CN112801610A - Full life cycle management system and method for electromechanical equipment - Google Patents
Full life cycle management system and method for electromechanical equipment Download PDFInfo
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Abstract
The invention belongs to the technical field of equipment control, and discloses a full life cycle management system and method for electromechanical equipment. The system comprises a big data analysis module, a main machine and a smart internet of things, wherein the big data analysis module acquires parameter information of electromechanical equipment, determines professional demand data according to the parameter information, and sends the professional demand data to the smart internet of things main machine; the monitoring module monitors the energy consumption of the electromechanical equipment in a preset scene to obtain the operation information of the electromechanical equipment, analyzes the operation information to obtain an analysis result, and sends the analysis result to the intelligent Internet of things host; the intelligent Internet of things host receives the professional demand data and the analysis result, and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result. The invention carries out unified platform operation management and control on the electromechanical equipment, realizes the maximum automatic and intelligent operation, effectively reduces the comprehensive operation cost of the existing electromechanical equipment, and greatly improves the overall operation management level of the electromechanical equipment.
Description
Technical Field
The invention relates to the technical field of equipment control, in particular to a full life cycle management system and method for electromechanical equipment.
Background
The competition in the field of high-speed railways worldwide in the future will depend to a great extent on the level of digitization and intelligence, and intelligent high-speed rails have become the leading development direction of railways worldwide. The method has important significance for continuously maintaining the global running position of the Chinese high-speed rail by grasping the rare opportunity brought by a new technological industrial revolution and accelerating the development of the Chinese intelligent high-speed rail. China railway general company clearly puts forward the requirements of railway informatization and intelligent development in railway informatization construction planning, and intelligent station construction is an important part of intelligent operation composition in an intelligent high-speed rail development system; is an important embodiment for promoting the intelligent development of railway passenger transportation. Therefore, the efficient and convenient passenger trip service is realized, the comprehensive operation management level of the modern high-speed rail passenger station is improved, and the informatization and intellectualization of the passenger station equipment are improved to solve the problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a full life cycle management system and method for electromechanical equipment, and aims to solve the technical problem that the electromechanical equipment cannot be efficiently and intelligently managed in the prior art.
In order to achieve the purpose, the invention provides a full-life-cycle management system of electromechanical equipment, which comprises a big data analysis module, an intelligent internet-of-things host and a monitoring module;
the big data analysis module is used for acquiring parameter information of the electromechanical equipment, determining professional demand data according to the parameter information and sending the professional demand data to the intelligent Internet of things host;
the monitoring module is used for monitoring the energy consumption of the electromechanical equipment in a preset scene, acquiring the operation information of the electromechanical equipment, analyzing the operation information, acquiring an analysis result and sending the analysis result to the intelligent Internet of things host;
the intelligent Internet of things host is used for receiving the professional demand data and the analysis result and carrying out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result.
Preferably, the full-life-cycle management system of the electromechanical device further comprises an energy consumption prediction module;
the energy consumption prediction module is used for predicting the predicted energy consumption data of the electromechanical equipment in the next operation period;
The energy consumption prediction module is also used for judging whether the predicted energy consumption data meets a preset condition; when the predicted energy consumption data meet the preset conditions, inputting the predicted energy consumption data into a preset prediction model to obtain a prediction curve graph corresponding to the predicted energy consumption data;
the energy consumption prediction module is further configured to obtain actual energy consumption data of the electromechanical device when the predicted energy consumption data does not meet the preset condition, extract training data from the actual energy consumption data, and establish an actual prediction model according to the training data.
Preferably, the full life cycle management system of the electromechanical device further comprises a fault prediction module and a file management module;
the fault prediction module is used for monitoring the running state of the electromechanical equipment in real time to obtain a monitoring result, and determining the fault rate of the electromechanical equipment according to the monitoring result;
the fault prediction module is also used for controlling an alarm device to send out an alarm signal when the fault rate is greater than a preset fault threshold value;
the archive management module is used for acquiring the operation state information and the maintenance history information of the electromechanical equipment and generating a digital equipment archive of the electromechanical equipment according to the operation state information and the maintenance history information.
Preferably, the full life cycle management system of the electromechanical device further comprises an inspection module and a maintenance management module;
the inspection module is used for carrying out health diagnosis on the electromechanical equipment to obtain a diagnosis result and sending the diagnosis result to the maintenance management module;
and the maintenance management module is used for receiving the diagnosis result and maintaining the corresponding electromechanical equipment according to the diagnosis result.
Preferably, the inspection module is further configured to obtain basic information data of the device to be predicted, and compare the basic information data with normal operation data of the device to obtain a comparison result;
the inspection module is further used for judging whether the equipment to be predicted is abnormal according to the comparison result, and determining fault types according to the comparison result when the equipment to be predicted is abnormal, wherein the fault types comprise soft faults and hard faults.
Preferably, the big data analysis module is further configured to construct a mathematical model, determine an optimal operation parameter according to the mathematical model, determine an operation strategy according to the operation parameter, and send the operation strategy to the intelligent internet of things host;
the intelligent Internet of things host is further used for receiving the operation strategy and carrying out intelligent Internet of things management on the electromechanical equipment according to the operation strategy.
