CN113291234A - Tractor trouble early warning system based on thing networking - Google Patents

Tractor trouble early warning system based on thing networking Download PDF

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Publication number
CN113291234A
CN113291234A CN202110475844.1A CN202110475844A CN113291234A CN 113291234 A CN113291234 A CN 113291234A CN 202110475844 A CN202110475844 A CN 202110475844A CN 113291234 A CN113291234 A CN 113291234A
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fault
tractor
time
index
monitoring
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冯秀云
王运峰
朱一峰
陈浩
郭安武
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Anhui Sizhou Tractor Manufacturing Co ltd
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Anhui Sizhou Tractor Manufacturing Co ltd
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Priority to CN202110475844.1A priority Critical patent/CN113291234A/en
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60QARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00Economic sectors
    • G16Y10/40Transportation
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00Information sensed or collected by the things
    • G16Y20/20Information sensed or collected by the things relating to the thing itself
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00IoT characterised by the purpose of the information processing
    • G16Y40/20Analytics; Diagnosis

Abstract

The invention discloses a tractor fault early warning system based on the Internet of things, which relates to the technical field of tractor fault early warning and solves the technical problem that the working strength of fault prediction is high because the parameter indexes of a tractor can not be screened in the prior art, the parameters of the tractor are analyzed and screened by a parameter analysis unit so as to monitor the tractor, a sample tractor is analyzed, key indexes corresponding to the sample tractor are screened to obtain abnormal key indexes of the fault tractor, then the fault key indexes are divided into a pumping monitoring index and a real-time monitoring index according to the weight value corresponding to the fault key indexes, the key indexes of the tractor are distinguished and screened, the working strength of the tractor fault early warning is reduced, the accuracy of the fault early warning is improved, and the indexes of the tractor which are not influenced much by the system are prevented from being monitored, the failure early warning efficiency is reduced, and the cost is wasted.

Description

Tractor trouble early warning system based on thing networking
Technical Field
The invention relates to the technical field of tractor fault early warning, in particular to a tractor fault early warning system based on the Internet of things.
Background
The tractor is a self-propelled power machine used for towing and driving the operation machinery to complete various mobile operations, and consists of systems or devices such as an engine, a transmission system, a walking system, a steering system, a hydraulic suspension system, a power output system, an electric instrument system, a driving control system, a towing system and the like; the power of an engine of a tractor is transmitted to a driving wheel by a transmission system to drive the tractor to run, and in real life, the common tractor is divided into the tractors with agriculture, industry, special use and the like according to functions and uses by taking a rubber belt as a power transmission medium, and is divided into a wheel type tractor, a crawler type tractor, a boat-shaped tractor, a self-propelled chassis and the like according to the structural types;
in the prior art, various faults often occur in the use process of the tractor, and parameter indexes of the tractor cannot be screened in fault prediction, so that the working strength of fault prediction is high, and the working efficiency is low;
in view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to provide an Internet of things-based tractor fault early warning system, which analyzes and screens parameters of a tractor through a parameter analysis unit so as to monitor the tractor, analyzes a sample tractor, screens key indexes corresponding to the sample tractor to obtain abnormal key indexes of the fault tractor, then divides the fault key indexes into a pumping-time monitoring index and a real-time monitoring index according to weight values corresponding to the fault key indexes, distinguishes and screens the key indexes of the tractor, reduces the working strength of tractor fault early warning, improves the accuracy of the fault early warning, and prevents the system from monitoring the indexes which are not influenced much by the tractor, so that the efficiency of the fault early warning is reduced and the cost is wasted.
