CN116522156A - Equipment state data analysis system and method based on energy management platform - Google Patents
Equipment state data analysis system and method based on energy management platform Download PDFInfo
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Abstract
The invention relates to the technical field of equipment state data management, in particular to an equipment state data analysis system and method based on an energy management platform. And adaptively adjusting the related early warning threshold in the early warning grading judging process by combining the judging data generated in each early warning grading judging process with the early warning data generated in the corresponding real-time monitoring data acquisition period, and outputting the early warning grading judging result of the next equipment state monitoring period.
Description
Technical Field
The invention relates to the technical field of equipment state data management, in particular to an equipment state data analysis system and method based on an energy management platform.
Background
The energy is the basis and the power of the improvement of the civilization of the human beings, is vital to the national life and the national security, relates to the survival and the development of the human beings, and is vital to the promotion of the development of the economy and the society and the improvement of the welfare of people. The energy equipment is related to energy development, energy use and energy conservation, and the running state of the energy equipment is directly related to the life and property safety of people and the ecological environment safety.
Therefore, possible faults of the energy equipment can be early warned in advance, and it is important to reduce losses caused by the faults of the energy equipment. However, in the prior art, the historical data of the energy equipment needs to be manually summarized to correct the early warning judgment threshold, and because the early warning threshold cannot be flexibly adjusted according to the actual running condition, early warning missing report and error early warning are generated on the energy equipment which runs normally, the early warning data of the actual running condition of the energy equipment cannot be reflected, and the judgment of equipment operation and maintenance personnel on the equipment running condition is not facilitated.
Disclosure of Invention
The invention aims to provide a system and a method for analyzing equipment state data based on an energy management platform, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an equipment state data analysis method based on an energy management platform, the method comprises the following steps:
step S100: collecting historical maintenance records of target equipment, constructing a maintenance database of the target equipment, collecting historical monitoring data of sensors for monitoring various operation and maintenance parameters of the target equipment, and constructing a first historical operation and maintenance database of the target equipment, wherein each monitoring sensor is used for correspondingly monitoring the operation and maintenance parameters of one item of target equipment;
step S200: setting a real-time monitoring data acquisition period T 2 Collecting and screening real-time monitoring data of the sensor to obtain corresponding real-time monitoring data collecting periods T 2 Is a real-time monitoring dataset of (1);
step S300: the data characteristics extracted from each real-time monitoring data set are compared with the data characteristics extracted from the corresponding second historical monitoring database in similarity to respectively obtain corresponding real-time monitoring data acquisition periods T 2 Data feature similarity comparison results of (2);
step S400: will correspond to each real-time monitoring data acquisition period T 2 Data characteristic similarity comparison result of (1) and different early warning threshold expansion are combined for each real-time monitoring data acquisition period T 2 Early warning grading judgment of (2);
step S500: combining judgment data generated in each early warning grading judgment process with corresponding real-time monitoring data acquisition period T 2 The early warning data generated in the early warning step judgment process is used for adaptively adjusting related early warning thresholds in the early warning step judgment process;
step S600: the adjusted related early warning threshold is used for the early warning grading judgment process of the next equipment state monitoring period, and the early warning grading judgment result of the next equipment state monitoring period is output.
Further, step S100 includes:
step S101: extracting historical maintenance records in the first historical operation and data database, extracting record generation time T corresponding to each historical maintenance record, and collecting a historical monitoring data collection period T before the record generation time T 1 In the system, monitoring the running state monitoring data of various devices presented by the energy source equipment of the target monitoring sensor;
step S102: collecting all collected operation state monitoring data of all equipment to obtain a first historical operation data base C 1 ，C 1 ＝{X 1 ，X 2 ，X 3 ，……，X N (wherein X is 1 ，X 2 ，X 3 ，……，X N Respectively expressed in time period T 1 And the historical state operation monitoring data sequences corresponding to the 1 st, 2 nd, 3 rd, … … th and N th operation and maintenance parameter items are provided.
