CN111956198A - Equipment state determination method and device - Google Patents
Equipment state determination method and device Download PDFInfo
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- CN111956198A CN111956198A CN202011129272.3A CN202011129272A CN111956198A CN 111956198 A CN111956198 A CN 111956198A CN 202011129272 A CN202011129272 A CN 202011129272A CN 111956198 A CN111956198 A CN 111956198A
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The method comprises the steps of comparing the similarity and the splicing vector corresponding to the current human body data acquired when the target equipment is used and the current equipment state data acquired when the target equipment is used with the similarity and the splicing vector under various conditions respectively to determine the most similar equipment state, and taking the equipment state as the current state of the target equipment.
Description
Technical Field
The application relates to the technical field of automatic detection, in particular to a method and a device for determining equipment states.
Background
For the comprehensive detection equipment capable of fixed-point delivery, people can do some routine detections without going to a hospital, for example: people can utilize comprehensive detection equipment to measure human body data such as blood pressure, blood sugar, heart rate, respiratory rate, electroencephalogram signals, electromyogram signals, electrooculogram signals, skin reaction signals and the like so as to use the data to diagnose, but for the comprehensive detection equipment which is put in at fixed points, because the working environment is relatively open, faults are easy to occur, when people use the equipment with faults to detect, the obtained detection data can be abnormal detection data of bodies, but people can not determine whether the bodies are abnormal or the equipment is abnormal, and the problem is urgently needed to be solved.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for determining a device state, so as to determine a current state of a device.
In a first aspect, an embodiment of the present application provides an apparatus state determining method, including:
inputting the normal average data of the human body and first equipment state data when target equipment is in a normal state into a prediction model to obtain first prediction data, and performing vector splicing processing on the normal average data of the human body and the first equipment state data to obtain a first spliced vector;
inputting the human body abnormal average data and second equipment state data when the target equipment is in abnormal state into the prediction model to obtain second prediction data, and performing vector splicing processing on the human body abnormal average data and the second equipment state data to obtain a second spliced vector;
inputting the human body normal average data and the second equipment state data into a prediction model to obtain third prediction data, and performing vector splicing processing on the human body normal average data and the second equipment state data to obtain a third spliced vector;
inputting the human body abnormal average data and the first equipment state data into the prediction model to obtain fourth prediction data, and performing vector splicing processing on the human body abnormal average data and the first equipment state data to obtain a fourth spliced vector;
inputting current human body data acquired when the target equipment is used and current equipment state data acquired when the target equipment is used into the prediction model to obtain fifth prediction data, and performing vector splicing processing on the current human body data and the current equipment state data to obtain a fourth splicing vector and obtain a fifth splicing vector;
calculating a first similarity of the fifth stitching vector and the first stitching vector, a second similarity of the fifth stitching vector and the second stitching vector, a third similarity of the fifth stitching vector and the third stitching vector, and a fourth similarity of the fifth stitching vector and the fourth stitching vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data;
calculating a first mean of the first similarity and the first ratio, a second mean of the second similarity and the second ratio, a third mean of the third similarity and the third ratio, and a fourth mean of the fourth similarity and the fourth ratio;
determining the maximum value of the first square difference, the second square difference, the third square difference and the fourth square difference, and taking the device state corresponding to the maximum value as the current state of the target device.
Optionally, when it is determined that the current state of the target device is abnormal, the method further includes:
for each detection sensor included in the target equipment, comparing the current sensor state data of the detection sensor with the standard state data corresponding to the detection sensor;
and when the error between the current sensor state data and the standard state data exceeds a preset threshold value, sending a prompt message for indicating that the detection sensor is abnormal.
