CN111144717B - Method and device for determining equipment state, storage medium and electronic equipment - Google Patents
Method and device for determining equipment state, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to a method, a device, a storage medium and an electronic device for determining a device state, so as to solve the problem of over-high health score of a fault device in the related art. The method comprises the following steps: acquiring state characteristic information of target equipment; acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment; according to state characteristic information of fault equipment in sample equipment, adjusting a state characteristic interval and/or a characteristic score corresponding to the state characteristic interval in a preset scoring rule to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, wherein the preset scoring rule comprises a plurality of state characteristic intervals and characteristic scores respectively corresponding to the state characteristic intervals; and determining the state score of the target equipment according to the adjusted preset scoring rule.
Description
Technical Field
The disclosure relates to the technical field of equipment detection, and in particular relates to a method, a device, a storage medium and electronic equipment for determining equipment states.
Background
In the field of industrial Internet of things, along with the acceleration of the industrial modernization process in China, the importance of the power equipment is increasingly highlighted, and the reliability and the safety of the power equipment are also required to be higher in industry, so that the evaluation of the health condition of the equipment is very important. The related art device evaluation method is generally performed based on expert experience. Specifically, scoring rules for different state characteristics of the equipment are subjectively formulated mainly through expert experience, and then the health condition of the equipment is evaluated based on the scoring rules.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, a storage medium, and an electronic device for determining a device status, so as to provide a new way of evaluating the device status.
To achieve the above object, in a first aspect, the present disclosure provides a method of determining a status of a device, the method comprising:
acquiring state characteristic information of target equipment;
acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
according to the state characteristic information of the fault equipment in the sample equipment, the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in the preset scoring rule are/is adjusted so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, and the preset scoring rule comprises a plurality of state characteristic intervals and the characteristic scores corresponding to the state characteristic intervals respectively;
And determining the state score of the target equipment according to the adjusted preset scoring rule.
Optionally, the adjusting the state feature interval and/or the feature score corresponding to the state feature interval in the preset scoring rule according to the state feature information of the fault device in the sample device includes:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining a target class cluster comprising fault equipment from class clusters obtained by clustering;
according to the target state characteristic information of the equipment in the target class cluster, adjusting a state characteristic interval in the preset scoring rule;
and determining the feature scores corresponding to the state feature intervals after adjustment according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the feature scores.
Optionally, the adjusting the status feature interval in the preset scoring rule according to the target status feature information of the device in the target cluster includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
And taking the maximum value of the state characteristic values as the upper limit value of the section, and taking the minimum value of the state characteristic values as the lower limit value of the section to obtain a new state characteristic section corresponding to the target state characteristic information.
Optionally, the adjusting the status feature interval in the preset scoring rule according to the target status feature information of the device in the target cluster includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises state characteristic values corresponding to the preset number of devices in the target cluster.
Optionally, the preset correspondence relationship is:
s new ＝s max -a·(s max -s min )·n k/k！ ·e -n ·P
wherein s is new Representing the adjusted feature scores, s max Sum s min Respectively representing a maximum characteristic score and a minimum characteristic score which are determined according to the preset scoring rule and correspond to the equipment in the target class cluster under the target state characteristic, wherein a represents a preset adjustment parameter, n represents the total number of the equipment in the target class cluster, k represents the number of fault equipment in the target class cluster, and P represents poisson distribution of target state characteristic information of the equipment in the target class cluster.
Optionally, before determining the state score of the target device according to the adjusted preset scoring rule, the method further includes:
determining initial feature weights corresponding to target state feature information of the sample equipment, wherein the target state feature information is any one of state feature information of the sample equipment;
according to the preset scoring rule and the target state characteristic information of the sample equipment, determining the characteristic scores corresponding to the sample equipment respectively, and determining low-score equipment with the characteristic scores lower than the preset characteristic scores in the sample equipment;
if the number of the fault devices in the low-score device is larger than the preset number, increasing the initial feature weight corresponding to the target state feature information;
the determining the state score of the target device according to the adjusted preset scoring rule includes:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
Optionally, the increasing the initial feature weight corresponding to the target state feature information includes:
increasing initial feature weights corresponding to the target state feature information according to the following formula:
w＝w 0 ·(1+m j/j！ ·e -m )
Wherein w represents the increased feature weight, w 0 Representing the initial feature weight, m represents the number of fault devices in the sample device, and j represents the number of fault devices in the low-score device.
