An intelligent liquid supply load estimation method for fully mechanized mining face

Abstract

The present invention discloses an intelligent liquid supply load estimation method for a fully-mechanized mining face, which relates to the technical field of fully-mechanized coal mining. The method includes obtaining the load calibration deviation values respectively corresponding to each temperature segmentation interval of the frequency converter, obtaining the interval speed calibration points respectively corresponding to each temperature segmentation interval, and calculating to obtain the output liquid supply load value; through the module for obtaining the output liquid supply load value, the deviation value of the estimated liquid supply load value of the emulsion pump affected by the temperature of the frequency converter is corrected, so that the output liquid supply load value is more accurate, and it is avoided that when the frequency converter executes the liquid supply load value of the emulsion pump according to the estimated liquid supply load value, the adjustment effect of the liquid supply load value of the emulsion pump is affected due to the different operating temperatures of the frequency converter, and then the situation that the control of the liquid supply load value of the emulsion pump cannot reach the value corresponding to the estimated liquid supply load, thereby affecting the evaluation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of fully mechanized coal mining, and specifically relates to an intelligent liquid supply load estimation method for a fully mechanized mining face. Background Art

[0002] In a fully mechanized coal mining face, the liquid supply system is a key component to ensure the safe and efficient mining of the mine. The intelligent liquid supply load estimation method for a fully mechanized mining face is a technology that evaluates and controls the load of the liquid supply system through intelligent technology. It can dynamically adjust the supply of emulsion liquid according to the real-time demand of the working face to ensure the effective operation of equipment such as hydraulic supports; the liquid supply system adopted in the fully mechanized mining face includes multiple emulsion pumps, multiple spray pumps, and multiple liquid tanks, which are used for the safe support and operation of hydraulic supports in the fully mechanized mining face, as well as for purposes such as spray dust reduction and equipment cooling of the shearer.

[0003] In a fully mechanized coal mining face, the estimated liquid supply load value obtained through a neural network will be executed by a frequency converter to control the liquid supply load of the emulsion pump to adapt to the actual working conditions.

[0004] However, in the process of adjusting the rotational speed and flow rate of the emulsion pump by adjusting the working state of the frequency converter, the adjustment effect of the liquid supply load value of the emulsion pump is affected due to the different operating temperatures of the frequency converter, and then the controlled liquid supply load value of the emulsion pump cannot reach the value corresponding to the estimated liquid supply load, which further affects the evaluation effect; based on this, an intelligent liquid supply load estimation method for a fully mechanized mining face is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent liquid supply load estimation method for a fully mechanized mining face, which solves the technical problem that the adjustment effect of the liquid supply load value of the emulsion pump is affected due to the different operating temperatures of the frequency converter, and then the controlled liquid supply load value of the emulsion pump cannot reach the value corresponding to the estimated liquid supply load, which further affects the evaluation effect.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent liquid supply load estimation method for a fully mechanized mining face includes the following steps:

[0008] Step 1: Analyze the equipment operating temperature of the frequency converter within a preset time period T to obtain the temperature segmentation interval of the frequency converter, where T≥1 day;

[0009] Step 2: Analyze the historical estimated liquid supply load value and the actual liquid supply load value of the emulsion pump in each temperature segmentation interval to obtain the load calibration deviation values respectively corresponding to the frequency converter in each temperature segmentation interval;

[0010] Step 3: Analyze the relationship between the historical estimated liquid supply load values of the emulsion pump in each temperature segment interval and the temperature of the frequency converter, and obtain the interval speed calibration points corresponding to each temperature segment interval respectively;

[0011] Step 4: Analyze the interval speed calibration points of each temperature segment interval to obtain the deviation coefficient between the emulsion pump load value and the frequency converter temperature;

[0012] Step 5: Calculate the output liquid supply load value according to the estimated liquid supply load value and the deviation coefficient between the emulsion pump load value and the frequency converter temperature.

[0013] As a further solution of the present invention: The way to obtain the temperature segment intervals of the frequency converter is as follows:

[0014] Obtain the equipment operating temperature values of the frequency converter within a preset time period T, obtain the maximum and minimum values of each operating temperature value according to the values, and generate the equipment operating temperature interval according to the maximum and minimum values. Divide the equipment operating temperature interval evenly to obtain the corresponding temperature segment intervals of the frequency converter.

