CN112508107A - Equipment state sensing method and system - Google Patents
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Abstract
The invention relates to an equipment state sensing method and system, which are used for classifying equipment according to the damage of the appearance of the equipment by acquiring equipment state data, further judging whether the equipment with the undamaged appearance is in fault and feeding back the equipment, improving the processing precision of the equipment state sensing data and realizing the real-time sensing and feedback of the equipment state.
Description
Technical Field
The invention relates to the technical field of equipment state perception data processing, in particular to an equipment state perception method and system.
Background
The existing equipment state perception data processing is finished by depending on subjective judgment of an equipment operator, the data processing method is strong in randomness and has no objectivity, and the equipment state data processing cannot objectively and faithfully reflect the equipment state. Therefore, the existing processing method for the equipment state perception data has the problem of low precision.
Disclosure of Invention
The invention aims to provide a method and a system for sensing equipment state, which are used for classifying equipment according to the condition that the equipment has no damage after acquiring equipment state data, further judging whether the equipment with the undamaged appearance fails and feeding back the equipment, improving the processing precision of the equipment state sensing data and realizing the real-time sensing and feedback of the equipment state.
In order to achieve the purpose, the invention provides the following scheme:
an equipment state awareness method comprising:
collecting equipment state data; the equipment state data comprises graphic data and working state data;
classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
comparing the working state data corresponding to the equipment with the undamaged equipment shape with a set threshold value to obtain a second sensing result of whether the equipment fails;
and feeding back the first sensing result and/or the second sensing result to the non-contact equipment.
Optionally, the classifying the equipment according to the graph data to obtain a classification result specifically includes:
processing the graphic data by an image recognition method to obtain a processing result;
judging whether the appearance of the equipment is damaged or not according to the processing result;
if the equipment has the damaged appearance, classifying the corresponding equipment into the classification result of the damaged appearance of the equipment;
and if the appearance of the equipment is not damaged, classifying the corresponding equipment into the classification result with the damaged appearance of the equipment.
Optionally, after the feeding back the first sensing result and/or the second sensing result to the contactless device, the method further includes:
counting the number of devices with appearance damage and faults to obtain the number of damages;
comparing the damage quantity with the equipment inventory quantity in an equipment database to obtain a comparison result;
if the comparison result is that the equipment inventory quantity is greater than or equal to the damage quantity, allocating equipment according with the damage quantity;
and if the comparison result shows that the equipment inventory quantity is smaller than the damage quantity, allocating the equipment according with the equipment inventory quantity.
Optionally, the first sensing result and/or the second sensing result are/is fed back to the contactless device in a wireless transmission manner.
Optionally, the working state data is divided into the working state data of the upper part and the working state data of the chassis part.
Optionally, the operating status data of the upper part includes runtime data and status bus time domain data.
Optionally, the comparing the working state data corresponding to the equipment without damage to the equipment shape with the set threshold value to obtain a second sensing result of whether the equipment fails includes:
determining a lower limit value and an upper limit value of the runtime data range;
comparing the running time data with the lower limit value and the upper limit value to obtain a comparison result;
if the comparison result is that the running time data is smaller than the lower limit value, judging that the upper installation part of the equipment has no fault;
if the comparison result is that the running time data is between the lower limit value and the upper limit value, judging that the probability of the fault of the upper installation part of the equipment exists;
and if the comparison result is that the running time data is larger than the upper limit value, judging that the loading part of the equipment is in fault.
Optionally, the comparing the working state data corresponding to the equipment without damage to the equipment shape with the set threshold value to obtain a second sensing result of whether the equipment fails, specifically further includes:
carrying out weighted average processing on the state bus time domain data to obtain weighted average data;
carrying out frequency domain transformation on the weighted average data to obtain frequency domain data;
acquiring two frequency domain data with different frequencies in the frequency domain data, and respectively using the two frequency domain data as first frequency domain data and second frequency domain data;
determining a first threshold value of the first frequency domain data and a second threshold value of the second frequency domain data;
respectively comparing the first frequency domain data with the first threshold value and the second frequency domain data with the second threshold value to obtain comparison results;
if the comparison result is that the first frequency domain data is larger than the first threshold value, judging that the loading part of the equipment has a fault;
if the comparison result is that the first frequency domain data is smaller than the first threshold value and the second frequency domain data is larger than the second threshold value, judging that the upper mounting part of the equipment has the probability of failure after a period of time;
and if the comparison result is that the first frequency domain data is smaller than the first threshold value and the second frequency domain data is smaller than the second threshold value, judging that the upper mounting part of the equipment has no fault.
