Coal mine energy fine management system based on Internet of Things

Abstract

The invention relates to the technical field of energy management, and discloses a coal mine energy fine management system based on the Internet of Things. The system uses a sensor network to collect coal mine energy related data through a data collection module, a data transmission and storage module transmits the collected data to a cloud server, a data analysis module analyzes the collected data, and an energy consumption monitoring and optimizing module calculates related values according to the analyzed data to monitor the energy consumption condition in real time. The early warning module calculates a coal mine energy management abnormal index by calling real-time data obtained by the data analysis module and the energy consumption monitoring module, compares the coal mine energy management abnormal index with a coal mine energy management normal threshold set by the system, monitors and finds abnormal conditions in real time, and automatically gives an alarm to inform a manager. And the user interface module is used for summarizing real-time data and historical data in all the modules and providing an interactive interface for management personnel to carry out data query, analysis result viewing and decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a coal mine energy refined management system based on the Internet of Things. Background Art

[0002] Coal mine energy is a key factor in the operation of the coal mining industry. It plays an important role in ensuring production efficiency, reducing production costs and achieving sustainable development. The mining, processing and transportation of coal all require a large amount of energy. Whether it is used for equipment operation or ventilation and drainage of mines, a stable energy supply is the basis for ensuring the continuity and efficiency of coal mine production. The coal mining industry is often accompanied by carbon emissions and other environmental pollution in the process of energy use. Therefore, improving energy efficiency, reducing unnecessary energy consumption, and improving the energy structure are crucial to reducing the impact on the environment. At the same time, effective energy management can help companies better fulfill their environmental responsibilities and enhance their social image and market competitiveness.

[0003] Traditional coal energy management often relies on manual records and manual audits, resulting in inefficient data collection and high error rates. At the same time, it fails to achieve real-time monitoring of energy consumption, making it difficult to promptly detect and resolve energy consumption anomalies and equipment failures, thus affecting the accuracy of decision-making. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a coal mine energy refined management system based on the Internet of Things, which has the function of using a sensor network to collect coal mine temperature, humidity, gas concentration and coal mine equipment operation status information through a data acquisition module; a data transmission and storage module regularly transmits the above-collected data to a cloud server through a wireless network and provides regular security data backup and redundant storage; a data analysis module receives all historical data and real-time data saved by the data transmission and storage module, and calculates the gas concentration change trend Mkqs, the temperature change rate Wdbh, the humidity change rate Mksb and the coal mine energy equipment failure rate Mngz in the coal mine according to the above data; an energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh, the coal mine equipment utilization efficiency Mlxl, the various energy use ratios Gnbl and the energy efficiency index N according to the data obtained from the data analysis module. xzb, monitors energy consumption in real time according to the above values, and compares and analyzes it with the historical coal mine energy data stored in the system to generate energy efficiency evaluation and optimization suggestions. The early warning module obtains real-time data from the data analysis module and the energy consumption monitoring module, calculates the coal mine energy management abnormality index Mkyb and compares it with the normal threshold value Zcyz of coal mine energy management set by the system, monitors and discovers abnormal situations in real time, and automatically issues an alarm to notify management personnel. The user interface module summarizes the real-time data and historical data in all the above modules, and provides an interactive interface for management personnel to query data, view analysis results and make decisions. Through intelligent and refined management, it avoids manual management errors and improves the efficiency of data collection. At the same time, it realizes real-time monitoring of energy consumption, promptly discovers and solves energy consumption abnormalities and equipment failures, improves the accuracy of management personnel's decision-making, and solves the above problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a coal mine energy refined management system based on the Internet of Things, comprising a data acquisition module, a data transmission and storage module, a data analysis module, an energy consumption monitoring and optimization module, an early warning module and a user interface module;

[0008] The data acquisition module collects the coal mine temperature, humidity, gas concentration and coal mine equipment operation status information through the sensor network, and sends the above collected data to the data transmission and storage module after preliminary cleaning;

[0009] The data transmission and storage module regularly transmits the collected data to the cloud server via a wireless network and provides regular secure data backup and redundant storage;

[0010] The data analysis module receives all historical data and real-time data saved by the data transmission and storage module, calculates the gas concentration change trend Mkqs, the temperature change rate Wdbh, the humidity change rate Mksb and the coal mine energy equipment failure rate Mngz in the coal mine according to the above data, and transmits the above collected data and calculated values ​​to the energy consumption monitoring and optimization module;