Preferably, the monitoring module is further configured to acquire actual energy consumption data of the electromechanical device;
the monitoring module is further configured to divide the actual energy consumption data according to an energy type, a type of the electromechanical device, or a region where the electromechanical device is located, obtain a division result, and store the division result in a server.
Preferably, the energy consumption prediction module is further configured to, when an abnormal value exists in the actual energy consumption data, obtain a reference value adjacent to the abnormal value;
the energy consumption prediction module is further configured to obtain an average value of the reference value, and adjust the abnormal value according to the average value.
Preferably, the energy consumption prediction module is further configured to adjust an abnormal value according to the prediction graph when the abnormal value exists in the actual energy consumption data.
In addition, to achieve the above object, the present invention further provides a full-life-cycle management method for an electromechanical device, the full-life-cycle management method for an electromechanical device being applied to a full-life-cycle management system for the electromechanical device, the method including:
the big data analysis module acquires parameter information of the electromechanical equipment, determines professional demand data according to the parameter information, and sends the professional demand data to the intelligent Internet of things host;
The monitoring module monitors the energy consumption of the electromechanical equipment in a preset scene to obtain the operation information of the electromechanical equipment, analyzes the operation information to obtain an analysis result, and sends the analysis result to the intelligent Internet of things host;
and the intelligent Internet of things host receives the professional demand data and the analysis result and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result.
The system comprises a big data analysis module, an intelligent Internet of things host and a monitoring module; the big data analysis module is used for acquiring parameter information of the electromechanical equipment, determining professional demand data according to the parameter information and sending the professional demand data to the intelligent Internet of things host; the monitoring module is used for monitoring the energy consumption of the electromechanical equipment in a preset scene, acquiring the operation information of the electromechanical equipment, analyzing the operation information, acquiring an analysis result and sending the analysis result to the intelligent Internet of things host; the intelligent Internet of things host is used for receiving the professional demand data and the analysis result and carrying out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result. The invention carries out unified platform operation management and control on the electromechanical equipment, realizes the maximum automatic and intelligent operation, effectively reduces the comprehensive operation cost of the existing electromechanical equipment, and greatly improves the overall operation management level of the electromechanical equipment.
Drawings
FIG. 1 is a schematic structural diagram of a hardware device that may be included in a system for full lifecycle management of mechatronic devices according to an embodiment of the present invention;
FIG. 2 is a schematic system diagram of a first embodiment of a full lifecycle management system for mechatronic devices of the present invention;
FIG. 3 is a system diagram illustrating a second embodiment of a full lifecycle management system for mechatronic devices in accordance with the present invention;
FIG. 4 is a schematic system diagram illustrating a third embodiment of a full lifecycle management system for mechatronic devices in accordance with the present invention;
FIG. 5 is a flowchart illustrating a third embodiment of a life-cycle management system for an electromechanical device according to the present invention;
FIG. 6 is a flowchart illustrating a method for full lifecycle management of an electromechanical device according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a hardware device that may be included in a full lifecycle management system of a mechatronic device according to an embodiment of the present invention.
As shown in fig. 1, a full lifecycle management system for a mechatronic device may include: the system comprises an internet of things host 1001, a communication bus 1002, field intelligent monitoring units 1004 and 1005, cold and heat source monitoring equipment 1006, heating ventilation and air conditioning monitoring equipment 1007, integrated air conditioner monitoring equipment 1008, illumination monitoring equipment 1009, water supply and drainage monitoring equipment 1010, power supply and distribution monitoring equipment 1011, elevator monitoring equipment 1012, fire protection monitoring equipment 1013, customer service monitoring equipment 1014, water supply and sewage discharge monitoring equipment 1015, building structure monitoring equipment 1016, an intelligent ammeter 1017, a remote water meter 1018, a gas meter 1019 and a heat meter 2020. The internet of things host 1001 is integrated with multiple functions of a PLC (programmable logic controller), a PC (personal computer), a gateway, motion control, I/O (input/output) data acquisition, a field bus protocol, machine vision, equipment networking and the like, and meanwhile, the functions of equipment motion control, data acquisition, operation and the like are realized. A communication bus 1002 is used to enable connection communications between these components. The intelligent on- site monitoring units 1004 and 1005 are complete boxes or cabinets and mainly comprise controllers such as a PLC (programmable logic controller), a DDC (direct digital control) and other electrical components. It should be noted that the specific number of monitoring units is determined according to the number and kinds of the accessed devices, and is not only two in the structural schematic diagram. 1006-1016 are schematic diagrams of controlled devices that need to be accessed through the internet of things on site, and different access modes may be provided according to different controlled devices, such as hard-wired access (access through status points of the field devices), communication access (access through communication integration), and the like, and corresponding sensors (such as temperature, humidity, air quality, pressure, illuminance, wind speed, differential pressure, liquid level, anti-freeze switches, and the like) that can acquire the field devices. The smart electric meter 1017 is a meter for monitoring power consumption, the remote water meter 1018 is a meter for monitoring water consumption, the gas meter 1019 is a meter for monitoring gas consumption, the heat meter 2020 is a meter for monitoring heat consumption, and the meters need specific data remote transmission functions.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the full lifecycle management system for mechatronic devices, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
Referring to fig. 2, fig. 2 is a system structure diagram of a first embodiment of the full lifecycle management system of the mechatronic device of the present invention, and the first embodiment of the present invention is proposed based on the system structure diagram of the first embodiment.
