CN113435689B - Automatic equipment state evaluation method and system for labeling digital bin modeling - Google Patents
Automatic equipment state evaluation method and system for labeling digital bin modeling Download PDFInfo
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Abstract
A method and a system for evaluating the state of automatic equipment for labeling several bins modeling comprise the following steps: step1, collecting metadata of a power system, and performing labeling operation on the metadata based on the data type of the metadata; step 2, based on the association influence factors of the preset automation equipment, evaluating metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation; and step 3, filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, and acquiring the health condition evaluation of the automation equipment and the operation and maintenance decision of the automation equipment. The technical scheme of the invention has the advantages of simple and quick algorithm, accurate result and good effect.
Description
Technical Field
The invention relates to the field of automatic scheduling of power systems, in particular to a method and a system for evaluating the state of automatic equipment for labeling several-bin modeling.
Background
At present, with the rapid development of intelligent technology of a transformer substation, the operation management content of equipment in the transformer substation is also changed day by day. Conventional periodic inspection of original electric power automation equipment is taken as a preventive electric power equipment maintenance mode, and the development requirement of an intelligent substation cannot be met due to various problems existing in the conventional periodic inspection.
Firstly, the intelligent substation cannot flexibly determine equipment overhaul time based on the actual running condition of the power equipment, so that the equipment overhaul period is usually determined according to actual experience, and the overhaul requirement of the power equipment cannot be fully met. And secondly, the periodic shutdown of the power equipment can be caused by the equipment fixed inspection, so that the running continuity of the power grid is destroyed, the power grid dispatching frequency is greatly increased to support the shutdown and recovery of the power grid, and the economic running efficiency of the power grid is further influenced. Thirdly, equipment is examined surely and is needed to consume a large amount of manpower physics, and inefficiency, with high costs.
For the above reasons, a state maintenance method based on operation data of an electric power automation device is receiving more and more attention and application. In recent years, the wide application of the substation automation technology leads the self-detection and self-diagnosis capabilities of the substation equipment to be remarkably improved. The electric power automation equipment can find out own abnormality through self-checking, and a basis for carrying out state maintenance according to the equipment self-checking alarm information is preliminarily provided.
However, the state maintenance realized according to the self-checking alarm information of the equipment is still a passive working mode for post-maintenance, the hidden trouble of the equipment cannot be detected and eliminated in time before the equipment fails, and a great amount of operation risks and potential accidents still exist in the electric power automation system. In addition, although the operation data of the automation devices can be collected in real time and in a large amount in the electric power automation system, the operation data of the automation devices still cannot be effectively utilized and fully mined. The electric power automation system in the prior art still has the problems that the operation data of the automation equipment cannot be effectively utilized, the comprehensive analysis and evaluation of the operation condition cannot be carried out from the system angle, the state maintenance of the automation equipment is supported, and the like.
Therefore, there is a need for an automated equipment state assessment method and system for labeling several bins modeling.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method and a system for evaluating the state of automatic equipment for labeling digital bin modeling, which are used for performing labeling operation on metadata of a power system and evaluating by adopting related influence factors based on the metadata and the labels thereof so as to obtain operation and maintenance decisions of the automatic equipment.
The invention adopts the following technical scheme.
The invention relates to a method for evaluating the state of automatic equipment for labeling several-bin modeling, which comprises the following steps: step 1, collecting metadata of a power system, and labeling the metadata based on the data type of the metadata; step 2, based on the association influence factors of the preset automation equipment, evaluating metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation; and step 3, filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, and acquiring the health condition evaluation of the automation equipment and the operation and maintenance decision of the automation equipment.
Preferably, the power system metadata includes equipment base data, patrol data, alarm attribute data, historical case data of automation equipment in the power system.
Preferably, the automation equipment at least comprises a power measurement and control device, a power system communication gateway, a power system monitoring host, a power system switch and a power system clock synchronization device.
Preferably, the data type of the metadata includes: measuring out-of-limit data and alarm state data.