In a second aspect, an embodiment of the present application provides an apparatus for determining a device status, including:
the processing unit is used for inputting the normal average data of the human body and the first equipment state data when the target equipment is in a normal state into a prediction model to obtain first prediction data, and performing vector splicing processing on the normal average data of the human body and the first equipment state data to obtain a first spliced vector;
the processing unit is further configured to input the human body abnormal average data and second device state data when the target device is in an abnormal state to the prediction model to obtain second prediction data, and perform vector splicing processing on the human body abnormal average data and the second device state data to obtain a second spliced vector;
the processing unit is further configured to input the human body normal average data and the second device state data to a prediction model to obtain third prediction data, and perform vector splicing processing on the human body normal average data and the second device state data to obtain a third spliced vector;
the processing unit is further configured to input the human body abnormal average data and the first device state data to the prediction model to obtain fourth prediction data, and perform vector splicing processing on the human body abnormal average data and the first device state data to obtain a fourth spliced vector;
the processing unit is further configured to input current human body data acquired when the target device is used and current device state data acquired when the target device is used to the prediction model to obtain fifth prediction data, and perform vector stitching on the current human body data and the current device state data to obtain a fourth stitching vector and obtain a fifth stitching vector;
a calculating unit, configured to calculate a first similarity between the fifth stitching vector and the first stitching vector, a second similarity between the fifth stitching vector and the second stitching vector, a third similarity between the fifth stitching vector and the third stitching vector, and a fourth similarity between the fifth stitching vector and the fourth stitching vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data;
the calculating unit is further configured to calculate a first mean square difference between the first similarity and the first ratio, a second mean square difference between the second similarity and the second ratio, a third mean square difference between the third similarity and the third ratio, and a fourth mean square difference between the fourth similarity and the fourth ratio;
a determining unit, configured to determine a maximum value of the first square difference, the second square difference, the third square difference, and the fourth square difference, so as to use a device state corresponding to the maximum value as a current state of the target device.
Optionally, the apparatus further comprises:
the comparison unit is used for comparing the current sensor state data of each detection sensor included in the target equipment with the standard state data corresponding to the detection sensor;
and the sending unit is used for sending a prompt message for indicating that the detection sensor is abnormal when the error between the current sensor state data and the standard state data exceeds a preset threshold value.
In the application, the obtained first prediction data is used for representing characteristic values when both a human body and equipment are normal, the first splicing vector is used for representing characteristic vectors when both the human body and the equipment are normal, the obtained second prediction data is used for representing characteristic values when the human body is abnormal and the equipment is normal, the second splicing vector is used for representing characteristic vectors when both the human body is abnormal and the equipment is normal, the obtained third prediction data is used for representing characteristic values when both the human body is normal and the equipment is abnormal, the obtained fourth prediction data is used for representing characteristic values when both the human body and the equipment are abnormal, the obtained fourth splicing vector is used for representing characteristic vectors when both the human body and the equipment are abnormal, and the obtained fifth prediction data is used for representing characteristic values corresponding to the current human body data of a tester and the current state data of target equipment, a fifth splicing vector is used for representing feature vectors corresponding to current human body data of a tester and current state data of target equipment, and then a first similarity of the fifth splicing vector and the first splicing vector, a second similarity of the fifth splicing vector and the second splicing vector, a third similarity of the fifth splicing vector and the third splicing vector, and a fourth similarity of the fifth splicing vector and the fourth splicing vector are calculated; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data, determining that the current human body data and the current state data of the target device of the test person are closer to one of the four cases according to the similarity, determining that the current human body data and the current state data of the target device of the test person are closer to one of the four cases according to the ratios, and then calculating a first mean square difference of the first similarity and the first ratio, a second mean square difference of the second similarity and the second ratio, and a third mean square difference of the third similarity and the third ratio, The fourth similarity and the fourth mean square of the fourth ratio, and only the state of the device corresponding to the maximum mean square of the similarity and the ratio is the current real state of the device, so that the maximum value among the first mean square, the second mean square, the third mean square and the fourth mean square needs to be determined, and the device state corresponding to the maximum value is taken as the current state of the target device.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required 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 application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a method for determining a device status according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another method for determining a device status according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus state determining device according to a second embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Example one
Fig. 1 is a schematic flowchart of a device status determining method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
And 102, inputting the human body abnormal average data and second equipment state data when the target equipment is in abnormal state into the prediction model to obtain second prediction data, and performing vector splicing processing on the human body abnormal average data and the second equipment state data to obtain a second spliced vector.
