Super-capacity production identification system and method based on coal mine underground power utilization monitoring

Abstract

The invention relates to a super-capacity production identification system and method based on coal mine underground power utilization monitoring, and belongs to the technical field of coal mine safety supervision and supervision. The system comprises a plurality of power utilization monitoring devices deployed at different underground nodes, a data processing unit used for receiving and processing monitoring data, a database module used for storing data, an identification module used for identifying abnormal power utilization and an early warning module used for carrying out early warning on abnormal power utilization conditions. The method comprises the following steps: comparing collected data with a preset power consumption interval [Qm, Qm] in a normal production period and a fluctuation threshold value f; and if the electric energy consumption of a certain node exceeds a preset range, the node is identified to be abnormal, and an early warning signal is sent to a manager. According to the invention, the electricity utilization condition is monitored and whether the super-capacity production theft exists is identified according to the electricity utilization condition, so that early warning and alarming are carried out on the potential super-capacity production behavior of the mine.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine safety supervision and inspection, and relates to an overcapacity production identification system and method based on underground coal mine electricity consumption monitoring. Background Art

[0002] In order to accurately identify and promptly eliminate the major accident hazards in coal mines, the Ministry of Emergency Management has formulated the "Criteria for Determining Major Accident Hazards in Coal Mines", which is applicable to the determination of major accident hazards in various types of coal mines, including the determination criteria for the major accident hazards of "organizing production beyond the designed or approved production capacity". Overcapacity production behavior, that is, production activities in coal mines that exceed the designed or approved production capacity, is a particularly worthy of attention. Although such behavior may bring short-term economic benefits in response to market demand, it also greatly increases the safety risks in mining areas and may lead to major safety accidents. The most effective way to determine whether there is overcapacity production behavior in coal mines is to start from the two aspects of underground wind and underground electricity use. As long as there is overcapacity production underground, there will inevitably be illegal acts such as hidden working faces or over-layer and over-boundary mining. The emergence of these mining sites will inevitably use wind and electricity to meet the labor of personnel and the operation of equipment. Therefore, it is of vital significance to explore effective supervision and inspection methods to effectively avoid the problems of time and space discontinuity, low precision, and low supervision efficiency in manual supervision and inspection to ensure the safe production of coal mines.

[0003] The prior art with publication number CN114961853A discloses an overcapacity production identification system and method based on mine air volume monitoring, which monitors the overcapacity production of coal mines in real time through the coal mine underground air volume monitoring system. When an abnormality is detected between the uploaded air volume data of each air-consuming location or the total air volume data of the mine and the air volume required data of all coal mining faces within the mine resource boundary, the overcapacity production behavior of the coal mine is identified. The background management system obtains the real-time position of the underground overcapacity production mining face based on the position of the air volume precision monitoring device and the data uploaded by it, compares the air volume deviation with the monitoring threshold through system calculation, completes the coal mine production status identification, and the monitoring system terminal issues early warning and alarm for the overcapacity production behavior and position of the mine.

[0004] Although modern mine air volume monitoring systems can monitor air volume data in real time and accurately, the accuracy and stability of the sensor may be affected to a certain extent by the complex underground environment (such as temperature, humidity, dust concentration, etc.), resulting in errors in the monitoring results. In addition, differences in geological conditions, mining processes and ventilation system designs in different mines may also affect the accuracy of the monitoring results.

[0005] Other aspects of coal mine overcapacity production monitoring, such as manual measurement and personnel positioning, are greatly affected by human factors, making it difficult to obtain real coal production information and are also labor-intensive, so they need to be improved. Summary of the invention

[0006] In view of this, an object of the present invention is to provide an overcapacity production identification system and method based on underground coal mine electricity consumption monitoring.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A system for identifying overcapacity production based on underground coal mine electricity consumption monitoring, comprising: a plurality of electricity consumption monitoring devices deployed at different nodes underground, a data processing unit for receiving and processing monitoring data, a database module for storing data, an identification module for identifying abnormal electricity consumption, and an early warning module for early warning of abnormal electricity consumption, wherein the electricity consumption monitoring device is provided with a current sensor and a voltage sensor for real-time monitoring of the current and voltage passing through the node, and the current and voltage data are transmitted to the data processing unit, the data processing unit calculates the real-time power consumption of each node according to the current and voltage data, and transmits the calculated real-time power consumption to the database module for storage, and the identification module determines whether there is overcapacity production behavior according to the past power consumption in the database and the identification result of the real-time power consumption transmitted by the data processing unit, and if there is overcapacity production behavior, the early warning module issues an early warning.

