CN109409621B - A kind of train air-conditioning Maintenance Scheduling system and its working method - Google Patents

A kind of train air-conditioning Maintenance Scheduling system and its working method Download PDF

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Publication number
CN109409621B
CN109409621B CN201910045999.4A CN201910045999A CN109409621B CN 109409621 B CN109409621 B CN 109409621B CN 201910045999 A CN201910045999 A CN 201910045999A CN 109409621 B CN109409621 B CN 109409621B
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air
conditioning
failure
compartment
train
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CN109409621A (en
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陈王永
仲启端
陈鑫铎
杜晓青
马丽丽
秦海刚
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New United Rail Transit Technology Co Ltd
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New United Rail Transit Technology Co Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/20Product repair or maintenance administration
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/30Transportation; Communications

Abstract

The present invention relates to Maintenance Scheduling field, specially a kind of train air-conditioning Maintenance Scheduling system and its working method, wherein train air-conditioning Maintenance Scheduling system includes: failure predication server and data screening module and data transmission module positioned at train air-conditioning end;Wherein the data screening module is suitable for the train air-conditioning real-time running data of screening being sent to failure predication server by data transmission module;The failure predication server is suitable for predicting air-conditioning failure according to train air-conditioning real-time running data.Realize the failure predication of train air-conditioning.

Description

A kind of train air-conditioning Maintenance Scheduling system and its working method
Technical field
The present invention relates to Maintenance Scheduling field, specially a kind of train air-conditioning Maintenance Scheduling system and its working method.
Background technique
Nowadays, the air-conditioning maintenance of train is all to arrange maintenance personal to repair after air-conditioning goes wrong, when prominent So occur that withdrawal of train is needed to repair when air-conditioning failure, will affect the operation of train, and the air-conditioning failure meeting to happen suddenly The trip experience of passenger is influenced, therefore to the prediction of train air-conditioning failure at the task of top priority.
Based on above-mentioned technical problem, need to design a kind of new train air-conditioning Maintenance Scheduling system and its working method.
Summary of the invention
The object of the present invention is to provide a kind of train air-conditioning Maintenance Scheduling system and its working methods.
In order to solve the above-mentioned technical problems, the present invention provides a kind of train air-conditioning Maintenance Scheduling systems, comprising:
Failure predication server and data screening module and data transmission module positioned at train air-conditioning end;Wherein
The train air-conditioning real-time running data that the data screening module is suitable for screen is sent by data transmission module To failure predication server;
The failure predication server is suitable for predicting air-conditioning failure according to train air-conditioning real-time running data.
Further, the train air-conditioning real-time running data of the data screening module screening, i.e.,
Full preset time is closed when real-time running data meets by-passing valve simultaneously, evaporation fan runs at high speed when completely presetting Between, after condensation fan opens full preset time, outer temperature is stablized in preset time the temperature difference lower than fiducial temperature and fresh air, useless row When pressure wave air door is full of the screening conditions of preset time, it is screened as suitable for the real time execution for predicting the failure of train air-conditioning Data;
The real-time running data includes: temperature in compartment, temperature, train speed, high pressure, low pressure, fresh air wind outside compartment Air pressure in door opening and closing number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment.
Further, the train air-conditioning Maintenance Scheduling system further include: train institute server-side;
Train institute's server-side is suitable for receiving the train air-conditioning real-time running data after screening and is sent to failure predication Server, and receive the prediction air-conditioning fault message that failure predication server is sent.
Further, the train air-conditioning Maintenance Scheduling system further include: the management end that is connected with failure predication server and Repair end;Wherein
The failure predication server is suitable for predict that air-conditioning fault message is sent to management end, and the management end is suitable for root It is predicted that air-conditioning fault message generates maintenance task, and it is sent to maintenance end;
The management end be suitable for from maintenance end obtain maintenance task feedback data, the failure predication server be suitable for from The management end transfers the feedback data of history maintenance task.
