Intelligent mine management system based on Internet of Things

Abstract

The invention relates to the field of data processing of the Internet of Things, in particular to an intelligent mine management system based on the Internet of Things, and the system comprises a data collection module which is used for collecting power supply parameters of feature equipment; the data analysis module is connected with the data acquisition module and is used for determining the category of the characteristic fluctuation of the equipment when the single fluctuation is preliminarily judged to meet a preset characteristic fluctuation standard according to the fluctuation characteristic value; the data storage module is connected with the data analysis module; the control module is respectively connected with the data analysis module and the data storage module, and is used for determining a storage strategy of power supply parameters according to the category of the characteristic fluctuation of the equipment, determining an initial optimization strategy of the category of the characteristic fluctuation according to the equipment association degree, and outputting the initial optimization strategy; and an iteration strategy of the category of the characteristic fluctuation is determined according to the calling frequency of the characteristic fluctuation data, so that the working efficiency and the safety are improved.

Description

Technical Field

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

[0002] Smart mines are a new type of mine management model that takes the digitization and informatization of mines as its premise and foundation, and comprehensively uses advanced technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to actively perceive, automatically analyze, and quickly process mine production, occupational health and safety, technical support, and logistics. The Internet of Things technology deploys various sensors and smart terminals to collect data such as mine environment, equipment status, and production progress in real time, building a digital twin of mine operations and providing a basis for intelligent decision-making.

[0003] Chinese patent publication number: CN117319957B, discloses a remote management system for mining 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 the mining equipment and send the monitoring data of the mining equipment to the wireless relay node; the wireless relay node is used to send the monitoring data of the mining equipment to the data aggregation unmanned vehicle; the data aggregation unmanned vehicle is used to change its position using an adaptive mobile cycle, and is used to send the received monitoring data to the remote management center via satellite communication; the remote management center is used to remotely manage the mining equipment based on the monitoring data. It can be seen that the above technical solution lacks analysis and processing of equipment data, which makes it difficult for technicians to quickly determine the cause of the problem when the equipment fails due to the lack of historical data records and analysis. Summary of the invention

[0004] To this end, 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 in the prior art due to the lack of processing of voltage fluctuation data of mining equipment.

[0005] To achieve the above object, the present invention provides an intelligent mine management system based on the Internet of Things, comprising: 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 length, or, the category of the characteristic fluctuation of the equipment is determined according to the heating rate of the emergency power supply within a second preset time length under a single power switching, wherein the categories of the characteristic fluctuation of the equipment include one to four characteristic fluctuations; 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 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 equipment, to determine the initial optimization strategy of the category of the characteristic fluctuation according to the equipment association degree, and to determine the iterative strategy of the category of the characteristic fluctuation according to the frequency of retrieval of the characteristic fluctuation data.

[0006] Further, the data analysis module preliminarily determines that a single fluctuation meets the preset characteristic fluctuation standard in response to the condition that the fluctuation characteristic value of the device is greater than or equal to the first preset fluctuation threshold; determines the category of the characteristic fluctuation according to the frequency of the characteristic fluctuation within the first preset time length when the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold; and determines the category of the characteristic fluctuation according to the heating rate of the emergency power supply within the second preset time length under a single power switching when the fluctuation characteristic value is greater than or equal to the second preset fluctuation threshold; The fluctuation characteristic value is determined based on the duration and amplitude of a single fluctuation.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, the control module determines a storage strategy for power supply parameters based on the category of characteristic fluctuation under the 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.

[0010] Furthermore, 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.

[0011] Furthermore, the device association degree of a single device is determined based on the data exchange volume of the associated device and the number of associated devices.

[0012] Furthermore, the control module upgrades the category of the characteristic fluctuation in the local storage unit in response to the first retrieval frequency being greater than or equal to the first preset retrieval frequency value under the condition that the storage duration of the characteristic fluctuation data is less than the preset iteration duration.

[0013] Furthermore, the control module iterates the characteristic fluctuation data in the local storage unit in response to the second retrieval frequency being less than the second preset retrieval frequency value under the condition that the storage time of the characteristic fluctuation data exceeds the preset iteration time, and downgrades the category of the characteristic fluctuation.

[0014] Furthermore, 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.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention determines whether a single fluctuation meets the preset characteristic fluctuation standard based on the fluctuation characteristic value of the equipment, wherein if the fluctuation characteristic value is less than the first preset fluctuation threshold, then the single fluctuation is determined to be not in compliance with the preset characteristic fluctuation standard, and the voltage fluctuation parameters of the equipment continue to be monitored; if the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold, then the single fluctuation is determined to be in compliance with the preset characteristic fluctuation standard. The present invention determines the characteristic fluctuation by setting a preset fluctuation threshold and judging the single fluctuation, thereby being able to analyze the valid data in a targeted manner.

