CN115931409A - Heating system fault early warning method and system - Google Patents
Heating system fault early warning method and system Download PDFInfo
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Abstract
The invention provides a heating system fault early warning method and a system, which relate to the technical field of fault monitoring and comprise the steps of sending first instruction information, wherein the first instruction information is a parameter instruction sent to a heating system, and determining whether the heating system is in a stable operation state or not according to the obtained parameter instruction; sending second instruction information, extracting a key frame image of the first video information, and performing anomaly detection on the extracted key frame image to obtain second video information; determining temperature conditions of different areas inside the heating system; and judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system, and sending out an early warning prompt. The method has the advantages that the method combines the extension correlation function and the elastic back propagation neural network algorithm, achieves the purpose of reflecting the system state by using less data, simplifies sample data and improves the accuracy of early warning.
Description
Technical Field
The invention relates to the technical field of fault monitoring, in particular to a heating system fault early warning method and system.
Background
In the existing medical cleaning machine, a double heating system is often adopted to dry the cleaned medical instruments, the double heating system consists of a fresh air system and an internal circulation system, and after cleaning is completed, the medical instruments in a cleaning cabin of the cleaning machine can be quickly dried, so that the drying time of the medical instruments is greatly shortened.
Disclosure of Invention
The invention aims to provide a heating system fault early warning method and a heating system fault early warning system to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a heating system fault early warning method, including:
step S100: sending first instruction information, wherein the first instruction information is a parameter instruction sent to a heating system, and determining whether the heating system is in a stable operation state according to the obtained parameter instruction;
step S200: sending second instruction information according to the first instruction information, wherein the second instruction information comprises first video information and vibration frequency information of the heating system, which are acquired according to electronic equipment when the heating system is in a stable operation state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of receiving vibration generated by the heating system in the working state;
step S300: extracting a key frame image of the first video information, and performing anomaly detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image;
step S400: identifying the second video information, and determining the temperature conditions of different areas in the heating system;
step S500: and judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system, and sending out an early warning prompt.
Preferably, in step S100, the sending first instruction information, where the first instruction information is a parameter instruction sent to a heating system, and determining whether the heating system is in a stable operation state according to the obtained parameter instruction, includes:
step S101: acquiring sample data of the heating system, carrying out standardization processing on the sample data to obtain first data information, and carrying out reprocessing on the first data information by using a coring linear dimension reduction method to obtain second data information;
step S102: calculating the second data information based on an extension correlation function and an elastic back propagation neural network algorithm to obtain an output result;
step S103: and judging the output result, if the output result is greater than zero, judging that the heating system is in a stable operation state, and if the output result is not greater than zero, judging that the heating system is abnormal.
Preferably, in step S200, the information about the vibration frequency of the heating system is information about the vibration generated by the heating system in the operating state, and includes:
step S201: acquiring first vibration data of the heating system which generates vibration in a working state;
step S202: removing the vibration data based on a preset condition to obtain preprocessed data, wherein the preset condition is a critical condition for removing abnormal vibration data;
step S203: determining all rejected section information based on the positions of the corresponding abnormal data in the preprocessed data, and acquiring second vibration data at the head end and the tail end corresponding to the rejected section information;
step S204: calculating the second vibration data to obtain an interpolation coefficient;
step S205: and calculating according to the interpolation coefficient and the second vibration data, and filling each removed section of information in unit time respectively to obtain the natural vibration data of the heating system which generates vibration in a working state.
Preferably, in step S300, the extracting a key frame image of the first video information and performing anomaly detection on the extracted key frame image to obtain second video information includes:
step S301: extracting a plurality of key frame images in the first video information, wherein the plurality of key frame images comprise images of a liquid adding system, a drying and cooling system, a temperature control system, a printing system, a door control system and an alarm system in a working state and a non-working state;
step S302: performing binarization processing on all pixel points in the key frame image to obtain a binarized key frame image;
step S303: sequentially performing expansion processing and corrosion processing on the binarized key frame image to obtain an image with noise removed;
step S304: marking the images, and sequencing the marked images according to the acquisition time to obtain sequenced image fragments;
step S305: traversing the image segments, judging whether the sequenced image segments are the same as the image segments in the video under the preset normal condition, if so, marking the first video information and the key frame images corresponding to the sequenced image segments as abnormal, and if so, marking the first video information and the key frame images as normal.