Further, the failure predication server be suitable for building failure predication physical model, with to train air-conditioning failure into Row prediction;
The failure predication physical model includes: that failure predication server is receiving the real-time running data by screening Afterwards, according to train within the predetermined period time fresh air air door, useless exhaust door, by-passing valve opening and closing number be greater than corresponding frequency threshold value And air pressure change is greater than air pressure threshold value to predict air-conditioning failure in compartment;And
Failure predication server from garbled real-time running data screen compartment in air conditioner corresponding two A air-conditioning system temperature, real-time when temperature, train speed condition outside compartment within the predetermined period time and in same compartment Operation data, based on two air-conditioning system high-low pressure trend air-conditioning fault types in compartment.
Further, the air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner failure;
Based on two air-conditioning system high-low pressure trend air conditioner ventilation failures in compartment, air-conditioning system event Barrier, air conditioner failure, i.e.,
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor the maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high pressure in predetermined period time interior compartment Value;LPFor the low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor predetermined period Maximum low voltage value in time interior compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 for high-voltage value and The corresponding ascending threshold of difference between high pressure average value;P2 be the corresponding rising threshold of difference between high-voltage value and minimum high-voltage value Value;P3 between high-voltage value and high pressure average value the corresponding falling-threshold value of difference;P4 is poor between high-voltage value and maximum high-voltage value It is worth corresponding falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and most The corresponding ascending threshold of difference between small low voltage value;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be the corresponding falling-threshold value of difference between low voltage value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then judge that an air-conditioning system goes out Existing air conditioner ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;With And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment changes different from the high and low pressure of another air-conditioning system respectively When trend, then predicts air conditioner failure, i.e., reveal failure or air inlet failure slowly.
Further, the management end is further adapted for arranging maintenance task according to prediction air-conditioning fault message, i.e., ought have train When air-conditioning predicts failure, the train where the maintenance task of generation is sent to the air-conditioning that prediction is broken down by management end is corresponding Repair end.
On the other hand, the present invention also provides a kind of working methods of train air-conditioning Maintenance Scheduling system, comprising:
Train air-conditioning real-time running data is screened;
Air-conditioning failure is predicted according to the train air-conditioning real-time running data after screening;And
Maintenance task is arranged according to prediction air-conditioning fault message.
Further, the method that the train air-conditioning real-time running data is screened include: by data screening module, when Real-time running data meets the full preset time of by-passing valve closing simultaneously, evaporation fan runs at high speed full preset time, condensation fan The temperature difference is opened lower than fiducial temperature and fresh air, useless row pressure Reeb air door in preset time after the full preset time of opening, outer temperature are stablized When the screening conditions of full preset time, it is screened as suitable for the real-time running data for predicting the failure of train air-conditioning;
The real-time running data includes: temperature in compartment, temperature, train speed, high pressure, low pressure, fresh air wind outside compartment Air pressure in door opening and closing number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment.
Further, the method according to the train air-conditioning real-time running data prediction air-conditioning failure after screening includes: base In failure predication physical model, train air-conditioning failure is predicted;
The failure predication physical model includes: that failure predication server is receiving the real-time running data by screening Afterwards, according to train within the predetermined period time fresh air air door, useless exhaust door, by-passing valve opening and closing number be greater than corresponding frequency threshold value And air pressure change is greater than air pressure threshold value to predict air-conditioning failure in compartment;And
Failure predication server from garbled real-time running data screen compartment in air conditioner corresponding two A air-conditioning system temperature, real-time when temperature, train speed condition outside compartment within the predetermined period time and in same compartment Operation data, based on two air-conditioning system high-low pressure trend air-conditioning fault types in compartment;
The air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner failure;
Based on two air-conditioning system high-low pressure trend air conditioner ventilation failures in compartment, air-conditioning system event Barrier, air conditioner failure, i.e.,
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor the maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high pressure in predetermined period time interior compartment Value;LPFor the low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor predetermined period Maximum low voltage value in time interior compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 for high-voltage value and The corresponding ascending threshold of difference between high pressure average value;P2 be the corresponding rising threshold of difference between high-voltage value and minimum high-voltage value Value;P3 between high-voltage value and high pressure average value the corresponding falling-threshold value of difference;P4 is poor between high-voltage value and maximum high-voltage value It is worth corresponding falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and most The corresponding ascending threshold of difference between small low voltage value;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be the corresponding falling-threshold value of difference between low voltage value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then judge that an air-conditioning system goes out Existing air conditioner ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;With And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment changes different from the high and low pressure of another air-conditioning system respectively When trend, then predicts air conditioner failure, i.e., reveal failure or air inlet failure slowly.