Preferably, the tagging of the metadata according to the data type of the metadata comprises: judging the degree of abnormality of the measurement threshold-crossing data according to the preset numerical range in which the measurement threshold-crossing data falls, and labeling the measurement threshold-crossing data according to the degree of abnormality; or labeling the alarm state data according to the alarm type of the alarm state data.
Preferably, the preset relevant influencing factors of the automation equipment comprise device communication rate, device defect condition, familial data and device operation age.
Preferably, step 2 further includes: step 2.1, collecting all metadata corresponding to the current automation equipment, and dividing or calculating all metadata according to the associated influence factors; step 2.2, calculating all the divided metadata based on the evaluation index and the evaluation standard corresponding to each associated influence factor to obtain the evaluation of the automation equipment; and 2.3, storing the evaluation of the automation equipment into a comprehensive evaluation data warehouse of the automation equipment.
Preferably, the related influence factors are taken as evaluation items, the current period data and the historical period data are taken as evaluation sub-items, and an analysis evaluation algorithm model is adopted to obtain the health condition evaluation of the automatic equipment; the calculation formula of the analysis and evaluation algorithm is as follows:
wherein S is a state score of health status evaluation of the automation equipment,
A j is the weight factor of the j-th evaluation item,
A k is the weight factor of the kth evaluation sub-term,
P k is the score of the kth evaluation sub-term,
M is the total number of evaluation items, and l is the total number of evaluation sub-items.
Preferably, the operation and maintenance decision of the automation device is determined based on the health evaluation of the automation device; the health evaluation of the automation device and the operation and maintenance decision of the automation device are displayed in a visual mode.
The second aspect of the invention relates to an automated equipment state assessment system for labeling several bins modeling, wherein: the system comprises a label generating unit, a data evaluating unit and an analysis decision unit; the tag generation unit is used for collecting metadata of the power system and labeling the metadata based on the data type of the metadata; the data evaluation unit is used for evaluating the metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model based on the association influence factors of the preset automation equipment, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation; and the analysis decision unit is used for filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, acquiring the health condition evaluation of the automation equipment and carrying out operation and maintenance decision of the automation equipment.
Compared with the prior art, the method and the system for evaluating the state of the automatic equipment for labeling the digital bin modeling have the advantages that the metadata of the power system can be labeled, the comprehensive evaluation data warehouse is generated for the labeled metadata by adopting the related influence factors, meanwhile, the health condition of the automatic equipment is evaluated based on the corresponding data in the data warehouse, and the operation and maintenance decision is generated. The technical scheme of the invention has the advantages of simple and quick algorithm, accurate result and good effect.
The beneficial effects of the invention also include:
1. The invention collects and uses various metadata such as equipment basic data, inspection data, alarm attribute, history case and the like, thereby leading the evaluation result of the automatic equipment generated by the invention to be more comprehensive and more accurate. In addition, the present invention can use a large amount of metadata of various types because the evaluation method adopted by the present invention has simple steps and few calculation processes, and redundant data is not generated.
2. The invention selects device communication rate, device defect condition, familial data, operation period and the like as the associated influencing factors of the automatic equipment. The selection of the influencing factors is based on analysis and processing experience of operation data in the power automation equipment for many years, and potential fault conditions of the power automation equipment can be accurately represented. Therefore, the metadata is classified and arranged by adopting the related influence factors, and the metadata is subjected to preliminary evaluation, so that the metadata is very necessary and accurate.
3. The method can accurately judge the fault state of the equipment under the condition that the transmission or generation errors occur to a certain item or individual item of metadata because the state evaluation is generated by comprehensively judging the items of metadata based on the related influence factors. Therefore, the method of the invention effectively improves the fault diagnosis accuracy and reduces the diagnosis error.
4. The method splits the step of establishing the comprehensive evaluation data warehouse and the step of generating the health condition evaluation and operation and maintenance decision of the automatic equipment. By the method, a data warehouse can be formed in advance, and then condition evaluation and decision schemes can be generated in a targeted manner according to operation and maintenance requirements or power grid monitoring requirements. Therefore, the operation speed of data processing is improved, the pertinence of the method is improved, the repeated redundancy process of data operation is prevented, and the overall efficiency is improved.
