An intelligent control system for industrial equipment using the Internet of Things

Abstract

The present invention belongs to the technical field of intelligent control of the Internet of Things, and specifically is an intelligent control system for industrial equipment using the Internet of Things, including an acquisition module, a load torque analysis module, an abnormal impact analysis module and a control module, aiming to optimize energy consumption management and equipment performance. The system monitors the energy consumption of the equipment motor in real time through the acquisition module, constructs an energy consumption curve and analyzes normal and abnormal intervals. The load torque analysis module compares the rated interval to determine the load state. The abnormal impact analysis module evaluates the correlation between energy consumption and load torque. The control module adjusts the motor speed accordingly to achieve load torque control and energy saving. The system uses the quartile range method to define the energy consumption baseline, compares the real-time energy consumption with the load torque curve, calculates the abnormal overlap rate, and guides the speed adjustment. The system uses the Internet of Things technology to achieve refined management and automatic control of industrial equipment energy consumption, improve energy utilization efficiency and production efficiency, and is suitable for various industrial scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of Internet of Things, and specifically is an intelligent control system for industrial equipment using Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, the Industrial 4.0 era requires smarter and more sophisticated equipment management methods. Internet of Things technology can achieve interconnection between devices, collect and analyze large amounts of real-time data, and provide possibilities for intelligent control of industrial equipment.

[0003] However, current IoT-driven control systems mostly focus on data collection and simple feedback mechanisms, lacking in-depth analysis of the deep correlation between equipment energy consumption and performance, especially in the processing of the dynamic relationship between motor load torque and energy consumption, where there is still room for improvement.

[0004] Therefore, it is particularly necessary to develop a system that can comprehensively analyze the energy consumption curve and load torque and intelligently adjust the operating status of the equipment accordingly. This will not only help save energy and reduce emissions, but also improve overall production efficiency and economic benefits.

[0005] To this end, the present invention provides an intelligent control system for industrial equipment using the Internet of Things. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: the industrial equipment intelligent control system using the Internet of Things described in the present invention includes:

[0008] The acquisition module collects the input power of the device motor in the time subunit, calculates the energy consumption and constructs an actual energy consumption curve chart;

[0009] The energy consumption analysis module constructs an energy consumption baseline through the actual energy consumption curve chart, and calculates the actual normal energy consumption range and the actual abnormal energy consumption range through the energy consumption baseline;

[0010] Obtain the real-time energy consumption of the equipment motor, compare the real-time energy consumption with the actual normal energy consumption range, and obtain energy consumption abnormality signals and energy consumption normal signals;

[0011] The load torque analysis module obtains the output power of the equipment motor through the abnormal energy consumption signal, and obtains the load torque of the equipment motor through calculation;

[0012] Compare the actual load torque value with the rated load torque range to generate a load torque normal signal and a load torque abnormal signal;

[0013] The abnormal impact analysis module analyzes the relationship between the energy consumption and load torque of the equipment motor through the energy consumption abnormal signal and the load torque abnormal signal, and obtains the abnormal impact signal and the abnormal non-impact signal;

[0014] The control module generates adjustment commands through abnormal impact signals and controls the load torque by adjusting the speed of the equipment motor, thereby reducing energy consumption;

[0015] Preferably, the energy consumption calculation method is specifically: multiplying the input power of the equipment motor by the specific time represented by the time subunit to obtain the energy consumption;

[0016] Preferably, constructing the actual energy consumption curve graph specifically comprises: integrating the energy consumption Pn data of the equipment motor in all time sub-units and the corresponding time series into an energy consumption data set;

[0017] Based on the energy consumption data set, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the energy consumption of the equipment motor as the vertical axis, to obtain an actual energy consumption curve of the equipment motor energy consumption changing with time;

[0018] Preferably, a normal range of energy consumption baseline is constructed for the equipment motor, specifically: integrating all energy consumption values ​​in the actual curve graph, and using the interquartile range method to obtain the energy consumption baseline;

[0019] Arrange all energy consumption values ​​in ascending order, and define the value corresponding to the boundary of the middle 25% of the data set as the lower quartile Q1, that is, at least 25% of the values ​​in the data set are less than or equal to Q1;

[0020] The value corresponding to the 75% boundary in the middle of the data set is defined as the upper quartile Q3, that is, at least 75% of the values ​​in the data set are less than or equal to Q3;

[0021] Calculate the difference between Q3 and Q1 to get the normal baseline of energy consumption, recorded as IQR;

[0022] Preferably, the actual normal energy consumption interval and the actual abnormal energy consumption interval are obtained, specifically:

[0023] According to the formula S1=Q1-1.5*IQR, the upper boundary value S1 of the normal energy consumption interval is obtained, and an upper boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the upper boundary value S1 of the normal energy consumption interval. According to the formula S2=Q3-1.5*IQR, the lower boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the lower boundary value S2 of the normal energy consumption interval;

[0024] The energy consumption curve is located above the lower boundary line of the normal energy consumption range and above the upper boundary line of the normal energy consumption range.