Further, the method for arranging maintenance task according to prediction air-conditioning fault message includes: to work as to have train air-conditioning pre- When surveying failure, the maintenance task of generation is sent to the corresponding maintenance of train where the air-conditioning that prediction is broken down by management end End, and repair end and the feedback data of maintenance task is sent to management end.
The invention has the advantages that the present invention passes through train air-conditioning subsystem and failure predication server;The train Air conditioning subsystem includes: processor module, the data transmission module and data screening module connecting with processor module;Wherein institute Data screening module is stated to be suitable for the train air-conditioning real-time running data of screening being sent to failure predication by data transmission module Server;The failure predication server is suitable for realizing train according to train air-conditioning real-time running data prediction air-conditioning failure The failure predication of air-conditioning.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the system block diagram of train air-conditioning Maintenance Scheduling system according to the present invention;
Fig. 2 is the flow chart of the method for the train air-conditioning real-time running data of screening according to the present invention;
Fig. 3 is the flow chart of the working method of train air-conditioning Maintenance Scheduling system according to the present invention.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with Illustration illustrates basic structure of the invention, therefore it only shows the composition relevant to the invention.
Embodiment 1
Fig. 1 is the system block diagram of train air-conditioning Maintenance Scheduling system according to the present invention.
As shown in Figure 1, present embodiments providing a kind of train air-conditioning Maintenance Scheduling system, comprising: failure predication server And data screening module and data transmission module positioned at train air-conditioning end;Wherein the data screening module is suitable for screen Train air-conditioning real-time running data failure predication server is sent to by data transmission module;The failure predication server Suitable for predicting air-conditioning failure according to train air-conditioning real-time running data;It is raw when failure predication server prediction train air-conditioning failure At prediction air-conditioning fault message and warning message;The data screening module can be, but not limited to useWI-FIModule;Existing Train data transport protocol in increase data screening field, mark the selection result of the train air-conditioning real-time running data;It is logical The screening to train air-conditioning real-time running data is crossed, failure predication server can predict the failure of train air-conditioning, improve element Service life, avoid periodic replacement function intact air conditioning system element in advance.
Fig. 2 is the flow chart of the method for the train air-conditioning real-time running data of screening according to the present invention.
As shown in Fig. 2, in the present embodiment, the train air-conditioning real-time running data of the data screening module screening, i.e., In the case where train air-conditioning the fault-free simultaneously full preset time of compressor operation of train air-conditioning complete machine, train air-conditioning is transported in real time Row data meet the full preset time of by-passing valve closing simultaneously, evaporation fan runs at high speed, and full preset time, condensation fan are opened completely Preset time, outer temperature stablize after in preset time the temperature difference be full of lower than fiducial temperature and fresh air, useless row pressure Reeb air door it is default When the screening conditions of time, it is screened as suitable for the real-time running data for predicting the failure of train air-conditioning;The real time execution Data include: temperature in compartment, temperature outside compartment, train speed, high pressure, low pressure, continuous working period, cumulative operation time, Air pressure in power, fresh air air door opening and closing number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment.
In the present embodiment, the train air-conditioning Maintenance Scheduling system further include: train institute server-side;The train is taken Business end is suitable for receiving the train air-conditioning real-time running data after screening and is sent to failure predication server, and reception failure is pre- Survey prediction air-conditioning fault message and warning message that server is sent;Train institute server-side can be, but not limited to pass throughMQ's Train air-conditioning real-time running data after screening is sent to failure predication server by mode.