And 104, inputting the human body abnormal average data and the first equipment state data into the prediction model to obtain fourth prediction data, and performing vector splicing processing on the human body abnormal average data and the first equipment state data to obtain a fourth spliced vector.
And 105, inputting the current human body data acquired when the target equipment is used and the current equipment state data acquired when the target equipment is used into the prediction model to obtain fifth prediction data, and performing vector splicing processing on the current human body data and the current equipment state data to obtain a fourth splicing vector and obtain a fifth splicing vector.
And step 108, determining the maximum value of the first square difference, the second square difference, the third square difference and the fourth square difference, and taking the device state corresponding to the maximum value as the current state of the target device.
Specifically, after obtaining the test data (i.e., the human body data), if it is not certain that the test data is abnormal due to the self-problem or the device problem, the user may click the detection button to execute the process shown in fig. 1, or the target device may directly execute the process shown in fig. 1 after detecting the human body data of the user, and after executing the process shown in fig. 1, the obtained result is displayed to the user, so that the user may determine whether the self-abnormality or the device abnormality occurs according to the displayed result.
The obtained first prediction data is used for representing characteristic values when both a human body and equipment are normal, the first splicing vector is used for representing characteristic vectors when both the human body and the equipment are normal, the obtained second prediction data is used for representing characteristic values when both the human body and the equipment are abnormal, the second splicing vector is used for representing characteristic vectors when both the human body and the equipment are normal, the obtained third prediction data is used for representing characteristic values when both the human body and the equipment are normal, the obtained third splicing vector is used for representing characteristic vectors when both the human body and the equipment are normal, the obtained fourth prediction data is used for representing characteristic values when both the human body and the equipment are abnormal, the obtained fourth splicing vector is used for representing characteristic vectors when both the human body and the equipment are abnormal, and the obtained fifth prediction data is used for representing characteristic values corresponding to the current human body data of a tester and the current state data of target equipment, and the fifth splicing vector is used for representing a characteristic vector corresponding to the current human body data of the tester and the current state data of the target equipment.
After the prediction data and the splicing vectors are obtained, calculating a first similarity between the fifth splicing vector and the first splicing vector, a second similarity between the fifth splicing vector and the second splicing vector, a third similarity between the fifth splicing vector and the third splicing vector, and a fourth similarity between the fifth splicing vector and the fourth splicing vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data, determining that the current human body data and the current state data of the target device of the test person are closer to one of the four cases according to the similarity, determining that the current human body data and the current state data of the target device of the test person are closer to one of the four cases according to the ratios, and then calculating a first mean square difference of the first similarity and the first ratio, a second mean square difference of the second similarity and the second ratio, and a third mean square difference of the third similarity and the third ratio, The fourth similarity and the fourth mean square of the fourth ratio, and only the state of the device corresponding to the maximum mean square of the similarity and the ratio is the current real state of the device, so that the maximum value among the first mean square, the second mean square, the third mean square and the fourth mean square needs to be determined, and the device state corresponding to the maximum value is taken as the current state of the target device.
Taking the maximum first variance example to obtain the current state of the target device as normal, taking the maximum second variance example to obtain the current state of the target device as abnormal, taking the maximum third variance example to obtain the current state of the target device as abnormal, and taking the maximum fourth variance example to obtain the current state of the target device as normal.