[0009] Furthermore, a data analysis algorithm is provided in the data processing unit, and the power consumption of each node is calculated according to the algorithm, and the formula is expressed as follows:

[0010] E=P×t=V×I×t

[0011] Among them, E represents the amount of electric energy consumed, P represents power, t represents time, V represents voltage, and I represents current;

[0012] The calculation formula for the real-time power consumption of node i is:

[0013]

[0014] Among them, I t and V t are the real-time values ​​of current and voltage at the tth moment respectively; E i Represents the real-time power consumption of node i from time t0 to time t1.

[0015] Furthermore, the database module stores the historical power consumption data of each monitoring node, and determines the power consumption range under normal production conditions based on the average value, standard deviation, maximum value, and minimum value of the historical power consumption data [Q m ,Qn ] and fluctuation threshold f, where the power consumption interval [Q m ,Q n ] is calculated as:

[0016] Qm=min(E1,E2,…,E N )

[0017] Qn=max(E1,E2,…,E N )

[0018] Among them, E1, E2, …, E N represents N energy consumption data points collected during the observation period; Qm is the minimum value of all energy consumption data points, indicating the lowest energy consumption level under normal production conditions; Qn is the maximum value of all energy consumption data points, indicating the highest energy consumption level under normal production conditions;

[0019] The calculation method of the fluctuation threshold f is:

[0020]

[0021] Among them, k is the set threshold strictness coefficient; σ is the standard deviation, which is used to measure the degree of dispersion of data distribution; is the average value of all energy consumption data.

[0022] Furthermore, the recognition module calculates the real-time power consumption E calculated by the data processing unit. i Compared with the normal production power consumption interval in the database module [Q m ,Q n ] and the fluctuation threshold f, and then identify the abnormal state. The identification module compares the real-time power consumption E in the following way: i And the power consumption range [Q m ,Q n ]:

[0023]

[0024] Among them, A i =1 indicates abnormal production behavior, A i =0 means normal production behavior;

[0025] The identification module compares the real-time power consumption E in the following way i With the fluctuation threshold f:

[0026]

[0027] Among them, F i =1 indicates abnormal production behavior, F i =0 means normal production behavior;

[0028] When A i =1 or F i =1When any of the situations occurs, the identification module marks node i as abnormal and sends an early warning instruction to the early warning module.

[0029] Furthermore, the early warning module includes a visual alarm interface and a wireless transmission unit, and the early warning module can send real-time early warning information to management personnel.

[0030] The present invention also proposes a method for identifying overcapacity production based on underground coal mine electricity consumption monitoring, which includes the following steps:

[0031] S1. Deploy multiple power monitoring devices underground;

[0032] S2. Collect and process the real-time power consumption data of each node, including current, voltage, power and energy consumption;

[0033] S3, compare the collected data with the preset normal production period power consumption interval [Q m ,Q n ] and the fluctuation threshold f for comparison;

[0034] S4. If it is found that the power consumption of a certain node exceeds the preset range, it is identified as abnormal and a warning signal is sent to the management personnel.

[0035] Further, in step S2, the power consumption of each node is calculated, and the formula is expressed as:

[0036] E=P×t=V×I×t

[0037] Among them, E represents the amount of electric energy consumed, P represents power, t represents time, V represents voltage, and I represents current;

[0038] The calculation formula for the real-time power consumption of node i is:

[0039]

[0040] Among them, I t and V t are the real-time values ​​of current and voltage at the tth moment respectively; E i Represents the real-time power consumption of node i from time t0 to time t1.