In a second aspect, the present disclosure also provides an apparatus for determining a status of a device, the apparatus comprising:
the first acquisition module is used for acquiring state characteristic information of the target equipment;
the second acquisition module is used for acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
the adjustment module is used for adjusting the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in the preset scoring rule according to the state characteristic information of the fault equipment in the sample equipment so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, wherein the preset scoring rule comprises a plurality of state characteristic intervals and the characteristic scores respectively corresponding to the state characteristic intervals;
and the determining module is used for determining the state score of the target equipment according to the adjusted preset scoring rule.
In a third aspect, the present disclosure also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
In a fourth aspect, the present disclosure also provides an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any of the first aspects.
Through the technical scheme, the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in the preset scoring rule can be adjusted according to the state characteristic information of the fault equipment so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, and therefore the state score of the target equipment can be determined according to the reduced characteristic score. Compared with the mode of making a scoring rule completely depending on expert experience in the related art, the method can avoid the condition scoring of the fault equipment from being too high, and obtain the scoring result more in line with the actual condition, so that the accuracy of equipment health condition evaluation can be improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flowchart illustrating a method of determining a device status according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a method of determining a device status according to another exemplary embodiment of the present disclosure;
FIG. 3 is a block diagram illustrating an apparatus for determining a device status according to an exemplary embodiment of the present disclosure;
fig. 4 is a block diagram of an electronic device, according to an exemplary embodiment of the present disclosure.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
In the field of industrial Internet of things, along with the acceleration of the industrial modernization process in China, the importance of the power equipment is increasingly highlighted, and the reliability and the safety of the power equipment are also required to be higher in industry, so that the scientific evaluation of the health condition of the equipment is very important. The related art device evaluation method is generally performed based on expert experience. Specifically, grading rules aiming at different state characteristics of the equipment are subjectively formulated mainly through expert experience, and then the health condition of the equipment is evaluated based on the grading rules. If expert experience deviates, the formulated scoring rules may not be well suited for the actual application scenario, resulting in errors in the health score of the device, e.g., too high a health score may be given for a failed device, etc.
In view of this, the embodiments of the present disclosure provide a method, an apparatus, a storage medium, and an electronic device for determining a device status, so as to solve the problem in the related art that the health score of a faulty device is too high, and improve the accuracy of the health status evaluation of the device.
Fig. 1 is a flowchart illustrating a method of determining a device status according to an exemplary embodiment of the present disclosure. Referring to fig. 1, the method may include:
step 101, obtaining state characteristic information of the target equipment.
Step 102, obtaining state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment.
And step 103, according to the state characteristic information of the fault equipment in the sample equipment, adjusting the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in a preset grading rule so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment. The preset scoring rule may include a plurality of state feature intervals and feature scores corresponding to the state feature intervals respectively.
And 104, determining the state score of the target equipment according to the adjusted preset scoring rule.
By the method, the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in the preset scoring rule can be adjusted according to the state characteristic information of the fault equipment so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, and therefore the state score of the target equipment can be determined according to the reduced characteristic score. Compared with the mode of making a scoring rule completely depending on expert experience in the related art, the method can avoid the condition scoring of the fault equipment from being too high, and obtain the scoring result more in line with the actual condition, so that the accuracy of equipment health condition evaluation can be improved.
In order to make those skilled in the art more aware of the method for determining the status of a device in the embodiments of the present disclosure, the above steps are illustrated in detail below.
In step 101, the target device may be a different type of device, such as a power device, to which embodiments of the present disclosure are not limited. The status characteristic information may be information that may be used to characterize the status of the device, such as time of commissioning of the device, lifetime of the device, operating status of the powertrain system, operating status of the driveline, number of historical failures, and the like, as well as embodiments of the present disclosure are not limited in this respect.
After the status characteristic information of the target device is acquired, status characteristic information of a sample device including the failed device may be acquired. For example, the sample device may be a plurality of devices including the fault device, which belong to the same batch, and may or may not include the target device.
After the state characteristic information of the sample equipment is obtained, the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in a preset scoring rule can be adjusted according to the state characteristic information of the fault equipment in the sample equipment so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment.
The preset scoring rule may include a plurality of state feature intervals and feature scores corresponding to the state feature intervals respectively. For example, the plurality of status feature sections may be different ranges of status feature values under certain status feature information, respectively. For example, in the case where the state characteristic information is the number of times of the history failure, the state characteristic values may be the state characteristic sections corresponding to the number of times of the history failure, respectively, in which the number of times of the failure in the last year is less than 1, the number of times of the failure in the last year is 1 to 3, and the number of times of the failure in the last year is 3 or more.