[0025] The area below the boundary is recorded as the actual normal energy consumption range;

[0026] The energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range

[0027] The area is recorded as the actual abnormal energy consumption interval;

[0028] Preferably, the load torque normal signal and the load torque abnormal signal are specifically:

[0029] The output power of the equipment motor is obtained, and the load torque of the equipment motor in each time sub-unit is obtained by calculation;

[0030] When the load torque of the motor increases, if the motor speed remains constant, then the motor must provide more output power to overcome the increased torque. Similarly, if the motor output power remains constant and the load torque increases, then the motor speed will decrease until a new equilibrium state is reached;

[0031] Get the rated load torque range (Tze1, Tze2) of the motor when the equipment is running, and compare the actual load torque value with the rated load torque range;

[0032] If Tze1<Tz<Tze2, it means that the load torque of the equipment motor is in a normal state, and a normal load torque signal of the motor is generated;

[0033] If Tze1>Tz, or Tz>Tze2, it means that the load torque of the equipment motor is in an abnormal state, and a motor load torque abnormality signal is generated;

[0034] Preferably, the equipment running time is taken as the analysis object, and the relationship between the energy consumption of the equipment motor and the load torque is analyzed, specifically:

[0035] Obtain the real-time energy consumption value and real-time load torque value of the motor when the equipment is running;

[0036] With the equipment running time as the horizontal axis and the real-time energy consumption value or real-time load torque value of the motor when the equipment is running as the vertical axis, a plane coordinate system is constructed, which is recorded as a real-time energy consumption or real-time load torque curve graph;

[0037] Based on the actual energy consumption curve graph, the actual energy consumption curve graph is compared with the energy consumption curve in the real-time energy consumption curve graph, and a lower boundary line of a normal energy consumption interval and an upper boundary line of a normal energy consumption interval are drawn in the real-time energy consumption curve graph to separate the real-time abnormal energy consumption interval and the real-time normal energy consumption interval;

[0038] Obtain the abnormal energy consumption time period corresponding to the area in the real-time abnormal energy consumption interval in the real-time energy consumption curve graph;

[0039] Based on the rated load torque range, on the real-time load torque curve, a rated load torque maximum boundary line parallel to the horizontal axis is drawn with the maximum value of the rated load torque of the equipment motor;

[0040] Draw a minimum boundary line of rated load torque parallel to the horizontal axis with the minimum value of rated load torque of the equipment motor;

[0041] The area below the maximum boundary line of the rated load torque and above the minimum boundary line of the rated load torque is recorded as the normal load torque range;

[0042] The area above the maximum boundary line of the rated load torque or below the minimum boundary line of the rated load torque is recorded as the abnormal load torque interval;

[0043] Obtaining the abnormal load torque time period corresponding to the area in the abnormal load torque interval in the real-time load torque curve diagram;

[0044] Preferably, the abnormal energy consumption time period is compared with the abnormal load torque time period, specifically:

[0045] Obtain the overlap time of the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal overlap time;

[0046] Obtain the non-coincidence time between the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal non-coincidence time;

[0047] The duration of the abnormal coincidence time and the duration of the abnormal non-coincidence time are summed up to obtain the total abnormal duration;

[0048] Calculate the ratio of the abnormal coincidence time to the total abnormal duration to obtain the abnormal coincidence rate Y1;

[0049] If the abnormal coincidence rate Y1 during the operation time of the equipment motor is greater than or equal to the abnormal coincidence rate threshold value Y2 during the operation time of the equipment motor, it means that the energy consumption abnormality during the operation of the equipment motor is related to the load torque abnormality, and an abnormal influence signal is generated;

[0050] If the abnormal coincidence rate Y1 during the equipment operation time is less than the abnormal coincidence rate threshold value Y2 during the equipment operation time, it means that the energy consumption abnormality during the operation of the equipment motor is not related to the load torque abnormality, and an abnormal non-influence signal is generated;