[0015] As a further solution of the present invention: The way to obtain the load calibration deviation values corresponding to each temperature segment interval of the frequency converter is as follows:

[0016] A1: Arbitrarily select one temperature segment interval as the analysis interval from each temperature segment interval. In the analysis interval, obtain the historical estimated liquid supply load values and the actual liquid supply load values of the emulsion pump in a preset number a of times. Obtain the absolute value of the difference CAa between the a historical estimated liquid supply load values of the emulsion pump in the analysis interval and their corresponding actual liquid supply load values. Calculate the discrete value W of the absolute value of the difference CAa, and analyze the discrete value W to obtain the load calibration deviation value corresponding to the emulsion pump in the analysis interval, a≥1;

[0017] A2: Repeat step A1 to obtain the load calibration deviation values Bn corresponding to the emulsion pump in each temperature segment interval respectively, where n represents the number of temperature segment intervals, n≥1.

[0018] As a further solution of the present invention: The way to obtain the load calibration deviation value corresponding to the emulsion pump in the analysis interval is as follows:

[0019] When the discrete value W < the preset value S1, the average value CAp of CAa is taken as the load calibration deviation value B1 of the emulsion pump corresponding to the analysis interval. When the discrete value W ≥ the preset value S1, the corresponding CAc is deleted in descending order according to the value of |CAc - CAp|, and after each deletion, the discrete value W of the remaining CAc is recalculated until the discrete value W < the preset value S1, then the calculation stops. At the same time, the number d of the deleted CAc is analyzed, and then the load calibration deviation value B1 of the emulsion pump corresponding to the analysis interval is obtained.

[0020] As a further solution of the present invention: The specific way to analyze the number d is:

[0021] The deleted number d is compared with the preset value S2. If the number d < S2, the average value of the remaining CAc is taken as the load calibration deviation value B1 of the emulsion pump corresponding to the analysis interval; if the number d ≥ S2, the average value of the maximum and minimum values of the remaining CAc is taken as the load calibration deviation value B1 of the emulsion pump corresponding to the analysis interval.

[0022] As a further solution of the present invention: The specific way to obtain the interval speed calibration points corresponding to each temperature segment interval is:

[0023] For each emulsion pump, the a - time historical estimated liquid supply load values in each temperature segment interval are averaged for the corresponding equipment temperatures of the frequency converters, and the obtained average value is taken as the temperature calibration value Wn corresponding to each temperature segment interval. The temperature calibration value Wn is taken as the abscissa of the interval speed calibration point of each temperature segment interval, and the load calibration deviation value Bn is taken as the ordinate of the interval speed calibration point of each temperature segment interval. Then the coordinates Dn(Wn, Bn) of the interval speed calibration points corresponding to each temperature segment interval are obtained.

[0024] As a further solution of the present invention: The specific way to obtain the deviation coefficient between the emulsion pump load value and the frequency converter temperature is:

[0025] The correlation coefficient H between the load calibration deviation value and the temperature calibration value is calculated through the correlation formula. When H ∉ [Y1, Y2], the coordinates (Wmax, E1) and (Wmin, E2) of the speed calibration points corresponding to the maximum and minimum values in Wn are obtained from the coordinates Dn(Wn, Bn) of the speed calibration points, where E1 and E2 are the numerical values of the ordinates corresponding to the speed calibration points with abscissas Wmax and Wmin respectively, Wmax is the maximum value in Wn, and Wmin is the minimum value in Wn. Then, according to the formula: R = (E2 - E1) / (Wmax - Wmin), the deviation coefficient R between the emulsion pump load value and the frequency converter temperature is calculated.

[0026] When H is within the range of [Y1, Y2], it indicates that there is no relationship between the load calibration deviation value and the temperature calibration value. In this case, the deviation coefficient R is taken as 0. Both Y1 and Y2 are preset values, and their specific numerical values are determined by relevant personnel according to the actual situation, with Y1 < Y2.