5. According to the invention, the metadata is classified into the measurement threshold-crossing data and the alarm state data, so that the labeling process is quicker and more accurate.
6. The evaluation method and the data warehouse establishing method can be well compatible and suitable for various different electric power automation equipment, and compared with the fault evaluation method specially provided for the current equipment based on the type, the function and the action of the equipment, the fault evaluation method has better universality, so that the fault evaluation method can be suitable for continuously updated and developed substation equipment and intelligent power grids.
Drawings
FIG. 1 is a schematic flow chart of steps of an automatic equipment state evaluation method for labeling several bins modeling in the present invention;
FIG. 2 is a schematic diagram of a data processing process in an automated equipment state assessment method for labeling several bins modeling according to the present invention;
FIG. 3 is a schematic diagram of a method for evaluating the health status and operation and maintenance decisions of an automated device in a labeled multi-bin modeling embodiment of the present invention;
fig. 4 is a schematic diagram of a visual display of health status evaluation and operation and maintenance decision of an automation device according to another embodiment of an automated device status evaluation method for labeling several bins modeling in the present invention.
Detailed Description
The application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
FIG. 1 is a schematic flow chart of steps of an automated equipment state evaluation method for labeling several bins modeling in the present invention. FIG. 2 is a schematic diagram of a data processing process in an automated equipment state assessment method for labeling a plurality of bins according to the present invention. 1-2, an automatic equipment state evaluation method for labeling several bins modeling comprises steps 1 to 3.
And step 1, collecting metadata of the power system, and labeling the metadata based on the data type of the metadata.
It will be appreciated that the power system metadata collected in the present invention may include various types of data collected directly or indirectly from the automation devices of the power system, and as such data may be capable of evaluating the operational status of the automation devices in the power system, such data may be collected and aggregated.
Preferably, the power system metadata includes equipment base data, patrol data, alarm attribute data, historical case data of automation equipment in the power system.
Specifically, the device base data may include base attribute data and operation data of each automation device, and the like, including, for example, an operation period of the automation device, a investment rate or a usage rate of a CPU, a memory, a disk, and the like. The inspection data can comprise voltage, current data, meter reading data and the like on each line in the power grid collected by the stator in the power inspection process. The alarm attribute data refers to alarm levels (such as critical, severe, general) of the alarm signals of the automatic equipment, alarm classifications (such as loop abnormality, channel abnormality, on-off abnormality, power plug-in abnormality, etc.), and the like. The historical case data may include historical summaries of the relevant data collected by the automation devices when the grid is operating in each typical state, such as a large area of historical faults occurring in a grid, alarm information of automation devices outside of or within such faults, and operational data of automation devices. From these relevant data, it is possible to summarize the operational data that an automation device may have when the grid is operating in a particular state, i.e. to predict the operational data of future automation devices.
Preferably, the automation equipment at least comprises a power measurement and control device, a power system communication gateway, a power system monitoring host, a power system switch and a power system clock synchronization device.
It will be appreciated that the relevant evaluation devices for performing the status evaluation of an automation device in the present invention may include the above five types of automation devices. Specifically, the information model of the national power grid 'four unification and four standardization' device can be used as the basis to collect corresponding data in the devices.
Tables 1 to 5 are the corresponding evaluation information of the above five kinds of devices. The specific contents are as follows:
Table 1 Equipment information Table of electric measurement and control device
As shown in table 1, the information collected by the power measurement and control device may include a device self-test alarm, an operation anomaly alarm, a Goose link anomaly alarm, an SV alarm, device operation condition information, and so on. The information such as GO01-A, GO-B can be sequentially corresponding to the numbers of different information points in the power grid, and the information such as GO01 and GO02 can be sequentially corresponding to the numbers of different Goose links in the power grid. Similarly, SV01, SV02, etc. correspond to the number of each different SV link in the power grid. According to the different model of the specific measurement and control device, the number of the working voltages in the device and the number of the optical ports and the objects are different. In an embodiment of the present invention, a measurement and control device of a specific model may be selected, and corresponding data of each port thereof may be collected, for example, the transmit-receive power of the optical port may be collected. The operation condition data of the measurement and control device is different from the content of the alarm data generated by the measurement and control device, and the data can be directly acquired by the measurement and control device or indirectly measured by external elements such as external sensors and the like. And, this part of data is taken as a data item with an actual acquisition value, and the attribute of the data is different from other alarm data.