The purpose of the invention can be realized by the following technical scheme:
a tractor fault early warning system based on the Internet of things comprises a parameter analysis unit, an equipment monitoring unit, an early warning unit, a fault early warning platform, a registration unit and a database;
the parameter analysis unit is used for analyzing and screening parameters of the tractor so as to monitor the tractor, and the specific analysis and screening process is as follows:
step S1: acquiring a tractor to be produced, marking the tractor to be produced as a sample tractor i, i is 1, 2, … …, n and n is a positive integer, putting the sample tractor into production at the same time, representing the tractor to be produced as a tractor which is not put into production after being manufactured, acquiring a key index Y of the sample tractor, and constructing a key index set { Y1, Y2, … … and Ym } of the sample tractor, wherein Y2 is a key index of the second ordered sample tractor in the set, m is a positive integer, and the key index of the sample tractor is represented as a performance parameter influencing the sample tractor;
step S2: setting an analysis time threshold t1, dividing a sample tractor into a failed tractor and a normal tractor within the analysis time threshold t1, obtaining a failure key index of the failed tractor in the sample tractor, marking the failure key index as an abnormal key index S, and then constructing an abnormal key index set { S1, S2, … …, Si }, wherein S2 represents the abnormal key index of the second-ranked failed tractor in the sample tractor;
step S3: marking the key indexes corresponding to the abnormal key indexes in the normal tractor as abnormal key index threshold values, marking the abnormal key indexes as o, o is 1, 2, … …, k and k is a positive integer, then marking the lowest threshold values corresponding to the abnormal key indexes as index critical values, and sending the index critical values to a fault early warning platform;
step S4: marking a fault tractor as p, wherein p is 1, 2, … …, j and j are positive integers, acquiring the maintenance time and the downtime of the fault tractor, marking the maintenance time and the downtime of the fault tractor as WHj and TGj, and acquiring an influence value Xj of a fault key index corresponding to the fault tractor through a formula Xj beta (WHj × a1+ TGj × a2), wherein the influence value Xj is a numerical value of the influence time of the fault key index on the working of the fault tractor, a1 and a2 are proportionality coefficients, a1 is more than a2 and more than 0, and beta is an error correction factor and takes a value of 1.32;
step S5: sorting the influence values Xj of the fault key indexes corresponding to the fault tractor according to the numerical sequence from large to small, constructing influence value sets { X1, X2, … …, Xj } by the sorted influence values, then assigning weights to subsets of the influence value sets, namely { gk1, gk2, … …, gkj }, wherein gk2 represents a weight value corresponding to the fault key index of the second sorted influence value set, and gk1+ gk2+ … … + gkj is 1, and then comparing the weight values of the fault key indexes with weight value thresholds: if the weighted value of the fault key index is larger than or equal to the weighted value threshold value, the corresponding fault key index is marked as a real-time monitoring index, if the weighted value of the fault key index is smaller than the weighted value threshold value, the corresponding fault key index is marked as a time-extraction monitoring index, the real-time monitoring index and the time-extraction monitoring index are sent to a fault early warning platform, and the time-extraction monitoring index is expressed as an index for monitoring random extraction of monitoring time.
Further, after the fault early warning platform receives the index critical value, the real-time monitoring index and the time-extraction monitoring index, an equipment monitoring signal is generated and sent to the equipment monitoring unit, and after the equipment monitoring unit receives the equipment monitoring signal, the tractor is monitored in real time, and the specific monitoring process is as follows:
step SS 1: the method comprises the steps of predicting the faults of tractors which are put into production, marking the tractors as predicted tractors, monitoring real-time monitoring indexes corresponding to the predicted tractors, setting predicted time t2, establishing a rectangular coordinate system by taking the predicted time t2 as an X axis and the real-time monitoring indexes as a Y axis, marking the real-time monitoring indexes corresponding to time points in the rectangular coordinate system, and establishing a real-time monitoring index curve;
step SS 2: comparing and monitoring the real-time monitoring index curve, marking the index critical value of each time point, then constructing the index critical curve in a rectangular coordinate system, and comparing the real-time monitoring index curve with the index critical curve:
if the real-time monitoring index curve is deviated from the index critical curve gradually, judging that the tractor equipment is in failure, then, marking the time point when the real-time monitoring index curve descends and the index critical curve ascends as equipment failure time, generating an equipment failure signal, and sending the equipment failure signal and the equipment failure time to an early warning unit;
if the real-time monitoring index curve and the index critical curve are suddenly and continuously deviated, judging that the tractor is predicted to be affected by external influences to generate faults, wherein the external influences comprise sudden weather or external force collision, marking a time point corresponding to the curve slope of the real-time monitoring index curve being more than or equal to 70 degrees as external fault time, generating an external fault signal, and sending the external fault signal and the external fault time to an early warning unit;
if the real-time monitoring index curve periodically deviates from the index critical curve, judging and predicting that the tractor operator has a fault due to improper operation, then taking a time point corresponding to the periodic floating end point of the real-time monitoring index curve as operation fault time, generating an operation fault signal and sending the operation fault signal and the operation fault time to the early warning unit.