[0041] Further, in step S3, the calculated real-time power consumption E i Compared with the normal production power consumption range [Q m ,Q n ] is compared with the fluctuation threshold f, wherein the real-time power consumption E is compared in the following manner: iAnd the power consumption range [Q m ,Q n ]:

[0042]

[0043] Among them, A i =1 indicates abnormal production behavior, A i =0 means normal production behavior;

[0044] Compare the real-time power consumption E as follows i With the fluctuation threshold f:

[0045]

[0046] Among them, F i =1 indicates abnormal production behavior, F i =0 indicates normal production behavior.

[0047] Further, in step S4, when A i =1 or F i =1When any of the situations occurs, the identification module marks node i as abnormal and sends an early warning instruction to the early warning module.

[0048] The beneficial effects of the present invention are:

[0049] The present invention can obtain the power consumption of each power point in the mine during normal production period by monitoring and analyzing the power consumption of each node in the mine, thereby improving the safety production level of the coal mine.

[0050] The present invention forms a normal period electricity consumption interval [Qm, Qn] and a fluctuation threshold f, providing a scientific basis for coal mine safety production. At the same time, the real-time data of the power consumption node is compared with the preset normal production period electricity consumption interval [Qm, Qn] and the fluctuation threshold f, and the abnormal state is determined, the abnormal node is marked, the coal mine production state is identified, and the coal mine safety production management level is improved.

[0051] The present invention provides early warning and alarm for potential over-capacity production behavior in a mine, avoids various human factors in manual supervision and inspection, and ensures safe production in the mine.

[0052] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0054] Figure 1 It is a structural schematic diagram of the underground super-capacity production identification system of the present invention;

[0055] Figure 2 It is a schematic diagram of the flow chart of the underground super-capacity production identification method of the present invention. DETAILED DESCRIPTION

[0056] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0057] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0058] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0059] See also Figure 1-2 , which is an overcapacity production identification system and method based on underground coal mine electricity consumption monitoring.

[0060] Example 1

[0061] This embodiment proposes an overcapacity production identification system based on underground coal mine power consumption monitoring, such as Figure 1As shown, the system includes multiple power consumption monitoring devices deployed at different nodes underground; a data processing unit connected to the power consumption monitoring devices for receiving and processing monitoring data; a database for storing the power consumption intervals during normal production [Q m ,Q n ] and fluctuation threshold f; an identification module for comparing the real-time power consumption data with the energy consumption range and fluctuation threshold stored in the database to identify abnormal growth; an early warning module for sending out an early warning signal when the identification module finds abnormal growth. The power consumption monitoring equipment includes a current sensor and a voltage sensor for real-time monitoring of the current and voltage passing through the node.

[0062] The power consumption monitoring equipment is first deployed at different nodes underground. Each device is equipped with a current sensor and a voltage sensor to monitor the current and voltage passing through the node in real time, and collect the original data transmitted to the data processing unit. These power consumption monitoring devices are connected to the central data processing unit through cables or wireless transmission to achieve real-time data upload. The data processing unit not only receives this data, but is also responsible for processing and analyzing it, and uses the built-in algorithm to calculate the real-time power consumption of each node. At the same time, the database is directly connected to the data processing unit to store the power consumption range during normal production [Q m ,Q n ] and fluctuation threshold f, provide the necessary comparison benchmark for the subsequent identification. The identification module is closely connected with the data processing unit, receives the processed power consumption data, and compares it with the energy consumption range and fluctuation threshold stored in the database to identify whether there is abnormal growth. This process is accurately determined with the help of the preset deviation value. Once the identification module detects abnormal growth, it will immediately pass the signal to the early warning module. The early warning module is directly connected to the identification module and receives instructions from the identification module. When receiving the signal of abnormal growth, the early warning module will be triggered and send a warning signal to the management personnel. The efficient operation of the entire system depends on the smooth connection and data flow between the modules. From the data collection of the monitoring equipment to the final warning signal, each link is completed automatically, reducing human intervention and improving the response speed and accuracy of the system.

[0063] The data processing unit is equipped with a data analysis algorithm to calculate the power consumption of each node. The specific formula is: E = P × t = V × I × t, where E is the power consumption (unit: kilowatt-hour, kWh), P is the power (unit: kilowatt, kW), V is the voltage (unit: volt V), I is the current (unit: ampere, A), and t is the time (unit: hour, h). Through the real-time collected data, the power usage of underground equipment can be monitored and analyzed in real time, which improves the accuracy of identifying overcapacity production behavior and reduces human errors.