[0011] The energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh, coal mine equipment utilization efficiency Mlxl, various energy use ratios Gnbl and energy efficiency index Nxzb based on the data obtained from the data analysis module, monitors the energy consumption in real time based on the above values, and compares and analyzes the coal mine energy historical data stored in the system to generate energy efficiency evaluation and optimization suggestions;

[0012] The early warning module obtains real-time data from the data analysis module and the energy consumption monitoring module, calculates the coal mine energy management abnormality index Mkyb and compares it with the coal mine energy management normal threshold Zcyz set by the system, monitors and finds abnormal situations in real time, and automatically issues an alarm to notify the management personnel;

[0013] The user interface module summarizes the real-time data and historical data in all the above modules, and provides an interactive interface for managers to query data, view analysis results and support decision-making.

[0014] Preferably, the data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the gas concentration change trend Mkqs in the coal mine based on the above data. The calculation formula is as follows:

[0015] Mkqs=k*t+b+∈

[0016] In the formula, Mkqs represents the trend of gas concentration change in the coal mine, k represents the slope of concentration change, t represents the change time, b represents the intercept, and ∈ represents the white noise term of normal distribution.

[0017] Preferably, the data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the temperature change rate Wdbh in the coal mine based on the above data. The calculation formula is as follows:

[0018]

[0019] In the formula, Wdbh represents the temperature change rate in the coal mine, T t represents the temperature at time t, T t-1 represents the temperature within time t-1, and Δt represents the time interval.

[0020] Preferably, the data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the humidity change rate Mksb in the coal mine according to the above data. The calculation formula is as follows:

[0021]

[0022] In the formula, Mksb represents the rate of change of humidity in the coal mine, n represents the number of time points involved in the calculation, starting from i=1, Ht i Represents the humidity value collected at time point i, Ht i-1 represents the humidity value collected at time point i-1, w i Represents the weight of time point i, which is assigned by the system based on the reliability of historical data and the importance of the collection period.

[0023] Preferably, the data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the failure rate Mngz of coal mine energy equipment based on the above data. The calculation formula is as follows:

[0024]

[0025] In the formula, Mngz represents the failure rate of coal mine energy equipment, N f represents the number of failures recorded during the observation period, T r Indicates the total operating time of the device.

[0026] Preferably, the energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh according to the data obtained from the data analysis module, and the calculation formula is as follows:

[0027]

[0028] In the formula, Dcnh represents the energy consumption per unit product, Ex t represents the total energy consumption within time t, and Px represents the total product output within time t.

[0029] Preferably, the energy consumption monitoring and optimization module calculates the coal mine equipment utilization efficiency Mlxl according to the data obtained from the data analysis module, and the calculation formula is as follows:

[0030]

[0031] In the formula, Mlxl represents the utilization efficiency of coal mine equipment, Sbsj represents the actual operating time of the equipment, and Sbky represents the available time of the equipment.

[0032] Preferably, the energy consumption monitoring and optimization module calculates the usage ratio of various energy sources Gnbl according to the data obtained from the data analysis module, and the calculation formula is as follows:

[0033]

[0034] In the formula, Gnbl represents the ratio of various energy usage, EN j represents the usage of the jth type of energy, EN total represents the total usage of all energy sources, and p represents the number of energy types.

[0035] Preferably, the energy consumption monitoring and optimization module calculates the energy efficiency index Nxzb according to the data obtained from the data analysis module, and the calculation formula is as follows:

[0036]

[0037] In the formula, Nxzb represents the energy efficiency index, Outp y Inpt represents the output of the yth product. y represents the energy consumption of the yth category of products, and q represents the number of product types.

[0038] Preferably, the early warning module calculates the coal mine energy management abnormality index Mkyb by retrieving the real-time data obtained by the data analysis module and the energy consumption monitoring module. The calculation formula is as follows:

[0039]

[0040] In the formula, Mkyb represents the abnormal index of coal mine energy management, Nhjs m represents the actual energy consumption in the mth time period, Yqxh m It represents the expected energy consumption in the mth time period based on historical data, e represents the number of observed time periods, and Zcyz represents the normal threshold of coal mine energy management set by the system. This indicator measures abnormal situations by comparing the difference between actual energy consumption and expected energy consumption. When the difference exceeds the set threshold, it is considered that abnormal energy use has occurred in this time period.