Preferably, the data type of the metadata includes: measuring out-of-limit data and alarm state data. For example, in table 1 of the embodiment of the present invention, in the data collected by the electric power measurement and control device, the content of self-checking alarm, abnormal operation alarm, link alarm and the like of the device all belong to alarm data, while the operation condition of the measurement and control device belongs to measurement out-of-limit data. Therefore, in the last column of table 1, specific evaluation information can be classified for different information names.
Alarm data, typically obtained based on self-checking alarms of the equipment, typically includes device anomalies and other device operational alarm information. Such data may be classified into critical alarms, severe alarms and general alarms according to the importance of each self-checking alarm. The magnitude out-of-limit data is obtained by monitoring the running condition of the device in real time. Specifically, the system can include various data such as a magnetic disk, a memory, a CPU, temperature, light intensity, power supply voltage, time synchronization state and the like. For the magnitude out-of-limit data, whether the magnitude out-of-limit data is out-of-limit or the magnitude of the magnitude is over can be determined according to the threshold value corresponding to each item of data. In particular, different tagging operations may be provided for different types of data content, depending on the different data types.
This will be described in detail below.
Table 2 device information table of power system monitoring host
As shown in table 2, the power system monitoring host may include information such as device operating conditions, time-tick state anomalies, CPU state monitoring, on-line memory detection, hard disk on-line monitoring, and process on-line monitoring. Most of the data in the above content can be obtained according to the monitoring information directly generated by the monitoring host after the monitoring host is connected to the power system, and part of the content, such as the CPU load rate, the memory usage rate and the disk usage rate, can determine the state of the device according to the specific values collected by the monitoring host. Therefore, the three items of data are data information of the magnitude-out-of-limit type.
In addition, the content of the process 1, the process 2, and the like may only represent some key processes running on the monitoring host. An alarm may occur, for example, when a corresponding process crashes or exits.
As a specific measurement and control device, the power grid of the invention may also comprise some telemechanical measurement and control devices.
Table 3 device information table for remote machine of electric power system
As shown in table 3, the remote machine as a measurement and control device can also be used for collecting the contents of operation conditions, on-line monitoring of approach, other operation alarm information and the like. The CPU load rate, the memory utilization rate and the disk utilization rate are of the magnitude out-of-limit type, and the others are alarm information. A. B is different network numbers, XX1, XX9 and the like are device numbers of measurement and control devices accessed by the remote machine.
Table 4 device information table of power system switch
As shown in table 4, the device information of the switch may include information of a device self-check alarm, a time tick state abnormality, a port state abnormality, a device operation condition, and the like. The device self-checking alarm may include an alarm caused by power loss, device configuration change, and the like. The device operation conditions can include CPU load rate, working voltage, board card temperature, CPU temperature and the like.
Table 5 equipment information table of clock synchronizer of electric power system
As shown in table 5, the clock synchronization device may include self-test alarm information of the device. Specifically, the synchronization principle of the clock synchronization device can include information such as an external time source signal state, an antenna state, a receiving module state, a time jump detection state and the like. The information is alarm data.
Preferably, the tagging of the metadata according to the data type of the metadata comprises: judging the degree of abnormality of the measurement threshold-crossing data according to the preset numerical range in which the measurement threshold-crossing data falls, and labeling the measurement threshold-crossing data according to the degree of abnormality; or labeling the alarm state data according to the alarm type of the alarm state data.
