CN114870684B - Intelligent stirring equipment based on internet of things control - Google Patents

Intelligent stirring equipment based on internet of things control Download PDF

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Publication number
CN114870684B
CN114870684B CN202210531976.6A CN202210531976A CN114870684B CN 114870684 B CN114870684 B CN 114870684B CN 202210531976 A CN202210531976 A CN 202210531976A CN 114870684 B CN114870684 B CN 114870684B
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equipment
stirring
internet
things
time
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CN114870684A (en
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万金荣
张前德
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Jiangsu Huijie Intelligent Mixing Technology Co ltd
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Jiangsu Huijie Intelligent Mixing Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F27/00—Mixers with rotary stirring devices in fixed receptacles; Kneaders
    • B01F27/80—Mixers with rotary stirring devices in fixed receptacles; Kneaders with stirrers rotating about a substantially vertical axis
    • B01F27/90—Mixers with rotary stirring devices in fixed receptacles; Kneaders with stirrers rotating about a substantially vertical axis with paddles or arms 
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F33/00—Other mixers; Mixing plants; Combinations of mixers
    • B01F33/80—Mixing plants; Combinations of mixers
    • B01F33/81—Combinations of similar mixers, e.g. with rotary stirring devices in two or more receptacles
    • B01F33/813—Combinations of similar mixers, e.g. with rotary stirring devices in two or more receptacles mixing simultaneously in two or more mixing receptacles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
    • B01F35/20—Measuring; Control or regulation
    • B01F35/22—Control or regulation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
    • G05B19/054—Input/output
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The invention relates to the field of stirring equipment, in particular to intelligent stirring equipment based on internet of things control and a fault detection method, which comprise a stirring device and a control system, wherein the control module comprises a touch screen, a PLC (programmable logic controller) module and a remote communication module.

Description

Intelligent stirring equipment based on internet of things control
Technical Field
The invention relates to the field of stirring equipment, in particular to intelligent stirring equipment based on internet of things control and a fault detection method.
Background
Compared with food stirring equipment, industrial stirring equipment has the characteristics of larger size, larger occupied area and higher concentration, and at present, the control system made by most manufacturers of stirring equipment is also the electrical control of low-voltage components in the traditional sense. With the rapid development of the internet, many customers need to monitor the running state of the stirring device at the mobile phone end, and control the stirring device at the mobile phone end. Thus, the conventional electrical control cannot meet the demands of customers.
On the other hand, in the aspect of re-fault elimination of the existing industrial stirring equipment, sensors are usually arranged on a motor and a circuit, wherein the sensors comprise a rotating speed sensor, a torque sensor, a rotating speed sensor and the like, whether the equipment has faults or not is judged by directly acquiring the values of the sensors, and then manual elimination is arranged.
CN102259390a discloses a mobile mortar stirring station based on the internet of things, specifically discloses adding GPS module and wireless module, can be real-time with the address information transmission of stirring station for remote server to through weighing sensor, the total quality of stirring at every turn can be convenient obtained, and the wireless module transmission is given remote server, thereby the convenience manager can monitor stirring station position and stirring volume in real time.
CN203471956U discloses a monitoring and managing system of a dry-mixed mortar storage mixer based on the internet of things technology, specifically discloses a real-time monitoring and storing mixer for producing and using conditions of dry-mixed mortar, and has a remote display function, so that multiple clients can share data.
However, the above prior art still has the following technical problems:
1. the existing stirring equipment is only additionally provided with a remote control module, can only work based on a preset control command or a control command actively sent by a user, and cannot actively prompt the user of a fault;
2. in the existing stirring equipment, the troubleshooting is dependent on the direct data of the sensor, and the fault can be reflected only when the direct data of the sensor is abnormal;
3. the existing stirring equipment has insufficient perception capability on 'non-fault abnormality', and even if hidden non-fault abnormality is found, the equipment cannot exert all the effects, so that the production efficiency is reduced; the term "non-fault abnormality" refers to the fact that no obvious abnormal signal is generated in the equipment, but due to the problems of poor debugging, insufficient lubrication and the like, the problems of reduced efficiency, increased fluctuation, reduced stability and the like are generated.