In the present embodiment, the train air-conditioning Maintenance Scheduling system further include: the pipe being connected with failure predication server Manage end and maintenance end;Wherein the failure predication server is suitable for predict that air-conditioning fault message is sent to management end, the pipe It manages end to be suitable for generating maintenance task according to prediction air-conditioning fault message, and is sent to maintenance end;The management end is suitable for from maintenance End obtains the feedback data of maintenance task, and the failure predication server is suitable for transferring history maintenance task from the management end Feedback data;It can be, but not limited to include train, the compartment where the train air-conditioning that breaks down of prediction in the maintenance task With the information such as air-conditioning fault type;The management end and maintenance end can be, but not limited to using mobile phone, computer etc.;Administrative staff It can be, but not limited to by mobile phoneAPPOr on computerWinformOrWebAdministration page after sale is logged in, to check management Maintenance task is sent to maintenance when train air-conditioning predicts failure by the fault condition of the train air-conditioning in personnel's compass of competency End, while management end can store maintenance task;The maintenance personal at maintenance end can be, but not limited to by mobile phoneAPPOr computer OnWinformOrWebCheck the maintenance task of oneself, maintenance personal can be, but not limited to by taking pictures in maintenance process Upload progress and performance that the modes such as management end upload maintenance task;Administrative staff can be monitored in real time by management end Schedule of the maintenance personal to maintenance task;Failure predication server be suitable for from the management end transfer history maintenance task with And the feedback data of history maintenance task, facilitate later period possible job rating, problem to call to account;Realize reasonable distribution maintenance Person works realize the track of the whole process to maintenance task, summarize convenient for later period maintenance personnel responsibility investigation and achievement.
In the present embodiment, the failure predication server is suitable for building failure predication physical model, to train air-conditioning Failure is predicted;The failure predication physical model includes: that failure predication server is receiving the real time execution by screening After data, according to train within the predetermined period time fresh air air door, useless exhaust door, by-passing valve opening and closing number be greater than corresponding number Air pressure change is greater than air pressure threshold value to predict air-conditioning failure in threshold value and compartment;And failure predication server is from garbled In real-time running data screen compartment in corresponding two air-conditioning systems of air conditioner within the predetermined period time and same Temperature in compartments, the real-time running data outside compartment when temperature, train speed condition are waited, based on two air-conditionings in compartment System high-low pressure trend air-conditioning fault type.
In the present embodiment, the air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner Failure;Based in compartment two air-conditioning system high-low pressure trend air conditioner ventilation failures, air-conditioning system failure, Air conditioner failure, i.e.,
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor the maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high pressure in predetermined period time interior compartment Value;LPFor the low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor predetermined period Maximum low voltage value in time interior compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 for high-voltage value and The corresponding ascending threshold of difference between high pressure average value;P2 be the corresponding rising threshold of difference between high-voltage value and minimum high-voltage value Value;P3 between high-voltage value and high pressure average value the corresponding falling-threshold value of difference;P4 is poor between high-voltage value and maximum high-voltage value It is worth corresponding falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and most The corresponding ascending threshold of difference between small low voltage value;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be the corresponding falling-threshold value of difference between low voltage value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then judge that an air-conditioning system goes out Existing air conditioner ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;With And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment changes different from the high and low pressure of another air-conditioning system respectively When trend, then predict air conditioner failure, i.e., slowly leakage failure (the slow leakage failure can be, but not limited to be to leak fluorine) or into Air port failure (the air inlet failure can be, but not limited to be slowly stifled).
In the present embodiment, the management end is further adapted for arranging maintenance task according to prediction air-conditioning fault message, i.e., ought have When the air-conditioning of train predicts failure, the maintenance task of generation is sent to the train where the air-conditioning that prediction is broken down by management end Corresponding maintenance end;Administrative staff are suitable for checking maintenance task by management end, and maintenance task are sent to prediction, event occurs The maintenance end that the corresponding maintenance personal of train where the train air-conditioning of barrier is held.
Embodiment 2
Fig. 3 is the flow chart of the working method of train air-conditioning Maintenance Scheduling system according to the present invention.
As shown in figure 3, on the basis of embodiment 1, the present embodiment 2 also provides a kind of train air-conditioning Maintenance Scheduling system Working method, comprising: train air-conditioning real-time running data is screened;It is pre- according to the train air-conditioning real-time running data after screening Survey air-conditioning failure;And maintenance task is arranged according to prediction air-conditioning fault message.