Further, when the early warning unit receives any one parameter of equipment fault signal and equipment fault time, external fault signal and external fault time and operation fault signal and operation fault time, the early warning unit generates early warning signals and sends the early warning signals to a mobile phone terminal of a manager, and then the early warning unit analyzes the accuracy of the early warning signals, wherein the specific analysis process is as follows:
when the early warning mode is that the fault occurrence time is predicted in advance, acquiring the time point when a manager receives an early warning signal, acquiring the fault prediction time point, calculating the early warning signal receiving time point and the fault prediction time point to acquire the fault occurrence time in advance, marking the fault occurrence time as YCT, comparing the fault occurrence time in advance YCT with a time threshold in advance, if the fault occurrence time in advance YCT is more than or equal to the time threshold in advance, judging that the accuracy of corresponding fault prediction is high, generating a high-accuracy prediction signal and sending the high-accuracy prediction signal to a monitoring person; if the failure occurrence predicted time YCT is less than the predicted time threshold, judging that the corresponding failure prediction accuracy is low, generating a predicted low-accuracy signal and sending the predicted low-accuracy signal to monitoring personnel;
when the early warning mode is the prediction fault occurrence time period, the prediction fault occurrence time period is acquired, the numerical value comparison is carried out on the prediction fault occurrence time period, if the prediction fault occurrence time period tends to be infinite, the fault prediction is judged to be invalid, and if the prediction fault occurrence time period is smaller than the time period threshold, the fault prediction accuracy is judged to be high.
Further, the registration login unit is used for the manager and the monitoring personnel to submit the manager information and the monitoring personnel information through the mobile phone terminals for registration, and data storage is carried out on the manager information and the monitoring personnel information which are successfully registered, the manager information comprises the name, the age, the time of entry and the mobile phone number of the real name authentication of the manager, and the monitoring personnel information comprises the name, the age, the time of entry and the mobile phone number of the real name authentication of the monitoring personnel.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, parameters of the tractor are analyzed and screened through a parameter analysis unit, so that the tractor is monitored, a sample tractor is analyzed, key indexes corresponding to the sample tractor are screened, abnormal key indexes of a fault tractor are obtained, then the fault key indexes are divided into a pumping-out monitoring index and a real-time monitoring index according to weight values corresponding to the fault key indexes, the key indexes of the tractor are distinguished and screened, the working strength of tractor fault early warning is reduced, the accuracy of the fault early warning is improved, and the condition that the system monitors indexes with little influence on the tractor, so that the fault early warning efficiency is reduced, and the cost is wasted;
2. in the invention, after receiving an equipment monitoring signal, an equipment monitoring unit carries out real-time monitoring on a tractor, constructs a monitoring index curve and an index critical value curve, carries out comparison monitoring on the real-time monitoring index curve, marks the index critical value of each time point, then constructs an index critical curve in a rectangular coordinate system, compares the real-time monitoring index curve with the index critical curve and generates an early warning signal; the real-time monitoring index curve is analyzed for fault reasons, different faults correspond to different curves, comprehensiveness of fault prediction is improved, meanwhile, the fault reasons are analyzed, fault solving efficiency is improved, and influences of the faults on the tractor are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a tractor fault early warning system based on the internet of things comprises a parameter analysis unit, an equipment monitoring unit, an early warning unit, a fault early warning platform, a registration unit and a database;
the registration login unit is used for submitting management personnel information and monitoring personnel information to register through mobile phone terminals by management personnel and monitoring personnel, and storing data of the management personnel information and the monitoring personnel information which are successfully registered, wherein the management personnel information comprises the name, the age, the time of entry of the management personnel and the mobile phone number of real name authentication of the person, and the monitoring personnel information comprises the name, the age, the time of entry of the monitoring personnel and the mobile phone number of real name authentication of the person;
the parameter analysis unit is used for analyzing and screening parameters of the tractor so as to monitor the tractor, and the specific analysis and screening process is as follows:
step S1: acquiring a tractor to be produced, marking the tractor to be produced as a sample tractor i, i is 1, 2, … …, n and n is a positive integer, putting the sample tractor into production at the same time, representing the tractor to be produced as a tractor which is not put into production after being manufactured, acquiring a key index Y of the sample tractor, and constructing a key index set { Y1, Y2, … … and Ym } of the sample tractor, wherein Y2 is a key index of the second ordered sample tractor in the set, m is a positive integer, and the key index of the sample tractor is represented as a performance parameter influencing the sample tractor;
step S2: setting an analysis time threshold t1, dividing a sample tractor into a failed tractor and a normal tractor within the analysis time threshold t1, obtaining a failure key index of the failed tractor in the sample tractor, marking the failure key index as an abnormal key index S, and then constructing an abnormal key index set { S1, S2, … …, Si }, wherein S2 represents the abnormal key index of the second-ranked failed tractor in the sample tractor;
step S3: marking the key indexes corresponding to the abnormal key indexes in the normal tractor as abnormal key index threshold values, marking the abnormal key indexes as o, o is 1, 2, … …, k and k is a positive integer, then marking the lowest threshold values corresponding to the abnormal key indexes as index critical values, and sending the index critical values to a fault early warning platform;