Further, step S200 includes:
step S201: setting a real-time operation and maintenance parameter acquisition period T 2 Acquisition T 2 Running monitoring data corresponding to the real-time states of each operation and maintenance parameter in a time period, and respectively arranging each item of real-time monitoring data according to the acquisition time to obtain N first real-time monitoring data sequences;
step S202: at each of respectivelyIn the first real-time monitoring data sequence, two adjacent state operation monitoring data are sequentially selected and recorded as g j And g j+1 The condition 1 is more than or equal to j and less than or equal to M-1, wherein M represents the total quantity of state operation monitoring data in each first real-time monitoring data sequence;
step S203: record g j And g j+1 The acquisition time interval of (1) is delta T, and g is recorded j And g j+1 The absolute value of the difference is deltae; setting a decision threshold P, when the condition is satisfiedIn the middle, wherein->When discarding g j+1 ；
Step S204: respectively collecting state operation monitoring data reserved after data screening of each first real-time monitoring data sequence to obtain a second real-time monitoring data sequence corresponding to each first real-time monitoring data sequence;
through screening the real-time monitoring data, the data with the change range smaller than a certain threshold in the time interval is removed, the calculation amount of the similarity between the real-time monitoring data and the historical detection data calculated by the system is reduced, the time for obtaining the similarity comparison result is shortened, and the comparison speed in the subsequent steps is improved.
Further, step S300 includes:
step S301: extracting data features of the second real-time monitoring data sequence of each operation and maintenance parameter item and data features of the corresponding historical state operation monitoring data sequence respectively, setting the data features of the second real-time monitoring data sequence of each operation and maintenance parameter item as S, setting the data features of the historical state operation monitoring data sequence of the second real-time monitoring data sequence of each operation and maintenance parameter item as S', and based on a formula: f ' =max (S, S ')/min (S, S ') to obtain the similarity F ' of S and S ';
further, the smaller the similarity value F ' of S and S ' is, the larger the similarity degree of the two groups of data is, and the larger the value F ' is, the smaller the similarity degree of the two groups of data is;
step S302: respectively collecting the similarity F' obtained after the similarity comparison of N operation and maintenance parameter items to obtain corresponding real-time monitoring data acquisition periods T 2 Data feature similarity set v= { V 1 ，v 2 ，v 3 ，……，v N }, where v 1 ，v 2 ，v 3 ，……，v N Respectively represent the acquisition period T of each real-time monitoring data 2 Corresponding to the similarity of the 1 st, 2 nd, 3 nd, … … th and N operation and maintenance parameter items.
Further, step S400 includes:
step S401: setting alarm threshold F for each operation and maintenance parameter item 1 And a threshold of interest F 2 Satisfy F 2 ＞F 1 Will monitor the data acquisition period T from each real time 2 The similarity result of all the collected operation and maintenance parameters is judged,
preferably, a single-side F test value with 95% confidence is selected as an alarm threshold F 1 ；
Step S402: for each real-time monitoring data acquisition period T 2 Is higher than the data alarm threshold F in the data feature similarity set V 1 Alarming the operation data items of the equipment;
step S403: for each real-time monitoring data acquisition period T 2 Is higher than the data focus threshold F in the data feature similarity set V of (2) 2 And (3) carrying out frequency statistics on the operation and maintenance data items.
Further, the system adjusts the attention threshold F by the correction value beta 2 The specific correction method is as follows:
step S501: recording a plurality of real-time monitoring data acquisition periods T 2 The number of times of over-alarming of one operation and maintenance parameter is an alarming frequency A, and the similarity of the characteristics of the occurrence data is smaller than a data concern threshold F 2 The number of times of (a) is the frequency of interest B;
step S502: the correction value β is the proportion of a to B, i.e., β=a/b×100%;
step S503: setting a decision threshold rho, theta and satisfying the barPart 0 is more than 2 rho is more than or equal to 2 theta, theta is more than or equal to 1, and when rho is more than or equal to beta and less than or equal to theta, the existing F is maintained 2 Unchanged, the correction step S504 is performed when β is less than or equal to ρ, and the correction step S505 is performed when β > θ;
step S504: when beta is less than or equal to rho, F 2 Is a correction value F of (2) 2 * Is that
Step S505: when beta > theta, F 2 Is a correction value F of (2) 2 * Is that
Step S506: executing a plurality of real-time monitoring data acquisition periods T 2 Is used for calculating a correction value F after judgment 2 * Correction value F 2 * Substitute for corresponding F 2 The value is used as a decision threshold of the operation and maintenance parameter attention threshold;
the relevant early warning threshold is adjusted according to the generated early warning data, the early warning rule can be flexibly adjusted according to the proportion of the number of alarms which are actually generated in the running process of the equipment and exceed the first early warning threshold to the number of alarms of the first early warning threshold, and the early warning content can be enabled to accord with the actual running condition of the equipment.