Disclosure of Invention
In order to solve the technical problems, the invention provides the following technical scheme:
the utility model provides an intelligent stirring equipment based on thing allies oneself with control, includes agitating unit and control system, control module includes touch-sensitive screen, PLC module and remote communication module, remote communication module includes the network card socket, the network card socket is used for connecting 4G network card, 4G network card is used for with user's cell-phone remote communication, the user logs in the backstage of remote communication module on the cell-phone, compiles the point position and the relevant parameter of the state of the equipment that will show on the touch-sensitive screen on remote communication module backstage, after the establishment, just can control equipment and look over the relevant running state of equipment at the cell-phone end.
Further, the control system is also in remote communication with the server of the Internet of things, a timer and a torque sensor are arranged on a motor main shaft of the stirring device, and the timer is used for determining stirring time T0 for the stirring device to finish one-time stirring;
the stirring time T0 refers to: and starting timing when the stirring device finishes loading and starting materials, continuously recording damping torque borne by the stirring main shaft by the torque sensor, and when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1 and the preset duration time does not exceed the upper threshold value M2 and the lower threshold value M1, considering that stirring is finished, and recording the duration time from the stirring starting time to the stirring finishing time T2 as stirring time T0.
Further, the plurality of intelligent stirring devices are in remote communication with the internet of things server, the control system sends the device number and the stirring time T of each work to the internet of things server, the internet of things server performs Gaussian distribution inspection based on the stirring time T of any device to determine whether the stirring time T of the device meets Gaussian distribution, if not, the intelligent stirring device gives an alarm to a mobile phone of a user, and feeds the alarm back to the intelligent stirring device, and the touch screen displays the alarm; if yes, marking the equipment as normal, calculating mathematical expectation mu and standard deviation sigma of the equipment marked as normal, carrying out cluster analysis on the mathematical expectation mu and standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as early warning, and sending the equipment number to a user mobile phone.
Further, the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
where x (i) is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the order statistic.
Further, an SPSS program is pre-stored in the internet of things server, the equipment meeting gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal.
A method for troubleshooting intelligent stirring equipment based on internet of things control, which is based on intelligent stirring equipment, the method for troubleshooting comprises the following steps:
s1, preparation: the engineer compiles the logic control program of the stirring equipment in the computer by using professional programming software, and then downloads the logic control program into the PLC module; and then, a professional touch screen control picture in the computer and related parameters are compiled and downloaded into the touch screen. Then, connecting the touch screen with the PLC by using a network cable; carrying out communication inspection on each intelligent stirring device, and completing remote communication connection between each intelligent stirring device and a mobile phone of a user and an Internet of things server;
s2, a pre-experiment step: loading a predetermined amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that stirring is completed based on a stirring standard and continuously stirring for a certain time, deriving the stirring time and torque relation curve, and determining an upper threshold M2 and a lower threshold M1 of damping torque and required duration based on the curve;
s3, working steps: working according to a preset stirring program;
s4, data checking: the method specifically comprises the following steps:
s41: gaussian analysis: the control system sends the equipment number and the stirring time T of each work to an Internet of things server, the Internet of things server performs Gaussian distribution test based on the stirring time T of any one piece of equipment to determine whether the stirring time T of the equipment meets Gaussian distribution, if not, the equipment is marked as abnormal, a mobile phone of a user is alarmed, and the alarm is fed back to the intelligent stirring equipment and displayed on a touch screen; if yes, calculating mathematical expectation mu and standard deviation sigma of the equipment;
s42: and (3) cluster analysis: and for the equipment with stirring time meeting Gaussian distribution, marking as normal, carrying out cluster analysis on mathematical expectation mu and standard deviation sigma of the equipment marked as normal, marking discrete equipment with the mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to a mobile phone of a user.
Further, the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
where x (i) is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the order statistic.
Further, an SPSS program is pre-stored in the internet of things server, the equipment meeting gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal.
Further, the cluster analysis is a k-means cluster algorithm.
Further, the cluster analysis is a hierarchical clustering algorithm.