For example, the feature scores corresponding to the plurality of state feature intervals may be preset according to the actual situation, which is not limited by the embodiment of the present disclosure. For example, in the case where the state feature information is the number of times of the history failure, the feature score corresponding to the number of times of the failure of the last year is less than 1 time and may be set to 100 points, the feature score corresponding to the number of times of the failure of the last year is 1 to 3 times and may be set to 60 points, the feature score corresponding to the number of times of the failure of the last year is 3 times or more and may be set to 30 points, and the like.
For example, for setting a preset scoring rule, a state feature interval corresponding to state feature information included in the scoring rule may be determined first, and then feature scores corresponding to each state feature interval may be determined respectively. For example, the state characteristic information includes a device production time and a device lifetime, a power system running state, a transmission system running state, and a historical failure number, in this case, a state characteristic interval corresponding to each state characteristic information and a characteristic score corresponding to each state characteristic interval may be set according to actual situations, so as to obtain a preset scoring rule as shown in table 1:
TABLE 1
If the target device is only scored for health according to the preset scoring rule, the health score of the faulty device may be too high, so that the faulty device is mistakenly considered to be a healthy device. For example, when the running state of the transmission system of the equipment fails and other performances are good, during health scoring according to the preset scoring rule, since other performances of the failed equipment are good, other feature scores may be higher except for the feature score corresponding to the running state of the transmission system, so that the overall state score of the failed equipment obtained by carrying out average value calculation or weight calculation according to the feature score may be too high.
In order to solve the technical problem, according to the state characteristic information of the fault device in the sample device, the state characteristic interval and/or the characteristic score corresponding to the state characteristic interval in the preset scoring rule may be adjusted to reduce the characteristic score of the state characteristic interval corresponding to the fault device, and finally, the state score of the target device is determined according to the adjusted preset scoring rule.
For example, as shown in table 1, if the state characteristic value of the fault device under the state characteristic information of "time to put into production and lifetime of the device" is 0.7, that is, the ratio between the time to put into production and lifetime of the fault device is 0.7, the state characteristic value is included in the state characteristic interval of ≡0.5 and <0.8 according to the preset scoring rule, so that it can be determined that the corresponding characteristic value is 60 points.
In this case, in order to reduce the feature score of the state feature interval corresponding to the fault device, the state feature interval in the preset scoring rule may be adjusted. For example, the state characteristic interval corresponding to the characteristic score of 60 minutes is adjusted to be equal to or more than 0.5 and less than 0.6, and the state characteristic interval corresponding to the characteristic score of 30 minutes is adjusted to be equal to or more than 0.6, so that the characteristic score of the characteristic interval corresponding to the fault equipment with the value of 0.7 can be reduced to 30. Alternatively, the feature scores in the preset scoring rules may be adjusted. For example, the feature score corresponding to the state feature interval of 0.5 or more and <0.8 is adjusted to 40 or the like. Alternatively, the state adjustment section and the feature score may be adjusted simultaneously, for example, the original state feature section corresponding to the feature score of 60 minutes is adjusted to be equal to or more than 0.5 and less than 0.6, and the original state feature section corresponding to the feature score of 30 minutes is adjusted to be equal to or more than 0.6. Then, the state characteristic interval is adjusted to be more than or equal to 0.6, the corresponding characteristic score is reduced from 30 minutes to 20 minutes, and the like.
It should be understood that, in the implementation of the present disclosure, an adjustment manner may be selected among an adjustment status feature interval, an adjustment feature score, and an adjustment status feature interval and a feature score according to actual situations, so as to achieve the purpose of reducing the status feature interval feature score corresponding to the fault device, which is not limited by the embodiments of the present disclosure.
In one possible manner, for the adjustment of the status feature interval and the feature score, the adjustment manner may be: the method comprises the steps of firstly clustering sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment. Then, among the clustered class clusters, a target class cluster including the failed device is determined. And then, according to the target state characteristic information of the equipment in the target class cluster, adjusting a state characteristic interval in a preset scoring rule. And finally, according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the characteristic scores, adjusting the characteristic scores corresponding to the state characteristic intervals in the preset scoring rule.
For example, the target state feature information is a production time and a device lifetime, and the sample devices are clustered according to the target state feature information, so that a plurality of class clusters can be obtained, and the ratio of the production time and the device lifetime of the sample devices in each class cluster is similar. For example, the ratio between the production time and the service life of the sample devices in a certain class of clusters can be 0.78, 0.79, 0.81 and 0.82 respectively, which are all similar values.