Preferably, in step S500, the determining whether the heating system fails at present based on the temperature condition and the vibration frequency information of the heating system, and sending an early warning prompt, and then:
step S501: judging whether the early warning prompt is a recoverable fault, if the early warning prompt is the recoverable fault, sending a first control command, wherein the first control command is that a cloud server is connected with a control center to send a control instruction to the heating system, and adjusting the heating system to a normal state; and if the fault is an unrecoverable fault, sending a second control command, wherein the second control command is that the cloud server is connected with the control center to send a control command to the heating system, and then displaying the contact way of maintenance personnel.
In a second aspect, the present application further provides a heating system fault early warning system, which includes a first sending module, a second sending module, an extraction module, an identification module, and a determination module, wherein:
a first sending module: the system comprises a control unit, a first instruction information and a second instruction information, wherein the control unit is used for sending a parameter instruction to a heating system and determining whether the heating system is in a stable operation state or not according to the obtained parameter instruction;
a second sending module: the system comprises a heating system, a first instruction information acquisition module, a second instruction information acquisition module, a vibration frequency acquisition module and a control module, wherein the first instruction information acquisition module is used for acquiring first video information and vibration frequency information of the heating system according to electronic equipment when the heating system is in a stable operation state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of receiving vibration generated by the heating system in the working state;
an extraction module: the first video information processing unit is used for extracting a key frame image of the first video information and carrying out abnormity detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image;
an identification module: the second video information is used for identifying the second video information and determining the temperature conditions of different areas in the heating system;
a judging module: and the system is used for judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system and sending out an early warning prompt.
In a third aspect, the present application further provides a heating system fault early warning device, including:
a memory for storing a computer program;
a processor for implementing the steps of the heating system fault pre-warning method when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the heating system fault early warning method are implemented.
The invention has the beneficial effects that:
the invention combines the extension correlation function and the elastic back propagation neural network algorithm, achieves the purpose of reflecting the system state by using less data, simplifies sample data and improves the accuracy of early warning.
According to the invention, through recording the video information of the heating system in the working state and the non-working state, accidents are prevented, meanwhile, the data information of the heating system is also recorded, a database is provided for the heating equipment, and whether the heating system is abnormal is determined through the judgment index of the vibration frequency generated by the heating system in the working process, so that the defects of easy error and subjective tendency of manual judgment are avoided, and the judgment efficiency and accuracy are improved.
The invention can ensure that the obtained temperature state is consistent with the current working temperature state of the heating system through Kalman filtering processing, thereby improving the reliability of the fault early warning of the heating system and being convenient for providing targeted heat dissipation processing for the heating system.
Additional features and advantages of the invention will be set forth in the detailed description which follows, and in part will be obvious from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart illustrating a heating system fault warning method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a heating system fault warning system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a heating system fault early warning device according to an embodiment of the present invention.
In the figure: 701. a first sending module; 7011. a first processing unit; 7012. a first calculation unit; 7013. a first judgment unit; 702. a second sending module; 7021. an acquisition unit; 7022. a rejection unit; 7023. a determination unit; 7024. a second calculation unit; 703. an extraction module; 7031. an extraction unit; 7032. a second processing unit; 7033. a third processing unit; 7034. a sorting unit; 7035. a second judgment unit; 704. an identification module; 705. a judgment module; 7051. a third judgment unit; 800. heating system fault early warning equipment; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. and a communication component.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
Example 1:
the embodiment provides a heating system fault early warning method.
Referring to fig. 1, the method is shown to include step S100, step S200, step S300, and steps S400 and S500.
S100, sending first instruction information, wherein the first instruction information is used for sending a parameter instruction to a heating system, and determining whether the heating system is in a stable operation state or not according to the obtained parameter instruction.