In the present embodiment, the method that the train air-conditioning real-time running data is screened includes: to pass through data screening Module closes full preset time when real-time running data meets by-passing valve simultaneously, evaporation fan runs at high speed full preset time, cold The temperature difference is lower than fiducial temperature and fresh air, useless row pressure Reeb in preset time after solidifying blower opens full preset time, outer temperature is stablized When air door is full of the screening conditions of preset time, it is screened as suitable for the real-time running data for predicting the failure of train air-conditioning; The real-time running data includes: temperature in compartment, temperature, train speed, high pressure, low pressure, fresh air air door opening and closing time outside compartment Air pressure in number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment.
In the present embodiment, the method packet according to the train air-conditioning real-time running data prediction air-conditioning failure after screening It includes: based on failure predication physical model, train air-conditioning failure being predicted;The failure predication physical model includes: failure Predictive server is receiving after the real-time running data of screening, according to train within the predetermined period time fresh air air door, useless Exhaust door, by-passing valve opening and closing number be greater than air pressure change in corresponding frequency threshold value and compartment and be greater than air pressure threshold value to predict sky Adjust failure;And failure predication server is corresponding from air conditioner in compartment is screened in garbled real-time running data Two air-conditioning systems temperature, the reality outside compartment when temperature, train speed condition within the predetermined period time and in same compartment When operation data, based on two air-conditioning system high-low pressure trend air-conditioning fault types in compartment;
The air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner failure;Based on train Two air-conditioning system high-low pressure trend air conditioner ventilation failures, air-conditioning system failures, air conditioner failure in compartment, I.e.
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor the maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high pressure in predetermined period time interior compartment Value;LPFor the low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor predetermined period Maximum low voltage value in time interior compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 for high-voltage value and The corresponding ascending threshold of difference between high pressure average value;P2 be the corresponding rising threshold of difference between high-voltage value and minimum high-voltage value Value;P3 between high-voltage value and high pressure average value the corresponding falling-threshold value of difference;P4 is poor between high-voltage value and maximum high-voltage value It is worth corresponding falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and most The corresponding ascending threshold of difference between small low voltage value;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be the corresponding falling-threshold value of difference between low voltage value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then judge that an air-conditioning system goes out Existing air conditioner ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;With And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment changes different from the high and low pressure of another air-conditioning system respectively When trend, then predicts air conditioner failure, i.e., reveal failure or air inlet failure slowly.
In the present embodiment, the method for arranging maintenance task according to prediction air-conditioning fault message includes: to work as to have train When air-conditioning predicts failure, the train where the maintenance task of generation is sent to the air-conditioning that prediction is broken down by management end is corresponding End is repaired, and repairs end and the feedback data of maintenance task is sent to management end.
In conclusion the present invention passes through train air-conditioning subsystem and failure predication server;The train air-conditioning subsystem It include: processor module, the data transmission module and data screening module being connect with processor module;The wherein data screening Module is suitable for the train air-conditioning real-time running data of screening being sent to failure predication server by data transmission module;It is described Failure predication server is suitable for predicting that air-conditioning failure, the failure for realizing train air-conditioning are pre- according to train air-conditioning real-time running data It surveys, improves the service life of element, periodic replacement function intact air conditioning system element in advance is avoided, to reach air-conditioning system The peak use rate of element.
The present invention is screened by train air-conditioning real-time running data;According to the train air-conditioning real time execution number after screening It is predicted that air-conditioning failure;And maintenance task is arranged according to prediction air-conditioning fault message, realize reasonable distribution maintenance personal's work Make, realize the track of the whole process to maintenance task, is summarized convenient for later period maintenance personnel responsibility investigation and achievement.
Taking the above-mentioned ideal embodiment according to the present invention as inspiration, through the above description, relevant staff is complete Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.