As shown in fig. 2, in this embodiment, the full-life-cycle management system of the electromechanical device includes a big data analysis module, an intelligent internet-of-things host, and a monitoring module;
the big data analysis module 10 is configured to obtain parameter information of the electromechanical device, determine professional demand data according to the parameter information, and send the professional demand data to the intelligent internet of things host.
It should be noted that the parameter information may be operation parameters, production data, report data, environment parameters, demand information, operation requirements of other auxiliary specialties on the equipment, data for ensuring equipment, production safety, operation requirement data, and the like, the environment parameters may be environment parameters such as indoor temperature, indoor humidity, indoor carbon dioxide concentration, outdoor temperature, outdoor humidity, illumination intensity, structure monitoring, passenger flow, and the like, and the professional requirement data may be professional requirement data formed by recording and analyzing the parameter information. And sending the professional requirement data to an intelligent Internet of things host so that the intelligent Internet of things host can perform intelligent control according to the professional requirement data, for example, some devices are not started or in a station scene when in a fault state or a maintenance state, and the like, some regions are not operated or passengers are not provided, and the devices in the regions can be suspended for use.
The monitoring module 20 is configured to monitor energy consumption of the electromechanical device in a preset scene, obtain operation information of the electromechanical device, analyze the operation information to obtain an analysis result, and send the analysis result to the host of the intelligent internet of things.
It should be noted that the preset scenario may be an actual scenario in which the current electromechanical device is located, or a test scenario manufactured for obtaining actual energy consumption data of the corresponding electromechanical device, or a scenario in which an end device is added in the energy consumption monitoring process, and the present embodiment is not limited herein. The terminal device may be an intelligent electric meter, a remote water meter, a gas meter, a heat meter, etc., and the implementation is not limited herein. The operation information can be operation parameters, energy consumption data, demand data and the like of the electromechanical equipment, and the operation information can be analyzed by classifying, performing item statistical analysis on the operation information and distributing according to needs, so that refined energy consumption management is achieved.
Further, in order to achieve more detailed energy management, the monitoring module is further configured to acquire actual energy consumption data of the electromechanical device, divide the actual energy consumption data according to an energy type, the type of the electromechanical device, or a region where the electromechanical device is located, obtain a division result, and store the division result in the server.
It should be noted that, the dividing the actual energy consumption data according to the energy type may be that the monitoring module divides the actual energy consumption data into electricity, water, gas, heat, and the like according to the energy type, divides the actual energy consumption data into a heating ventilation air conditioner, a lighting socket, general power, passenger traffic information, commercial electricity, special electricity, and the like according to the device type, and divides the actual energy consumption data into an outbound layer, a station layer, an overhead layer, and the like according to the area, where the division scene may be a usage scene of the electromechanical device in the passenger traffic station, and in the usage scene of other electromechanical devices, different classification bases may be set according to specific usage situations, which is not limited herein. The following description will briefly describe the power consumption and water usage items in the energy consumption according to the use of the electromechanical device in the passenger station.
The station water use items can be classified according to regions or directions, and the detailed classification can refer to a water energy item table in the following table 1:
TABLE 1 Water energy itemizing table
The power utilization items can be divided into the following items according to the types of equipment: lighting sockets, heating ventilation air conditioners, general power, passenger traffic information, special electricity utilization and commercial electricity utilization. The detailed classification can refer to the electric energy subentry of the table 2:
meter 2 electric energy itemizing meter
The intelligent internet of things host 30 is configured to receive the professional demand data and the analysis result, and perform intelligent internet of things management on the electromechanical device according to the professional demand data and/or the analysis result.
It should be noted that, taking a guest station scene as an example, performing intelligent internet of things management on the electromechanical devices may be performing intelligent internet of things management on various service type electromechanical devices in the guest station, including performing intelligent internet of things management on cold and heat source system devices, heating, ventilation and air conditioning system devices, integrated air conditioning system devices, lighting system devices, water supply and drainage system devices, power supply and distribution system devices, elevator system devices, fire fighting system devices, guest service system devices, passenger car water supply and sewage drainage system devices, building structure monitoring system devices, and the like.
It should be understood that each type of electromechanical device has different interface types, and therefore, the technology of the intelligent internet of things needs to be applied. Simultaneously for the all kinds of equipment of better access, provide an intelligence thing networking host computer in this embodiment. The intelligent Internet of things host is integrated by multiple fields of functions such as a PLC (programmable logic controller), a PC (personal computer), a gateway, motion control, I/O (input/output) data acquisition, a field bus protocol, machine vision and equipment networking, and simultaneously realizes the functions of equipment motion control, data acquisition, operation and the like.