It is understood that step S100 includes steps S101, S102, and S103, where:
s101, obtaining sample data of the heating system, carrying out standardization processing on the sample data to obtain first data information, and carrying out reprocessing on the first data information by using a nuclear linear dimensionality reduction method to obtain second data information;
it should be noted that, in the conventional neural network, a standard value a is preset, the output result b is compared with the standard value a, if not, the result is determined to be abnormal, and an alarm is issued, or a standard interval is set, and if the output result does not belong to the standard interval, the system is in an abnormal state.
However, in the existing heating system, two upper lines and two lower lines which cause quality change and quantity change are often existed in the operation process. If the operator sets a rated current, a safe current, while the medical washer is operating, this indicates that the washer is below the rated current and not outside the safe current range, there is no danger of damage. However, problems may arise with certain components of the system, and once the safe current is exceeded, the system may be damaged to a large extent, and it may be certain where the problem is present and an alarm may need to be issued. According to the invention, a correlation function in the topology is introduced into the neural network so as to solve the problem of early warning accuracy of the heating system.
Before the data kernel principal component analysis is carried out, a large amount of data needs to be collected, and the more the amount of the collected data is, the more beneficial the result is in the subsequent training process. In order to eliminate the influence of different dimensions in different data, the sample data needs to be standardized, so that the data is simplified, and an optimization effect is achieved. Secondly, the first data information is reprocessed by utilizing a coring linear dimension reduction method, a main body of a main analytical analysis method is determined, a principal element is extracted, and unimportant components are removed, so that the purpose of reflecting the system state by less data is achieved, and original sample data is simplified.
S102, calculating the second data information based on an extension correlation function and an elastic back propagation neural network algorithm to obtain an output result;
it should be noted that the elastic back propagation neural network algorithm has good adaptivity, the second data information sample set is input into the neural network model to be trained, and meanwhile, the extension function is combined to improve the neural network model, so that the final neural network model is established, the system is diagnosed, and the feasibility of the result is determined. The formula is as follows:
wherein S represents the standard interval of the neural network, S = (a, b), the feasible interval is W = (c, d), S and W have no common endpoint, O K D is a value obtained by a correlation function that takes a maximum value at the midpoint of S with respect to S and W, k (x) is an elementary correlation function that takes a maximum value with respect to point x, and ρ (x, y) is a distance between two points x, y on the real number axis.
S103, judging the output result, if the output result is larger than zero, judging that the heating system is in a stable operation state, and if the output result is not larger than zero, judging that the heating system is abnormal.
It should be noted that, when the output result k (x) is greater than zero, the heating system is in a normal state; when-1 < -k (x) ≦ 0, it indicates that some components in the heating system are abnormal at this time, but the entire system may still be in operation; when k (x) is less than or equal to-1, a serious problem already occurs in the heating system at the moment, and a serious abnormality occurs in a dangerous office state. The invention combines the extension correlation function and the elastic back propagation neural network algorithm, achieves the purpose of reflecting the system state by less data, simplifies sample data and improves the accuracy of early warning.
S200, sending second instruction information according to the first instruction information, wherein the second instruction information comprises first video information and vibration frequency information of the heating system acquired according to electronic equipment when the heating system is in a stable running state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of vibration generated by the heating system in the working state.
It should be noted that, in this step, by recording the video information of the heating system in the working state and the non-working state, accidents are prevented, and at the same time, the data information of the heating system is also recorded, so as to provide a database for the heating device, and the judgment index of the vibration frequency generated by the heating system during working is used to determine whether the heating system is abnormal, thereby reducing the errors and subjectivity of manual judgment, and improving the judgment efficiency and accuracy.
It is understood that step S200 includes steps S201, S202, S203, S204 and S205, where:
s201, acquiring first vibration data of vibration generated by the heating system in a working state;
s202, removing the vibration data based on a preset condition to obtain preprocessed data, wherein the preset condition is a critical condition for removing abnormal vibration data;
it can be understood that, in this step, abnormal values (parameters which may be caused by external conditions and obviously different from ordinary observed values) are removed from the vibration data according to preset conditions, so as to obtain preprocessed data.
The abnormal data eliminating method comprises the steps of calculating a vibration average value and a vibration standard difference value based on the vibration data, and calculating according to the calculation result and by using a lapel criterion to obtain a preset condition. The formula calculation preset condition is as follows:
|X i -x|>3δ
wherein：X i The vibration mean value, x vibration data, and δ vibration standard deviation value.