[0064] The database further stores the historical power consumption data of each monitoring node. Based on the statistical analysis of the historical energy consumption data, including the calculation of the average value, standard deviation, minimum and maximum values, the power consumption range under normal production conditions is determined. m ,Q n ] and set the fluctuation threshold f, the process is expressed as follows:

[0065] Calculate the power consumption interval [Q m ,Q n ]:

[0066] Qm=min(E1,E2,…,E N )

[0067] Qn=max(E1,E2,…,E N )

[0068] Among them, E1, E2, …, E N represents the N energy consumption data points collected during the observation period, Qm is the minimum value of all energy consumption data points, indicating the lowest energy consumption level that may be observed under normal production conditions, and Qn is the maximum value of all energy consumption data points, indicating the highest energy consumption level that may be observed under normal production conditions. These two formulas directly extract the minimum and maximum values ​​from the historical energy consumption data to determine the lower and upper limits of normal production energy consumption;

[0069] Calculate the fluctuation threshold f:

[0070]

[0071] Among them, k is a strict coefficient for setting the threshold, and σ is the standard deviation, which is used to measure the degree of dispersion of data distribution, that is, the degree to which each data point deviates from the mean value. It is the average value of all energy consumption data. The formula is calculated by multiplying the standard deviation by the coefficient k. It aims to define a normal energy consumption variation range. When the energy consumption data exceeds this range, it indicates an abnormal situation.

[0072] The database not only stores real-time power consumption data, but also further saves the historical power consumption data of each monitoring node. These historical data are processed by statistical analysis methods, including calculating the average value, standard deviation, and minimum and maximum values. This process determines the power consumption range under normal production conditions [Q m ,Q n ] and the basis for setting the fluctuation threshold f, which can provide a scientific basis for safe production in coal mines.

[0073] The identification module uses a preset deviation value to determine whether there is an abnormal increase in power consumption. The early warning module includes a visual alarm interface and a wireless transmission function to send real-time early warning information to management personnel.

[0074] The identification module uses the preset deviation value to analyze and determine whether there is an abnormal increase in power consumption. This process involves comparing the real-time monitored data with the normal production energy consumption range stored in the database [Q m ,Q n ] and fluctuation threshold f. If the data exceeds these preset thresholds, especially when the degree of excess reaches the set deviation value, the identification module will immediately identify it as abnormal growth and trigger the early warning module. The early warning module is equipped with a visual alarm interface, so that managers can intuitively see the abnormal status, and with the help of wireless transmission function, early warning information is sent to managers in real time. The instant feedback mechanism ensures that any potential over-capacity production behavior can be quickly discovered and dealt with, which not only enhances production safety, but also improves the response efficiency and management level of the monitoring system.

[0075] Example 2

[0076] This embodiment proposes a method for identifying overcapacity production based on underground coal mine electricity consumption monitoring, which includes the following steps:

[0077] S1. Deploy multiple power monitoring devices underground;

[0078] S2. Collect and process the real-time power consumption data of each node, including current, voltage, power and energy consumption;

[0079] S3, compare the collected data with the preset normal production period power consumption interval [Q m ,Q n ] and the fluctuation threshold f for comparison;

[0080] S4. If it is found that the power consumption of a certain node exceeds the preset range, it is identified as abnormal and a warning signal is sent to the management personnel.

[0081] The method first deploys multiple power consumption monitoring devices in the coal mine to collect real-time data on current, voltage, power and energy consumption. These data are then transmitted to the central processing unit, where the real-time power consumption data of each node is processed using a data analysis algorithm. Then, the system compares the real-time collected data with the preset power consumption interval during normal production [Q m ,Q n ] and the fluctuation threshold f to identify whether there is abnormal growth. Once the power consumption of a node is found to exceed the preset range, the system will identify it as an abnormal state and immediately send a warning signal to the management personnel. This method not only improves the accuracy of identifying overcapacity production behavior and reduces human errors, but also enhances the timeliness of the warning, allowing managers to take quick measures to effectively avoid possible safety accidents.