Optionally, the comparing the working state data corresponding to the equipment without damage to the equipment shape with the set threshold value to obtain a second sensing result of whether the equipment fails, specifically further includes:
carrying out weighted average processing on the working state data of the chassis part to obtain weighted working state data of the chassis part;
determining a third threshold value of the weighted chassis part working state data;
comparing the weighted working state data of the chassis part with the third threshold value to obtain a comparison result;
if the comparison result is that the weighted working state data of the chassis part is larger than the third threshold value, judging that the chassis part of the equipment has a fault;
and if the comparison result is that the weighted working state data of the chassis part is less than or equal to the third threshold, judging that the chassis part of the equipment has no fault.
An equipment state awareness system comprising:
a contactless device and a base control center;
the contactless device is wirelessly connected with the base control center;
the non-contact equipment is used for collecting equipment state data; the equipment state data comprises graphic data and working state data;
the base control center is used for receiving the equipment state data and processing the equipment state data;
the base control center specifically comprises a classification module, a fault judgment module and a feedback module;
the classification module is used for classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
the fault judging module is used for comparing the working state data corresponding to the equipment with no damage to the appearance of the equipment with a set threshold value to obtain a second sensing result of whether the equipment is in fault or not;
the feedback module is used for feeding back the first sensing result and/or the second sensing result to the non-contact equipment.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
compared with the prior art which does not adopt a method for directly dividing the equipment state without judging whether the equipment has the appearance hard damage, the classification of the equipment state sensing method is more accurate, and the subsequent processing can be easily processed and the position of the equipment between the two edges can be distinguished due to the more accurate classification, so that the overall efficiency is higher; the invention respectively carries out smooth processing on the working state data of the equipment chassis part, the equipment running time data and the equipment state bus time domain data, and the processing process has higher precision, thereby reflecting the equipment state more truly.
In addition, the non-contact equipment is adopted to sense the equipment state data, various physical structures of the equipment are not damaged, the influence on the equipment performance is smaller compared with a mode of additionally arranging sensors on various interfaces of the equipment, and the sensed data is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an equipment state sensing method according to embodiment 1 of the present invention;
fig. 2 is a block diagram of an equipment state sensing system according to embodiment 2 of the present invention.
Description of the drawings: 1 is a non-contact device, and 2 is a base control center.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for sensing equipment state.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
referring to fig. 1, an equipment state sensing method includes:
step 101: collecting equipment state data; the equipment state data comprises graphic data and working state data;
step 102: classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
step 103: comparing the working state data corresponding to the equipment with the undamaged equipment shape with a set threshold value to obtain a second sensing result of whether the equipment fails;
step 104: and feeding back the first sensing result and/or the second sensing result to the non-contact equipment.
It should be noted that the present invention does not limit the sequence of steps 101-104.
As an optional implementation manner, in step 102, the specific method includes:
processing the graphic data by an image recognition method to obtain a processing result;
judging whether the appearance of the equipment is damaged or not according to the processing result;
if the equipment has the damaged appearance, classifying the corresponding equipment into the classification result of the damaged appearance of the equipment;
and if the appearance of the equipment is not damaged, classifying the corresponding equipment into the classification result with the damaged appearance of the equipment.
As an optional implementation manner, after step 104, the method further includes:
counting the number of devices with appearance damage and faults to obtain the number of damages;
comparing the damage quantity with the equipment inventory quantity in an equipment database to obtain a comparison result;
if the comparison result is that the equipment inventory quantity is greater than or equal to the damage quantity, allocating equipment according with the damage quantity;
and if the comparison result shows that the equipment inventory quantity is smaller than the damage quantity, allocating the equipment according with the equipment inventory quantity.
For clarity of description, the embodiment illustrates a method for allocating the inventory amount of the equipment. For example, when the number of damaged equipment is 10 and the equipment inventory number is 12, 10 corresponding equipment are allocated from the equipment database and sent to the front to continue to complete the task; and when the equipment inventory number is 8, namely the equipment inventory number does not meet the damage number, allocating 8 corresponding equipment from the equipment database, and sending the equipment to the front to continue to complete the task.