And substituting each vibration data into the calculation according to the formula, marking the abnormal vibration data which exceed the range of the preset condition, and then eliminating the abnormal vibration data to obtain the eliminated vibration data.
S203, determining all removed section information based on the positions of the corresponding abnormal data in the preprocessed data, and acquiring second vibration data at the head end and the tail end corresponding to the removed section information;
s204, calculating the second vibration data to obtain an interpolation coefficient;
in this step, the interpolation coefficient is calculated according to the following formula:
wherein: alpha is an interpolation coefficient; a, rejecting vibration data corresponding to any point in the middle of the section information; a is 1 Vibration data corresponding to the head end of the information of the removed section is obtained; a is 2 And vibration data corresponding to the tail end of the information of the removed section is obtained.
S205, calculating according to the interpolation coefficient and the second vibration data, and filling each removed section information in unit time respectively to obtain the natural vibration data of the heating system generating vibration in the working state.
It should be noted that the formula is filled with the removed sections, and the vibration data in the required unit time is intercepted as the natural vibration data, and the formula is as follows:
y＝(1-α)y 0 +αy 1
wherein: y is vibration data corresponding to any time in the middle of the removed section information; alpha is an interpolation coefficient; y is 0 Vibration data corresponding to the head end of the removed section information is obtained; y is 1 And vibration data corresponding to the tail end of the information of the removed section is obtained.
S300, extracting a key frame image of the first video information, and performing anomaly detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image.
It is understood that step S300 includes steps S301, S302, S303, S304, and S305, where:
s301, extracting a plurality of key frame images in the first video information, wherein the plurality of key frame images comprise images of a liquid adding system, a drying and cooling system, a temperature control system, a printing system, a door control system and an alarm system in a working state and a non-working state;
it should be noted that, in the present application, the heating system includes a rack, a cabin assembly, a door control system, a fresh air heating system, an internal circulation system, a control system, and a cleaning rack. Wherein, the new trend system includes fan, high efficiency filter, drying and heating case, temperature sensor and pipe-line system.
The fresh air is sucked by the fan, heated by the drying and heating box and enters the cleaning cabin of the cleaning machine, and gas exhausted from the cleaning cabin is divided into two parts. One part of the air with the water vapor is discharged from the exhaust port, and the other part of the air enters the internal circulation system to be heated and reused. The internal circulation system comprises a fan, a heat exchanger, a temperature sensor, a drain valve and a pipeline system. The fan sucks the other part of air exhausted from the cabin, the air enters the cabin body after passing through the heat exchange system, the heat exchange system heats the air through steam, the heated air is sent into the cabin body for cyclic utilization, and condensed water is discharged through the condensation pipe. The door control system comprises a glass door, a door motor, a detection switch and a sealing ring. The control system comprises a programmable controller, a touch screen, a switching power supply, a contactor and an air switch. Controlling the heating temperature of the drying oven and the heating temperature of the heat exchange system: and keeping the temperature constant, stopping heating when the temperature reaches the upper limit value of the temperature, and starting heating when the temperature is lower than the set value. And respectively extracting key frames of the images of the liquid adding system, the drying and cooling system, the temperature control system, the printing system, the door control system and the alarm system so as to carry out image processing operation.
S302, performing binarization processing on all pixel points in the key frame image to obtain a binarized key frame image;
it should be noted that, the key frame image is binarized, and then the background image of the key frame image is converted into the same pixel point, so that the image contour in the system can be determined quickly.
S303, sequentially performing expansion processing and corrosion processing on the binarized key frame image to obtain an image with noise removed;
it should be noted that, by performing expansion processing and corrosion processing on the binarized image, noise pixel points in the image are eliminated, so that the calculation amount of the key point information can be reduced, and the determination efficiency of the key points is improved.
S304, marking the images, and sequencing the marked images according to the acquisition time to obtain sequenced image fragments;
s305, traversing the image segments, judging whether the sequenced image segments are the same as the image segments in the video under the preset normal condition, if so, marking the first video information and the key frame images corresponding to the sequenced image segments as abnormal, and if so, marking the first video information and the key frame images as normal.