Claims (4)

1. a kind of train air-conditioning Maintenance Scheduling system characterized by comprising
Failure predication server and data screening module and data transmission module positioned at train air-conditioning end;Wherein
The data screening module is suitable for the train air-conditioning real-time running data of screening being sent to event by data transmission module Hinder predictive server;
The failure predication server is suitable for predicting air-conditioning failure according to train air-conditioning real-time running data;
The train air-conditioning real-time running data of the data screening module screening, i.e.,
Full preset time is closed when real-time running data meets by-passing valve simultaneously, evaporation fan runs at high speed full preset time, cold The temperature difference is lower than fiducial temperature and fresh air, useless row pressure Reeb in preset time after solidifying blower opens full preset time, outer temperature is stablized When air door is full of the screening conditions of preset time, it is screened as suitable for the real-time running data for predicting the failure of train air-conditioning;
The real-time running data includes: temperature in compartment, temperature, train speed, high pressure, low pressure, fresh air air door are opened outside compartment Close the air pressure in number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment;
The train air-conditioning Maintenance Scheduling system further include: train institute server-side;
Train institute's server-side is suitable for receiving the train air-conditioning real-time running data after screening and is sent to failure predication service Device, and receive the prediction air-conditioning fault message that failure predication server is sent;
The train air-conditioning Maintenance Scheduling system further include: the management end being connected with failure predication server and maintenance end;Wherein
The failure predication server is suitable for predict that air-conditioning fault message is sent to management end, and the management end is suitable for according to pre- It surveys air-conditioning fault message and generates maintenance task, and be sent to maintenance end;
The management end is suitable for obtaining the feedback data of maintenance task from maintenance end, and the failure predication server is suitable for from described Management end transfers the feedback data of history maintenance task;
The failure predication server is suitable for building failure predication physical model, to predict train air-conditioning failure;
The failure predication physical model includes: that failure predication server is receiving the root after the real-time running data of screening According to train within the predetermined period time fresh air air door, useless exhaust door, by-passing valve opening and closing number be greater than corresponding frequency threshold value and Air pressure change is greater than air pressure threshold value to predict air-conditioning failure in compartment;And
Failure predication server from garbled real-time running data screen compartment in corresponding two skies of air conditioner Adjusting system temperature, the real time execution outside compartment when temperature, train speed condition within the predetermined period time and in same compartment Data, based on two air-conditioning system high-low pressure trend air-conditioning fault types in compartment.
2. train air-conditioning Maintenance Scheduling system as described in claim 1 characterized by comprising
The air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner failure;
Based in compartment two air-conditioning system high-low pressure trend air conditioner ventilation failures, air-conditioning system failure, Air conditioner failure, i.e.,
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor Maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high-voltage value in predetermined period time interior compartment;LPFor Low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor in the predetermined period time Maximum low voltage value in compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 is high-voltage value and high-voltage flat The corresponding ascending threshold of difference between mean value;P2 be the corresponding ascending threshold of difference between high-voltage value and minimum high-voltage value;P3 are The corresponding falling-threshold value of difference between high-voltage value and high pressure average value;P4 between high-voltage value and maximum high-voltage value difference it is corresponding Falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and minimum low voltage value Between the corresponding ascending threshold of difference;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be low pressure The corresponding falling-threshold value of difference between value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then it is empty to judge that an air-conditioning system occurs Adjust ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment is respectively different from the high and low pressure variation tendency of another air-conditioning system When, then it predicts air conditioner failure, i.e., reveals failure or air inlet failure slowly.
3. train air-conditioning Maintenance Scheduling system as claimed in claim 2, which is characterized in that
The management end is further adapted for arranging maintenance task according to prediction air-conditioning fault message, i.e., ought have the air-conditioning prediction failure of train When, the maintenance task of generation is sent to the corresponding maintenance end of train where the air-conditioning that prediction is broken down by management end.