[0041] Compared with the prior art, the present invention provides a coal mine energy refined management system based on the Internet of Things, which has the following beneficial effects:

[0042] The present invention uses a sensor network to collect coal mine temperature, humidity, gas concentration and coal mine equipment operating status information through a data acquisition module. The data transmission and storage module regularly transmits the above-collected data to a cloud server through a wireless network and provides regular security data backup and redundant storage. The data analysis module receives all historical data and real-time data saved by the data transmission and storage module, and calculates the gas concentration change trend Mkqs, the temperature change rate Wdbh, the humidity change rate Mksb and the coal mine energy equipment failure rate Mngz in the coal mine according to the above data. The energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh, the coal mine equipment utilization efficiency Mlxl, the various energy use ratios Gnbl and the energy efficiency index Nxzb according to the data obtained from the data analysis module. Monitor energy consumption and compare and analyze it with the historical coal mine energy data stored in the system to generate energy efficiency evaluation and optimization suggestions. The early warning module obtains real-time data from the data analysis module and the energy consumption monitoring module, calculates the coal mine energy management abnormality index Mkyb and compares it with the normal threshold value Zcyz of coal mine energy management set by the system, monitors and discovers abnormal situations in real time, and automatically issues an alarm to notify management personnel. The user interface module summarizes the real-time data and historical data in all the above modules, and provides an interactive interface for management personnel to query data, view analysis results and make decisions. Through intelligent and refined management, manual management errors are avoided, the efficiency of data collection is improved, and real-time monitoring of energy consumption is achieved. Abnormal energy consumption and equipment failures are discovered and resolved in a timely manner, which improves the accuracy of management personnel's decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Traditional coal energy management often relies on manual records and manual audits, resulting in low efficiency and high error rate in data collection. At the same time, it fails to achieve real-time monitoring of energy consumption, making it difficult to timely discover and solve energy consumption anomalies or equipment failures, thus affecting the accuracy of decision-making. Therefore, a coal mine energy refined management system based on the Internet of Things is proposed. Please refer to Figure 1,The system includes a data acquisition module, a data transmission and storage module, a data analysis module, an energy consumption monitoring and optimization module, an early warning module and a user interface module;

[0046] The data acquisition module first relies on a series of high-performance sensors. The distribution of these sensors in coal mines is usually achieved through wireless sensor networks. The composition of the sensor network includes:

[0047] Temperature sensor: monitors heat changes inside coal mines to ensure that ambient temperature remains within a safe range and prevent safety hazards caused by excessive temperature;

[0048] Humidity sensor: Real-time detection of humidity levels in the mine to prevent geological disasters or equipment corrosion caused by excessive moisture;

[0049] Gas sensors: These may include a variety of gas detectors, such as methane, carbon dioxide, and carbon monoxide sensors. These sensors can continuously monitor the concentration of harmful gases in coal mines to ensure the safety of workers and issue alarms in a timely manner;

[0050] Equipment status sensor: monitors the operating status of various equipment in the mine, including motors, conveyors, compressors, etc., and collects equipment working efficiency, fault information, and maintenance status;

[0051] After the sensor completes data collection, the data collection module cleans the collected data, including:

[0052] Data filtering: removes noise data caused by sensor errors, interference or failures to ensure that the collected data has high accuracy and reliability;

[0053] Missing value processing: For missing data caused by signal loss or sensor failure during the acquisition process, the system will use interpolation and other methods to fill in the missing values ​​to maintain the integrity of the data set;

[0054] Data standardization and normalization: standardize the collected data to make it conform to a unified dimension and format to facilitate subsequent data analysis. The cleaned data will be sent to the data transmission and storage module;

[0055] The data transmission and storage module uses wireless communication technology to send the cleaned data to the cloud server in real time. This process requires an efficient and stable data transmission channel to ensure effective data transmission even in the complex environment inside the coal mine. The collected data is usually stored in the database, supporting the storage of structured and unstructured data. The storage solution uses the relational database MySQL or the non-relational database MongoDB, and is optimized according to the frequency and scale of data generation to facilitate subsequent query and retrieval. In the process of data transmission and storage, data encryption and access control mechanisms are implemented to ensure that sensitive data is not illegally accessed or tampered with. At the same time, data backup is performed regularly to prevent the impact caused by data loss;

