic fluctuation, executing a storage strategy of the power supply parameter of storing the characteristic fluctuation data in the local storage unit to the cloud storage unit and deleting the characteristic fluctuation data in the local storage unit; If the characteristic fluctuation is a fourth type of characteristic fluctuation, a storage strategy of backing up the characteristic fluctuation data in the local storage unit to the power supply parameter of the cloud storage unit is executed; The first preset condition is that the power supply parameters complete the storage of the local storage unit and the storage time reaches the preset iteration time; The control module upgrades the category of the characteristic fluctuation in response to the condition that the device association degree is greater than or equal to a preset association degree threshold; The device association degree of a single device is determined based on the data exchange volume of the associated devices and the number of associated devices. The determination process includes: The square root of the ratio of the data exchange volume to the data exchange volume threshold is multiplied by the third evaluation coefficient to obtain the data exchange volume evaluation value, where the third evaluation coefficient is 0.55 and the data exchange volume threshold is 1×10 5 GB; The square root of the ratio of the number of associated devices to the threshold of the number of associated devices is multiplied by the fourth evaluation coefficient to obtain an evaluation value of the number of associated devices, where the fourth evaluation coefficient is 0.42 and the threshold of the number of associated devices is 3; The sum of the data exchange volume evaluation value and the associated device quantity evaluation value is recorded as the device association degree.

2. The intelligent mine management system based on the Internet of Things according to claim 1 is characterized in that: The data analysis module preliminarily determines that a single fluctuation meets a preset characteristic fluctuation standard in response to the condition that the fluctuation characteristic value of the device is greater than or equal to a first preset fluctuation threshold; When the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, the category of the characteristic fluctuation is determined according to the frequency of the characteristic fluctuation within the first preset time length, and when the fluctuation characteristic value is greater than or equal to the second preset fluctuation threshold, the category of the characteristic fluctuation is determined according to the heating rate of the emergency power supply within the second preset time length under a single power switching; The fluctuation characteristic value is determined based on the duration and amplitude of a single fluctuation.

3. The intelligent mine management system based on the Internet of Things according to claim 2 is characterized in that: The data analysis module determines the category of the characteristic fluctuation according to the characteristic fluctuation frequency value within the first preset time length, wherein: If the characteristic fluctuation frequency value is greater than or equal to the first preset characteristic fluctuation frequency value and less than the second preset characteristic fluctuation frequency value, the characteristic fluctuation is marked as a type of characteristic fluctuation, and the type of characteristic fluctuation data is stored in the local storage unit, wherein the type of characteristic fluctuation data is the fluctuation power supply data under the first preset duration; If the characteristic fluctuation frequency value is greater than or equal to the second preset characteristic fluctuation frequency value, the characteristic fluctuation is marked as Class II characteristic fluctuation, and Class II characteristic fluctuation data is stored in the local storage unit, where the Class II characteristic fluctuation data is all power supply data under the first preset duration.

4. The intelligent mine management system based on the Internet of Things according to claim 3 is characterized in that: The data analysis module determines the category of characteristic fluctuation according to the temperature rise rate of the emergency power supply within the second preset time period under a single power switching, wherein: If the heating rate is less than the preset heating rate, the characteristic fluctuation is marked as the third type of characteristic fluctuation, and the three types of characteristic fluctuation data are stored in the local storage unit, where the three types of characteristic fluctuation data are the fluctuation power supply data under the second preset time length; If the heating rate is greater than or equal to the preset heating rate, the characteristic fluctuation is marked as four types of characteristic fluctuations, and the four types of characteristic fluctuation data are stored in the local storage unit, and the four types of characteristic fluctuation data are all power supply data under the second preset time length.

5. The intelligent mine management system based on the Internet of Things according to claim 4 is characterized in that: The control module upgrades the category of the characteristic fluctuation in the local storage unit in response to a first retrieval frequency being greater than or equal to a first preset retrieval frequency value under the condition that the storage duration of the characteristic fluctuation data is less than the preset iteration duration.

6. The intelligent mine management system based on the Internet of Things according to claim 5 is characterized in that: The control module iterates the characteristic fluctuation data in the local storage unit under the condition that the storage time of the characteristic fluctuation data exceeds the preset iteration time and in response to the second retrieval frequency being less than the second preset retrieval frequency value, and downgrades the category of the characteristic fluctuation.

7. The intelligent mine management system based on the Internet of Things according to claim 6 is characterized in that: When the storage time of the characteristic fluctuation data reaches a third preset time, the control module deletes the characteristic fluctuation data of the four types of characteristic fluctuations in the local storage unit.

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