Further, the system comprises a historical monitoring data acquisition module, a real-time monitoring data acquisition and screening module, a similarity comparison module and an early warning grading judgment module, wherein the historical monitoring data acquisition module is used for acquiring historical monitoring data of sensors for monitoring various operation and maintenance parameters of target equipment, the real-time monitoring data acquisition and screening module is used for acquiring and screening the real-time monitoring data of the sensors, the similarity comparison module is used for comparing the similarity of data features extracted from all real-time monitoring data sets with data features extracted from corresponding second historical monitoring databases, and the early warning grading judgment module is used for comparing the similarity of the data features and combining different early warning threshold expansion for each real-time monitoring data acquisition period T 2 Early warning and grading judgment.
Further, the real-time monitoring data acquisition and screening module comprises a monitoring data acquisition and sorting unit and a monitoring data selection unit, wherein the monitoring data acquisition and sorting unit is used for sequentially arranging each item of real-time monitoring data according to acquisition time, and the monitoring data selection unit is used for selecting the real-time monitoring data meeting judgment requirements.
Further, the early warning grading judging module comprises a numerical value judging unit, a data statistics unit and a threshold value correcting unit, wherein the numerical value judging unit is used for judging a threshold value section where the similarity degree value is located, the data statistics unit is used for counting the number of judging results in the threshold value section, and the threshold value correcting unit is used for correcting the threshold value.
Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of comparing the similarity between monitoring of the operation parameters of the energy equipment and data before historical fault record, early warning the fault type of the energy equipment, counting the frequency of the energy equipment approaching to the fault state in one period, setting the equipment with high frequency approaching to the fault state to pay attention to, and optimizing an early warning threshold by analyzing early warning frequency change, so that early warning content is flexibly adjusted according to the actual operation condition of the energy equipment.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a device status data analysis system based on an energy management platform according to the present invention;
fig. 2 is a schematic flow chart of a device state data analysis method based on an energy management platform.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
step S100: collecting historical maintenance records of target equipment, constructing a maintenance database of the target equipment, collecting historical monitoring data of sensors for monitoring various operation and maintenance parameters of the target equipment, and constructing a first historical operation and maintenance database of the target equipment; wherein, each monitoring sensor correspondingly monitors the operation and maintenance parameters of a project label device;
wherein, step S100 includes:
step S101: extracting historical maintenance records in the first historical operation and data database, extracting record generation time T corresponding to each historical maintenance record, and collecting a historical monitoring data collection period T before the record generation time T 1 In the system, monitoring the running state monitoring data of various devices presented by the energy source equipment of the target monitoring sensor;
step S102: collecting all collected operation state monitoring data of all equipment to obtain a first historical operation data base C 1 ，C 1 ＝{X 1 ，X 2 ，X 3 ，……，X N (wherein X is 1 ，X 2 ，X 3 ，……，X N Respectively expressed in time period T 1 And the historical state operation monitoring data sequences corresponding to the 1 st, 2 nd, 3 rd, … … th and N th operation and maintenance parameter items are provided.