It will be appreciated that the alarm data described above is often referred to in the art as a state quantity, and the measurement out-of-limit data is often referred to as an analog quantity.
For example, if the current value of the analog point of a certain device is 3.7, the data is metadata of one measurement threshold-crossing data in the current apparatus. To implement a tagging operation for the data, a threshold range of the data needs to be obtained. Empirically, three sets of thresholds for this data can be set to-2 and 2, -4 and 4, -6 and 6. When the data falls between [ -2,2], the current data is in a "normal" range, when the data falls between [ -4, -2) or (2, 4], the current data is in a "general alarm" range, when the data falls between [ -6, -4) or (4, 6], the current data is in a "serious alarm" range, and when the data falls (- +, -6) or (6, +), the current data is in a "critical alarm" range. I.e., the data can be labeled according to general, severe and critical alarms.
On the other hand, as described above, for the alarm data, the alarm is classified as critical alarm, serious alarm or general alarm according to the specific contents of the alarm, and is labeled accordingly.
And 2, evaluating metadata corresponding to the automation equipment based on a preset association influence factor of the automation equipment and an analysis evaluation algorithm model, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation.
After the classification and labeling processes of the data in the step 1 are completed, the metadata corresponding to the automation equipment can be evaluated.
Preferably, the preset relevant influencing factors of the automation equipment comprise device communication rate, device defect condition, familial data and operation age.
Table 6 is a table of information about related influencing factors of the automation device preset in the present invention. As shown in table 6, the influencing factors include the device communication rate, the device defect condition, the familial data, and the operational years.
TABLE 6 preset associated influence factor information Table for Automation devices
The device communication rate data can be obtained by dividing the normal communication duration of the device in the current period by the duration of the current period. The defect condition of the device can be obtained according to various alarm data in tables 1 to 5. The judging method of the defect condition of the device is shown in the table, and under the condition of one-time general alarm, serious alarm or critical alarm, the corresponding scores can be deducted respectively, so that the final defect data is obtained.
Specifically, in tables 1-5 of step 1, the basis for determining that various types of alarm information are general alarms, serious alarms or critical alarms may be determined according to the reasons and influence degrees of different alarms. In the present invention. Critical alarms are those equipment operating defects that cause problems with automated equipment and loops, resulting in loss of primary function, direct threat to safe operation and need immediate disposal. The serious alarm refers to the defect of equipment operation that the automatic equipment and the loop have problems, so that part of protection functions are lost or the performance is reduced, but the equipment can still operate in a short time and needs to be processed as soon as possible. The general alarm refers to equipment operation defects which are not affected by critical and serious alarms and do not directly affect the safe operation and power supply capacity of the equipment, the functions of the automatic device are not affected substantially, the quality is general and the degree is light, the influence on the safe operation is small, and the treatment can be suspended.
As shown in table 6, the data of each automation device acquired in step 1 and subjected to the labeling processing may be divided according to the relevant influencing factors in table 6, and after the division is completed, according to the content of each evaluation item and each evaluation sub-item, the score of the evaluation may be obtained according to the content of the data.
Specifically, according to the evaluation experience of the actual automation device, for example, the accuracy of the evaluation history index, or according to the evaluation requirement of the automation device, the evaluation score condition in table 6, even the evaluation item and the evaluation sub-item in table 6, or the weight factors of the evaluation item and the evaluation sub-item, may be modified, so as to obtain the results of different evaluation scores according to different requirements or evaluation accuracy.
According to the metadata obtained in the step 1 after the labeling treatment and the associated influence factors of the automation equipment obtained in the step 2, a comprehensive evaluation data warehouse of the automation equipment can be generated. The data warehouse may organize and store metadata, automation devices corresponding to the metadata, tags corresponding to the metadata, associated influencing factors corresponding to the metadata, and the like in a certain form. Correspondingly, slice data of a certain device, certain labels and a certain related influence factor can be quickly fetched from the data warehouse at any time according to different requirements of evaluation, operation, display and the like, and calculated and analyzed.