[0027] As a further solution of the present invention: The specific method for obtaining the output liquid supply load value is as follows:

[0028] By using the calculation formula: BC = [K + (1 + R × |BF - JF|)] × β1, the output liquid supply load value BC of the emulsion pump is calculated, where K is the estimated liquid supply load value, JF is the real-time operating temperature of the frequency converter, and BF is the calibrated operating temperature of the frequency converter.

[0029] As a further solution of the present invention: The specific correlation formula is: , where Wp and Bp are the mean values of the temperature calibration value Wn and the load calibration deviation value Bn respectively, and n ≥ e ≥ 1.

[0030] Advantages of the present invention:

[0031] In the present invention, through the module for obtaining the output liquid supply load value, the deviation value of the estimated liquid supply load value of the emulsion pump affected by the temperature of the frequency converter is corrected, making the output liquid supply load value more accurate. This avoids the situation where when the frequency converter executes the liquid supply load value of the emulsion pump according to the estimated liquid supply load value, the adjustment effect of the liquid supply load value of the emulsion pump is affected due to the different operating temperatures of the frequency converter, resulting in the inability to reach the value corresponding to the estimated liquid supply load for controlling the liquid supply load value of the emulsion pump, and further affecting the evaluation effect. It can effectively solve the influence of the frequency converter temperature on the adjustment effect of the liquid supply load of the emulsion pump, and improve the accuracy and stability of the estimated liquid supply load. Description of the Drawings

[0032] The present invention will be further described below with reference to the accompanying drawings.

[0033] Figure 1 It is a schematic diagram of the framework structure of an intelligent liquid supply load estimation method for a fully mechanized coal mining face in the present invention. Detailed Embodiments

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0035] Please refer to Figure 1As shown in the figure, the present invention is an intelligent liquid supply load estimation method for a fully-mechanized mining face, including the following steps:

[0036] Step 1: Obtain the equipment operating temperature of the frequency converter within a preset time period, analyze it, and obtain the temperature segmentation interval of the frequency converter according to the analysis result. The specific method is as follows:

[0037] Obtain the equipment operating temperature value of the frequency converter within a preset time period T, obtain the maximum and minimum values among the respective operating temperature values, generate an equipment operating temperature interval based on the maximum and minimum values, and evenly divide the equipment operating temperature interval to obtain the respective temperature segmentation intervals corresponding to the frequency converter;

[0038] It should be noted that the preset time period T is a time period that starts from the current moment of data acquisition and goes back 180 days. The day of data acquisition is included. 180 ≥ T ≥ 1. The equipment operating temperature of the frequency converter is obtained and recorded through a temperature sensor set on the frequency converter:

[0039] Step 2: Obtain the a - time historical estimated liquid supply load values within the respective temperature segmentation intervals of the frequency converter. At the same time, obtain the actual liquid supply load values corresponding to the a - time historical estimated liquid supply load values of the emulsion pump respectively and analyze them. Obtain the load calibration deviation values corresponding to the frequency converter in the respective temperature segmentation intervals according to the analysis result. The specific method is as follows:

[0040] A1: Arbitrarily select one temperature segmentation interval as the analysis interval among the respective temperature segmentation intervals;

[0041] A2: Obtain the historical estimated liquid supply load values of the emulsion pump within the preset number of times a in the analysis interval, and mark them as ZAa respectively. At the same time, obtain the actual liquid supply load values corresponding to the historical estimated liquid supply load values of the emulsion pump within the preset number of times a respectively, and mark them as ZBa respectively, where a is the preset number of times, and the specific value is determined by relevant personnel according to actual needs. Here, 128 ≥ a ≥ 1;

[0042] Through the formula CAa = |ZAa - ZBa|, obtain the absolute value of the difference CAa between the a - time historical estimated liquid supply load value of the emulsion pump in the analysis interval and its corresponding actual liquid supply load value;

[0043] Through the standard deviation calculation formula , calculate the discrete value W of the absolute value of the difference CAa, where CAc is any value in CAa, CAp is the mean value of CAa, and a ≥ c ≥ 1;

[0044] Compare and analyze the discrete value W with the preset value S1. When the discrete value W < the preset value S1, it indicates that the difference between the absolute values of each difference CAa is small. Then, take the average value CAp of CAa as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval. If the discrete value W ≥ the preset value S1, it indicates that the difference between the absolute values of each difference CAa is large. Then, delete the corresponding CAc according to the value of |CAc - CAp| in descending order, and recalculate the discrete value W of the remaining CAc after each deletion until the discrete value W < the preset value S1, and then stop the calculation. At the same time, record the number d of the deleted CAc.