After the sample devices are clustered, a target class cluster including the failed device may be determined from the clustered class clusters. The target class cluster may be a class cluster including at least one faulty device, or may also be a class cluster including a number of faulty devices greater than or equal to a preset threshold, which is not limited by the embodiments of the present disclosure. It should be appreciated that in order to avoid occasional errors, it is preferable to determine the target class cluster as a class cluster that includes a number of failed devices greater than or equal to a preset threshold. The preset threshold may be set according to an actual situation, which is not limited in the embodiment of the present disclosure.
After the target class cluster is determined, the state characteristic interval in the preset scoring rule can be adjusted according to the target state characteristic information of the equipment in the target class cluster.
In one possible manner, the state characteristic values corresponding to the target state characteristic information of the devices in the target class cluster can be determined first, then the maximum value in the state characteristic values is taken as the upper limit value of the interval, and the minimum value in the state characteristic values is taken as the lower limit value of the interval, so that a new state characteristic interval is obtained.
For example, the target state characteristic information is a production time and a service life of the device, and the state characteristic values of the devices in the target class cluster under the state characteristic information can be a ratio between the production time and the service life of the device, and are respectively 0.78, 0.79, 0.81 and 0.82. If the preset scoring rule is shown in table 1, it can be determined that the state characteristic interval corresponding to the equipment includes two state characteristic intervals of greater than or equal to 0.5, less than 0.8 and greater than or equal to 0.8. However, since the status feature values of the devices in the target class cluster are similar, it is obviously not reasonable to correspond to different status feature intervals.
Therefore, in the embodiment of the present disclosure, the state characteristic value of the device under the target state characteristic information may be determined first, then the maximum value of the state characteristic values is taken as the upper limit value of the section, and the minimum value of the state characteristic values is taken as the lower limit value of the section, so that a new state characteristic section is obtained. For example, in the above example, the maximum state characteristic value of the device in the target class cluster in the target state characteristic information is 0.82, and the minimum state characteristic value is 0.78, so that the new state characteristic interval can be determined to be 0.82 to 0.78.
In another possible manner, the adjustment of the state feature interval in the preset scoring rule may be: firstly, determining state characteristic values corresponding to target state characteristic information of devices in a target class cluster, and then determining a new state characteristic interval according to the average value or the median value of the state characteristic values so that the new state characteristic interval comprises state characteristic values corresponding to the preset number of devices in the target class cluster.
For example, the preset number may be set according to an actual situation, for example, the preset number may be set to 25% (rounded or rounded) of the total number of devices in the target class cluster, or the preset number may be set to the total number of devices in the target class cluster, or the like, which is not limited by the embodiment of the present disclosure. It should be appreciated that in order to avoid dividing the devices in the target class cluster into different status feature intervals, it is preferable to set the preset number to the total number of devices in the target class cluster. In addition, since each device has only one state characteristic value under the state information of the target characteristic, the number of devices in the target class cluster covered by the state characteristic interval can be determined according to the state characteristic values of the devices included in the state characteristic interval.
For example, the target state characteristic information is a production time and a service life of the device, and the state characteristic values of the devices in the target class cluster under the state characteristic information can be a ratio between the production time and the service life of the device, and are respectively 0.78, 0.79, 0.81 and 0.82. First, it may be determined that the average value of the state feature values of the devices is 0.795, then the value expansion may be performed upward and downward with 0.795 as a reference value, for example, in order to make the expanded value range include the state feature values of all the devices in the target class cluster, the value range may be expanded upward and downward by 0.02 to obtain a value range of 0.77-0.81, and then the value range may be used as a new state feature interval corresponding to the target state feature information.
Alternatively, if the median of the state feature values of the devices may be determined to be 0.79, then the value of 0.79 may be used as a reference value, and the numerical expansion may be performed upward and downward, for example, in order to make the expanded numerical range include the state feature values of all the devices in the target class cluster, the numerical range may be expanded upward and downward by 0.02, to obtain a numerical range of 0.77-0.81, and then the numerical range may be used as a new state feature section corresponding to the target state feature information.
After obtaining a new state characteristic interval corresponding to the target state characteristic information, a characteristic score corresponding to the new state characteristic interval can be determined. Specifically, the feature score corresponding to the new state feature interval may be determined according to the number of fault devices in the target class cluster and a preset correspondence between the number of fault devices and the feature score.