step S4: marking a fault tractor as p, wherein p is 1, 2, … …, j and j are positive integers, acquiring the maintenance time and the downtime of the fault tractor, marking the maintenance time and the downtime of the fault tractor as WHj and TGj, and acquiring an influence value Xj of a fault key index corresponding to the fault tractor through a formula Xj beta (WHj × a1+ TGj × a2), wherein the influence value Xj is a numerical value of the influence time of the fault key index on the working of the fault tractor, a1 and a2 are proportionality coefficients, a1 is more than a2 and more than 0, and beta is an error correction factor and takes a value of 1.32;
step S5: sorting the influence values Xj of the fault key indexes corresponding to the fault tractor according to the numerical sequence from large to small, constructing influence value sets { X1, X2, … …, Xj } by the sorted influence values, then assigning weights to subsets of the influence value sets, namely { gk1, gk2, … …, gkj }, wherein gk2 represents a weight value corresponding to the fault key index of the second sorted influence value set, and gk1+ gk2+ … … + gkj is 1, and then comparing the weight values of the fault key indexes with weight value thresholds: if the weighted value of the fault key index is larger than or equal to the weighted value threshold value, marking the corresponding fault key index as a real-time monitoring index, if the weighted value of the fault key index is smaller than the weighted value threshold value, marking the corresponding fault key index as a time-extraction monitoring index, and sending the real-time monitoring index and the time-extraction monitoring index to a fault early warning platform, wherein the time-extraction monitoring index is an index for monitoring random extraction monitoring time; key indexes of the tractor are distinguished and screened, so that the working strength of tractor fault early warning is reduced, and the accuracy of the fault early warning is improved;
after the fault early warning platform receives the index critical value and the real-time monitoring index and the time-extraction monitoring index, generating an equipment monitoring signal and sending the equipment monitoring signal to an equipment monitoring unit, wherein the equipment monitoring unit carries out real-time monitoring on the tractor after receiving the equipment monitoring signal, and the specific monitoring process is as follows:
step SS 1: the method comprises the steps of predicting the faults of tractors which are put into production, marking the tractors as predicted tractors, monitoring real-time monitoring indexes corresponding to the predicted tractors, setting predicted time t2, establishing a rectangular coordinate system by taking the predicted time t2 as an X axis and the real-time monitoring indexes as a Y axis, marking the real-time monitoring indexes corresponding to time points in the rectangular coordinate system, and establishing a real-time monitoring index curve;
step SS 2: comparing and monitoring the real-time monitoring index curve, marking the index critical value of each time point, then constructing the index critical curve in a rectangular coordinate system, and comparing the real-time monitoring index curve with the index critical curve:
if the real-time monitoring index curve is deviated from the index critical curve gradually, judging that the tractor equipment is in failure, then, marking the time point when the real-time monitoring index curve descends and the index critical curve ascends as equipment failure time, generating an equipment failure signal, and sending the equipment failure signal and the equipment failure time to an early warning unit;
if the real-time monitoring index curve and the index critical curve are suddenly and continuously deviated, judging that the tractor is predicted to be affected by external influences to generate faults, wherein the external influences comprise sudden weather or external force collision, marking a time point corresponding to the curve slope of the real-time monitoring index curve being more than or equal to 70 degrees as external fault time, generating an external fault signal, and sending the external fault signal and the external fault time to an early warning unit;
if the real-time monitoring index curve periodically deviates from the index critical curve, judging and predicting that a fault is generated due to improper operation of a tractor operator, then taking a time point corresponding to a periodic floating terminal point of the real-time monitoring index curve as operation fault time, generating an operation fault signal and sending the operation fault signal and the operation fault time to an early warning unit; the comprehensiveness of fault prediction is improved, fault reasons are analyzed, the fault solving efficiency is improved, and the influence of the fault on the tractor is reduced;
when the early warning unit receives any one parameter of equipment fault signal and equipment fault time, external fault signal and external fault time and operation fault signal and operation fault time, all generate early warning signal and send early warning signal to managers's cell phone terminal, early warning unit carries out the analysis to the early warning signal accuracy afterwards, and concrete analytic process is as follows:
when the early warning mode is that the fault occurrence time is predicted in advance, acquiring the time point when a manager receives an early warning signal, acquiring the fault prediction time point, calculating the early warning signal receiving time point and the fault prediction time point to acquire the fault occurrence time in advance, marking the fault occurrence time as YCT, comparing the fault occurrence time in advance YCT with a time threshold in advance, if the fault occurrence time in advance YCT is more than or equal to the time threshold in advance, judging that the accuracy of corresponding fault prediction is high, generating a high-accuracy prediction signal and sending the high-accuracy prediction signal to a monitoring person; if the failure occurrence predicted time YCT is less than the predicted time threshold, judging that the corresponding failure prediction accuracy is low, generating a predicted low-accuracy signal and sending the predicted low-accuracy signal to monitoring personnel;
when the early warning mode is the prediction fault occurrence time period, the prediction fault occurrence time period is acquired, the numerical value comparison is carried out on the prediction fault occurrence time period, if the prediction fault occurrence time period tends to be infinite, the fault prediction is judged to be invalid, and if the prediction fault occurrence time period is smaller than the time period threshold, the fault prediction accuracy is judged to be high.