[0056] The data analysis module receives all historical data and real-time data saved by the data transmission and storage module, and calculates the gas concentration change trend Mkqs, the temperature change rate Wdbh, the humidity change rate Mksb and the coal mine energy equipment failure rate Mngz in the coal mine based on the above data, where:

[0057] The calculation formula for the trend of gas concentration change in coal mines is as follows:

[0058] Mkqs=k*t+b+∈

[0059] By real-time monitoring of changes in gas concentration in coal mines, the system can automatically identify abnormal gas concentrations. For example, an increase in methane concentration may lead to explosion risks. Timely warnings can help managers take emergency measures to ensure the safety of the working environment and protect the lives of miners. In the formula, Mkqs represents the trend of gas concentration changes in coal mines, k represents the slope of concentration changes, t represents the change time, b represents the intercept, and ε represents the white noise term of the normal distribution. Trend analysis can help identify potential high-risk areas and provide a basis for subsequent safety planning and risk assessment, thereby reducing the probability of accidents.

[0060] The calculation formula of the temperature change rate in the coal mine is as follows:

[0061]

[0062] Monitoring the temperature change rate can help timely identify abnormal high temperatures inside coal mines, warn of potential fire risks early, and ensure production safety. In the formula, Wdbh represents the temperature change rate inside the coal mine, T t represents the temperature at time t, T t-1 It represents the temperature within time t-1, and Δt represents the time interval. By analyzing the temperature change rate, the operating efficiency of the ventilation equipment can be optimized, unnecessary energy consumption can be reduced, and better energy management can be achieved;

[0063] The calculation formula of humidity change rate in coal mine is as follows:

[0064]

[0065] A high humidity environment may cause the coal seam to become damp, increasing the risk of spontaneous combustion. By monitoring the humidity change rate and taking timely measures to maintain an appropriate humidity level, it helps prevent fire and explosion incidents. In the formula, Mksb represents the humidity change rate in the coal mine, n represents the number of time points involved in the calculation, starting from i=1, and Ht i Represents the humidity value collected at time point i, Ht i-1 represents the humidity value collected at time point i-1, w i Represents the weight of time point i. This weight is assigned by the system based on the reliability of historical data and the importance of the collection period. Humidity changes have a greater impact on mine equipment. Too high or too low humidity can cause equipment failure or accelerated wear. Monitoring the humidity change rate can help managers take maintenance measures in a timely manner and extend the service life of equipment.

[0066] The calculation formula for the failure rate of coal mine energy equipment is as follows:

[0067]

[0068] By analyzing the failure rate data and combining it with historical failure records, a prediction model can be established to predict the failure time of equipment and formulate a maintenance plan to reduce sudden failures. In the formula, Mngz represents the failure rate of coal mine energy equipment, and N f represents the number of failures recorded during the observation period, T r Indicates the total operating time of the equipment. Failure rate data can provide intuitive information for management, helping them make more scientific decisions and optimize operating procedures and management strategies;

[0069] The energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh, coal mine equipment utilization efficiency Mlxl, various energy usage ratios Gnbl and energy efficiency index Nxzb based on the data obtained from the data analysis module. It monitors the energy consumption in real time based on the above values, and compares and analyzes the historical coal mine energy data stored in the system to generate energy efficiency evaluation and optimization suggestions, including:

[0070] The formula for calculating energy consumption per unit product is as follows:

[0071]

[0072] Real-time monitoring of unit product energy consumption can help managers identify links with excessive energy consumption, so as to take corresponding technical improvements and management measures to optimize energy efficiency. In the formula, Dcnh represents unit product energy consumption, Ex tPx represents the total energy consumption in time t, and Px represents the total product output in time t. By understanding the energy consumption per unit product in real time, enterprises can better adjust production plans and improve production flexibility and response speed;

[0073] The calculation formula for coal mine equipment utilization efficiency is as follows:

[0074]

[0075] Real-time monitoring of equipment utilization efficiency can help managers optimize equipment scheduling and allocation, make the production process more flexible and efficient, and reduce unnecessary waiting and idle time. In the formula, Mlxl represents the utilization efficiency of coal mine equipment, Sbsj represents the actual operation time of the equipment, and Sbky represents the available time of the equipment. By analyzing the use and operation time of the equipment, the inefficient or bottleneck links can be found, and targeted improvements can be made to improve the overall production efficiency.