[0016] Furthermore, the present invention determines the category of characteristic fluctuations according to the characteristic fluctuation frequency value within the first preset time length, and divides the characteristic fluctuations into Class I characteristic fluctuations and Class II characteristic fluctuations, and determines the category of characteristic fluctuations according to the heating rate of the emergency power supply within the second preset time length under a single power switching, and divides the characteristic fluctuations into Class III characteristic fluctuations and Class IV characteristic fluctuations. By classifying the characteristic fluctuations, the efficiency of data management is improved.

[0017] Furthermore, the present invention determines the storage strategy of power supply parameters based on the category of characteristic fluctuations under a first preset condition, wherein the storage strategy for one category of characteristic fluctuation data is to delete the characteristic fluctuation data in the local storage unit; the storage strategy for the second category of characteristic fluctuation data is to compress the characteristic fluctuation data in the local storage unit and store them in the cloud storage unit, and delete the characteristic fluctuation data in the local storage unit; the storage strategy for the third category of characteristic fluctuation data is to store the data in the local storage unit in the cloud storage unit, and delete the characteristic fluctuation data in the local storage unit; the storage strategy for the fourth category of characteristic fluctuation data is to back up the characteristic fluctuation data in the local storage unit to the cloud storage unit. Different storage strategies are implemented for different categories to reduce the cost of data storage.

[0018] Furthermore, the present invention determines whether to upgrade the category of feature fluctuations based on device association, wherein if the device association is less than a preset association threshold, the category of feature fluctuations is not upgraded; if the device association is greater than or equal to the preset association threshold, the category of feature fluctuations is upgraded. The present invention introduces the concept of device association, determines the association between devices based on the data exchange volume and data transmission distance between devices, and improves the accuracy and efficiency of data processing.

[0019] Furthermore, the process of determining whether to upgrade the category of characteristic fluctuations in a local storage unit based on the first retrieval frequency under the condition that the storage time of characteristic fluctuation data is less than a preset iteration time in the present invention includes: comparing the first retrieval frequency with a first preset retrieval frequency value; if the first retrieval frequency is less than the first preset retrieval frequency value, not upgrading the category of characteristic fluctuations in the local storage unit; if the first retrieval frequency is greater than or equal to the first preset retrieval frequency value, upgrading the category of characteristic fluctuations in the local storage unit, thereby maintaining the timeliness and accuracy of the data and reducing the cost of data storage and maintenance.

[0020] Furthermore, the present invention determines whether to iterate the characteristic fluctuation data in the local storage unit according to the second retrieval frequency under the condition that the storage time of the characteristic fluctuation data reaches the preset iteration time, wherein if the second retrieval frequency is less than the second preset retrieval frequency value, the characteristic fluctuation data in the local storage unit is iterated, and the category of the characteristic fluctuation is downgraded; 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, which can efficiently utilize local storage and cloud storage resources to ensure timely storage and backup of key data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This 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; Figure 2 A flow chart of an embodiment of the present invention for determining whether a single fluctuation meets a preset characteristic fluctuation standard; Figure 3 A flow chart of determining the category of a characteristic fluctuation according to a characteristic fluctuation frequency value according to an embodiment of the present invention; Figure 4 The present invention is a flowchart of a storage strategy for determining power supply parameters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objects and advantages of the present invention more clearly understood, the present invention is 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.

[0023] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0024] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively a module connection diagram of an intelligent mine management system based on the Internet of Things according to an embodiment of the present invention; a flowchart of an embodiment of the present invention for determining whether a single fluctuation meets a preset characteristic fluctuation standard; a flowchart of an embodiment of the present invention for determining the category of a characteristic fluctuation according to a characteristic fluctuation frequency value; and a flowchart of an embodiment of the present invention for determining a storage strategy for power supply parameters.

[0025] An embodiment of the present invention provides an intelligent mine management system based on the Internet of Things, including: 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 length, or, the category of the characteristic fluctuation of the equipment is determined according to the heating rate of the emergency power supply within a second preset time length under a single power switching, wherein the categories of the characteristic fluctuation of the equipment include one to four characteristic fluctuations; 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 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 equipment, to determine the initial optimization strategy of the category of the characteristic fluctuation according to the equipment association degree, and to determine the iterative strategy of the category of the characteristic fluctuation according to the frequency of retrieval of the characteristic fluctuation data.