Step S200: setting a real-time monitoring data acquisition period T 2 Collecting real-time monitoring data of a sensorScreeningObtaining the corresponding real-time monitoring data acquisition period T 2 Is a real-time monitoring dataset of (1);
wherein, step S200 includes:
step S201: setting a real-time operation and maintenance parameter acquisition period T 2 Acquisition T 2 Running the monitoring data corresponding to the real-time state of each operation and maintenance parameter in the time period, and respectively arranging each item of real-time monitoring data according to the acquisition time to obtainN first real-time monitoring data sequences;
step S202: respectively selecting two adjacent state operation monitoring data in each first real-time monitoring data sequence in sequence and marking the two adjacent state operation monitoring data as g j And g j+1 The condition 1 is more than or equal to j and less than or equal to M-1, wherein M represents the total quantity of state operation monitoring data in each first real-time monitoring data sequence;
step S203: record g j And g j+1 The acquisition time interval of (1) is delta T, and g is recorded j And g j+1 The absolute value of the difference is deltae; setting a decision threshold P, when the condition is satisfiedIn the middle, wherein->When discarding g j+1 ；
For example g 1 ＝25.5、g 2 ＝28.3、g 3 ＝28.2，g 1 ，g 2 Acquisition time interval DeltaT 1 1 unit time g 2 ，g 3 Acquisition time interval DeltaT 2 The real-time operation and maintenance parameter acquisition period is 50 units of time, 10 real-time monitoring data are acquired in 50 units of time,so keep g 2 ，/> Therefore discard g 3 ；
Step S204: and respectively collecting state operation monitoring data reserved after data screening is carried out on each first real-time monitoring data sequence to obtain a second real-time monitoring data sequence corresponding to each first real-time monitoring data sequence.
Step S300: will be extracted from each real-time monitoring datasetThe data features are subjected to similarity comparison with the data features extracted from the corresponding second historical monitoring database; respectively obtaining corresponding real-time monitoring data acquisition periods T 2 Data feature similarity comparison results of (2);
wherein, step S300 includes:
step S301: extracting data features of the second real-time monitoring data sequence of each operation and maintenance parameter item and data features of the corresponding historical state operation monitoring data sequence respectively, setting the data features of the second real-time monitoring data sequence of each operation and maintenance parameter item as S, setting the data features of the historical state operation monitoring data sequence of the second real-time monitoring data sequence of each operation and maintenance parameter item as S', and based on a formula: f ' =max (S, S ')/min (S, S ') to obtain the similarity F ' of S and S ';
step S302: respectively collecting the similarity F' obtained after the similarity comparison of N operation and maintenance parameter items to obtain corresponding real-time monitoring data acquisition periods T 2 Data feature similarity set v= { V 1 ，v 2 ，v 3 ，……，v N }, where v 1 ，v 2 ，v 3 ，……，v N Respectively represent the acquisition period T of each real-time monitoring data 2 Corresponding to the similarity of the 1 st, 2 nd, 3 nd, … … th and N operation and maintenance parameter items.
Step S400: will correspond to each real-time monitoring data acquisition period T 2 Data characteristic similarity comparison result of (1) and different early warning threshold expansion are combined for each real-time monitoring data acquisition period T 2 Early warning grading judgment of (2);
wherein, step S400 includes:
step S401: setting alarm threshold F for each operation and maintenance parameter item 1 And a threshold of interest F 2 Satisfy F 2 ＞F 1 Will monitor the data acquisition period T from each real time 2 Judging the similarity results of all the collected operation and maintenance parameters;
step S402: for each real-time monitoring data acquisition period T 2 Is higher than the data alarm threshold in the data feature similarity set VValue F 1 Alarming the operation data items of the equipment;
step S403: for each real-time monitoring data acquisition period T 2 Is higher than the data focus threshold F in the data feature similarity set V of (2) 2 And (3) carrying out frequency statistics on the operation and maintenance data items.