[0051] Preferably, the load torque is controlled by adjusting the speed of the equipment motor to reduce energy consumption, specifically:

[0052] When the output power of the equipment motor remains unchanged, the speed of the equipment motor is inversely proportional to the load torque of the equipment motor;

[0053] Obtain the number of pole pairs and power supply frequency of the equipment motor, and calculate the synchronous speed of the equipment motor;

[0054] The speed of the equipment motor is calculated according to the speed formula through the synchronous speed of the equipment motor;

[0055] Preferably, under the condition of constant output power of the equipment motor, the synchronous speed of the equipment motor is increased by increasing the power supply frequency of the equipment motor, thereby increasing the actual speed of the equipment motor, and the load torque of the equipment motor is reduced by increasing the actual speed of the equipment motor, and the energy consumption of the equipment motor is reduced by reducing the load torque of the equipment motor.

[0056] The beneficial effects of the present invention are as follows:

[0057] 1. The intelligent control system for industrial equipment using the Internet of Things described in the present invention monitors the energy consumption of the equipment motor in real time, constructs an actual energy consumption curve and a baseline, and automatically identifies normal and abnormal energy consumption intervals. This precise energy consumption management mechanism can promptly detect energy consumption anomalies, warn of potential faults, and effectively prevent equipment shutdowns caused by energy consumption anomalies, thereby improving production efficiency and safety. At the same time, the instant feedback of energy consumption anomaly signals can trigger subsequent load torque analysis and regulation, forming a closed-loop control and enhancing the system's adaptive capabilities.

[0058] 2. The present invention describes an intelligent control system for industrial equipment using the Internet of Things. The system adopts advanced algorithms to analyze the load torque state based on abnormal energy consumption signals. By comparing the actual load torque with the rated range, it can quickly determine whether the load is abnormal. For abnormal load torque, the system can automatically generate control commands, control the load torque by adjusting the speed of the equipment motor, and achieve energy consumption optimization. This intelligent load management can not only reduce energy consumption, but also reduce equipment wear caused by overload, extend the service life of the equipment, and save maintenance costs.

[0059] 3. The present invention describes an intelligent control system for industrial equipment using the Internet of Things. The system deeply analyzes the relationship between energy consumption and load torque during equipment operation. By comparing the actual energy consumption curve with the real-time energy consumption curve and combining the changes in load torque, the system accurately identifies the correlation between abnormal energy consumption and abnormal load torque. This comprehensive analysis capability helps to accurately locate the source of problems in equipment operation and guide more effective maintenance strategies. At the same time, it reduces energy consumption through intelligent regulation and achieves the goal of energy conservation and emission reduction, responds to the call for green manufacturing, and enhances the social responsibility and market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] Figure 1It is a block diagram of an intelligent control system for industrial equipment using the Internet of Things;

[0062] Figure 2 It is a flow chart for obtaining the actual abnormal energy consumption range in the intelligent control system of industrial equipment using the Internet of Things;

[0063] Figure 3 It is a flow chart for obtaining abnormal load torque time period in the intelligent control system of industrial equipment using the Internet of Things;

[0064] Figure 4 It is a flow chart for obtaining abnormal impact signals in the intelligent control system of industrial equipment using the Internet of Things; DETAILED DESCRIPTION

[0065] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0066] Embodiment 1

[0067] like Figure 1 As shown, an intelligent control system for industrial equipment using the Internet of Things according to an embodiment of the present invention includes a collection module, an energy consumption analysis module, a load torque analysis module, an abnormal impact analysis module and a control module;

[0068] The acquisition module collects the input power of the device motor in the time subunit, calculates the energy consumption and constructs an actual energy consumption curve chart;

[0069] The energy consumption analysis module constructs an energy consumption baseline through the actual energy consumption curve chart, and calculates the actual normal energy consumption range and the actual abnormal energy consumption range through the energy consumption baseline;

[0070] Obtain the real-time energy consumption of the equipment motor, compare the real-time energy consumption with the actual normal energy consumption range, and obtain energy consumption abnormality signals and energy consumption normal signals;

[0071] The load torque analysis module obtains the output power of the equipment motor through the abnormal energy consumption signal, and obtains the load torque of the equipment motor through calculation;

[0072] Compare the actual load torque value with the rated load torque range to generate a load torque normal signal and a load torque abnormal signal;

[0073] The abnormal impact analysis module analyzes the relationship between the energy consumption and load torque of the equipment motor through the energy consumption abnormal signal and the load torque abnormal signal, and obtains the abnormal impact signal and the abnormal non-impact signal;

[0074] The control module generates adjustment commands through abnormal impact signals and controls the load torque by adjusting the speed of the equipment motor, thereby reducing energy consumption.