As an optional implementation manner, the first sensing result and/or the second sensing result are/is fed back to the contactless device by means of wireless transmission.
As an optional implementation manner, the operating state data is divided into operating state data of the top loading part and operating state data of the chassis part, and the operating state data of the top loading part includes runtime data and state bus time domain data.
In an optional manner, in step 103, the method specifically includes:
determining a lower limit value and an upper limit value of the runtime data range;
comparing the running time data with the lower limit value and the upper limit value to obtain a comparison result;
if the comparison result is that the running time data is smaller than the lower limit value, judging that the upper installation part of the equipment has no fault;
if the comparison result is that the running time data is between the lower limit value and the upper limit value, judging that the probability of the fault of the upper installation part of the equipment exists;
and if the comparison result is that the running time data is larger than the upper limit value, judging that the loading part of the equipment is in fault.
In order to make those skilled in the art better understand the above-mentioned scheme for processing runtime data disclosed in the present embodiment, the following description is provided for example.
The operating time data is represented by S1, and if the upper limit value of S1 is set to 100 hours and the lower limit value is set to 50 hours, then:
when the time S1 is less than 50 hours, the upper part of the equipment is judged to be fault-free, and meanwhile, the equipment user does not need to be prompted with any information and can normally operate and use the equipment;
when the time S1 is between 50 and 100 hours, the upper installation part of the equipment can be in failure, and meanwhile, the equipment user is prompted to carry out the test of the equipment subsystem through wireless remote transmission;
and when the S1 is more than 100 hours, judging that the upper mounting part of the equipment is in fault, and simultaneously prompting the equipment user to overhaul the equipment in a wireless remote transmission mode.
In an optional manner, in step 103, the method specifically further includes:
carrying out weighted average processing on the state bus time domain data to obtain weighted average data;
carrying out frequency domain transformation on the weighted average data to obtain frequency domain data;
acquiring two frequency domain data with different frequencies in the frequency domain data, and respectively using the two frequency domain data as first frequency domain data and second frequency domain data;
determining a first threshold value of the first frequency domain data and a second threshold value of the second frequency domain data;
respectively comparing the first frequency domain data with the first threshold value and the second frequency domain data with the second threshold value to obtain comparison results;
if the comparison result is that the first frequency domain data is larger than the first threshold value, judging that the loading part of the equipment has a fault;
and if the comparison result is that the first frequency domain data is smaller than the first threshold value and the second frequency domain data is larger than the second threshold value, judging that the upper-mounted part of the equipment has the probability of failure after a period of time.
In order to make those skilled in the art better understand the above-mentioned scheme for processing the status bus time domain data disclosed in the present embodiment, the following description is made.
The status bus time domain data is represented by S2, S1 includes x and y, where x represents the phase noise over time on the status bus and y represents the standing wave ratio over time on the status bus.
Carrying out weighted average on x and y by utilizing a trisection mode to obtain weighted average data z, wherein
Transforming z to a frequency domain by utilizing Fourier transform to obtain frequency domain data;
selecting frequency domain data with the frequency of 15KHz as first frequency domain data, wherein the first frequency domain data, namely the frequency of 15KHz, has a corresponding amplitude value in a spectrogram, and setting a first threshold value to be 0.6 based on the amplitude value;
selecting frequency domain data with the frequency of 30KHz as second frequency domain data, wherein the second frequency domain data, namely the frequency of 30KHz, has a corresponding amplitude value in a spectrogram, and setting a first threshold value to be 0.7 based on the amplitude value;
therefore, when the amplitude value of the first frequency domain data is greater than 0.6 and the output of the amplitude value of the second frequency domain data is less than 0.7, the upper part of the equipment is judged to be in fault, and meanwhile, a maintenance prompt is sent to an equipment user to suggest the equipment user to carry out comprehensive inspection and maintenance on the equipment;
when the amplitude value of the first frequency domain data is less than 0.6 and the amplitude value output of the second frequency domain data is more than 0.7, the upper part of the equipment is very likely to have a fault after 10 hours, and meanwhile, the user of the equipment is prompted to make the next judgment.