[0082] In step S3, the comparison process is:

[0083] Calculate real-time energy consumption:

[0084]

[0085] Among them, I t and V t are the real-time values ​​of current and voltage at the tth moment respectively; E i Represents the real-time power consumption of node i from time t0 to time t1.

[0086] Compare energy consumption range:

[0087] If E i m or E i >Q n , then node i is identified as abnormal; here it is expressed as:

[0088] Among them A i =1 indicates abnormality, A i =0 means normal.

[0089] Compare fluctuation threshold:

[0090] If |E i+1 -E i |>f, then node i is identified as abnormal;

[0091] Here it is represented as:

[0092] where F i =1 indicates abnormality, F i =0 means normal.

[0093] In the above formula, E i is the real-time energy consumption of the i-th node in a certain time interval [t0, t1], Q m and Q n are the minimum and maximum energy consumption values ​​during normal production, respectively; f is the fluctuation threshold; and otherwise is used to indicate the default or normal state when the conditions are not met.

[0094] The real-time energy consumption in the i-th time interval is calculated, where and are the real-time values ​​of current and voltage respectively, and this real-time energy consumption is compared with the preset power consumption interval [Q m ,Q n ] for comparison, if the real-time energy consumption is lower than the minimum energy consumption value Q m Or higher than the maximum energy consumption value Q n ​, the system identifies it as an abnormal state and takes corresponding early warning measures. At the same time, it examines the fluctuation of energy consumption. If the difference between the real-time energy consumption and the average energy consumption in the previous time period exceeds the set fluctuation threshold f, it will also be regarded as an abnormality. The double comparison mechanism is realized, combined with the evaluation of energy consumption range and fluctuation threshold, which significantly improves the accuracy and timeliness of identification.

[0095] In step S4, based on the comparison result in S3, the abnormal state is determined. i =1 or F i =1, marking node i as abnormal, thus sending an early warning signal.

[0096] Based on the comparison results in S3, the abnormal state is determined. When the real-time energy consumption data exceeds the preset normal production power consumption range [Q m ,Q n ] or fluctuation threshold f, the system will mark the node as abnormal and immediately send an early warning signal. This process not only relies on comprehensive monitoring and analysis of underground power consumption, but also involves precise positioning technology to ensure timely identification and response to overcapacity production behavior. Compared with the traditional method of monitoring only by underground power consumption and positioning angle, it can more effectively identify whether there is overcapacity production behavior. Traditional methods are usually limited to monitoring and analyzing whether there is over-layer and over-boundary mining, but are not enough to accurately judge whether there is overcapacity production in production activities, whether there is a hidden working face, and whether the energy consumption exceeds the safe operation range of the equipment. Therefore, the present invention provides a more comprehensive and accurate monitoring solution through real-time data analysis and energy consumption comparison, which significantly enhances safety risk management, reduces human errors, and improves production efficiency and resource utilization efficiency.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. An overcapacity production identification system based on underground coal mine electricity monitoring, Features: It includes: Several power consumption monitoring devices deployed at different nodes underground, a data processing unit for receiving and processing monitoring data, a database module for storing data, a recognition module for identifying abnormal power consumption, and an early warning module for early warning of abnormal power consumption, wherein: The electricity consumption monitoring equipment is equipped with current sensors and voltage sensors to monitor the current and voltage passing through the nodes in real time, and transmit the current and voltage data to the data processing unit. The data processing unit calculates the real-time power consumption of each node based on the current and voltage data, and transmits the calculated real-time power consumption to the database module for storage. The identification module determines whether there is over-capacity production behavior based on the past power consumption in the database and the identification results of the real-time power consumption transmitted by the data processing unit. If there is over-capacity production behavior, the early warning module will issue an early warning.

2. According to claim 1, a super capacity production identification system based on underground coal mine electricity consumption monitoring, Features: The data processing unit is equipped with a data analysis algorithm, which calculates the power consumption of each node according to the algorithm. The formula is expressed as follows: E=P×t=V×I×t Among them, E represents the amount of electric energy consumed, P represents power, t represents time, V represents voltage, and I represents current; The calculation formula for the real-time power consumption of node i is: Among them, I t and V t are the real-time values ​​of current and voltage at the tth moment respectively; E i Indicates that node i is from t 0 Time to t 1 Real-time power consumption at any given moment.