[0075] Example 2

[0076] Divide the monitoring period into time sub-units of equal length, and obtain the input power of the equipment motor in each time sub-unit;

[0077] The energy consumption Pn is obtained by multiplying the input power of the equipment motor by the specific time represented by the time subunit;

[0078] Energy consumption refers to the energy consumption of the equipment motor after the equipment motor runs continuously for one time sub-unit at a constant input power;

[0079] Factors that affect the energy consumption of equipment motors include heat losses and mechanical losses caused by the current in the motor windings passing through resistance;

[0080] The heat loss is proportional to the resistance of the motor winding and the square of the current passing through the winding;

[0081] If the abnormal energy consumption is caused by heat loss, it can be adjusted by controlling the current. If the controlled current cannot prevent the abnormal heat loss, it means that the motor winding is abnormal, and the heat loss can be controlled by replacing the motor winding.

[0082] Mechanical losses include the contact friction between the equipment motor shaft and the bearing, that is, bearing friction. The loss caused by bearing friction will increase as the speed of the equipment motor increases;

[0083] If the shaft or bearing of the equipment motor is abnormal, it will cause abnormal energy consumption of the equipment motor. The energy consumption can be controlled by replacing the abnormal parts.

[0084] Integrate the energy consumption Pn data of the equipment motor in all time sub-units and the corresponding time series into an energy consumption data set;

[0085] Based on the energy consumption data set, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the energy consumption of the equipment motor as the vertical axis, to obtain an actual energy consumption curve showing the energy consumption of the equipment motor changing with time.

[0086] Example 3

[0087] like Figure 2 As shown, based on the actual energy consumption curve, a normal range of energy consumption baseline is constructed for the equipment. Specifically, all energy consumption values ​​in the actual curve are integrated and the energy consumption baseline is obtained using the interquartile range method.

[0088] Arrange all energy consumption values ​​in ascending order, and define the value corresponding to the boundary of the middle 25% of the data set as the lower quartile Q1, that is, at least 25% of the values ​​in the data set are less than or equal to Q1;

[0089] The value corresponding to the 75% boundary in the middle of the data set is defined as the upper quartile Q3, that is, at least 75% of the values ​​in the data set are less than or equal to Q3;

[0090] The difference between Q3 and Q1 is calculated to obtain the normal baseline of the energy consumption index, which is recorded as IQR;

[0091] According to the formula S1=Q1-1.5*IQR, the upper boundary value S1 of the normal energy consumption interval is obtained, and an upper boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the upper boundary value S1 of the normal energy consumption interval. According to the formula S2=Q3-1.5*IQR, the lower boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the lower boundary value S2 of the normal energy consumption interval;

[0092] The normal range of energy consumption during the actual operation of the equipment is obtained by the above method, that is, the area where the energy consumption curve is above the lower boundary line of the normal energy consumption range and below the upper boundary line of the normal energy consumption range is recorded as the actual normal energy consumption range;

[0093] The area where the energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range is recorded as the actual abnormal energy consumption range;

[0094] Obtain the real-time energy consumption of the equipment motor in the time sub-unit, and compare it with the actual normal energy consumption range after processing;

[0095] Specific:

[0096] Obtain the abnormal time period corresponding to the actual abnormal energy consumption interval, integrate all abnormal time periods within the monitoring period, and record them as the total abnormal time;

[0097] If the total abnormal time is greater than or equal to the preset time threshold, it means that the equipment motor is in an abnormal energy consumption state, and an abnormal energy consumption signal is generated;

[0098] If the total abnormal time is less than the preset time threshold, it means that the equipment motor is in a non-abnormal energy consumption state, and a non-abnormal energy consumption signal is generated.