And when the amplitude value of the first frequency domain data is greater than 0.6 and the amplitude value output of the second frequency domain data is greater than 0.7, judging that the upper part of the equipment is in fault, and simultaneously prompting a user of the equipment to stop using the equipment immediately to carry out all-around inspection on the equipment.
When the amplitude value of the first frequency domain data is less than 0.6 and the amplitude value output of the second frequency domain data is less than 0.7, it is determined that the upper-mounted portion of the equipment is not faulty.
Amplitude values corresponding to other frequencies in the frequency domain data do not affect the use of the equipment.
In an optional manner, in step 103, the method specifically further includes:
carrying out weighted average processing on the working state data of the chassis part to obtain weighted working state data of the chassis part;
determining a third threshold value of the weighted chassis part working state data;
comparing the weighted working state data of the chassis part with the third threshold value to obtain a comparison result;
if the comparison result is that the weighted working state data of the chassis part is larger than the third threshold value, judging that the chassis part of the equipment has a fault;
and if the comparison result is that the weighted working state data of the chassis part is less than or equal to the third threshold, judging that the chassis part of the equipment has no fault.
In order to make the technical personnel in this embodiment better understand the above-mentioned scheme for processing the working state data of the chassis part, the following description is given by way of example.
The operation state data of the chassis part is represented by S3, and the weighted average processing is carried out on the operation state data S3 by utilizing S1 and S2 to obtain the weighted operation state data S4 of the chassis part, namely
Setting the third threshold value to be 15, judging that the equipment chassis has no fault when S4 is less than or equal to 15, and continuing to use; and when the S4 is larger than 15, judging that the equipment chassis is in fault.
In the embodiment, the equipment is firstly judged whether the equipment has the hard appearance damage or not, and then the equipment state is classified, so that the classification is more accurate compared with the prior art, the subsequent treatment can be easily carried out and the position of the equipment between the two edges can be distinguished due to the more accurate classification, and the overall efficiency is higher; and the embodiment respectively performs smoothing processing on S1, S2 and S3, so that the processing precision is higher, and the state of the equipment can be reflected more truly.
Example 2:
referring to fig. 2, the present embodiment provides an equipment status sensing system, including:
a contactless device 1 and a base control center 2;
the contactless device 1 and the base control center 2 are wirelessly connected;
the non-contact device 1 is used for collecting equipment state data; the equipment state data comprises graphic data and working state data;
the base control center 2 is used for receiving the equipment state data and processing the equipment state data;
the base control center specifically comprises a classification module M1, a fault judgment module M2 and a feedback module M3;
the classification module M1 is used for classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
the fault judgment module M2 is configured to compare the working state data corresponding to the equipment without damage to the equipment shape with a set threshold, and obtain a second sensing result of whether the equipment is faulty;
the feedback module M3 is configured to feed back the first sensing result and/or the second sensing result to the contactless device 1.
The equipment state sensing system provided by the embodiment realizes accurate processing and timely feedback of equipment state data; in addition, the existing equipment state data acquisition is mainly realized by a mode of additionally arranging sensors on various interfaces of the equipment, and the mode has the defects and shortcomings that the performance of the equipment is damaged by additionally arranging the sensors, and the additionally arranged sensors cannot sense the data accurately. In the embodiment, the problem in the prior art is solved by adopting the non-contact device.
The emphasis of each embodiment in the present specification is on the difference from the other embodiments, and the same and similar parts among the various embodiments may be referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. An equipment state awareness method, comprising:
collecting equipment state data; the equipment state data comprises graphic data and working state data;
classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
comparing the working state data corresponding to the equipment with the undamaged equipment shape with a set threshold value to obtain a second sensing result of whether the equipment fails;
and feeding back the first sensing result and/or the second sensing result to the non-contact equipment.
2. The equipment state sensing method according to claim 1, wherein the classifying the equipment according to the graphic data to obtain a classification result specifically comprises:
processing the graphic data by an image recognition method to obtain a processing result;
judging whether the appearance of the equipment is damaged or not according to the processing result;
if the equipment has the damaged appearance, classifying the corresponding equipment into the classification result of the damaged appearance of the equipment;
and if the appearance of the equipment is not damaged, classifying the corresponding equipment into the classification result with the damaged appearance of the equipment.