[0045] Compare the deleted number d with the preset value S2. If the number d < S2, calculate the average value of the remaining CAc and take it as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval. If the number d ≥ S2, take the average value of the maximum and minimum values of the remaining CAc as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval, where both S1 and S2 are preset values, and the specific values are determined by relevant personnel according to the actual situation.

[0046] A3: Repeat steps A1 - A2 to obtain the load calibration deviation values Bn corresponding to the emulsion pump in each temperature segment interval, where n represents the number of temperature segment intervals, and n ≥ 1.

[0047] Step three: Among the a historical estimated liquid supply load values of the emulsion pump in each temperature segment interval, obtain the corresponding equipment temperature of the frequency converter for each, and analyze it to obtain the interval speed calibration points corresponding to each temperature segment interval. The specific method is as follows:

[0048] Perform averaging processing on the corresponding equipment temperature of the frequency converter in the a historical estimated liquid supply load values of each emulsion pump in each temperature segment interval, and take the obtained average value as the temperature calibration value Wn corresponding to each temperature segment interval. Obtain the interval speed calibration points Dn corresponding to each temperature segment interval according to the load calibration deviation value Bn and the temperature calibration value Wn corresponding to each temperature segment interval. Take the temperature calibration value Wn as the abscissa of the interval speed calibration point of each temperature segment interval, and the load calibration deviation value Bn as the ordinate of the interval speed calibration point of each temperature segment interval, and then obtain the coordinates Dn(Wn, Bn) of the interval speed calibration points corresponding to each temperature segment interval.

[0049] Step 4: Import the interval speed calibration points corresponding to each temperature segment interval into the same two-dimensional coordinate system for representation. Connect the speed calibration points in ascending order according to the size of the abscissa to obtain a speed deviation curve, and analyze the speed deviation curve to further obtain the deviation coefficient between the emulsion pump load value and the frequency converter temperature. The specific method is as follows:

[0050] Through the correlation formula , calculate the correlation coefficient H between the load calibration deviation value and the temperature calibration value, where Wp and Bp are the means of the temperature calibration value Wn and the load calibration deviation value Bn respectively, and n≥e≥1;

[0051] When H∉[Y1, Y2], it indicates that there is a positive or negative correlation between the load calibration deviation value and the temperature calibration value, that is, the load calibration deviation value increases as the calibration deviation value increases, or the load calibration deviation value decreases as the calibration deviation value increases. Obtain the coordinates of the speed calibration points (Wmax, E1) and (Wmin, E2) corresponding to the maximum and minimum values in Wn from the coordinates Dn (Wn, Bn) of the speed calibration points, where E1 and E2 are the numerical values of the ordinates corresponding to the speed calibration points with abscissas Wmax and Wmin respectively, Wmax is the maximum value in Wn, and Wmin is the minimum value in Wn. Then, according to the formula: R = (E2 - E1) / (Wmax - Wmin), calculate the deviation coefficient R between the emulsion pump load value and the frequency converter temperature. It should be noted that when R is positive, it indicates that there is a positive correlation between the load calibration deviation value and the temperature calibration value, and when R is negative, it indicates that there is a negative correlation between the load calibration deviation value and the temperature calibration value;

[0052] When H∈[Y1, Y2], it indicates that there is no relationship between the load calibration deviation value and the temperature calibration value, and the deviation coefficient R takes the value of 0. Y1 and Y2 are both preset values, and the specific numerical values are determined by relevant personnel according to the actual situation, and Y1 < Y2;

[0053] Step 5: Obtain the output liquid supply load value according to the estimated liquid supply load value and the deviation coefficient between the emulsion pump load value and the frequency converter temperature. The specific method is as follows:

[0054] Through the calculation formula: BC = [K + (1 + R×|BF - JF|)]×β1, calculate the output liquid supply load value BC of the emulsion pump, where K is the estimated liquid supply load value, JF is the actual operating temperature of the frequency converter, and BF is the calibrated operating temperature of the frequency converter;