In a possible implementation, fig. 2 is a schematic flowchart of another method for determining a device status provided in the first embodiment of the present application, and as shown in fig. 2, the method further includes the following steps:
Specifically, the target device can detect various human body data, such as: the blood pressure, the blood sugar, the heart rate, the respiratory rate, the electroencephalogram signal, the electromyogram signal, the electrooculogram signal, the galvanic skin response signal and other human body data, wherein each data can be measured by a sensor, for example, the blood pressure can be measured by a blood pressure sensor, the heart rate can be measured by a heart rate sensor, and the like, when an abnormality of a target device is detected, each detection sensor needs to be detected to determine which sensor is abnormal, when whether a certain sensor is normal is detected, the current sensor state data of the detection sensor can be compared with the standard state data corresponding to the detection sensor, if the error between the current sensor state data and the standard state data exceeds a preset threshold value, the sensor is indicated to be abnormal, and at the moment, a prompt message for indicating that the detection sensor is abnormal is sent, for reference by maintenance personnel.
Example two
Fig. 3 is a schematic structural diagram of an apparatus state determining device according to a second embodiment of the present application, and as shown in fig. 3, the device includes:
the processing unit 31 is configured to input the human body normal average data and first device state data when the target device is in a normal state to a prediction model to obtain first prediction data, and perform vector splicing processing on the human body normal average data and the first device state data to obtain a first spliced vector;
the processing unit 31 is further configured to input the human body abnormal average data and second device state data when the target device is in an abnormal state to the prediction model to obtain second prediction data, and perform vector splicing processing on the human body abnormal average data and the second device state data to obtain a second spliced vector;
the processing unit 31 is further configured to input the human body normal average data and the second device state data to a prediction model to obtain third prediction data, and perform vector splicing processing on the human body normal average data and the second device state data to obtain a third spliced vector;
the processing unit 31 is further configured to input the human body abnormal average data and the first device state data to the prediction model to obtain fourth prediction data, and perform vector stitching on the human body abnormal average data and the first device state data to obtain a fourth stitching vector;
the processing unit 31 is further configured to input current human body data acquired when the target device is used and current device state data acquired when the target device is used to the prediction model to obtain fifth prediction data, and perform vector stitching processing on the current human body data and the current device state data to obtain a fourth stitching vector and obtain a fifth stitching vector;
a calculating unit 32, configured to calculate a first similarity between the fifth stitching vector and the first stitching vector, a second similarity between the fifth stitching vector and the second stitching vector, a third similarity between the fifth stitching vector and the third stitching vector, and a fourth similarity between the fifth stitching vector and the fourth stitching vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data;
the calculating unit 32 is further configured to calculate a first mean of the first similarity and the first ratio, a second mean of the second similarity and the second ratio, a third mean of the third similarity and the third ratio, and a fourth mean of the fourth similarity and the fourth ratio;
a determining unit 33, configured to determine a maximum value of the first square difference, the second square difference, the third square difference, and the fourth square difference, so as to use a device state corresponding to the maximum value as the current state of the target device.
In one possible embodiment, the apparatus further comprises:
a comparison unit 34, configured to compare, for each detection sensor included in the target device, current sensor state data of the detection sensor with standard state data corresponding to the detection sensor;
and the sending unit 35 is configured to send a prompt message for indicating that the detection sensor is abnormal when an error between the current sensor state data and the standard state data exceeds a preset threshold.
For the principle explanation of the second embodiment, reference may be made to the related explanation of the first embodiment, and detailed explanation is not provided herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
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, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. A method for determining a device status, comprising:
inputting the normal average data of the human body and first equipment state data when target equipment is in a normal state into a prediction model to obtain first prediction data, and performing vector splicing processing on the normal average data of the human body and the first equipment state data to obtain a first spliced vector;
inputting the human body abnormal average data and second equipment state data when the target equipment is in abnormal state into the prediction model to obtain second prediction data, and performing vector splicing processing on the human body abnormal average data and the second equipment state data to obtain a second spliced vector;
inputting the human body normal average data and the second equipment state data into a prediction model to obtain third prediction data, and performing vector splicing processing on the human body normal average data and the second equipment state data to obtain a third spliced vector;
inputting the human body abnormal average data and the first equipment state data into the prediction model to obtain fourth prediction data, and performing vector splicing processing on the human body abnormal average data and the first equipment state data to obtain a fourth spliced vector;
inputting current human body data acquired when the target equipment is used and current equipment state data acquired when the target equipment is used into the prediction model to obtain fifth prediction data, and performing vector splicing processing on the current human body data and the current equipment state data to obtain a fourth splicing vector and obtain a fifth splicing vector;
calculating a first similarity of the fifth stitching vector and the first stitching vector, a second similarity of the fifth stitching vector and the second stitching vector, a third similarity of the fifth stitching vector and the third stitching vector, and a fourth similarity of the fifth stitching vector and the fourth stitching vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data;
calculating a first mean of the first similarity and the first ratio, a second mean of the second similarity and the second ratio, a third mean of the third similarity and the third ratio, and a fourth mean of the fourth similarity and the fourth ratio;
determining the maximum value of the first square difference, the second square difference, the third square difference and the fourth square difference, and taking the device state corresponding to the maximum value as the current state of the target device.