[0026] Specifically, the data analysis module determines whether a single fluctuation meets the preset characteristic fluctuation standard according to the fluctuation characteristic value of the device, wherein: If the fluctuation characteristic value is less than the first preset fluctuation threshold value of 0.56, it is determined that the single fluctuation does not meet the preset characteristic fluctuation standard, and the power supply parameters of the device are continuously monitored; If the fluctuation characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold 0.70, it is preliminarily determined that the 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 time length of 1 hour; If the fluctuation characteristic value is greater than or equal to the second preset fluctuation threshold, it is determined that the single fluctuation meets the preset characteristic fluctuation standard, and the type of characteristic fluctuation is determined according to the heating rate of the emergency power supply within the second preset time of 30 minutes under the single power switching; The process of jointly determining the fluctuation characteristic value according to the duration and amplitude of a single fluctuation includes: The ratio of the duration of a single fluctuation to the duration threshold is squared and then multiplied by the first evaluation coefficient to obtain a duration evaluation value, where the first evaluation coefficient is 0.52 and the duration threshold is 2s; The ratio of the absolute value of the amplitude of a single fluctuation to the amplitude threshold is squared and then multiplied by the second evaluation coefficient to obtain an amplitude evaluation value, wherein the second evaluation coefficient is 0.45 and the amplitude threshold is 10V; The duration evaluation value and the amplitude evaluation value are added together and recorded as the fluctuation characteristic value.

[0027] Specifically, the duration and amplitude of the single fluctuation can be measured by a digital multimeter, an oscilloscope or a voltage monitor, without limitation.

[0028] Specifically, 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 less than the first preset characteristic fluctuation frequency value 2, the characteristic fluctuation is not marked and is not stored in the local storage unit; 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 5, the characteristic fluctuation is marked as a type of characteristic fluctuation, and a type of characteristic fluctuation data is stored in the local storage unit, and 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.

[0029] Specifically, the characteristic fluctuation frequency value can be measured by a voltage fluctuation meter or a handheld power quality analyzer, and is not specifically limited.

[0030] Specifically, 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 of 5°C / h, 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.

[0031] Specifically, 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 of 168 hours.

[0032] Specifically, the control module determines whether to upgrade the category of feature fluctuation based on the device association degree, wherein: If the device correlation is less than the preset correlation threshold of 0.85, the category of feature fluctuation is not upgraded; If the device association degree is greater than or equal to the preset association degree threshold, the category of the characteristic fluctuation is upgraded.

[0033] Specifically, the process of determining the device association degree of a single device based on the data exchange volume of the associated devices and the number of associated devices 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.

[0034] Specifically, the process of the control module determining whether to upgrade the category of the characteristic fluctuation in the local storage unit according to the first retrieval frequency under the condition that the storage time of the characteristic fluctuation data is less than the preset iteration time includes: Compare the first access frequency with a first preset access frequency value of 5; If the first retrieval frequency is less than the first preset retrieval frequency value, the category of the characteristic fluctuation in the local storage unit is not upgraded; If the first retrieval frequency is greater than or equal to the first preset retrieval frequency value, the category of the characteristic fluctuation in the local storage unit is upgraded.

[0035] Specifically, the upgrade process is to increase the category of the characteristic fluctuation in the local storage unit by one level. The storage strategy of the power supply parameters can be changed through the upgrade process, so that this type of characteristic fluctuation data can be easily retrieved.

[0036] Specifically, the control module determines whether to iterate the characteristic fluctuation data in the local storage unit according to the second retrieval frequency under the condition that the storage time of the characteristic fluctuation data reaches the preset iteration time, wherein: If the second retrieval frequency is less than the second preset retrieval frequency value 10, iterating the characteristic fluctuation data in the local storage unit and downgrading the category of the characteristic fluctuation; 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.

[0037] Specifically, the downgrading process is to reduce the category of the characteristic fluctuation in the local storage unit by one level.

[0038] Specifically, when the storage time of the characteristic fluctuation data reaches the third preset time of 240 hours, the control module deletes the characteristic fluctuation data of the four types of characteristic fluctuations in the local storage unit.

[0039] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying 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 substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. 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 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 equipment, to determine the initial optimization strategy of the category of the characteristic fluctuation according to the equipment association degree, and to determine the iterative strategy of the category of the characteristic fluctuation according to the frequency of retrieval of the characteristic fluctuation data.

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 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.

6. The intelligent mine management system based on the Internet of Things according to claim 5 is characterized in that: 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.

7. The intelligent mine management system based on the Internet of Things according to claim 6 is characterized in that: The device association degree of a single device is determined based on the data exchange volume of the associated device and the number of associated devices.

8. The intelligent mine management system based on the Internet of Things according to claim 7 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.

9. The intelligent mine management system based on the Internet of Things according to claim 8 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.

10. The intelligent mine management system based on the Internet of Things according to claim 9 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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