It can be understood that, in this step, whether the key frame image is normal or not is checked by sequencing each video clip; and if the image meets the regulations, the key frame image is normal, and the first video information corresponding to the key frame image marked as normal is sent to the subsequent processing step to prepare for the subsequent identification.
S400, identifying the second video information, and determining the temperature conditions of different areas in the heating system.
It can be understood that, in this step, the infrared image in the second video information is identified, and the kalman filtering process is performed on the returned infrared image, where the kalman filtering process is actually two steps, the first step is prediction, and this step usually predicts the value of the next time according to the estimated value of the previous time to become a priori estimation, and at the same time, this step predicts the error of the next time to become a priori error; the second step is updating, and the actual step is correcting, firstly, kalman gain (a weight for balancing prior estimation and a measured value) is calculated, then the prior estimation of the last step is utilized to calculate posterior estimation, and meanwhile, the prior error is updated to the posterior error. In the application, the background noise of the infrared image is removed firstly; and then identifying the infrared radiation intensity distribution contained in the infrared image so as to determine respective temperature values of different areas in the heating system and temperature difference values among the different areas, thereby determining the temperature condition in the heating system. The invention can ensure that the obtained temperature state is consistent with the current working temperature state of the heating system through Kalman filtering processing, thereby improving the reliability of the fault early warning of the heating system and being convenient for providing targeted heat dissipation processing for the heating system.
S500, judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system, and sending out an early warning prompt.
It is understood that step S500 is followed by step S501:
s501, judging whether the early warning prompt is a recoverable fault, if the recoverable fault is detected, sending a first control command, wherein the first control command is that a cloud server is connected with a control center to send a control instruction to the heating system, and adjusting the heating system to a normal state; and if the fault is an unrecoverable fault, sending a second control command, wherein the second control command is that the cloud server is connected with the control center to send a control command to the heating system, and then displaying the contact way of maintenance personnel.
The early warning prompt is adopted in the step, the fault is judged, and the fault early warning on the related node equipment with abnormal detection can be avoided; the equipment in the heating system is effectively maintained and early warned, and the normal and stable operation of the system is ensured.
Example 2:
as shown in fig. 2, the present embodiment provides a heating system fault early warning system, which includes a first sending module 701, a second sending module 702, an extracting module 703, an identifying module 704, and a determining module 705 with reference to fig. 2, wherein:
the first transmission module 701: the system comprises a control unit, a first instruction information and a second instruction information, wherein the control unit is used for sending a parameter instruction to a heating system and determining whether the heating system is in a stable operation state or not according to the obtained parameter instruction;
the second sending module 702: the system comprises a heating system, a first instruction information acquisition module, a second instruction information acquisition module, a vibration frequency acquisition module and a control module, wherein the first instruction information acquisition module is used for acquiring first video information and vibration frequency information of the heating system according to electronic equipment when the heating system is in a stable operation state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of receiving vibration generated by the heating system in the working state;
the extraction module 703: the first video information processing unit is used for extracting a key frame image of the first video information and carrying out abnormity detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image;
the identification module 704: the second video information is used for identifying the second video information and determining the temperature conditions of different areas in the heating system;
the judgment module 705: and the system is used for judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system and sending out an early warning prompt.
Specifically, the first sending module 701 includes a first processing unit 7011, a first calculating unit 7012, and a first determining unit 7013, where:
first processing unit 7011: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring sample data of the heating system, carrying out standardization processing on the sample data to obtain first data information, and carrying out reprocessing on the first data information by utilizing a coring linear dimension reduction method to obtain second data information;
first calculation unit 7012: the second data information is calculated based on an extension correlation function and an elastic back propagation neural network algorithm to obtain an output result;
first determining unit 7013: and the controller is used for judging the output result, judging that the heating system is in a stable operation state if the output result is greater than zero, and judging that the heating system is abnormal if the output result is not greater than zero.