2. The method of claim 1, wherein when the current state of the target device is determined to be abnormal, the method further comprises:
for each detection sensor included in the target equipment, comparing the current sensor state data of the detection sensor with the standard state data corresponding to the detection sensor;
and when the error between the current sensor state data and the standard state data exceeds a preset threshold value, sending a prompt message for indicating that the detection sensor is abnormal.
3. An apparatus for determining a device status, comprising:
the processing unit is used for inputting the normal average data of the human body and the first equipment state data when the target equipment is in a normal state into a prediction model to obtain first prediction data, and performing vector splicing processing on the normal average data of the human body and the first equipment state data to obtain a first spliced vector;
the processing unit is further configured to input the human body abnormal average data and second device state data when the target device is in an abnormal state to the prediction model to obtain second prediction data, and perform vector splicing processing on the human body abnormal average data and the second device state data to obtain a second spliced vector;
the processing unit is further configured to input the human body normal average data and the second device state data to a prediction model to obtain third prediction data, and perform vector splicing processing on the human body normal average data and the second device state data to obtain a third spliced vector;
the processing unit is further configured to input the human body abnormal average data and the first device state data to the prediction model to obtain fourth prediction data, and perform vector splicing processing on the human body abnormal average data and the first device state data to obtain a fourth spliced vector;
the processing unit is further configured to input current human body data acquired when the target device is used and current device state data acquired when the target device is used to the prediction model to obtain fifth prediction data, and perform vector splicing processing on the current human body data and the current device state data to obtain a fourth spliced vector and obtain a fifth spliced vector;
a calculating unit, configured to calculate a first similarity between the fifth stitching vector and the first stitching vector, a second similarity between the fifth stitching vector and the second stitching vector, a third similarity between the fifth stitching vector and the third stitching vector, and a fourth similarity between the fifth stitching vector and the fourth stitching vector; and calculating a first ratio of the fifth prediction data to the first prediction data, a second ratio of the fifth prediction data to the second prediction data, a third ratio of the fifth prediction data to the third prediction data, and a fourth ratio of the fifth prediction data to the fourth prediction data;
the calculating unit is further configured to calculate a first mean square difference between the first similarity and the first ratio, a second mean square difference between the second similarity and the second ratio, a third mean square difference between the third similarity and the third ratio, and a fourth mean square difference between the fourth similarity and the fourth ratio;
a determining unit, configured to determine a maximum value of the first square difference, the second square difference, the third square difference, and the fourth square difference, so as to use a device state corresponding to the maximum value as a current state of the target device.
4. The apparatus of claim 3, wherein the apparatus further comprises:
the comparison unit is used for comparing the current sensor state data of each detection sensor included in the target equipment with the standard state data corresponding to the detection sensor;
and the sending unit is used for sending a prompt message for indicating that the detection sensor is abnormal when the error between the current sensor state data and the standard state data exceeds a preset threshold value.
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| CN117224151A (en) * | 2023-11-14 | 2023-12-15 | 江西科技学院 | Early warning method and system for electroencephalogram abnormal signals |
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