Further, the pre-experiment step of the invention further comprises drawing a weight-stirring time curve, and the concrete method comprises the steps of determining the mass of the loaded materials at the bottom of the stirring equipment according to a weight sensor, starting the stirring equipment, continuously recording damping torque borne by a stirring main shaft by a torque sensor, considering that stirring is completed when the damping torque falls between a preset upper threshold M2 and a preset lower threshold M1 and the preset duration does not exceed the upper threshold M2 and the lower threshold M1, recording the duration from the stirring starting time to the stirring finishing time T2 as stirring time T0, and determining whether two-dimensional Gaussian distribution is met or not by using the weight of the materials and the stirring time as parameters in the following S4 and data correction steps.
(III) beneficial effects
Compared with the prior art, the invention has the following beneficial effects:
1. the stirring equipment provided by the invention is transformed by Internet of things, can work based on remote control of a user, and can also work based on remote programming, so that the networking capability of the equipment is improved, and the remote monitoring and operating capability of the user on the equipment is improved.
2. According to the invention, the 'equipment abnormality' is checked based on a statistical rule, specifically, the debugged stirring equipment is completed, the working time of the preset stirring task is required to meet the Gaussian distribution, whether the Gaussian distribution is met or not is determined by counting a plurality of times of working time, when the Gaussian distribution is not met, the equipment abnormality can be determined, a user can be actively prompted to maintain, and the reliability of the equipment is ensured.
3. According to the invention, the investigation of 'non-fault abnormality' is performed based on a statistical rule, specifically, when the stirring equipment works in daily life, the problems of motor coil aging, lack of lubrication oil and the like possibly occur, the problems do not cause direct faults of the equipment, but the efficiency of the equipment is reduced, namely 'non-fault abnormality', the plurality of pieces of equipment conforming to Gaussian distribution are subjected to cluster analysis through a statistical method, the equipment with discrete conditions can be found, specifically, the increase of mathematical expectation mu of Gaussian distribution is caused due to the increase of stirring time caused by magnetic leakage of a motor, the decrease of detection precision caused by pollution of a torque sensor is likely, the fact that the stirring of equipment with unstable material resistance is judged in advance is completed, the decrease of mathematical expectation mu of Gaussian distribution is likely, and the uneven stirring force caused by lack of shafting lubrication is likely, and the increase of standard deviation sigma is likely to occur; the equipment and the method can timely detect the non-fault abnormality, avoid equipment condition deterioration and ensure production efficiency.
Drawings
FIG. 1 is a schematic structural view of a stirring apparatus of the present invention;
fig. 2 is a schematic diagram of an internet of things structure based on a plurality of stirring devices;
FIG. 3 is a schematic view of a control Yuanjiang of the stirring device of the present invention;
fig. 4 is a graph of torque versus agitation time for the present invention.
In the figure: 1. a node; 11. an electronic map module; 12. a gridding module; 13. a task broadcasting module; 14. a consensus module; 141. a release sub-module; 142. a resource calling sub-module; 143. an evaluation sub-module; 144. a decision sub-module; 145. a ticket casting sub-module; 15. and executing the module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to the drawings in which like reference numerals refer to like elements,
the utility model provides an intelligent agitated vessel based on thing allies oneself with control, includes agitating unit 1 and control system 2, control module includes touch-sensitive screen 21, PLC module 22 and remote communication module 23, its characterized in that: the remote communication module 23 comprises a network card socket, the network card socket is used for connecting a 4G network card, the 4G network card is used for remote communication with a mobile phone of a user, the user logs in a background of the remote communication module on the mobile phone, points of states of equipment to be displayed on a touch screen and related parameters are compiled on the background of the remote communication module, and after the compiling is completed, the equipment can be controlled at the mobile phone end and related running states of the equipment can be checked. The control system 2 is also in remote communication with the Internet of things server 3, a timer 4 and a torque sensor 5 are arranged on a motor main shaft of the stirring device 1, and the timer 4 is used for determining stirring time T0 for the stirring device 1 to finish one-time stirring;
the stirring time T0 refers to: starting timing when the stirring device 1 finishes loading and starting materials, continuously recording damping torque borne by a stirring main shaft by the torque sensor 5, recording the moment T1 when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1, continuously timing, judging whether the duration is greater than a preset duration threshold value when the damping torque exceeds the preset upper threshold value M2 and the preset lower threshold value M1, if not, judging that the timing is invalid, repeatedly detecting, if so, judging that stirring is finished, and recording the duration from the stirring starting moment to the stirring finishing moment T2 as stirring time T0.