Specifically, the second sending module 702 includes an obtaining unit 7021, a removing unit 7022, a determining unit 7023, and a second calculating unit 7024, where:
acquisition unit 7021: the method comprises the steps of acquiring first vibration data of the heating system which generates vibration in a working state;
reject unit 7022: the vibration data are removed based on a preset condition to obtain preprocessed data, wherein the preset condition is a critical condition for removing abnormal vibration data;
determination unit 7023: the vibration data processing device is used for determining all removed section information based on the positions of corresponding abnormal data in the preprocessed data and acquiring second vibration data of the head end and the tail end corresponding to the removed section information;
second calculation unit 7024: the second vibration data are used for calculating to obtain an interpolation coefficient; and calculating according to the interpolation coefficient and the second vibration data, and filling each removed section of information in unit time respectively to obtain the natural vibration data of the heating system which generates vibration in a working state.
Specifically, the extracting module 703 includes an extracting unit 7031, a second processing unit 7032, a third processing unit 7033, a sorting unit 7034, and a second determining unit 7035, where:
extraction unit 7031: the system comprises a plurality of key frame images, a plurality of image acquisition units and a plurality of image acquisition units, wherein the key frame images are used for extracting a plurality of key frame images in the first video information, and the key frame images comprise images of a liquid adding system, a drying and cooling system, a temperature control system, a printing system, a door control system and an alarm system in a working state and a non-working state;
second processing unit 7032: the key frame image binarization processing module is used for carrying out binarization processing on all pixel points in the key frame image to obtain a binarized key frame image;
third processing unit 7033: the key frame image after binarization is sequentially subjected to expansion processing and corrosion processing to obtain an image after noise removal;
sorting unit 7034: the image acquisition device is used for marking the images and sequencing the marked images according to the acquisition time to obtain sequenced image fragments;
second determining unit 7035: the image fragment traversing module is used for traversing the image fragments, judging whether the sequenced image fragments are the same as the image fragments in the video under the preset normal condition, if the sequenced image fragments are different, marking the first video information and the key frame image corresponding to the sequenced image fragments as abnormal, and if the sequenced image fragments are the same, marking the first video information and the key frame image as normal.
Specifically, the determining module 705 then includes a third determining unit 7051:
third determining unit 7051: the system comprises a control center, a cloud server, a heating system and a warning prompt, wherein the control center is used for sending a warning prompt to the heating system, and the warning prompt is used for judging whether the warning prompt is a recoverable fault or not, and sending a first control command if the warning prompt is the recoverable fault, wherein the first control command is used for enabling the cloud server to be connected with the control center to send a control command to the heating system and adjusting the heating system to be in a normal state; and if the fault is an unrecoverable fault, sending a second control command, wherein the second control command is that the cloud server is connected with the control center to send a control command to the heating system, and then displaying the contact way of maintenance personnel.
It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3:
corresponding to the above method embodiment, the present embodiment further provides a heating system fault early warning device, and a heating system fault early warning device described below and a heating system fault early warning method described above may be referred to in correspondence.
Fig. 3 is a block diagram illustrating a heating system fault warning device 800 according to an exemplary embodiment. As shown in fig. 3, the heating system malfunction early warning apparatus 800 includes a processor 801 and a memory 802. The heating system fault warning device 800 may further include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the heating system fault early warning apparatus 800, so as to complete all or part of the steps in the heating system fault early warning method. The memory 802 is used to store various types of data to support the operation of the heating system fault warning device 800, which may include, for example, instructions for any application or method operating on the heating system fault warning device 800, as well as application-related data, such as contact data, messages sent or received, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, and the like. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the heating system fault warning device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the heating system fault warning Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described heating system fault warning method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the heating system fault warning method described above is also provided. For example, the computer readable storage medium may be the memory 802 described above including program instructions that are executable by the processor 801 of the heating system fault warning device 800 to perform the heating system fault warning method described above.