For example, the preset corresponding relationship may be a corresponding relationship characterizing negative correlation between the number of fault devices and the feature score, so that the more the number of fault devices in the target class cluster is, the lower the feature score corresponding to the new state feature interval is, and further the purpose of reducing the feature score of the state feature interval corresponding to the fault device is achieved.
In one possible manner, the preset correspondence may be:
s new ＝s max -a·(s max -s min )·n k/k！ ·e -n ·P (1)
wherein s is new Representing the adjusted feature scores, s max Sum s min Respectively representing a maximum characteristic score and a minimum characteristic score which are determined according to a preset scoring rule and correspond to the equipment in the target class cluster under the target state characteristic, wherein a represents a preset adjustment parameter, n represents the total number of the equipment in the target class cluster, k represents the number of fault equipment in the target class cluster, and P represents poisson distribution of the target state characteristic information of the equipment in the target class cluster.
For example, the target state characteristic information is a production time and a service life of the device, and the state characteristic values of the devices in the target class cluster under the state characteristic information can be a ratio between the production time and the service life of the device, and are respectively 0.78, 0.79, 0.81 and 0.82. According to a preset scoring rule, the maximum feature score of the equipment under the target state feature is 60 minutes, and the minimum feature score is 30 minutes. The total number of devices in the target class cluster and the number of failed devices may then be determined. Finally, the determined maximum feature score (60), minimum feature score (30), total equipment number in the target cluster and fault equipment number are substituted into formula (1) to calculate, so that the feature score corresponding to the new state feature interval is obtained.
After the new state characteristic interval and the characteristic score corresponding to the new state characteristic interval are obtained according to the above manner, the existing state characteristic interval in the preset scoring rule can be correspondingly adjusted, so that the new state characteristic interval and the characteristic score corresponding to the new state characteristic interval are added into the preset scoring rule, and the state score of the target equipment is determined according to the adjusted preset scoring rule.
In a possible manner, the feature scores of the target device under different state features may be determined according to the adjusted preset scoring rule, and then the feature scores of the target device under different state features are weighted and summed, so as to obtain a state score for reflecting the health condition of the target device. In the related art, the weight value for performing the weighted summation is mainly subjectively determined by expert experience. If the equipment fails, but the expert judges the importance of the failed state characteristic information to have deviation, the weight corresponding to the state characteristic information is possibly lower, so that an excessively high state score is obtained, the failed equipment is determined to be the non-failed equipment, and an equipment state result which does not accord with the actual situation is obtained.
In order to solve the above-mentioned problem, the embodiment of the present disclosure may further determine, before determining the state score of the target device according to the adjusted preset scoring rule, an initial feature weight corresponding to target state feature information of the sample device, where the target state feature information is any one of the state feature information of the sample device. And then, according to a preset scoring rule and target state characteristic information of the sample equipment, determining the characteristic scores corresponding to the sample equipment respectively, and determining low-score equipment with the characteristic scores lower than the preset scores in the sample equipment. If the number of the fault devices in the low-score device is greater than the preset number, the initial feature weight is increased. Accordingly, according to the adjusted preset scoring rule, the determining the state score of the target device may be: and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial characteristic weight.
By way of example, the initial feature weights may be determined in a manner known in the relevant art, to which embodiments of the present disclosure are not limited. For example, the sample device includes 6 devices, each including four pieces of state feature information. In this case, a decision matrix may be constructed for the sample device first. Specifically, the value of each element in the decision matrix may be subjectively determined by an expert according to the rules shown in table 2, thereby obtaining a value example shown in table 3.
TABLE 2
| Value taking | Meaning of | Value taking | Meaning of |
| 1 | The importance of the element A is the same as that of the element B | 1/3 | The B element is slightly more important than the A element |
| 3 | The A element is slightly more important than the B element | 1/5 | B element is more important than A element |
| 5 | The element A is more important than the element B | 1/7 | The B element is obviously important than the A element |
| 7 | The A element is obviously important than the B element | 1/9 | The B element is more important than the A element |
| 9 | The A element is more important than the B element |
TABLE 3 Table 3
For example, the preset score may be set according to an actual situation, which is not limited by the embodiment of the present disclosure. In a possible manner, the preset score may be determined according to the number of state feature intervals corresponding to the target state feature information. Specifically, if the target state feature information corresponds to three state feature intervals, the score between the two lower feature scores may be determined as the preset score. For example, the historical failure times shown in table 1 correspond to three status feature intervals, the lower two feature scores being 60 and 30, and then the score between 60 and 30 may be determined as a preset score, such as 45 as a preset score, and so on. In this way, the device having the lowest feature score at the number of times of the history of failures among the sample devices can be selected. If the equipment with the lowest feature score has more fault equipment, the weight of the state feature information, namely the historical fault times, during weighted summation can be increased, so that the overall state score of the fault equipment is improved.