The intelligent stirring devices are in remote communication with the internet of things server 3, the control system 2 sends the device number and the stirring time T of each work to the internet of things server 3, the internet of things server 3 performs Gaussian distribution check based on the stirring time T of any device to determine whether the stirring time T of the device meets Gaussian distribution, if not, a mobile phone of a user is given an alarm, the alarm is fed back to the intelligent stirring device, and the touch screen 21 displays the alarm; if yes, marking the equipment as normal, calculating mathematical expectation mu and standard deviation sigma of the equipment marked as normal, carrying out cluster analysis on the mathematical expectation mu and standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as early warning, and sending the equipment number to a user mobile phone.
The Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
where x (i) is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the order statistic.
The internet of things server 3 is pre-stored with an SPSS program, the equipment meeting gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal.
Embodiment two:
the method for performing fault investigation on the intelligent stirring equipment based on the internet of things control is characterized by comprising the following steps of:
s1, preparation: the engineer compiles the logic control program of the stirring equipment in the computer by using professional programming software, and then downloads the logic control program into the PLC module; and then, a professional touch screen control picture in the computer and related parameters are compiled and downloaded into the touch screen. Then, connecting the touch screen with the PLC by using a network cable; each intelligent stirring device is subjected to communication inspection and is connected with a mobile phone of a user and the Internet of things server 3 in a remote communication manner;
s2, a pre-experiment step: loading a predetermined amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that stirring is completed based on a stirring standard and continuously stirring for a certain time, deriving the stirring time and torque relation curve, and determining an upper threshold M2 and a lower threshold M1 of damping torque and required duration based on the curve;
s3, working steps: working according to a preset stirring program;
s4, data checking: the method specifically comprises the following steps:
s41: gaussian analysis: the control system 2 sends the equipment number and the stirring time T of each work to the Internet of things server 3, the Internet of things server 3 carries out Gaussian distribution test based on the stirring time T of any piece of equipment to determine whether the stirring time T of the equipment meets Gaussian distribution, if not, the equipment is marked as abnormal, the mobile phone of a user is alarmed, the alarm is fed back to the intelligent stirring equipment, and the touch screen 21 displays the alarm; if yes, calculating mathematical expectation mu and standard deviation sigma of the equipment;
s42: and (3) cluster analysis: and for the equipment with stirring time meeting Gaussian distribution, marking as normal, carrying out cluster analysis on mathematical expectation mu and standard deviation sigma of the equipment marked as normal, marking discrete equipment with the mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to a mobile phone of a user.
The Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
where x (i) is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the order statistic.
The internet of things server 3 is pre-stored with an SPSS program, the equipment meeting gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal. The cluster analysis is a k-means cluster algorithm or a hierarchical cluster algorithm.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. The utility model provides an intelligent agitated vessel based on thing allies oneself with control, includes agitating unit (1) and control system (2), and control module includes touch-sensitive screen (21), PLC module (22) and remote communication module (23), its characterized in that: the remote communication module (23) comprises a network card socket, the network card socket is used for connecting a 4G network card, the 4G network card is used for remotely communicating with a mobile phone of a user, the user logs in a background of the remote communication module on the mobile phone, points of states of equipment to be displayed on a touch screen and related parameters are compiled on the background of the remote communication module, and after the compiling is completed, the equipment can be controlled at a mobile phone end and related running states of the equipment can be checked;
the control system (2) is also in remote communication with the Internet of things server (3), a timer (4) and a torque sensor (5) are arranged on a motor main shaft of the stirring device (1), and the timer (4) is used for determining stirring time T0 for completing one-time stirring of the stirring device (1);
the stirring time T0 refers to: starting timing when the stirring device (1) finishes loading and starting materials, continuously recording damping torque borne by a stirring main shaft by the torque sensor (5), considering that stirring is finished when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1 and the preset duration time does not exceed the upper threshold value M2 and the lower threshold value M1, and recording the time length from the stirring starting time to the stirring finishing time T2 as stirring time T0;
the intelligent stirring equipment is in remote communication with the Internet of things server (3), the control system (2) sends equipment numbers and stirring time T of each work to the Internet of things server (3), the Internet of things server (3) performs Gaussian distribution detection based on the stirring time T of any piece of equipment to determine whether the stirring time T of the equipment meets Gaussian distribution, if not, the equipment is determined to be abnormal, the mobile phone of a user is given an alarm, the user is actively prompted to carry out maintenance, the alarm is fed back to the intelligent stirring equipment, and the touch screen (21) displays the alarm; if yes, marking the equipment as normal, calculating mathematical expectation mu and standard deviation sigma of the equipment marked as normal, carrying out cluster analysis on the mathematical expectation mu and standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as early warning, and sending the equipment number to a user mobile phone.