Example 4:
corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a heating system fault early warning method described above may be referred to in correspondence.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the heating system fault early warning method of the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A heating system fault early warning method is characterized by comprising the following steps:
step S100: sending first instruction information, wherein the first instruction information is a parameter instruction sent to a heating system, and determining whether the heating system is in a stable operation state according to the obtained parameter instruction;
step S200: sending second instruction information according to the first instruction information, wherein the second instruction information comprises first video information and vibration frequency information of the heating system, which are acquired according to electronic equipment when the heating system is in a stable operation state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of receiving vibration generated by the heating system in the working state;
step S300: extracting a key frame image of the first video information, and performing anomaly detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image;
step S400: identifying the second video information, and determining the temperature conditions of different areas in the heating system;
step S500: and judging whether the heating system breaks down currently or not based on the temperature condition and the vibration frequency information of the heating system, and sending out an early warning prompt.
2. The heating system fault warning method according to claim 1, wherein the step S100 includes:
step S101: acquiring sample data of the heating system, carrying out standardization processing on the sample data to obtain first data information, and carrying out reprocessing on the first data information by using a coring linear dimension reduction method to obtain second data information;
step S102: calculating the second data information based on an extensible correlation function and an elastic back propagation neural network algorithm to obtain an output result;
step S103: and judging the output result, if the output result is greater than zero, judging that the heating system is in a stable operation state, and if the output result is not greater than zero, judging that the heating system is abnormal.
3. The heating system fault early warning method according to claim 1, wherein in step S200, the information about the vibration frequency of the heating system is information about the vibration generated by the heating system in the operating state, and includes:
step S201: acquiring first vibration data of vibration generated by the heating system in a working state;
step S202: removing the vibration data based on a preset condition to obtain preprocessed data, wherein the preset condition is a critical condition for removing abnormal vibration data;
step S203: determining all removed section information based on the positions of the corresponding abnormal data in the preprocessed data, and acquiring second vibration data of the head end and the tail end corresponding to the removed section information;
step S204: calculating the second vibration data to obtain an interpolation coefficient;
step S205: and calculating according to the interpolation coefficient and the second vibration data, and filling each removed section of information in unit time respectively to obtain the natural vibration data of the heating system which generates vibration in a working state.
4. The heating system fault early warning method according to claim 1, wherein in step S300, the extracting a key frame image of the first video information, and performing anomaly detection on the extracted key frame image to obtain second video information includes:
step S301: extracting a plurality of key frame images in the first video information, wherein the plurality of key frame images comprise images of a liquid adding system, a drying and cooling system, a temperature control system, a printing system, a door control system and an alarm system in a working state and a non-working state;
step S302: performing binarization processing on all pixel points in the key frame image to obtain a binarized key frame image;
step S303: sequentially performing expansion processing and corrosion processing on the binarized key frame image to obtain an image with noise removed;
step S304: marking the images, and sequencing the marked images according to the acquisition time to obtain sequenced image fragments;
step S305: traversing the image segments, judging whether the sequenced image segments are the same as the image segments in the video under the preset normal condition, if so, marking the first video information and the key frame images corresponding to the sequenced image segments as abnormal, and if so, marking the first video information and the key frame images as normal.
5. The heating system fault early warning method according to claim 1, wherein in step S500, the determining whether the heating system is currently in fault based on the temperature condition and vibration frequency information of the heating system, and issuing an early warning prompt includes:
step S501: judging whether the early warning prompt is a recoverable fault, if the early warning prompt is the recoverable fault, sending a first control command, wherein the first control command is that a cloud server is connected with a control center to send a control instruction to the heating system, and adjusting the heating system to a normal state; and if the fault is an unrecoverable fault, sending a second control command, wherein the second control command is that the cloud server is connected with the control center to send a control command to the heating system, and then displaying the contact way of maintenance personnel.
6. A heating system fault early warning system, comprising:
a first sending module: the system comprises a control unit, a first instruction information and a second instruction information, wherein the control unit is used for sending a parameter instruction to a heating system and determining whether the heating system is in a stable operation state or not according to the obtained parameter instruction;
a second sending module: the system comprises a heating system, a first instruction information acquisition module, a second instruction information acquisition module, a vibration frequency acquisition module and a control module, wherein the first instruction information acquisition module is used for acquiring first video information and vibration frequency information of the heating system according to electronic equipment when the heating system is in a stable operation state, the first video information comprises video information of the heating system in a working state and video information of the heating system in a non-working state, and the vibration frequency information of the heating system is information of receiving vibration generated by the heating system in the working state;
an extraction module: the first video information processing unit is used for extracting a key frame image of the first video information and carrying out abnormity detection on the extracted key frame image to obtain second video information, wherein the second video information comprises the first video information corresponding to the fact that the key frame image is judged to be a normal image;
an identification module: the second video information is used for identifying the second video information and determining the temperature conditions of different areas in the heating system;
a judging module: and the system is used for judging whether the heating system fails currently or not based on the temperature condition and the vibration frequency information of the heating system and sending out an early warning prompt.