[0055] By obtaining the output liquid supply load value module, the deviation value of the estimated liquid supply load value of the emulsion pump affected by the frequency converter temperature is corrected, making the output liquid supply load value more accurate. This avoids the situation where when the frequency converter executes the liquid supply load value of the emulsion pump according to the estimated liquid supply load value, the adjustment effect of the liquid supply load value of the emulsion pump is affected due to the different operating temperatures of the frequency converter, resulting in the inability to reach the value corresponding to the estimated liquid supply load when controlling the liquid supply load value of the emulsion pump, and further affecting the evaluation effect. It can effectively solve the influence of the frequency converter temperature on the adjustment effect of the liquid supply load of the emulsion pump and improve the accuracy and stability of the liquid supply load estimation. Embodiment 2

[0056] As Embodiment 2 of the present invention, when the present application is specifically implemented, compared with Embodiment 1, the difference in the technical solution of this embodiment from that of Embodiment 1 lies only in the method for obtaining the estimated liquid supply load value involved in this embodiment. The specific steps are as follows:

[0057] Collect real-time data from the sensor network of the fully-mechanized coal face. This data includes pressure and flow signals X1 to X6 at different positions, action switch signals X7 to X7+m of the hydraulic supports, and a time signal X8+m. Through a three-layer structure of a preset BP neural network, the input layer, hidden layer, and output layer, the estimated liquid supply load value is obtained to form a pressure setting, and further control the frequency converter to adjust the emulsion pump. The output layer has only one neuron for outputting the prediction result; the hidden layer has two layers, each with ten neurons. This structure helps to improve the expression ability and prediction accuracy of the network. Set three pressure and flow signals X1 to X6 at different positions, set the switch signals X7 to X7+m related to the actions of the hydraulic supports, which can reflect the working state of the hydraulic supports, and set the time signal X8+m to introduce the prediction of the system efficiency decay, which helps to improve the prediction accuracy;

[0058] Preprocess the collected real-time data, handle missing values, use linear interpolation to fill in the missing data points, use the 3-sigma method or box plot method to detect outliers, and perform processing to eliminate or replace them, and perform smoothing processing. Use the moving average method to smooth the data to reduce noise and fluctuations;

[0059] Use the Sigmoid function to perform normalization processing on the collected data, scale the numerical range to between 0 and 1, so that the neural network is easier to process. This step is crucial for improving the learning efficiency and prediction accuracy of the neural network;

[0060] The above are all existing and mature technologies, so no further elaboration will be made here.

[0061] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0062] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for estimating intelligent fluid supply load of a fully mechanized mining face, characterized in that: The following steps are involved: Step 1: Analyze the operating temperature of the inverter within a preset time T to obtain the temperature segmentation range of the inverter, T ≥ 1 day; Step 2: Analyze the historical estimated liquid supply load value and the actual liquid supply load value of the emulsion pump in each temperature segment interval, and obtain the load calibration deviation value corresponding to each temperature segment interval of the frequency converter; Step 3: Analyze the relationship between the historical estimated liquid supply load value of the emulsion pump in each temperature segment and the inverter temperature, and obtain the corresponding interval speed calibration points in each temperature segment; Step 4: Analyze the speed calibration points of each temperature segment interval to obtain the deviation coefficient between the emulsion pump load value and the inverter temperature; Step 5: Calculate the output liquid supply load value based on the estimated liquid supply load value and the deviation coefficient between the emulsion pump load value and the inverter temperature; The specific method of obtaining the interval speed calibration points corresponding to each temperature segment interval is: The inverter performs average processing on the corresponding equipment temperature in the a-time historical estimated liquid supply load value of each emulsion pump in each temperature segment interval, and uses the obtained average value as the temperature calibration value Wn corresponding to each temperature segment interval, and uses the temperature calibration value Wn as the horizontal coordinate of the interval speed calibration point of each temperature segment interval, and the load calibration deviation value Bn as the vertical coordinate of the interval speed calibration point of each temperature segment interval, thereby obtaining the coordinates Dn (Wn, Bn) of the interval speed calibration point corresponding to each temperature segment interval, where n refers to the number of temperature segment intervals, and n≥1; The specific method for obtaining the deviation coefficient between the emulsion pump load value and the inverter temperature is: The correlation coefficient H between the load calibration deviation value and the temperature calibration value is calculated by the correlation formula. When H∉[Y1, Y2], the coordinates (Wmax, E1) and (Wmin, E2) of the speed calibration points corresponding to the maximum and minimum values ​​in Wn are obtained from the coordinates Dn (Wn, Bn) of the speed calibration points, where E1 and E2 are the values ​​of the vertical coordinates corresponding to the speed calibration points with horizontal coordinates Wmax and Wmin, respectively. Wmax is the maximum value in Wn, and Wmin is the minimum value in Wn. Then, according to the formula: R=(E2-E1) / (Wmax-Wmin), the deviation coefficient R between the emulsion pump load value and the inverter temperature is calculated; When H∈[Y1, Y2], it means that there is no relationship between the load calibration deviation value and the temperature calibration value, then the deviation coefficient R takes the value of 0, Y1 and Y2 are both preset values, and Y1<Y2.