Alternatively, if the target state characteristic information corresponds to four or five state characteristic intervals, the score between the two characteristic scores of the second and third largest may be determined as a preset score, and so on, so that a device having a lower characteristic score under the target state characteristic information among the sample devices may be selected. If the equipment with the lowest feature score has more fault equipment, the weight of the target state feature information in the weighted summation can be increased, and then the overall state score of the fault equipment is improved.
In a possible manner, the initial feature weight corresponding to the target state feature information may be increased according to the following formula:
w＝w 0 ·(1+m j/j！ ·e -m ) (2)
wherein w represents the increased feature weight, w 0 Representing the initial feature weight, m represents the number of failed devices in the sample device, and j represents the number of failed devices in the low score device.
It should be understood that, according to the formula (2), if the number of fault devices in the low-score device is greater under the target state feature information, the feature weight corresponding to the target state feature information is greater, so that the influence of the target state feature information on the overall state score can be increased, the condition score is prevented from being too high, and the accuracy of the device score is improved.
For example, after obtaining the status score of the target device according to the above manner, the operation status of the target device may be determined according to the status score. For example, it may be preset that the state score is 80 or more (excluding 80), the operation state of the target device is determined to be healthy, the state score is 60 to 80, the operation state of the target device is determined to be good, the state score is 60 or less, the operation state of the target device is determined to be unhealthy, and so on, so that the operation state of the target device may be determined according to the state score. In addition, the condition scoring of the fault equipment can be prevented from being too high through the mode, and the scoring result which is more in line with the actual condition is obtained, so that the accuracy of equipment health condition evaluation is improved.
A method of determining a device status in the present disclosure is described below by way of another exemplary embodiment.
Referring to fig. 2, the process includes:
step 201, status feature information of a sample device is acquired. Wherein the sample device includes a fault device.
Step 202, clustering sample devices according to target state characteristic information of the sample devices. Wherein the target state characteristic information is any one of state characteristic information of the sample device.
And 203, determining target class clusters with the number of the included fault devices being greater than or equal to a preset threshold value from the class clusters obtained by clustering.
Step 204, determining state characteristic values corresponding to the target state characteristic information of the devices in the target class cluster respectively.
In step 205, a new state feature section corresponding to the target state feature information is obtained by using the maximum value of the state feature values as the upper limit value of the section and the minimum value of the state feature values as the lower limit value of the section.
And 206, determining the feature scores corresponding to the new state feature intervals according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the feature scores.
Step 207, adding the new state feature interval and the feature score corresponding to the new state feature interval to a preset scoring rule to obtain an adjusted initial scoring rule;
step 208, determining an initial feature weight corresponding to the target state feature information of the sample device.
Step 209, determining the feature scores corresponding to the sample devices respectively according to the preset scoring rule and the target state feature information of the sample devices.
In step 210, a low score device with a feature score lower than a preset feature score is determined in the sample device.
Step 211, if the number of the fault devices in the low-score device is greater than the preset number, increasing the initial feature weight corresponding to the target state feature information.
Step 212, determining the state score of the target device according to the adjusted preset scoring rule and the increased initial feature weight.
The specific embodiments of the above steps are illustrated in detail above, and will not be repeated here. It should be further understood that for the purposes of simplicity of explanation of the above method embodiments, all of them are depicted as a series of acts in combination, but it should be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts described above. Further, it should also be appreciated by those skilled in the art that the embodiments described above are preferred embodiments and that the steps involved are not necessarily required by the present disclosure.
By the method, the feature scores of the state feature intervals corresponding to the fault equipment under the target state features can be reduced, and the feature weights of the target state features are increased, so that the state scores of the target equipment can be determined according to the reduced feature scores and the increased feature weights. Compared with the mode of making a scoring rule and a characteristic weight completely depending on expert experience in the related art, the method can avoid excessively high health scoring of the fault equipment and obtain a scoring result more in line with actual conditions, so that the accuracy of equipment health condition evaluation can be improved.