Preferably, step 2 further includes: step 2.1, collecting all metadata corresponding to the current automation equipment, and dividing or calculating all metadata according to the associated influence factors; step 2.2, calculating all the divided metadata based on the evaluation index and the evaluation standard corresponding to each associated influence factor to obtain the evaluation of the automation equipment; and 2.3, storing the evaluation of the automation equipment into a comprehensive evaluation data warehouse of the automation equipment.
Specifically, the current requirement to be evaluated is obtained, for example, a certain automation device is selected, and all metadata of the current automation device is called from the data warehouse. All metadata of this device is partitioned according to the associated impact criteria. For example, if the current automation device is a measurement and control device of an electric power system as described in table 1, the contents of device self-detection, abnormal operation alarm, device operation condition and the like of the device can be classified into a device defect condition. In addition, according to the communication condition of the device in the current period, the device communication rate of the current period of the communication rate of the device=the normal duration of the device communication in the current period/the current period duration is calculated.
After the division is completed, each evaluation item can be scored according to the evaluation mode of the evaluation item, and the final score is the evaluation of the automation equipment. The evaluation of the automation device may also be stored in a data warehouse as historical data for providing basis for subsequent evaluation steps.
Table 7 is an example of a data information table in the comprehensive evaluation data warehouse of the automated equipment obtained in the present invention. As shown in table 7, a comprehensive evaluation data warehouse of an automation device can be obtained based on the metadata obtained in step 1 and the associated influencing factors obtained in step 2. In the repository, not only the content of each item of rating, but also the type, identity, and whether the item of data is present in the real-time repository may be included. The evaluation item is the final calculation result of the metadata, has less content, high calling speed and can accurately represent the characteristics of the data, so that the evaluation item can be stored in a real-time library for quick calling, and the state of the equipment can be judged in real time.
Table 7 example data information table in comprehensive evaluation data warehouse of automation equipment
Specifically, from each content of the associated influence factors, data items such as the protection device id, the historical evaluation communication rate score, the historical evaluation defect score, the historical evaluation family defect score, the historical evaluation operation age score, the last period communication rate, the present period communication rate, and the like described in table 7 can be obtained.
The status scores in table 7, as a total score of all the relevant influencing factors, can be combined to reflect the status of one automation device.
Preferably, the related influence factors are taken as evaluation items, the current period data and the historical period data are taken as evaluation sub-items, and an analysis evaluation algorithm model is adopted to obtain the health condition evaluation of the automatic equipment; the calculation formula of the analysis and evaluation algorithm is as follows:
wherein S is a state score of health status evaluation of the automation equipment,
A j is the weight factor of the j-th evaluation item,
A k is the weight factor of the kth evaluation sub-term,
P k is the score of the kth evaluation sub-term,
M is the total number of evaluation items, and l is the total number of evaluation sub-items.
It will be appreciated that the score and weighting factor of each evaluation item, evaluation sub-item are multiplied and then summed to obtain a total evaluation score. Since the score of each data item is between 0 and 100 points and the total weight is 1, the total evaluation score obtained according to the formula is also between 0 and 100 points.
Table 7 also includes an online evaluation state quantity score and an online evaluation analog quantity score. The two data are alternative algorithms which can be adopted when the algorithm real-time degree in the invention is poor, can simply divide each item of data of the current device into analog quantity and state quantity, and adopts a simpler evaluation method to evaluate the equipment state. In addition, table 7 also includes a state grade, which can be obtained according to simple estimation of the state quantity and analog quantity, and the calculation method is quicker and the calculation process is reduced because the state grade is not obtained based on the state division obtained by complex calculation. On the other hand, the algorithms behind the status level and the status division are different, so that the status level and the status division are applicable to different health status monitoring requirements of different devices, and can be more accurately and widely applied to different profile status monitoring methods.
Specifically, the analog quantity of the current equipment is magnitude out-of-limit data, and the state quantity of the current equipment is alarm state data; and generating the health condition of the automatic equipment according to the communication state of the equipment, the alarm state data and the limit crossing data of the quantity value.