2. The method for estimating the intelligent liquid supply load of a fully mechanized mining face according to claim 1 is characterized in that: Get the temperature segmentation range of the inverter. The specific method is: The device operating temperature value of the inverter within the preset time length T is obtained, and the maximum and minimum values ​​of each operating temperature value are obtained. The device operating temperature range is generated according to the maximum and minimum values, and the device operating temperature range is evenly divided to obtain each temperature segment range corresponding to the inverter.

3. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 2 is characterized in that: The load calibration deviation values ​​corresponding to each temperature segment of the inverter are obtained in the following ways: A1: Randomly select one temperature segment interval from each temperature segment interval as the analysis interval, obtain the historical estimated liquid supply load value and the actual liquid supply load value of the emulsion pump in the preset number of times a in the analysis interval, obtain the absolute value CAa of the difference between the historical estimated liquid supply load value a times of the emulsion pump in the analysis interval and the corresponding actual liquid supply load value, calculate the discrete value W of the absolute value CAa of the difference, analyze the discrete value W and then obtain the load calibration deviation value corresponding to the emulsion pump in the analysis interval, a≥1; A2: Repeat step A1 to obtain the load calibration deviation value Bn corresponding to each temperature segment of the emulsion pump, where n refers to the number of temperature segment intervals, and n≥1.

4. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 3 is characterized in that: The specific method for obtaining the load calibration deviation value corresponding to the emulsion pump in the analysis interval is: When the discrete value W is less than the preset value S1, the mean value CAp of CAa is used as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval. When the discrete value W is greater than or equal to the preset value S1, the corresponding CAc is deleted in descending order according to the value of |CAc-CAp|, and the discrete value W of the remaining CAc is recalculated after each deletion until the discrete value W is less than the preset value S1, then the calculation is stopped. At the same time, the number d of deleted CAc is analyzed to obtain the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval, where CAc is any value from Ca1 to CAa, CAp is the mean value from Ca1 to CAa, and a≥c≥1.

5. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 4 is characterized in that: The specific way to analyze the quantity d is: The deleted quantity d is compared with the preset value S2. If the quantity d<S2, the mean of the remaining CAc is used as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval; if the quantity d≥S2, the mean of the maximum and minimum values ​​of the remaining CAc is used as the load calibration deviation value B1 corresponding to the emulsion pump in the analysis interval.

6. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 1, characterized in that: The specific method for obtaining the output liquid supply load value is: The output liquid supply load value BC of the emulsion pump is calculated by the calculation formula: BC=[K+(1+R×|BF-JF|)]×β1, where K is the estimated liquid supply load value, JF is the real-time operating temperature of the inverter, and BF is the calibrated operating temperature of the inverter.

7. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 1, characterized in that: The correlation formula is as follows: , where Wp and Bp are the average of the temperature calibration value Wn and the load calibration deviation value Bn respectively, n≥e≥1.

8. The method for estimating intelligent fluid supply load of a fully mechanized mining face according to claim 1, characterized in that: When R is a positive value, it indicates that there is a positive correlation between the load calibration deviation value and the temperature calibration value. When R is a negative value, it indicates that there is a negative correlation between the load calibration deviation value and the temperature calibration value.

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