Based on the same inventive concept, the embodiments of the present disclosure also provide an apparatus for determining a device state, where the apparatus may be part or all of an electronic device by software, hardware, or a combination of both. Referring to fig. 3, the apparatus 300 for determining a device state may include:
a first obtaining module 301, configured to obtain state feature information of a target device;
a second obtaining module 302, configured to obtain status feature information of a sample device, where the sample device includes a fault device;
the adjusting module 303 is configured to adjust, according to status feature information of the fault device in the sample device, a status feature interval and/or feature scores corresponding to the status feature interval in the preset scoring rule to reduce the feature scores of the status feature interval corresponding to the fault device, where the preset scoring rule includes a plurality of status feature intervals and feature scores corresponding to the plurality of status feature intervals respectively;
and the determining module 304 is configured to determine a status score of the target device according to the adjusted preset scoring rule.
Optionally, the adjusting module 303 is configured to:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
Determining a target class cluster comprising fault equipment from class clusters obtained by clustering;
according to the target state characteristic information of the equipment in the target class cluster, adjusting a state characteristic interval in the preset scoring rule;
and determining the feature scores corresponding to the state feature intervals after adjustment according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the feature scores.
Optionally, the adjusting module 303 is configured to:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
and taking the maximum value of the state characteristic values as the upper limit value of the section, and taking the minimum value of the state characteristic values as the lower limit value of the section to obtain a new state characteristic section corresponding to the target state characteristic information.
Optionally, the adjusting module 303 is configured to:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises state characteristic values corresponding to the preset number of devices in the target cluster.
Optionally, the preset correspondence relationship is:
s new ＝s max -a·(s max -s min )·n k/k！ ·e -n ·P (1)
wherein s is new Representing the adjusted feature scores, s max Sum s min Respectively representing a maximum characteristic score and a minimum characteristic score which are determined according to the preset scoring rule and correspond to the equipment in the target class cluster under the target state characteristic, wherein a represents a preset adjustment parameter, n represents the total number of the equipment in the target class cluster, k represents the number of fault equipment in the target class cluster, and P represents poisson distribution of target state characteristic information of the equipment in the target class cluster.
Optionally, the apparatus 300 further includes:
the first determining module is used for determining initial feature weights corresponding to target state feature information of the sample equipment before determining the state score of the target equipment according to the adjusted preset scoring rule, wherein the target state feature information is any one of the state feature information of the sample equipment;
the second determining module is used for determining the characteristic scores corresponding to the sample devices respectively according to the preset scoring rule and the target state characteristic information of the sample devices, and determining low-score devices with the characteristic scores lower than the preset characteristic scores in the sample devices;
The processing module is used for increasing the initial feature weight corresponding to the target state feature information when the number of the fault devices in the low-score device is larger than the preset number;
the determining module 304 is configured to:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
Optionally, the processing module is configured to:
increasing initial feature weights corresponding to the target state feature information according to the following formula:
w＝w 0 ·(1+m j/j！ ·e -m ) (2)
wherein w represents the increased feature weight, w 0 Representing the initial feature weight, m represents the number of fault devices in the sample device, and j represents the number of fault devices in the low-score device.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Based on the same inventive concept, the embodiments of the present disclosure further provide an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of determining a device status described above.
In a possible manner, the block diagram of the electronic device may be as shown in fig. 4. Referring to fig. 4, the electronic device may include: a processor 401, a memory 402. The electronic device 400 may also include one or more of a multimedia component 403, an input/output (I/O) interface 404, and a communication component 405.
Wherein the processor 401 is configured to control the overall operation of the electronic device 400 to perform all or part of the steps in the method for determining a device status described above. The memory 402 is used to store various types of data to support operations at the electronic device 400, which may include, for example, instructions for any application or method operating on the electronic device 400, as well as application-related data, such as preset scoring rules, preset numbers, and the like.
The Memory 402 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The multimedia component 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 402 or transmitted through the communication component 405. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 405 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processor (Digital Signal Processor, DSP), digital signal processing device (Digital Signal Processing Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field Programmable Gate Array, FPGA), controller, microcontroller, microprocessor, or other electronic element(s) for performing the above-described method of determining a device state.