[0099] Example 4

[0100] Based on the abnormal energy consumption signal of the equipment motor generated above, since the abnormal motor load torque during equipment operation will lead to the abnormal motor energy consumption during equipment operation, it is analyzed and judged whether the abnormal energy consumption of the equipment motor is related to the abnormal load torque of the equipment motor. If there is a relationship of load torque influence, it is convenient to adjust the motor load torque in time during the subsequent equipment operation to reduce the influence of the equipment motor load torque on the energy consumption of the equipment motor, effectively maintain the energy consumption stability of the equipment motor during operation, and thus reduce the energy consumption and equipment damage of the equipment motor during operation. Specifically:

[0101] Get the output power Ps of the equipment motor through the formula The load torque Tz of the equipment motor in each time sub-unit is calculated, and Nz is the speed of the equipment motor;

[0102] Among them, when the load torque Tz of the motor increases, if the motor speed Nz remains unchanged, then the motor must provide more output power Ps to overcome the increased torque. Similarly, if the motor output power Ps remains unchanged and the load torque Tz increases, then the motor speed Nz will decrease until a new equilibrium state is reached;

[0103] Get the rated load torque range (Tze1, Tze2) of the motor when the equipment is running, and compare the actual load torque value with the rated load torque range;

[0104] If Tze1<Tz<Tze2, it means that the load torque of the equipment motor is in a normal state, and a normal load torque signal of the motor is generated;

[0105] If Tze1>Tz, or Tz>Tze2, it means that the load torque of the equipment motor is in an abnormal state, and a motor load torque abnormality signal is generated.

[0106] Example 5

[0107] like Figure 3-Figure 4 As shown, based on the energy consumption abnormality signal and the load torque abnormality signal in the above embodiment, the relationship between the energy consumption and the load torque of the equipment motor is analyzed with the equipment running time as the analysis object, specifically:

[0108] Obtain the real-time energy consumption value and real-time load torque value of the motor when the equipment is running;

[0109] With the equipment running time as the horizontal axis and the real-time energy consumption value or real-time load torque value of the motor when the equipment is running as the vertical axis, a plane coordinate system is constructed, which is recorded as a real-time energy consumption or real-time load torque curve graph;

[0110] Based on the actual energy consumption curve graph, the actual energy consumption curve graph is compared with the energy consumption curve in the real-time energy consumption curve graph, and a lower boundary line of a normal energy consumption interval and an upper boundary line of a normal energy consumption interval are drawn in the real-time energy consumption curve graph to separate the real-time abnormal energy consumption interval and the real-time normal energy consumption interval;

[0111] Obtain the abnormal energy consumption time period corresponding to the area in the real-time abnormal energy consumption interval in the real-time energy consumption curve graph;

[0112] Based on the rated load torque range, on the real-time load torque curve, a rated load torque maximum boundary line parallel to the horizontal axis is drawn with the rated load torque maximum value Tze2 of the equipment motor;

[0113] Draw a minimum boundary line of rated load torque parallel to the horizontal axis using the minimum value Tze1 of the rated load torque of the equipment motor;

[0114] The area below the maximum boundary line of the rated load torque and above the minimum boundary line of the rated load torque is recorded as the normal load torque range;

[0115] The area above the maximum boundary line of the rated load torque or below the minimum boundary line of the rated load torque is recorded as the abnormal load torque interval;

[0116] Obtaining the abnormal load torque time period corresponding to the area in the abnormal load torque interval in the real-time load torque curve diagram;

[0117] like Figure 4 As shown, the abnormal energy consumption time period is compared with the abnormal load torque time period;

[0118] Specific:

[0119] Obtain the overlap time of the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal overlap time;

[0120] Obtain the non-coincidence time between the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal non-coincidence time;

[0121] The duration of the abnormal coincidence time and the duration of the abnormal non-coincidence time are summed up to obtain the total abnormal duration;

[0122] Calculate the ratio of the abnormal coincidence time to the total abnormal duration to obtain the abnormal coincidence rate Y1;

[0123] If the abnormal coincidence rate Y1 during the operation time of the equipment motor is greater than or equal to the abnormal coincidence rate threshold value Y2 during the operation time of the equipment motor, it means that the energy consumption abnormality during the operation of the equipment motor is related to the load torque abnormality, and an abnormal influence signal is generated;

[0124] If the abnormal coincidence rate Y1 during the equipment operation time is less than the abnormal coincidence rate threshold value Y2 during the equipment operation time, it means that the energy consumption abnormality during the operation of the equipment motor is not related to the load torque abnormality, and an abnormal non-influence signal is generated.