[0076] The calculation formula for the energy usage ratio of each type is as follows:

[0077]

[0078] By analyzing the usage ratio of various energy sources, enterprises can identify the main energy sources and the actual contribution of various energy sources in production, providing data support for energy allocation. In the formula, Gnbl represents the usage ratio of various energy sources, EN j represents the usage of the jth type of energy, EN total represents the total amount of energy used, and p represents the number of energy types. By optimizing the usage ratio of various energy types, energy costs can be effectively reduced, corporate expenditures can be reduced, and economic benefits can be improved;

[0079] The energy efficiency index calculation formula is as follows:

[0080]

[0081] Improving energy efficiency indicators means that enterprises can complete production with lower energy consumption while maintaining the same output, thereby directly reducing energy costs. In the formula, Nxzb represents the energy efficiency indicator, Outp y Inpt represents the output of the yth product. y represents the energy consumption of the yth category of products, q represents the number of product types, and energy efficiency indicators can help discover abnormal equipment operation or overload operation, reduce equipment failure rate, and enhance safety management;

[0082] The early warning module obtains real-time data from the data analysis module and the energy consumption monitoring module, calculates the coal mine energy management abnormality index Mkyb and compares it with the coal mine energy management normal threshold Zcyz set by the system;

[0083] The calculation formula for abnormal index of coal mine energy management is as follows:

[0084]

[0085] Through the analysis of abnormal indicators, management can make more accurate and timely decisions based on objective data to support the improvement of enterprise energy management. In the formula, Mkyb represents the abnormal index of coal mine energy management, and Nhjs represents the abnormal index of coal mine energy management. m represents the actual energy consumption in the mth time period, Yqxh m represents the expected energy consumption in the mth time period, based on historical data, e represents the number of observed time periods, and Zcyz represents the normal threshold of coal mine energy management set by the system. This indicator measures abnormal conditions by comparing the difference between actual energy consumption and expected energy consumption. If the difference exceeds the set threshold, it is considered that abnormal energy use has occurred in this time period;

[0086] Once an abnormal situation is detected, the early warning module will immediately send an alert to managers through multiple channels, including SMS, email and mobile application push notifications, so that they can take timely action. At the same time as the alarm notification, the system will take countermeasures, including automatically adjusting equipment settings, starting backup equipment and deploying human resources to troubleshoot the fault. Each alarm will be recorded by the system, including the type of abnormality, occurrence time, response time and processing results, forming a complete event log. Through the analysis of abnormal events, the management team can identify potential system problems and operational bottlenecks, adjust normal threshold settings and optimize energy management strategies to reduce future abnormalities.

[0087] The user interface module extracts and summarizes data from each module. Whether it is real-time monitoring of energy consumption data or trend analysis of historical records, the system can efficiently integrate them. By providing different data views, including tables, charts and dashboards, managers can select indicators of concern as needed, which is convenient for quickly locating problems in emergencies or gaining deep insights in routine analysis.

[0088] Through intelligent and refined management, manual management errors are avoided, the efficiency of data collection is improved, and real-time monitoring of energy consumption is achieved. Abnormal energy consumption and equipment failures are discovered and resolved in a timely manner, thereby improving the accuracy of managers' decision-making.

[0089] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A coal mine energy refined management system based on the Internet of Things, characterized by: It includes data acquisition module, data transmission and storage module, data analysis module, energy consumption monitoring and optimization module, early warning module and user interface module; The data acquisition module collects the coal mine temperature, humidity, gas concentration and coal mine equipment operation status information through the sensor network, and sends the above collected data to the data transmission and storage module after preliminary cleaning; The data transmission and storage module regularly transmits the collected data to the cloud server via a wireless network and provides regular secure data backup and redundant storage; The data analysis module receives all historical data and real-time data saved by the data transmission and storage module, calculates the gas concentration change trend Mkqs, the temperature change rate Wdbh, the humidity change rate Mksb and the coal mine energy equipment failure rate Mngz in the coal mine according to the above data, and transmits the above collected data and calculated values ​​to the energy consumption monitoring and optimization module; The energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh, coal mine equipment utilization efficiency Mlxl, various energy use ratios Gnbl and energy efficiency index Nxzb based on the data obtained from the data analysis module, monitors the energy consumption in real time based on the above values, and compares and analyzes the coal mine energy historical data stored in the system to generate energy efficiency evaluation and optimization suggestions; The early warning module obtains real-time data from the data analysis module and the energy consumption monitoring module, calculates the coal mine energy management abnormality index Mkyb and compares it with the coal mine energy management normal threshold Zcyz set by the system, monitors and finds abnormal situations in real time, and automatically issues an alarm to notify the management personnel; The user interface module summarizes the real-time data and historical data in all the above modules, and provides an interactive interface for managers to query data, view analysis results and support decision-making.