Furthermore, in order to better integrate various electromechanical devices, the intelligent internet of things host classifies the types of the device interfaces. The interfaces of various devices mainly comprise Ethernet interfaces, serial interfaces and hard-point interfaces. The specific interface requirements are as follows:
(1) Ethernet interface
The Ethernet interface should accord with IEEE 802.3CSMA/CD standard, should support at least the unshielded twisted pair cable of the ultra-five types, shield the twisted pair cable, the network fault should be able to detect and isolate automatically, the normal operation and operation of both sides will not be influenced by the cut-in or take-off of the network equipment. The design principle of the network should be such that any single point of failure does not disrupt the overall network operation. Meanwhile, the system ethernet interface should also meet the following requirements:
1)100Mbps/1000Mbps adaptive Ethernet interface
2) Supporting TCP/IP protocol
3) The Ethernet interface adopts RJ45 standard interface
4) Supporting universal, open, software decoding protocols
5) When any change occurs in the field, the data on the interface should be updated in real time
6) The communication of the interface is usually performed by adopting a query or event triggering mode
(2) Serial interface
The serial interface adopts RS422 or RS485 meeting EIA standard, and when the communication distance is not more than 1200 meters and the repeater is not used, the communication speed is not lower than 9600 bps. Meanwhile, the system serial interface should also meet the following requirements:
1) support for generic, open, software-decoded protocols;
2) when any change occurs on the site, the data on the interface should be updated in real time;
3) The communication of the interface is typically performed in a query or event triggered manner.
(3) Hard contact interface
The hard contact interface is a switching value interface and adopts a cross-sectional area of 1mm2Or 1.5mm2The cable of (1).
In the embodiment, parameter information of electromechanical equipment is acquired through a big data analysis module, professional demand data is determined according to the parameter information, and the professional demand data is sent to an intelligent Internet of things host; the monitoring module monitors the energy consumption of the electromechanical equipment in a preset scene to obtain the operation information of the electromechanical equipment, analyzes the operation information to obtain an analysis result, and sends the analysis result to the intelligent Internet of things host; the intelligent Internet of things host receives the professional demand data and the analysis result, and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result. The embodiment performs unified platform-based operation management and control on the electromechanical equipment, realizes the maximum automatic and intelligent operation, effectively reduces the comprehensive operation cost of the existing electromechanical equipment, and greatly improves the overall operation management level of the electromechanical equipment.
A second embodiment of the present invention is proposed based on the first embodiment of the present invention described above.
Referring to fig. 3, fig. 3 is a schematic system structure diagram of a full lifecycle management system of an electromechanical device according to a second embodiment of the present invention.
As shown in fig. 3, in this embodiment, the system for managing the full life cycle of the electromechanical device further includes an energy consumption predicting module 40;
the energy consumption prediction module 40 is configured to predict predicted energy consumption data of the electromechanical device in a next operation cycle.
It should be noted that, the predicting of the predicted energy consumption data of the electromechanical device in the next operating cycle may be a short-term prediction and/or a long-term prediction of energy consumption respectively obtained by using an artificial intelligence method, the short-term prediction may be an energy consumption prediction for half an hour in the future, the long-term prediction may be an energy consumption prediction for 24 hours in the future, the length of the cycle may be adaptively adjusted according to actual conditions, and the implementation is not limited herein.
Further, to obtain a more accurate prediction result, for short-term energy consumption prediction, a statistical regression method may be used to establish a thermal response model f of the building, which describes the statistical relationship between various influencing factors, such as weather, time periods, refrigeration/heat, and average temperature changes within the room within a certain time period. From this model an inverse function f-1 is derived, which describes how much cooling/heating is required for a given magnitude of change in the indoor temperature required for a given time period with the current impact factor (e.g. weather, time period). With the inverse function f-1, the total load demand in a future time period can be calculated as short-term load prediction by directly utilizing the indoor average temperature and humidity data of the building, the relevant influence factors and the expected indoor temperature. For long-term load prediction, several typical patterns of load change are obtained from historical data by adopting a time series data clustering method, and then a 24h load change pattern closest to conditions such as current weather, holidays and the like is obtained from the typical patterns to serve as long-term load prediction. Compared with long-term prediction, the short-term prediction can obtain more accurate estimation by utilizing more known conditions, such as the supply and return water temperature at the current time point, the current outdoor temperature and humidity and the like, and the long-term prediction can reflect the change trend or the mode of the load over a large time span.
The energy consumption prediction module is also used for judging whether the predicted energy consumption data meets a preset condition; and when the predicted energy consumption data meet the preset conditions, inputting the predicted energy consumption data into a preset prediction model to obtain a prediction curve graph corresponding to the predicted energy consumption data.
It should be noted that the preset condition may be a set condition for determining whether the predicted energy consumption data meets a desired condition, for example, the preset condition may be a condition such as whether an energy consumption value in the predicted energy consumption data exceeds a preset threshold, and the preset prediction model may be a model that may generate a corresponding prediction graph according to the predicted energy consumption data.
The energy consumption prediction module is further configured to obtain actual energy consumption data of the electromechanical device when the predicted energy consumption data does not meet the preset condition, extract training data from the actual energy consumption data, and establish an actual prediction model according to the training data.