2. The intelligent stirring device based on the internet of things control according to claim 1, wherein: the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
wherein x is (i) Is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the ordered statistic.
3. The intelligent stirring device based on the internet of things control according to claim 2, wherein: the SPPS program is pre-stored in the server (3) of the Internet of things, equipment meeting Gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal.
4. A method for troubleshooting an intelligent stirring apparatus based on internet of things control, characterized in that the method for troubleshooting is based on the intelligent stirring apparatus according to any one of claims 1-3, comprising the steps of:
s1, preparation: the engineer compiles the logic control program of the stirring equipment in the computer by using professional programming software, and then downloads the logic control program into the PLC module; then, a professional touch screen control picture in a computer and related parameters are compiled and then downloaded into the touch screen; then, connecting the touch screen with the PLC by using a network cable; each intelligent stirring device is subjected to communication inspection and is connected with a mobile phone of a user and an Internet of things server (3) in a remote communication manner;
s2, a pre-experiment step: loading a predetermined amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that stirring is completed based on a stirring standard and continuously stirring for a certain time, deriving the stirring time and torque relation curve, and determining an upper threshold M2 and a lower threshold M1 of damping torque and required duration based on the curve;
s3, working steps: working according to a preset stirring program;
s4, data checking: the method specifically comprises the following steps:
s41: gaussian analysis: the control system (2) sends the equipment number and the stirring time T of each work to the Internet of things server (3), the Internet of things server (3) carries out Gaussian distribution test based on the stirring time T of any piece of equipment to determine whether the stirring time T of the equipment meets Gaussian distribution, if not, the equipment is marked as abnormal, the mobile phone of a user is alarmed, the alarm is fed back to the intelligent stirring equipment, and the touch screen (21) displays the alarm; if yes, calculating mathematical expectation mu and standard deviation sigma of the equipment;
s42: and (3) cluster analysis: and for the equipment with stirring time meeting Gaussian distribution, marking as normal, carrying out cluster analysis on mathematical expectation mu and standard deviation sigma of the equipment marked as normal, marking discrete equipment with the mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to a mobile phone of a user.
5. The method for troubleshooting an intelligent stirring device based on internet of things control according to claim 4, wherein the method comprises the following steps: the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
wherein x is (i) Is the ith order statistic, i.e., the ith minimum in the sample;
is the average value of the samples;
constant a i Calculated by the following formula:
wherein m= (m 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of ordered independent co-distributed statistics sampled from a standard gaussian distributed random variable, said m 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the ordered statistic.
6. The method for troubleshooting an intelligent stirring device based on internet of things control according to claim 5, wherein the method comprises the following steps: the SPPS program is pre-stored in the server (3) of the Internet of things, equipment meeting Gaussian distribution is marked as normal, and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each piece of equipment marked as normal.
7. The method for troubleshooting an intelligent stirring device based on internet of things control according to claim 6, wherein the method comprises the following steps: the cluster analysis is a k-means clustering algorithm.
8. The method for troubleshooting an intelligent stirring device based on internet of things control according to claim 7, wherein the method comprises the following steps: the cluster analysis is a hierarchical clustering algorithm.
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