In another exemplary embodiment, a computer readable storage medium is also provided comprising program instructions which, when executed by a processor, implement the steps of the method of determining a device state described above. For example, the computer readable storage medium may be the memory 402 including program instructions described above that are executable by the processor 401 of the electronic device 400 to perform the method of determining a device state described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of determining a device state when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (9)
1. A method of determining a status of a device, the method comprising:
acquiring state characteristic information of target equipment, wherein the state characteristic information is used for representing the state of the equipment;
acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
according to the state characteristic information of the fault equipment in the sample equipment, the state characteristic interval and the characteristic score corresponding to the state characteristic interval in a preset grading rule are adjusted to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, wherein the preset grading rule comprises a plurality of state characteristic intervals and the characteristic scores corresponding to the state characteristic intervals respectively;
determining the state score of the target equipment according to the adjusted preset scoring rule;
the step of adjusting the state characteristic interval and the characteristic score corresponding to the state characteristic interval in the preset scoring rule according to the state characteristic information of the fault equipment in the sample equipment comprises the following steps:
Clustering the sample devices aiming at target state characteristic information of the sample devices, wherein the target state characteristic information is any one of the state characteristic information of the sample devices;
determining a target class cluster comprising fault equipment from class clusters obtained by clustering;
according to the target state characteristic information of the equipment in the target class cluster, adjusting a state characteristic interval in the preset scoring rule;
and determining the feature scores corresponding to the state feature intervals after adjustment according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the feature scores.
2. The method according to claim 1, wherein the adjusting the status feature interval in the preset scoring rule according to the target status feature information of the devices in the target class cluster includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
and taking the maximum value of the state characteristic values as the upper limit value of the section, and taking the minimum value of the state characteristic values as the lower limit value of the section to obtain a new state characteristic section corresponding to the target state characteristic information.
3. The method according to claim 1, wherein the adjusting the status feature interval in the preset scoring rule according to the target status feature information of the devices in the target class cluster includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target class cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises state characteristic values corresponding to a preset number of devices in the target cluster.
4. A method according to any one of claims 1-3, wherein the predetermined correspondence relationship is:
s new ＝s max -a·(s max -s min )·n k/k！ ·e -n ·P
wherein s is new Representing the adjusted feature scores, s max Sum s min Respectively representing a maximum characteristic score and a minimum characteristic score which are determined according to the preset scoring rule and correspond to the equipment in the target class cluster under the target state characteristic, wherein a represents a preset adjustment parameter, n represents the total number of the equipment in the target class cluster, k represents the number of fault equipment in the target class cluster, and P represents poisson distribution of target state characteristic information of the equipment in the target class cluster.
5. The method of claim 1, wherein prior to determining the status score for the target device according to the adjusted preset scoring rules, the method further comprises:
determining initial feature weights corresponding to target state feature information of the sample equipment, wherein the target state feature information is any one of state feature information of the sample equipment;
according to the preset scoring rule and the target state characteristic information of the sample equipment, determining the characteristic scores corresponding to the sample equipment respectively, and determining low-score equipment with the characteristic scores lower than the preset characteristic scores in the sample equipment;
if the number of the fault devices in the low-score device is larger than the preset number, increasing the initial feature weight corresponding to the target state feature information;
the determining the state score of the target device according to the adjusted preset scoring rule includes:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
6. The method of claim 5, wherein the increasing the initial feature weight corresponding to the target state feature information comprises:
Increasing initial feature weights corresponding to the target state feature information according to the following formula:
w＝w 0 ·(1+m j/j！ ·e -m )
wherein w represents the increased feature weight, w 0 Representing the initial feature weight, m represents the number of fault devices in the sample device, and j represents the number of fault devices in the low-score device.
7. An apparatus for determining a status of a device, the apparatus comprising:
the first acquisition module is used for acquiring state characteristic information of the target equipment, wherein the state characteristic information is used for representing the state of the equipment;
the second acquisition module is used for acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
the adjustment module is used for adjusting the state characteristic interval and the characteristic score corresponding to the state characteristic interval in a preset scoring rule according to the state characteristic information of the fault equipment in the sample equipment so as to reduce the characteristic score of the state characteristic interval corresponding to the fault equipment, wherein the preset scoring rule comprises a plurality of state characteristic intervals and the characteristic scores corresponding to the state characteristic intervals respectively;
the determining module is used for determining the state score of the target equipment according to the adjusted preset scoring rule;
The adjusting module is used for:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining a target class cluster comprising fault equipment from class clusters obtained by clustering;
according to the target state characteristic information of the equipment in the target class cluster, adjusting a state characteristic interval in the preset scoring rule;
and determining the feature scores corresponding to the state feature intervals after adjustment according to the number of the fault devices in the target class cluster and the preset corresponding relation between the number of the fault devices and the feature scores.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-6.
9. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-6.
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