It should be noted that the actual energy consumption data may be energy consumption data acquired by the electromechanical device in a preset scene, the training data may be data selected according to the actual energy consumption data to establish an actual prediction model, the sampled training data may be selected nearby according to a value to be predicted, and meanwhile, data of the same ratio is selected to perform big data operation, if data of a next day is to be predicted, data of the previous 3 to 5 days needs to be extracted, meanwhile, a predicted change rate is obtained by comparing the data of the previous month, a prediction coefficient is obtained by combining the data of the previous month and factors such as the number of devices to be turned on in the next day, weather, and the like, wherein if the data of the previous month is found to have a large change with the data of the month through data operation, the data of the previous month is not used, and finally, the actual prediction model is established according to the predicted change rate and the prediction coefficient, the predicted energy consumption data do not meet the preset condition may be due to the fact that the number of selected data values is too small during prediction, so that the predicted energy consumption data do not meet the preset condition, and at the moment, the predicted energy consumption data are predicted again according to the actual prediction model, so that the predicted energy consumption data meet the preset condition.
Furthermore, when the actual measurement energy consumption data is collected, data loss or abnormal phenomena may occur at a certain specific moment due to temporary power failure, equipment restart or external interference. In order to mark the missing data or abnormal data, the energy consumption prediction module is further used for adjusting an abnormal value according to the prediction graph when the abnormal value exists in the actual energy consumption data. For example, if the energy consumption data in the time period of 12:30 to 13:00 in the measured energy consumption data mapping relation table is lost, the predicted energy consumption data in the time period of 12:30 to 13:00 may be obtained according to the prediction graph or the data predicted according to the actual prediction model, and the lost energy consumption data may be adjusted according to the predicted energy consumption data.
In this embodiment, the full life cycle management system of the electromechanical device further includes an energy consumption prediction module; the energy consumption prediction module is used for predicting the predicted energy consumption data of the electromechanical equipment in the next operation period; judging whether the predicted energy consumption data meets a preset condition or not; when the predicted energy consumption data meet the preset conditions, inputting the predicted energy consumption data into a preset prediction model to obtain a prediction curve graph corresponding to the predicted energy consumption data; and the energy consumption prediction module is further used for acquiring actual energy consumption data of the electromechanical equipment when the predicted energy consumption data does not meet the preset condition, extracting training data from the actual energy consumption data, and establishing an actual prediction model according to the training data. Energy consumption can be predicted through the energy consumption prediction module to perform energy saving comparison, abnormal data correction, or energy consumption threshold management of a user and the like. The refined energy consumption management of the equipment is achieved.
Referring to fig. 4, fig. 4 is a schematic system structure diagram of a third embodiment of the full-life-cycle management system of the electromechanical device according to the present invention.
Based on the above embodiments, as shown in fig. 4, in this embodiment, the full-life-cycle management system of the electromechanical device further includes a failure prediction module, an inspection module, a maintenance management module, and an archive management module.
The failure prediction module 50 is configured to monitor the operation state of the electromechanical device in real time, obtain a monitoring result, and determine a failure rate of the electromechanical device according to the monitoring result.
It should be noted that, the real-time monitoring of the operation state of the electromechanical device may be monitoring the operation state of the electromechanical device, for example, whether the electromechanical device is always in operation, whether the operation is terminated or abnormal operation such as jamming and slow down of operation occurs, and determining a failure rate of the electromechanical device according to the monitoring result.
The failure prediction module 50 is further configured to control an alarm device to send an alarm signal when the failure rate is greater than a preset failure threshold value.
It should be noted that the preset failure threshold may be a customized failure threshold that needs to be repaired, for example, the customized failure threshold is 60%, when the failure rate of the electromechanical device is 50%, the failure threshold is not exceeded, the electromechanical device may not be temporarily repaired, and when the failure rate is 70%, the electromechanical device needs to be repaired. The alarm device is controlled to send out an alarm signal, so that maintenance personnel can carry out maintenance.
The inspection module 60 is configured to perform health diagnosis on the electromechanical device, obtain a diagnosis result, and send the diagnosis result to the maintenance management module.
The inspection module 60 is further configured to obtain basic information data of the device to be predicted, compare the basic information data with normal operation data of the device, and obtain a comparison result. And the device is also used for judging whether the equipment to be predicted is abnormal or not according to the comparison result, and determining the fault type according to the comparison result when the equipment to be predicted is abnormal, wherein the fault type comprises a soft fault and a hard fault.
It should be noted that the basic information data may be basic information data such as operation data, energy consumption data, sensing data and the like on the device to be predicted, then, the basic information data is compared with normal data under different working conditions, a comparison result is obtained by comparing the basic information data with the normal data under different working conditions, whether the device to be predicted is abnormal or not is determined according to the comparison result, and when the device to be predicted is abnormal, a fault type is determined according to the comparison result, wherein the fault type includes a soft fault and a hard fault.
The hard fault mainly refers to the fault that the equipment and the device are completely failed, such as sudden shutdown of a fan, belt breakage, no output signal or bad data output of a sensor, complete blockage of a valve and the like. From the time course of the occurrence of the fault, the fault is a sudden fault, and the fault is large and is easy to detect.
Soft faults refer to various faults such as fouling of fan coils (gradual coil blockage), leakage when valves are closed, drift of instruments, etc., in which the performance of equipment is degraded or partially fails. Soft failures are generally progressive, with the symptoms being less pronounced before they occur and often difficult to detect initially. Progressive faults are in fact due to progressive deterioration of the system parameters, in the sense that soft faults are more harmful than hard faults.