4. a kind of working method of train air-conditioning Maintenance Scheduling system characterized by comprising
Train air-conditioning real-time running data is screened;
Air-conditioning failure is predicted according to the train air-conditioning real-time running data after screening;And
Maintenance task is arranged according to prediction air-conditioning fault message;
The method that the train air-conditioning real-time running data is screened includes: by data screening module, when real time execution number According to meet simultaneously by-passing valve close full preset time, evaporation fan run at high speed full preset time, condensation fan open it is full default The temperature difference is lower than fiducial temperature in preset time after time, outer temperature are stablized and fresh air, useless row pressure Reeb air door are full of preset time Screening conditions when, be screened as suitable for the real-time running data for predicting the failure of train air-conditioning;
The real-time running data includes: temperature in compartment, temperature, train speed, high pressure, low pressure, fresh air air door are opened outside compartment Close the air pressure in number, useless exhaust door opening and closing number, by-passing valve opening and closing number and compartment;
The method according to the train air-conditioning real-time running data prediction air-conditioning failure after screening includes: based on failure predication object Model is managed, train air-conditioning failure is predicted;
The failure predication physical model includes: that failure predication server is receiving the root after the real-time running data of screening According to train within the predetermined period time fresh air air door, useless exhaust door, by-passing valve opening and closing number be greater than corresponding frequency threshold value and Air pressure change is greater than air pressure threshold value to predict air-conditioning failure in compartment;And
Failure predication server from garbled real-time running data screen compartment in corresponding two skies of air conditioner Adjusting system temperature, the real time execution outside compartment when temperature, train speed condition within the predetermined period time and in same compartment Data, based on two air-conditioning system high-low pressure trend air-conditioning fault types in compartment;
The air-conditioning fault type includes: air conditioner ventilation failure, air-conditioning system failure, air conditioner failure;
Based in compartment two air-conditioning system high-low pressure trend air conditioner ventilation failures, air-conditioning system failure, Air conditioner failure, i.e.,
Set breakdown judge model:
IfHP-HPAvg>P1, andHP-HPMin>P2, then judge high pressure ascendant trend in compartment;
IfHP-HPAvg<P3, andHP-HPMax<P4, then judge high pressure downward trend in compartment;
IfLP-LPAvg>P5, andLP-LPMin>P6, then judge low pressure ascendant trend in compartment;
IfLP-LPAvg<P7, andLP-LPMax<P8, then judge low pressure downward trend in compartment;
In above formula,HPFor the high-voltage value in compartment;HPAvgFor the high pressure average value in predetermined period time interior compartment;HPMaxFor Maximum high-voltage value in predetermined period time interior compartment;HPMinFor the minimum high-voltage value in predetermined period time interior compartment;LPFor Low voltage value in compartment;LPAvgFor the low pressure average value in predetermined period time interior compartment;LPMaxFor in the predetermined period time Maximum low voltage value in compartment;LPMinFor the minimum low voltage value in predetermined period time interior compartment;P1 is high-voltage value and high-voltage flat The corresponding ascending threshold of difference between mean value;P2 be the corresponding ascending threshold of difference between high-voltage value and minimum high-voltage value;P3 are The corresponding falling-threshold value of difference between high-voltage value and high pressure average value;P4 between high-voltage value and maximum high-voltage value difference it is corresponding Falling-threshold value;P5 between low voltage value and low pressure average value the corresponding ascending threshold of difference;P6 be low voltage value and minimum low voltage value Between the corresponding ascending threshold of difference;P7 between low voltage value and low pressure average value the corresponding falling-threshold value of difference;P8 be low pressure The corresponding falling-threshold value of difference between value and maximum low voltage value;
According to the breakdown judge model, the high pressure variation tendency of two air-conditioning systems is judged, i.e.,
When the high pressure variation tendency of two air-conditioning systems is identical and low pressure variation tendency is abnormal, then it is empty to judge that an air-conditioning system occurs Adjust ventilation failure;
According to the breakdown judge model, the high pressure variation tendency of single air conditioning system is judged, i.e.,
When any air-conditioning system high and low pressure variation tendency be more than above-mentioned respective threshold, then judge air-conditioning system failure;And
According to the breakdown judge model, judgement is combined to two air-conditioning systems, i.e.,
When the high and low pressure variation tendency of an air-conditioning system in compartment is respectively different from the high and low pressure variation tendency of another air-conditioning system When, then it predicts air conditioner failure, i.e., reveals failure or air inlet failure slowly;And
The method for arranging maintenance task according to prediction air-conditioning fault message includes: the pipe when there is train air-conditioning to predict failure The maintenance task of generation is sent to the corresponding maintenance end of train where the air-conditioning that prediction is broken down by reason end, and repairs end The feedback data of maintenance task is sent to management end.
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