2. According to the Internet of Things-based coal mine energy refined management system according to claim 1, it is characterized by: The data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the gas concentration change trend Mkqs in the coal mine based on the above data. The calculation formula is as follows: Mkqs=k*t+b+∈ In the formula, Mkqs represents the trend of gas concentration change in the coal mine, k represents the slope of concentration change, t represents the change time, b represents the intercept, and ∈ represents the white noise term of normal distribution.

3. According to claim 2, a coal mine energy refined management system based on the Internet of Things is characterized by: The data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the temperature change rate Wdbh in the coal mine based on the above data. The calculation formula is as follows: In the formula, Wdbh represents the temperature change rate in the coal mine, T t represents the temperature at time t, T t-1 represents the temperature within time t-1, and Δt represents the time interval.

4. According to claim 3, a coal mine energy refined management system based on the Internet of Things is characterized by: The data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the humidity change rate Mksb in the coal mine based on the above data. The calculation formula is as follows: In the formula, Mksb represents the rate of change of humidity in the coal mine, n represents the number of time points involved in the calculation, starting from i=1, Ht i Represents the humidity value collected at time point i, Ht i-1 represents the humidity value collected at time point i-1, w i Represents the weight of time point i, which is assigned by the system based on the reliability of historical data and the importance of the collection period.

5. According to claim 4, a coal mine energy refined management system based on the Internet of Things is characterized by: The data analysis module receives all historical data and real-time data stored in the data transmission and storage module, and calculates the failure rate Mngz of coal mine energy equipment based on the above data. The calculation formula is as follows: In the formula, Mngz represents the failure rate of coal mine energy equipment, N f represents the number of failures recorded during the observation period, T r Indicates the total operating time of the device.

6. According to claim 5, a coal mine energy refined management system based on the Internet of Things is characterized by: The energy consumption monitoring and optimization module calculates the unit product energy consumption Dcnh based on the data obtained from the data analysis module. The calculation formula is as follows: In the formula, Dcnh represents the energy consumption per unit product, Ex t represents the total energy consumption within time t, and Px represents the total product output within time t.

7. The coal mine energy refined management system based on the Internet of Things according to claim 6 is characterized by: The energy consumption monitoring and optimization module calculates the coal mine equipment utilization efficiency Mlxl based on the data obtained from the data analysis module. The calculation formula is as follows: In the formula, Mlxl represents the utilization efficiency of coal mine equipment, Sbsj represents the actual operating time of the equipment, and Sbky represents the available time of the equipment.

8. The coal mine energy refined management system based on the Internet of Things according to claim 7 is characterized by: The energy consumption monitoring and optimization module calculates the energy usage ratio Gnbl of each type based on the data obtained from the data analysis module. The calculation formula is as follows: In the formula, Gnbl represents the ratio of various energy usage, EN j represents the usage of the jth type of energy, EN total represents the total usage of all energy sources, and p represents the number of energy types.

9. The coal mine energy refined management system based on the Internet of Things according to claim 8 is characterized by: The energy consumption monitoring and optimization module calculates the energy efficiency index Nxzb according to the data obtained from the data analysis module. The calculation formula is as follows: In the formula, Nxzb represents the energy efficiency index, Outp y Inpt represents the output of the yth product. y represents the energy consumption of the yth category of products, and q represents the number of product types.

10. The coal mine energy refined management system based on the Internet of Things according to claim 9, characterized in that: The early warning module calculates the coal mine energy management abnormality index Mkyb by retrieving the real-time data obtained by the data analysis module and the energy consumption monitoring module. The calculation formula is as follows: In the formula, Mkyb represents the abnormal index of coal mine energy management, Nhjs m represents the actual energy consumption in the mth time period, Yqxh m It represents the expected energy consumption in the mth time period based on historical data, e represents the number of observed time periods, and Zcyz represents the normal threshold of coal mine energy management set by the system. This indicator measures abnormal situations by comparing the difference between actual energy consumption and expected energy consumption. When the difference exceeds the set threshold, it is considered that abnormal energy use has occurred in this time period.

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