Here, for better understanding, taking the failure of the hvac system as an example, the following is explained:
after the heating, ventilating and air conditioning equipment is used for a period of time, faults occurring in the system are mostly accidental faults, therefore, the fault characteristics are random, and the occurrence process of the faults is a non-stable random process related to time. The equipment is formed by combining various subsystems and elements according to a certain rule and has hierarchy. Therefore, the failure occurrence is hierarchical due to its hierarchy. In addition, the heating, ventilating and air conditioning system is composed of a plurality of subsystems which are mutually associated, and some subsystem faults can be caused by faults of the subsystems or links related to the subsystem faults, so that the system fault transmissibility is called. Depending on the location of the fault, the fault may be an equipment fault or a sensor fault, either a hard fault or a soft fault. Often interleaved in the same system, increasing the complexity of fault diagnosis of the hvac system.
The system mainly diagnoses and predicts faults of a heating, ventilation and air conditioning refrigeration system of a high-speed rail intelligent station, and the heating, ventilation and air conditioning refrigeration system mainly comprises a compressor, a heat exchanger, a throttling device, a reversing device, auxiliary equipment, electric control, a terminal device, cold (heat) medium conveying and other subsystems. The main fault diagnosis method for each subsystem is as follows:
(1) compressor abnormal shutdown failure
The abnormal shutdown of the compressor is generally caused by an electrical fault or by high and low voltage protection actions of the compressor. When the compressor is stopped, the whole refrigeration cycle system is separated by the compressor, and the refrigerant is no longer in a circulating state. However, at this time, the condenser still exchanges heat with the outside, the refrigerant temperature and pressure rapidly decrease in a short time, the differential pressure across the expansion valve decreases, and the refrigerant flow rate rapidly decreases to 0. The amount of heat exchange between the evaporator and the condenser and the outside is rapidly decreased, and mainly the refrigerant change heat.
(2) Blower fan of condenser side finned tube heat exchanger stops rotating
The reasons for causing the shutdown of the fan of the finned tube heat exchanger are as follows: the burning of relays, the burning of fan motors, the damage of belts, the failure of electrical circuits, etc. After the fan of the finned tube heat exchanger stops rotating, the heat exchange quantity between air and the condenser is sharply reduced, and the more heat is accumulated in the condenser, so that the condensing pressure is increased, and the pressure difference between the front and the rear of the expansion valve is increased. The flow of refrigerant will increase and the amount of evaporator heat exchange will increase.
(3) Stop of circulating water pump
After the circulating water pump stops running, the heat exchange mode of the water and the evaporator side is changed into a heat conduction mode from a heat exchange mode taking convection heat exchange as a leading mode, the heat exchange quantity is reduced suddenly, so that the internal energy of a refrigerant in the evaporator is reduced, the evaporation temperature and the evaporation pressure of the refrigerant are reduced, and the amount of the refrigerant in the evaporator is accumulated. This reduces the condensing temperature and pressure, and reduces the refrigerant flow rate. Because the system has certain time lag, the change of each thermodynamic parameter is not very obvious in the initial period of the fault.
And the maintenance management module 70 is configured to receive the diagnosis result, and maintain the corresponding electromechanical device according to the diagnosis result.
It should be noted that, the maintaining the corresponding electromechanical device according to the diagnosis result may be to generate a maintenance plan according to the device operation condition and the corresponding component information, and track the maintenance condition to ensure the service life of the device. The spare part management system mainly uses equipment prevention and maintenance to establish a spare part management system by comprehensively analyzing the requirement condition of the spare parts, guiding the purchasing work of the equipment spare parts, providing transparent management and multilevel management on the inventory of the spare parts and combining the quantity of the equipment through various contents of the service condition, the consumption period, the maintenance and maintenance plan and the like of the spare parts of the equipment
The archive management module 80 is configured to obtain the operation state information and the maintenance history information of the electromechanical device, and generate a digital device archive of the electromechanical device according to the operation state information and the maintenance history information.
It should be noted that, the digital device file for generating the electromechanical device may be a digital device file with a "one-file-by-one" full life cycle, which is formed by organically fusing the dynamic and static data of the device with the configuration of the device as the center, in combination with the state data of the device and information such as a failure history and a maintenance history from service to scrapping.
Referring to fig. 5, fig. 5 is a flowchart illustrating a third embodiment of a full lifecycle management system of an electromechanical device according to the present invention.
As shown in fig. 5, the patrol inspection module inspects the electromechanical device according to a preset patrol inspection period and generates a patrol inspection record, when the device is abnormal, the patrol inspection is finished, when the device is confirmed to be abnormal, a preset alarm device is started to perform fault alarm so as to inform maintenance personnel to perform maintenance to confirm whether the fault and the fault reason or confirm the fault through self-inspection of the device, whether overhaul is needed or not is judged according to the fault reason, if overhaul is needed, a manufacturer of the device is informed to perform overhaul or replace the device, if overhaul is not needed, field maintenance is performed, a maintenance work order is backfilled according to a maintenance result so as to generate a digital file of the device, and after the maintenance is finished, the fault alarm of the device is turned off.