[0125] Example 6

[0126] Based on the abnormal impact signal, the load torque is controlled by adjusting the speed of the equipment motor to achieve the purpose of reducing energy consumption;

[0127] Specific:

[0128] Based on Example 4, when the output power of the device motor remains unchanged, the speed of the device motor is inversely proportional to the load torque of the device motor;

[0129] Get the number of pole pairs pd and power frequency f of the equipment motor, through the formula Get the synchronous speed ns of the equipment motor through the formula Get the slip rate of the equipment motor;

[0130] According to the speed formula Get the relationship between the speed of the equipment motor and the power supply frequency;

[0131] It should be noted that, when the equipment motor has a constant output power, after receiving an abnormal impact signal, the synchronous speed of the equipment motor can be increased by increasing the power supply frequency f of the equipment motor, thereby increasing the actual speed of the equipment motor, thereby achieving the purpose of reducing the load torque of the equipment motor by increasing the actual speed of the equipment motor, and reducing the energy consumption of the equipment motor by reducing the load torque of the equipment motor.

[0132] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent control system for industrial equipment using the Internet of Things, characterized by: It includes acquisition module, energy consumption analysis module, load torque analysis module, abnormal impact analysis module and control module; The acquisition module collects the input power of the device motor in the time subunit, calculates the energy consumption and constructs an actual energy consumption curve chart; The energy consumption analysis module constructs an energy consumption baseline through the actual energy consumption curve chart, and calculates the actual normal energy consumption range and the actual abnormal energy consumption range through the energy consumption baseline; Obtain the real-time energy consumption of the equipment motor, compare the real-time energy consumption with the actual normal energy consumption range, and obtain energy consumption abnormality signals and energy consumption normal signals; The load torque analysis module obtains the output power of the equipment motor through the abnormal energy consumption signal, and obtains the load torque of the equipment motor through calculation; Compare the actual load torque value with the rated load torque range to generate a load torque normal signal and a load torque abnormal signal; The abnormal impact analysis module analyzes the relationship between the energy consumption and load torque of the equipment motor through the energy consumption abnormal signal and the load torque abnormal signal, and obtains the abnormal impact signal and the abnormal non-impact signal; The control module generates adjustment commands through abnormal impact signals to complete equipment control; The abnormal energy consumption time period is compared with the abnormal load torque time period. Specifically: Obtain the overlap time of the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal overlap time. between; Obtain the non-coincidence time between the abnormal energy consumption time period and the abnormal load torque time period, and record it as the abnormal non-coincidence time; The duration of the abnormal coincidence time and the duration of the abnormal non-coincidence time are summed up to obtain the total abnormal duration; Calculate the ratio of the abnormal coincidence time to the total abnormal duration to obtain the abnormal coincidence rate Y1; If the abnormal coincidence rate Y1 during the operation time of the equipment motor is greater than or equal to the abnormal coincidence rate threshold value Y2 during the operation time of the equipment motor, it means that the energy consumption abnormality during the operation of the equipment motor is related to the load torque abnormality, and an abnormal influence signal is generated.

2. According to claim 1, an intelligent control system for industrial equipment using the Internet of Things is characterized by: The energy consumption of the equipment motor is the product of the input power of the equipment motor and the time subunit, and the energy consumption is recorded as Pn.

3. According to claim 2, an intelligent control system for industrial equipment using the Internet of Things is characterized by: The construction process of the actual energy consumption curve is as follows: Integrate the energy consumption Pn data of the equipment motor in all time sub-units and the corresponding time series into an energy consumption data set; Based on the energy consumption data set, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the energy consumption of the equipment motor as the vertical axis. The energy consumption obtained by the time sub-unit is plotted in the plane coordinate system to construct an actual energy consumption curve showing the energy consumption of the equipment motor changing with time.

4. According to claim 3, an intelligent control system for industrial equipment using the Internet of Things is characterized by: The process of building an energy consumption baseline for equipment motors is as follows: Integrate all energy consumption values ​​in the actual curve and use the interquartile range method to obtain the energy consumption baseline; Arrange all energy consumption values ​​in ascending order, and define the value corresponding to the boundary of the middle 25% of the data set as the lower quartile Q1, that is, at least 25% of the values ​​in the data set are less than or equal to Q1; The value corresponding to the 75% boundary in the middle of the data set is defined as the upper quartile Q3, that is, at least 75% of the values ​​in the data set are less than or equal to Q3; The difference between Q3 and Q1 is calculated to obtain the normal baseline of energy consumption, which is recorded as IQR.