7. The heating system malfunction early warning system according to claim 6, wherein the first transmitting module includes:
a first processing unit: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring sample data of the heating system, carrying out standardization processing on the sample data to obtain first data information, and carrying out reprocessing on the first data information by using a coring linear dimension reduction method to obtain second data information;
the first calculation unit: the second data information is calculated based on an extension correlation function and an elastic back propagation neural network algorithm to obtain an output result;
a first judgment unit: and the controller is used for judging the output result, judging that the heating system is in a stable operation state if the output result is greater than zero, and judging that the heating system is abnormal if the output result is not greater than zero.
8. The heating system malfunction early warning system according to claim 6, wherein the second transmitting module includes:
an acquisition unit: the method comprises the steps of acquiring first vibration data of the heating system which generates vibration in a working state;
a rejection unit: the vibration data are removed based on a preset condition to obtain preprocessed data, wherein the preset condition is a critical condition for removing abnormal vibration data;
a determination unit: the vibration data processing device is used for determining all removed section information based on the positions of corresponding abnormal data in the preprocessed data and acquiring second vibration data of the head end and the tail end corresponding to the removed section information;
a second calculation unit: the second vibration data are used for calculating to obtain an interpolation coefficient; and calculating according to the interpolation coefficient and the second vibration data, and filling each removed section of information in unit time respectively to obtain the natural vibration data of the heating system which generates vibration in a working state.
9. The heating system malfunction early warning system of claim 6, wherein the extraction module comprises:
an extraction unit: the system comprises a plurality of key frame images, a plurality of image acquisition units and a plurality of image acquisition units, wherein the key frame images are used for extracting a plurality of key frame images in the first video information, and the key frame images comprise images of a liquid adding system, a drying and cooling system, a temperature control system, a printing system, a door control system and an alarm system in a working state and a non-working state;
a second processing unit: the key frame image binarization processing module is used for carrying out binarization processing on all pixel points in the key frame image to obtain a binarized key frame image;
a third processing unit: the key frame image after binarization is sequentially subjected to expansion processing and corrosion processing to obtain an image after noise removal;
a sorting unit: the image acquisition device is used for marking the images and sequencing the marked images according to the acquisition time to obtain sequenced image fragments;
a second judgment unit: the image fragment is traversed, whether the sequenced image fragment is the same as the image fragment in the video under the preset normal condition or not is judged, if the sequenced image fragment is different from the image fragment in the video under the preset normal condition, the first video information and the key frame image corresponding to the sequenced image fragment are marked as abnormal, and if the sequenced image fragment is the same as the key frame image, the first video information and the key frame image are marked as normal.
10. The heating system fault warning system of claim 6, wherein the determining module thereafter comprises:
a third judging unit: the system comprises a control center, a cloud server, a heating system and a warning prompt, wherein the control center is used for sending a warning prompt to the heating system, and the warning prompt is used for judging whether the warning prompt is a recoverable fault or not, and sending a first control command if the warning prompt is the recoverable fault, wherein the first control command is used for enabling the cloud server to be connected with the control center to send a control command to the heating system and adjusting the heating system to be in a normal state; and if the fault is an unrecoverable fault, sending a second control command, wherein the second control command is that the cloud server is connected with the control center to send a control command to the heating system, and then displaying the contact way of maintenance personnel.
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| CN117851956A (en) * | 2024-03-07 | 2024-04-09 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
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| CN117851956A (en) * | 2024-03-07 | 2024-04-09 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
| CN117851956B (en) * | 2024-03-07 | 2024-05-10 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
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