3. According to claim 1, a super capacity production identification system based on underground coal mine electricity monitoring, Features: The database module stores the historical power consumption data of each monitoring node, and determines the power consumption range under normal production conditions based on the average value, standard deviation, maximum value and minimum value of the historical power consumption data [Q m ,Q n ] and fluctuation threshold f, where the power consumption interval [Q m ,Q n ] is calculated as: Qm=min(E 1 ,E 2 ,…,E N ) Qn=max(E 1 ,AND 2 ,…,AND N ) Among them, E 1 ,E 2 ,…,E N represents N energy consumption data points collected during the observation period; Qm is the minimum value of all energy consumption data points, indicating the lowest energy consumption level under normal production conditions; Qn is the maximum value of all energy consumption data points, indicating the highest energy consumption level under normal production conditions; The calculation method of the fluctuation threshold f is: Among them, k is the set threshold strictness coefficient; σ is the standard deviation, which is used to measure the degree of dispersion of data distribution; is the average value of all energy consumption data.

4. According to claim 1, a super capacity production identification system based on underground coal mine electricity monitoring, Features: The recognition module calculates the real-time power consumption E calculated by the data processing unit. i Compared with the normal production power consumption interval in the database module [Q m ,Q n ] and the fluctuation threshold f, and then identify the abnormal state. The identification module compares the real-time power consumption E in the following way: i And the power consumption range [Q m ,Q n ]: Among them, A i =1 indicates abnormal production behavior, A i =0 means normal production behavior; The identification module compares the real-time power consumption E in the following way i With the fluctuation threshold f: Among them, F i =1 indicates abnormal production behavior, F i =0 means normal production behavior; When A i =1 or F i =1When any of the situations occurs, the identification module marks node i as abnormal and sends an early warning instruction to the early warning module.

5. According to claim 1, a super capacity production identification system based on underground coal mine electricity monitoring, Features: The early warning module includes a visual alarm interface and a wireless transmission unit, and the early warning module can send real-time early warning information to management personnel.

6. A method for identifying overcapacity production based on underground coal mine electricity monitoring, Features: The following steps are involved: S1. Deploy multiple power monitoring devices underground; S2. Collect and process the real-time power consumption data of each node, including current, voltage, power and energy consumption; S3, compare the collected data with the preset normal production period power consumption interval [Q m ,Q n ] and the fluctuation threshold f for comparison; S4. If it is found that the power consumption of a certain node exceeds the preset range, it is identified as abnormal and a warning signal is sent to the management personnel.

7. A method for identifying overcapacity production based on underground coal mine electricity consumption monitoring according to claim 6, Features: In step S2, the power consumption of each node is calculated, and the formula is expressed as: E=P×t=V×I×t Among them, E represents the amount of electric energy consumed, P represents power, t represents time, V represents voltage, and I represents current; The calculation formula for the real-time power consumption of node i is: Among them, I t and V t are the real-time values ​​of current and voltage at the tth moment respectively; E i Indicates that node i is from t 0 Time to t 1 Real-time power consumption at any given moment.

8. A method for identifying overcapacity production based on underground coal mine electricity consumption monitoring according to claim 7, Features: In step S3, the calculated real-time power consumption E i Compared with the normal production power consumption range [Q m ,Q n ] is compared with the fluctuation threshold f, wherein the real-time power consumption E is compared in the following manner: i And the power consumption range [Q m ,Q n ]: Among them, A i =1 indicates abnormal production behavior, A i =0 means normal production behavior; Compare the real-time power consumption E as follows i With the fluctuation threshold f: Among them, F i =1 indicates abnormal production behavior, F i =0 indicates normal production behavior.

9. A method for identifying overcapacity production based on underground coal mine electricity consumption monitoring according to claim 8, Features: In step S4, when A i =1 or F i =1When any of the situations occurs, the identification module marks node i as abnormal and sends an early warning instruction to the early warning module.

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