When the tractor failure early warning system works, parameters of the tractor are analyzed and screened through the parameter analysis unit, so that the tractor is monitored, key indexes of the tractor are distinguished and screened, the working strength of tractor failure early warning is reduced, the accuracy of failure early warning is improved, then the tractor is monitored in real time after the equipment monitoring unit receives an equipment monitoring signal, a monitoring index curve and an index critical value curve are constructed, then the curves are compared, an early warning signal is generated, and then the accuracy of the early warning signal is analyzed through the early warning unit.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (4)

1. A tractor fault early warning system based on the Internet of things is characterized by comprising a parameter analysis unit, an equipment monitoring unit, an early warning unit, a fault early warning platform, a registration unit and a database;
the parameter analysis unit is used for analyzing and screening parameters of the tractor so as to monitor the tractor, and the specific analysis and screening process is as follows:
step S1: acquiring a tractor to be produced, marking the tractor to be produced as a sample tractor i, i is 1, 2, … …, n and n is a positive integer, putting the sample tractor into production at the same time, representing the tractor to be produced as a tractor which is not put into production after being manufactured, acquiring a key index Y of the sample tractor, and constructing a key index set { Y1, Y2, … … and Ym } of the sample tractor, wherein Y2 is a key index of the second ordered sample tractor in the set, m is a positive integer, and the key index of the sample tractor is represented as a performance parameter influencing the sample tractor;
step S2: setting an analysis time threshold t1, dividing a sample tractor into a failed tractor and a normal tractor within the analysis time threshold t1, obtaining a failure key index of the failed tractor in the sample tractor, marking the failure key index as an abnormal key index S, and then constructing an abnormal key index set { S1, S2, … …, Si }, wherein S2 represents the abnormal key index of the second-ranked failed tractor in the sample tractor;
step S3: marking the key indexes corresponding to the abnormal key indexes in the normal tractor as abnormal key index threshold values, marking the abnormal key indexes as o, o is 1, 2, … …, k and k is a positive integer, then marking the lowest threshold values corresponding to the abnormal key indexes as index critical values, and sending the index critical values to a fault early warning platform;
step S4: marking a fault tractor as p, wherein p is 1, 2, … …, j and j are positive integers, acquiring the maintenance time and the downtime of the fault tractor, marking the maintenance time and the downtime of the fault tractor as WHj and TGj, and acquiring an influence value Xj of a fault key index corresponding to the fault tractor through a formula Xj beta (WHj × a1+ TGj × a2), wherein the influence value Xj is a numerical value of the influence time of the fault key index on the working of the fault tractor, a1 and a2 are proportionality coefficients, a1 is more than a2 and more than 0, and beta is an error correction factor and takes a value of 1.32;
step S5: sorting the influence values Xj of the fault key indexes corresponding to the fault tractor according to the numerical sequence from large to small, constructing influence value sets { X1, X2, … …, Xj } by the sorted influence values, then assigning weights to subsets of the influence value sets, namely { gk1, gk2, … …, gkj }, wherein gk2 represents a weight value corresponding to the fault key index of the second sorted influence value set, and gk1+ gk2+ … … + gkj is 1, and then comparing the weight values of the fault key indexes with weight value thresholds: if the weighted value of the fault key index is larger than or equal to the weighted value threshold value, the corresponding fault key index is marked as a real-time monitoring index, if the weighted value of the fault key index is smaller than the weighted value threshold value, the corresponding fault key index is marked as a time-extraction monitoring index, the real-time monitoring index and the time-extraction monitoring index are sent to a fault early warning platform, and the time-extraction monitoring index is expressed as an index for monitoring random extraction of monitoring time.