When the communication state of the equipment is normal, the alarm state data in the device are all alarm-free, and the magnitude out-of-limit data in the device are all in normal non-out-of-limit state, so that the health condition of the current device can be judged to be normal. When the communication state of the equipment is normal, the alarm state data in the device are all alarm-free, and the general out-of-limit state exists in the magnitude out-of-limit data in the device, so that the health condition of the current device can be judged to be attention. When the communication state of the equipment is normal, general alarm items exist in alarm state data in the device, for example, the equipment in table 1 has abnormal time setting state, but no serious alarm item exists, and then the health condition of the current device is judged to be in a serious out-of-limit state. When the communication state of the equipment is normal, the critical alarm item exists in the alarm state data in the device, for example, if power failure occurs in the table 1, the health condition of the current device is judged to be the critical alarm item. According to the above mode, the state of the device can be classified into four types of normal, general alarm, serious alarm and critical alarm.
In particular, data information tables in data warehouses of different contents can be generated in this step according to different evaluation requirements and various different actual requirements. For example, to achieve a faster evaluation, a data warehouse as shown in Table 8 may also be employed.
Table 8 yet another example data information table in a comprehensive evaluation data warehouse of an automation device
And step 3, filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, and acquiring the health condition evaluation of the automation equipment and the operation and maintenance decision of the automation equipment.
Preferably, the operation and maintenance decision of the automation device is determined based on the health evaluation of the automation device; the health evaluation of the automation device and the operation and maintenance decision of the automation device are displayed in a visual mode.
Fig. 3 is a schematic diagram of a visual display of health status evaluation and operation and maintenance decision of an automation device in an embodiment of a method for evaluating a status of an automation device for labeling a plurality of bins according to the present invention. Fig. 4 is a schematic diagram of a visual display of health status evaluation and operation and maintenance decision of an automation device according to another embodiment of an automated device status evaluation method for labeling several bins modeling in the present invention. As shown in fig. 3-4, the operational decision of the visualized automation device may be generated from the data warehouse.
Fig. 3 is a substation level evaluation result display screen, where the left side of the screen is a model tree, and a corresponding substation can be consulted or quickly searched, for example: lang Feng, the upper half of the right side of the picture is summary information, and the content comprises the number of serious devices, the number of abnormal devices, the number of attention devices, the number of normal devices and the number of unknown devices; the lower right half of the screen is detailed information, and the content includes the equipment name, the station, the health status, the evaluation time, and the like.
FIG. 4 is a device-level evaluation result display screen, the screen layout being divided into three parts, namely a left side, a middle and a right side, wherein the left side is the model tree as in FIG. 3, and the device level can be referred to; the middle upper half part is device information, and the middle lower half part is latest evaluation time selection and evaluation result information, including evaluation time, equipment health status, communication status and the like. The upper half part of the rightmost picture is equipment state information displayed in a mode of combining a circular disc graph and a curve graph, the lower half part of the rightmost picture is a detailed evaluation result, and the content comprises real-time analog quantity information, real-time state quantity information, historical analog quantity information and historical state quantity information.
The second aspect of the invention relates to an automatic equipment state evaluation system for labeling digital bin modeling, which comprises a label generating unit, a data evaluating unit and an analysis decision unit; the tag generation unit is used for collecting metadata of the power system and labeling the metadata based on the data type of the metadata; the data evaluation unit is used for evaluating metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model based on a preset association influence factor of the automation equipment, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation; and the analysis decision unit is used for filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, acquiring the health condition evaluation of the automation equipment and carrying out operation and maintenance decision of the automation equipment.