3. The equipment state perception method according to claim 1, wherein after the feeding back the first perception result and/or the second perception result to the contactless device, the method further includes:
counting the number of devices with appearance damage and faults to obtain the number of damages;
comparing the damage quantity with the equipment inventory quantity in an equipment database to obtain a comparison result;
if the comparison result is that the equipment inventory quantity is greater than or equal to the damage quantity, allocating equipment according with the damage quantity;
and if the comparison result shows that the equipment inventory quantity is smaller than the damage quantity, allocating the equipment according with the equipment inventory quantity.
4. The equipment state perception method according to claim 1, wherein the first perception result and/or the second perception result are/is fed back to the contactless device through wireless transmission.
5. The equipment state sensing method of claim 1, wherein the working state data is divided into working state data of a top loading part and working state data of a chassis part.
6. The equipment state awareness method of claim 5, wherein the operating state data of the upper-mounted part comprises runtime data and state bus time domain data.
7. The method for sensing the equipment state according to claim 6, wherein the step of comparing the working state data corresponding to the equipment without damage to the equipment shape with a set threshold value to obtain a second sensing result of whether the equipment is in a fault includes:
determining a lower limit value and an upper limit value of the runtime data range;
comparing the running time data with the lower limit value and the upper limit value to obtain a comparison result;
if the comparison result is that the running time data is smaller than the lower limit value, judging that the upper installation part of the equipment has no fault;
if the comparison result is that the running time data is between the lower limit value and the upper limit value, judging that the probability of the fault of the upper installation part of the equipment exists;
and if the comparison result is that the running time data is larger than the upper limit value, judging that the loading part of the equipment is in fault.
8. The method for sensing the equipment state according to claim 6, wherein the step of comparing the working state data corresponding to the equipment without damage to the equipment shape with a set threshold value to obtain a second sensing result of whether the equipment is in fault further comprises:
carrying out weighted average processing on the state bus time domain data to obtain weighted average data;
carrying out frequency domain transformation on the weighted average data to obtain frequency domain data;
selecting two frequency domain data with different frequencies in the frequency domain data as first frequency domain data and second frequency domain data respectively;
determining a first threshold value of the first frequency domain data and a second threshold value of the second frequency domain data;
respectively comparing the first frequency domain data with the first threshold value and the second frequency domain data with the second threshold value to obtain comparison results;
if the comparison result is that the first frequency domain data is larger than the first threshold value, judging that the loading part of the equipment has a fault;
if the comparison result is that the first frequency domain data is smaller than the first threshold value and the second frequency domain data is larger than the second threshold value, judging that the upper mounting part of the equipment has the probability of failure after a period of time;
and if the comparison result is that the first frequency domain data is smaller than the first threshold value and the second frequency domain data is smaller than the second threshold value, judging that the upper mounting part of the equipment has no fault.
9. The method for sensing the equipment state according to claim 5, wherein the step of comparing the working state data corresponding to the equipment without damage to the equipment shape with a set threshold value to obtain a second sensing result of whether the equipment is in fault further comprises:
carrying out weighted average processing on the working state data of the chassis part to obtain weighted working state data of the chassis part;
determining a third threshold value of the weighted chassis part working state data;
comparing the weighted working state data of the chassis part with the third threshold value to obtain a comparison result;
if the comparison result is that the weighted working state data of the chassis part is larger than the third threshold value, judging that the chassis part of the equipment has a fault;
and if the comparison result is that the weighted working state data of the chassis part is less than or equal to the third threshold, judging that the chassis part of the equipment has no fault.
10. An equipment state awareness system, comprising:
a contactless device and a base control center;
the contactless device is wirelessly connected with the base control center;
the non-contact equipment is used for collecting equipment state data; the equipment state data comprises graphic data and working state data;
the base control center is used for receiving the equipment state data and processing the equipment state data;
the base control center specifically comprises a classification module, a fault judgment module and a feedback module;
the classification module is used for classifying the equipment according to the graphic data to obtain a classification result; the classification result comprises equipment appearance damage and equipment appearance damage; taking the equipment shape damage as a first perception result;
the fault judging module is used for comparing the working state data corresponding to the equipment with no damage to the appearance of the equipment with a set threshold value to obtain a second sensing result of whether the equipment is in fault or not;
the feedback module is used for feeding back the first sensing result and/or the second sensing result to the non-contact equipment.
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