2. The tractor fault early warning system based on the internet of things as claimed in claim 1, wherein the fault early warning platform generates a device monitoring signal and sends the device monitoring signal to a device monitoring unit after receiving an index critical value, a real-time monitoring index and a timing monitoring index, the device monitoring unit monitors the tractor in real time after receiving the device monitoring signal, and the specific monitoring process is as follows:
step SS 1: the method comprises the steps of predicting the faults of tractors which are put into production, marking the tractors as predicted tractors, monitoring real-time monitoring indexes corresponding to the predicted tractors, setting predicted time t2, establishing a rectangular coordinate system by taking the predicted time t2 as an X axis and the real-time monitoring indexes as a Y axis, marking the real-time monitoring indexes corresponding to time points in the rectangular coordinate system, and establishing a real-time monitoring index curve;
step SS 2: comparing and monitoring the real-time monitoring index curve, marking the index critical value of each time point, then constructing the index critical curve in a rectangular coordinate system, and comparing the real-time monitoring index curve with the index critical curve:
if the real-time monitoring index curve is deviated from the index critical curve gradually, judging that the tractor equipment is in failure, then, marking the time point when the real-time monitoring index curve descends and the index critical curve ascends as equipment failure time, generating an equipment failure signal, and sending the equipment failure signal and the equipment failure time to an early warning unit;
if the real-time monitoring index curve and the index critical curve are suddenly and continuously deviated, judging that the tractor is predicted to be affected by external influences to generate faults, wherein the external influences comprise sudden weather or external force collision, marking a time point corresponding to the curve slope of the real-time monitoring index curve being more than or equal to 70 degrees as external fault time, generating an external fault signal, and sending the external fault signal and the external fault time to an early warning unit;
if the real-time monitoring index curve periodically deviates from the index critical curve, judging and predicting that the tractor operator has a fault due to improper operation, then taking a time point corresponding to the periodic floating end point of the real-time monitoring index curve as operation fault time, generating an operation fault signal and sending the operation fault signal and the operation fault time to the early warning unit.
3. The tractor fault early warning system based on the internet of things as claimed in claim 2, wherein when the early warning unit receives any one of parameters of equipment fault signals, equipment fault time, external fault signals, external fault time, operation fault signals and operation fault time, the early warning unit generates early warning signals and sends the early warning signals to a mobile phone terminal of a manager, and then the early warning unit analyzes the accuracy of the early warning signals, and the specific analysis process is as follows:
when the early warning mode is that the fault occurrence time is predicted in advance, acquiring the time point when a manager receives an early warning signal, acquiring the fault prediction time point, calculating the early warning signal receiving time point and the fault prediction time point to acquire the fault occurrence time in advance, marking the fault occurrence time as YCT, comparing the fault occurrence time in advance YCT with a time threshold in advance, if the fault occurrence time in advance YCT is more than or equal to the time threshold in advance, judging that the accuracy of corresponding fault prediction is high, generating a high-accuracy prediction signal and sending the high-accuracy prediction signal to a monitoring person; if the failure occurrence predicted time YCT is less than the predicted time threshold, judging that the corresponding failure prediction accuracy is low, generating a predicted low-accuracy signal and sending the predicted low-accuracy signal to monitoring personnel;
when the early warning mode is the prediction fault occurrence time period, the prediction fault occurrence time period is acquired, the numerical value comparison is carried out on the prediction fault occurrence time period, if the prediction fault occurrence time period tends to be infinite, the fault prediction is judged to be invalid, and if the prediction fault occurrence time period is smaller than the time period threshold, the fault prediction accuracy is judged to be high.
4. The tractor fault early warning system based on the internet of things as claimed in claim 1, wherein the registration login unit is used for a manager and a monitoring person to submit manager information and monitoring person information through mobile phone terminals for registration, and to store the manager information and the monitoring person information which are successfully registered in a data manner, the manager information includes a name, an age, an attendance time and a mobile phone number for authenticating the real name of the manager, and the monitoring person information includes a name, an age, an attendance time and a mobile phone number for authenticating the real name of the monitoring person.
CN202110475844.1A 2021-04-29 2021-04-29 Tractor trouble early warning system based on thing networking Pending CN113291234A (en)

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Cited By (5)

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Application publication date: 20210824