In the embodiment, the running state of the electromechanical device is monitored in real time through a fault prediction module to obtain a monitoring result, and the fault rate of the electromechanical device is determined according to the monitoring result; when the fault rate is greater than a preset fault threshold value, controlling an alarm device to send out an alarm signal; and acquiring the running state information and the maintenance history information of the electromechanical equipment through the file management module, and generating a digital equipment file of the electromechanical equipment according to the running state information and the maintenance history information. The inspection module is used for carrying out health diagnosis on the electromechanical equipment to obtain a diagnosis result, and the diagnosis result is sent to the maintenance management module; the maintenance management module receives the diagnosis result, and maintains the corresponding electromechanical device according to the diagnosis result, so that the comprehensive operation cost of the electromechanical device is effectively reduced from the aspects of equipment failure, maintenance management, periodic inspection, equipment archives and the like through the synergistic effect of the modules, and the management and control efficiency of the operation of the electromechanical device is improved.
Further, referring to fig. 6, fig. 6 is a schematic flowchart of a first embodiment of a full-life-cycle management method of an electromechanical device according to the present invention, where the full-life-cycle management method of the electromechanical device is applied to a full-life-cycle management system of the electromechanical device, and the full-life-cycle management system of the electromechanical device includes a big data analysis module, an intelligent internet of things host, and a monitoring module;
The full life cycle management method of the electromechanical device comprises the following steps:
step S100: the big data analysis module obtains parameter information of the electromechanical equipment, determines professional demand data according to the parameter information, and sends the professional demand data to the intelligent Internet of things host.
It should be noted that the parameter information may be operation parameters, production data, report data, environment parameters, demand information of the electromechanical device, operation requirements of other auxiliary specialties on the electromechanical device, data (operation requirement data) for ensuring the safety of the electromechanical device and the production, and the like, the environment parameters may be environment parameters such as indoor temperature, indoor humidity, indoor carbon dioxide concentration, outdoor temperature, outdoor humidity, illumination intensity, structure monitoring, passenger flow volume, and the like, and the professional demand data may be professional demand data formed by recording and analyzing the parameter information. And sending the professional requirement data to an intelligent Internet of things host so that the intelligent Internet of things host can perform intelligent control according to the professional requirement data, for example, some devices are not started or in a station scene when in a fault state or a maintenance state, and the like, some regions are not operated or passengers are not provided, and the devices in the regions can be suspended for use.
Step S200: the monitoring module monitors the energy consumption of the electromechanical equipment in a preset scene, obtains the operation information of the electromechanical equipment, analyzes the operation information to obtain an analysis result, and sends the analysis result to the intelligent Internet of things host.
It should be noted that the preset scenario may be an actual scenario in which the current electromechanical device is located, or a test scenario manufactured for obtaining actual energy consumption data of the corresponding electromechanical device, or a scenario in which an end device is added in the energy consumption monitoring process, and the present embodiment is not limited herein. The terminal device may be an intelligent electric meter, a remote water meter, a gas meter, a heat meter, etc., and the implementation is not limited herein. The operation information can be operation parameters, energy consumption data, demand data and the like of the electromechanical equipment, and the operation information can be analyzed by classifying, performing item statistical analysis on the operation information and distributing according to needs, so that refined energy consumption management is achieved.
Further, in order to achieve more detailed energy management, the monitoring module is further configured to acquire actual energy consumption data of the electromechanical device, divide the actual energy consumption data according to an energy type, the type of the electromechanical device, or a region where the electromechanical device is located, obtain a division result, and store the division result in the server.
It should be noted that, the dividing the actual energy consumption data according to the energy type may be that the monitoring module divides the actual energy consumption data into electricity, water, gas, heat, and the like according to the energy type, divides the actual energy consumption data into a heating ventilation air conditioner, a lighting socket, general power, passenger traffic information, commercial electricity, special electricity, and the like according to the device type, and divides the actual energy consumption data into an outbound layer, a station layer, an overhead layer, and the like according to the area, where the division scene may be a usage scene of the electromechanical device in the passenger traffic station, and in the usage scene of other electromechanical devices, different classification bases may be set according to specific usage situations, which is not limited herein.
Step S300: and the intelligent Internet of things host receives the professional demand data and the analysis result and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result.
It should be noted that, taking a guest station scene as an example, performing intelligent internet of things management on the electromechanical devices may be performing intelligent internet of things management on various service type electromechanical devices in the guest station, including performing intelligent internet of things management on cold and heat source system devices, heating, ventilation and air conditioning system devices, integrated air conditioning system devices, lighting system devices, water supply and drainage system devices, power supply and distribution system devices, elevator system devices, fire fighting system devices, guest service system devices, passenger car water supply and sewage drainage system devices, building structure monitoring system devices, and the like.
It should be understood that each type of electromechanical device has different interface types, and therefore, the technology of the intelligent internet of things needs to be applied. Simultaneously for the all kinds of equipment of better access, provide an intelligence thing networking host computer in this embodiment. The intelligent Internet of things host is integrated by multiple fields of functions such as a PLC (programmable logic controller), a PC (personal computer), a gateway, motion control, I/O (input/output) data acquisition, a field bus protocol, machine vision and equipment networking, and simultaneously realizes the functions of equipment motion control, data acquisition, operation and the like.