5. According to claim 4, an intelligent control system for industrial equipment using the Internet of Things is characterized by: The division process of the actual normal energy consumption interval and the actual abnormal energy consumption interval is as follows: According to the formula S1=Q1-1.5*IQR, the lower boundary value S1 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the lower boundary value S1 of the normal energy consumption interval. According to the formula S2=Q3-1.5*IQR, the upper boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the upper boundary value S2 of the normal energy consumption interval; The energy consumption curve is located above the lower boundary line of the normal energy consumption range and above the upper boundary line of the normal energy consumption range. The area below the boundary is recorded as the actual normal energy consumption range; The energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range The area is recorded as the actual abnormal energy consumption range.

6. The intelligent control system for industrial equipment using the Internet of Things according to claim 1, characterized in that: The process of obtaining the normal load torque signal and the abnormal load torque signal is as follows: The output power of the equipment motor is obtained, and the load torque of the equipment motor in each time sub-unit is obtained by calculation; Obtain the rated load torque range (Tze1, Tze2) of the motor when the equipment is running, and compare the actual load torque value with the rated load torque range; If Tze1<Tz<Tze2, it means that the load torque of the equipment motor is in a normal state, and a normal load torque signal of the motor is generated; If Tze1>Tz, or Tz>Tze2, it means that the load torque of the equipment motor is in an abnormal state, and a motor load torque abnormality signal is generated.

7. The intelligent control system for industrial equipment using the Internet of Things according to claim 6 is characterized by: Taking the equipment running time as the analysis object, the relationship between the energy consumption of the equipment motor and the load torque is analyzed, specifically: Obtain the real-time energy consumption value and real-time load torque value of the motor when the equipment is running; With the equipment running time as the horizontal axis and the real-time energy consumption value or real-time load torque value of the motor when the equipment is running as the vertical axis, a plane coordinate system is constructed, which is recorded as a real-time energy consumption or real-time load torque curve graph; Based on the actual energy consumption curve graph, the actual energy consumption curve graph is compared with the energy consumption curve in the real-time energy consumption curve graph, and a lower boundary line of a normal energy consumption interval and an upper boundary line of a normal energy consumption interval are drawn in the real-time energy consumption curve graph to separate the real-time abnormal energy consumption interval and the real-time normal energy consumption interval; Obtain the abnormal energy consumption time period corresponding to the area in the real-time abnormal energy consumption interval in the real-time energy consumption curve graph; Based on the rated load torque range, on the real-time load torque curve, a rated load torque maximum boundary line parallel to the horizontal axis is drawn with the maximum value of the rated load torque of the equipment motor; Draw a minimum boundary line of rated load torque parallel to the horizontal axis with the minimum value of rated load torque of the equipment motor; The area below the maximum boundary line of the rated load torque and above the minimum boundary line of the rated load torque is recorded as the normal load torque range; The area above the maximum boundary line of the rated load torque or below the minimum boundary line of the rated load torque is recorded as the abnormal load torque interval; The abnormal load torque time period corresponding to the area in the abnormal load torque interval in the real-time load torque curve diagram is obtained.

8. The intelligent control system for industrial equipment using the Internet of Things according to claim 7 is characterized by: If the abnormal coincidence rate Y1 during the equipment operation time is less than the abnormal coincidence rate threshold value Y2 during the equipment operation time, it means that the energy consumption abnormality during the operation of the equipment motor is not related to the load torque abnormality, and an abnormal non-influence signal is generated.

9. The intelligent control system for industrial equipment using the Internet of Things according to claim 8, characterized in that: Based on the abnormal impact signal, the speed of the equipment motor is taken as the analysis object, and the relationship between the power supply frequency of the equipment motor and the load torque of the equipment motor is analyzed. Specifically: When the output power of the equipment motor remains unchanged, the speed of the equipment motor is inversely proportional to the load torque of the equipment motor; Obtain the number of pole pairs and power supply frequency of the equipment motor, and calculate the synchronous speed of the equipment motor; Based on the synchronous speed of the equipment motor, the speed of the equipment motor is calculated according to the speed formula and the speed is proportional to the power supply frequency; That is, the power supply frequency of the equipment motor is inversely proportional to the load torque of the equipment motor.

10. The intelligent control system for industrial equipment using the Internet of Things according to claim 9, characterized in that: Based on the relationship between the power supply frequency of the equipment motor and the load torque of the equipment motor, the energy consumption of the equipment motor is regulated by the power supply frequency of the equipment motor.

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