Compared with the prior art, the method and the system for evaluating the state of the automatic equipment for labeling the digital bin modeling have the advantages that the metadata of the power system can be labeled, the comprehensive evaluation data warehouse is generated for the labeled metadata by adopting the related influence factors, meanwhile, the health condition of the automatic equipment is evaluated based on the corresponding data in the data warehouse, and the operation and maintenance decision is generated. The technical scheme of the invention has the advantages of simple and quick algorithm, accurate result and good effect.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (7)
1. The automatic equipment state evaluation method for labeling several bins modeling is characterized by comprising the following steps:
Step 1, collecting metadata of power system automation equipment, and labeling the metadata based on the data type of the metadata; the automatic equipment at least comprises an electric power measurement and control device, an electric power system communication gateway, an electric power system monitoring host, an electric power system switch and an electric power system clock synchronization device;
Step 2, based on preset associated influence factors of the automation equipment, namely device communication rate, device defect condition, familial data and device operation years, taking the associated influence factors as evaluation items, taking current period data and historical period data as evaluation sub-items, evaluating metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation;
the device communication rate is obtained by dividing the normal communication duration of the device in the current period by the duration of the current period;
The defect condition and familial data of the device are obtained according to the alarm data;
the average operation period of the device operation period reference device is obtained after calculation;
the calculation formula of the analysis and evaluation algorithm model is as follows:
Wherein S is a state score of the health condition evaluation of the automation equipment,
A j is the weight factor of the j-th evaluation item,
A k is the weight factor of the kth evaluation sub-term,
P k is the score of the kth evaluation sub-term,
M is the total number of evaluation items, and l is the total number of evaluation sub-items;
step 3, filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, and acquiring the health condition evaluation of the automation equipment and the operation and maintenance decision of the automation equipment;
the filtering comprises the steps of calling slice data of a certain device, certain labels and a certain related influence factor from the comprehensive evaluation data warehouse according to different evaluation, calculation and display requirements, and calculating and analyzing.
2. An automated equipment state assessment method for labeling of a plurality of bins modeling as defined in claim 1, wherein:
the power system metadata includes equipment base data, inspection data, alarm attribute data, and historical case data of the automation equipment in the power system.
3. An automated equipment state assessment method for labeling of a plurality of bins modeling as defined in claim 1, wherein:
The data types of the metadata include: measuring out-of-limit data and alarm state data.
4. An automated equipment state assessment method for labeled digital camera modeling according to claim 3, wherein:
Tagging the metadata according to the data type of the metadata comprises:
Judging the degree of abnormality of the measurement threshold-crossing data according to a preset numerical range in which the measurement threshold-crossing data falls, and labeling the measurement threshold-crossing data according to the degree of abnormality; or alternatively
Labeling the alarm state data according to the alarm type of the alarm state data.
5. An automated equipment state assessment method for labeling of a plurality of bins modeling as defined in claim 1, wherein:
the step 2 further comprises:
Step 2.1, collecting all metadata corresponding to the current automation equipment, and dividing or calculating all metadata according to the association influence factors;
Step 2.2, calculating all the divided metadata based on the evaluation index and the evaluation standard corresponding to each associated influence factor to obtain the evaluation of the automation equipment;
And 2.3, storing the evaluation of the automation equipment into a comprehensive evaluation data warehouse of the automation equipment.
6. An automated equipment state assessment method for labeling of a plurality of bins modeling as defined in claim 1, wherein:
The operation and maintenance decision of the automation equipment is determined based on the health condition evaluation of the automation equipment;
the health evaluation of the automation equipment and the operation and maintenance decision of the automation equipment are displayed in a visual mode.
7. An automated equipment state assessment system for labeled digital cartridge modeling according to any of claims 1-6, characterized in that:
The system comprises a label generating unit, a data evaluating unit and an analysis decision unit; wherein,
The tag generation unit is used for collecting metadata of the power system and labeling the metadata based on the data type of the metadata;
The data evaluation unit is used for evaluating the metadata corresponding to the automation equipment by adopting an analysis evaluation algorithm model based on the association influence factors of the preset automation equipment, and establishing a comprehensive evaluation data warehouse of the automation equipment based on the evaluation;
And the analysis decision unit is used for filtering the corresponding information of the automation equipment in the comprehensive evaluation data warehouse, and acquiring the health condition evaluation of the automation equipment and the operation and maintenance decision of the automation equipment.
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