According to the embodiment, parameter information of the electromechanical equipment is obtained, professional demand data are determined according to the parameter information, and the professional demand data are sent to the intelligent Internet of things host; monitoring the energy consumption of the electromechanical equipment in a preset scene to obtain the operation information of the electromechanical equipment, analyzing the operation information to obtain an analysis result, and sending the analysis result to the intelligent Internet of things host; the intelligent Internet of things host receives the professional demand data and the analysis result, and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result. The embodiment performs unified platform-based operation management and control on the electromechanical equipment, realizes the maximum automatic and intelligent operation, effectively reduces the comprehensive operation cost of the existing electromechanical equipment, and greatly improves the overall operation management level of the electromechanical equipment.
Other embodiments or specific implementation manners of the full-life-cycle management method of the electromechanical device of the present invention may refer to the above system embodiments, and are not described herein again.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the parameter operation method provided in any embodiment of the present invention, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The full-life-cycle management system of the electromechanical equipment is characterized by comprising a big data analysis module, an intelligent Internet of things host and a monitoring module;
the big data analysis module is used for acquiring parameter information of the electromechanical equipment, determining professional demand data according to the parameter information and sending the professional demand data to the intelligent Internet of things host;
the monitoring module is used for monitoring the energy consumption of the electromechanical equipment in a preset scene, acquiring the operation information of the electromechanical equipment, analyzing the operation information, acquiring an analysis result and sending the analysis result to the intelligent Internet of things host;
the intelligent Internet of things host is used for receiving the professional demand data and the analysis result and carrying out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result.
2. The system of claim 1, wherein the full lifecycle management system of the mechatronic device further comprises an energy consumption prediction module;
the energy consumption prediction module is used for predicting the predicted energy consumption data of the electromechanical equipment in the next operation period;
The energy consumption prediction module is also used for judging whether the predicted energy consumption data meets a preset condition; when the predicted energy consumption data meet the preset conditions, inputting the predicted energy consumption data into a preset prediction model to obtain a prediction curve graph corresponding to the predicted energy consumption data;
the energy consumption prediction module is further configured to obtain actual energy consumption data of the electromechanical device when the predicted energy consumption data does not meet the preset condition, extract training data from the actual energy consumption data, and establish an actual prediction model according to the training data.
3. The system of claim 1, wherein the full lifecycle management system of the mechatronic device further comprises a fault prediction module and a profile management module;
the fault prediction module is used for monitoring the running state of the electromechanical equipment in real time to obtain a monitoring result, and determining the fault rate of the electromechanical equipment according to the monitoring result;
the fault prediction module is also used for controlling an alarm device to send out an alarm signal when the fault rate is greater than a preset fault threshold value;
the archive management module is used for acquiring the operation state information and the maintenance history information of the electromechanical equipment and generating a digital equipment archive of the electromechanical equipment according to the operation state information and the maintenance history information.
4. The system of claim 1, wherein the full lifecycle management system of the mechatronic device further comprises an inspection module and a maintenance management module;
the inspection module is used for carrying out health diagnosis on the electromechanical equipment to obtain a diagnosis result and sending the diagnosis result to the maintenance management module;
and the maintenance management module is used for receiving the diagnosis result and maintaining the corresponding electromechanical equipment according to the diagnosis result.
5. The system of claim 4, wherein the inspection module is further configured to obtain basic information data of the device to be predicted, compare the basic information data with normal operation data of the device, and obtain a comparison result;
the inspection module is further used for judging whether the equipment to be predicted is abnormal according to the comparison result, and determining fault types according to the comparison result when the equipment to be predicted is abnormal, wherein the fault types comprise soft faults and hard faults.
6. The system of claim 1, wherein the big data analysis module is further configured to construct a mathematical model, determine optimal operating parameters according to the mathematical model, determine an operating strategy according to the operating parameters, and send the operating strategy to the intelligent internet of things host;
The intelligent Internet of things host is further used for receiving the operation strategy and carrying out intelligent Internet of things management on the electromechanical equipment according to the operation strategy.
7. The system of claim 1, wherein the monitoring module is further configured to collect actual energy consumption data of the mechatronic device;
the monitoring module is further configured to divide the actual energy consumption data according to an energy type, a type of the electromechanical device, or a region where the electromechanical device is located, obtain a division result, and store the division result in a server.
8. The system of claim 2, wherein the energy consumption prediction module is further configured to, when an outlier exists in the actual energy consumption data, obtain a reference value adjacent to the outlier;
the energy consumption prediction module is further configured to obtain an average value of the reference value, and adjust the abnormal value according to the average value.
9. The system of claim 8, wherein the energy consumption prediction module is further configured to adjust an outlier in the actual energy consumption data based on the prediction graph when the outlier exists.
10. A full lifecycle management method of a mechatronic device based on the full lifecycle management system of a mechatronic device of any one of claims 1 to 9, comprising:
The big data analysis module acquires parameter information of the electromechanical equipment, determines professional demand data according to the parameter information, and sends the professional demand data to the intelligent Internet of things host;
the monitoring module monitors the energy consumption of the electromechanical equipment in a preset scene to obtain the operation information of the electromechanical equipment, analyzes the operation information to obtain an analysis result, and sends the analysis result to the intelligent Internet of things host;
and the intelligent Internet of things host receives the professional demand data and the analysis result and carries out intelligent Internet of things management on the electromechanical equipment according to the professional demand data and/or the analysis result.
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