Step S500: combining judgment data generated in each early warning grading judgment process with corresponding real-time monitoring data acquisition period T 2 The early warning data generated in the early warning step judgment process is used for adaptively adjusting related early warning thresholds in the early warning step judgment process;
wherein the system adjusts the attention threshold F by the correction value beta 2 The specific correction method is as follows:
step S501: recording a plurality of real-time monitoring data acquisition periods T 2 The number of times that one operation and maintenance parameter has an over-alarming is an alarming frequency A, and the number of times that the similarity of the characteristics of the data is smaller than a data concern threshold F2 is a concern frequency B;
step S502: the correction value β is the proportion of a to B, i.e., β=a/b×100%;
step S503: setting decision threshold rho and theta, satisfying condition 0 < 2rho < theta < 1 < 2θ, and maintaining the existing F when rho < beta < θ 2 Unchanged, the correction step S504 is performed when β is less than or equal to ρ, and the correction step S505 is performed when β > θ;
step S504: when beta is less than or equal to rho, F 2 Is a correction value F of (2) 2 * Is that
Step S505: when beta > theta, F 2 Is a correction value F of (2) 2 * Is that
Step S506: executing a plurality of real-time monitoring data acquisition periods T 2 Is used for calculating a correction value F after judgment 2 * Correction value F 2 * Substitute for corresponding F 2 The value is used as a decision threshold of the operation and maintenance parameter attention threshold;
step S600: the adjusted related early warning threshold is used for the early warning grading judgment process of the next equipment state monitoring period, and the early warning grading judgment result of the next equipment state monitoring period is output;
for example, alarm threshold F 1 =5 and a threshold of interest F 2 12, the number of times of alarm occurrence of one operation and maintenance parameter of a plurality of real-time monitoring data acquisition periods is alarm frequency A=6, the number of times of occurrence of data characteristic similarity smaller than the data attention threshold F2 is attention frequency B=8, decision threshold ρ=25%, θ=60%, β=6/8×100% =75% > θ, F 2 * =15.5 replacement of the attention threshold F in the early warning hierarchical judgment process for the next device state monitoring period 2 =12 as the correlation early warning threshold.
The system comprises a historical monitoring data acquisition module, a real-time monitoring data acquisition and screening module, a similarity comparison module and an early warning grading judgment module, wherein the historical monitoring data acquisition module is used for acquiring historical monitoring data of sensors for monitoring various operation and maintenance parameters of target equipment, the real-time monitoring data acquisition and screening module is used for acquiring and screening the real-time monitoring data of the sensors, the similarity comparison module is used for comparing the similarity of data features extracted from all real-time monitoring data sets with data features extracted from corresponding second historical monitoring databases, and the early warning grading judgment module is used for expanding all real-time monitoring data acquisition periods T according to the similarity comparison result of the data features and combining different early warning thresholds 2 Early warning and grading judgment.
The real-time monitoring data acquisition and screening module comprises a monitoring data acquisition and sorting unit and a monitoring data selection unit, wherein the monitoring data acquisition and sorting unit is used for sequentially arranging each item of real-time monitoring data according to acquisition time, and the monitoring data selection unit is used for selecting the real-time monitoring data meeting judgment requirements.
The early warning grading judging module comprises a numerical value judging unit, a data statistics unit and a threshold value correcting unit, wherein the numerical value judging unit is used for judging a threshold value section where the similarity degree value is located, the data statistics unit is used for counting the number of judging results in the threshold value section, and the threshold value correcting unit is used for correcting the threshold value.
Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of comparing the similarity between monitoring of the operation parameters of the energy equipment and data before historical fault record, early warning the fault type of the energy equipment, counting the frequency of the energy equipment approaching to the fault state in one period, setting the equipment with high frequency approaching to the fault state to pay attention to, and optimizing an early warning threshold by analyzing early warning frequency change, so that early warning content is flexibly adjusted according to the actual operation condition of the energy equipment.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An equipment state data analysis method based on an energy management platform is characterized in that: the method comprises the following steps:
step S100: collecting historical maintenance records of target equipment, constructing a maintenance database of the target equipment, collecting historical monitoring data of sensors for monitoring various operation and maintenance parameters of the target equipment, and constructing a first historical operation and maintenance database of the target equipment; wherein, each monitoring sensor correspondingly monitors the operation and maintenance parameters of a project label device;
step S200: setting a real-time monitoring data acquisition period T 2 Collecting and screening real-time monitoring data of the sensor to obtain corresponding real-time monitoring data collecting periods T 2 Is a real-time monitoring dataset of (1);
step S300: similarity comparison is carried out on the data characteristics extracted from the real-time monitoring data sets and the data characteristics extracted from the corresponding second historical monitoring databases; respectively obtaining corresponding real-time monitoring data acquisition periods T 2 Data feature similarity comparison results of (2);
step S400: will correspond to each real-time monitoring data acquisition period T 2 Data characteristic similarity comparison result of (1) and different early warning threshold expansion are combined for each real-time monitoring data acquisition period T 2 Early warning grading judgment of (2);
step S500: combining judgment data generated in each early warning grading judgment process with corresponding real-time monitoring data acquisition period T 2 The early warning data generated in the early warning step judgment process is used for adaptively adjusting related early warning thresholds in the early warning step judgment process;
step S600: the adjusted related early warning threshold is used for the early warning grading judgment process of the next equipment state monitoring period, and the early warning grading judgment result of the next equipment state monitoring period is output.
2. The method for analyzing device status data based on an energy management platform according to claim 1, wherein: the step S100 includes:
step S101: extracting historical maintenance records in the first historical operation and data database, extracting record generation time T corresponding to each historical maintenance record, and collecting a historical monitoring data collection period T before the record generation time T 1 In the system, monitoring the running state monitoring data of various devices presented by the energy source equipment of the target monitoring sensor;
step S102: collecting all collected operation state monitoring data of all equipment to obtain a first historical operation data base C 1 ，C 1 ＝{X 1 ，X 2 ，X 3 ，……，X N (wherein X is 1 ，X 2 ，X 3 ，……，X N Respectively expressed in time period T 1 And the historical state operation monitoring data sequences corresponding to the 1 st, 2 nd, 3 rd, … … th and N th operation and maintenance parameter items are provided.
3. The method for analyzing device status data based on an energy management platform according to claim 1, wherein: the step S200 includes:
step S201: setting a real-time operation and maintenance parameter acquisition period T 2 Acquisition T 2 Running monitoring data corresponding to the real-time states of each operation and maintenance parameter in a time period, and respectively arranging each item of real-time monitoring data according to the acquisition time to obtain N first real-time monitoring data sequences;
step S202: respectively selecting two adjacent state operation monitoring data in each first real-time monitoring data sequence in sequence and marking the two adjacent state operation monitoring data as g j And g j+1 The condition that j is more than or equal to 1 and less than or equal to M-1 is satisfied; wherein M represents the total amount of state operation monitoring data in each first real-time monitoring data sequence;
step S203: record g j And g j+1 The acquisition time interval of (1) is delta T, and g is recorded j And g j+1 The absolute value of the difference is deltae; setting a decision threshold P, when the condition is satisfiedIn the middle, wherein->When discarding g j+1 ；
Step S204: and respectively collecting state operation monitoring data reserved after data screening is carried out on each first real-time monitoring data sequence to obtain a second real-time monitoring data sequence corresponding to each first real-time monitoring data sequence.
4. The method for analyzing device status data based on an energy management platform according to claim 1, wherein: the step S300 includes:
step S301: extracting data characteristics of a second real-time monitoring data sequence of each operation and maintenance parameter item and data characteristics of a corresponding historical state operation monitoring data sequence respectively, setting the data characteristics of the second real-time monitoring data sequence of each operation and maintenance parameter item as S, and setting the data characteristics of the historical state operation monitoring data sequence of the second real-time monitoring data sequence of each operation and maintenance parameter item as S'; based on the formula: f ' =max (S, S ')/min (S, S ') to obtain the similarity F ' of S and S ';
step S302: respectively collecting the similarity F' obtained after the similarity comparison of N operation and maintenance parameter items to obtain corresponding real-time monitoring data acquisition periods T 2 Data feature similarity set v= { V 1 ，v 2 ，v 3 ，……，v N }, where v 1 ，v 2 ，v 3 ，……，v N Respectively represent the acquisition period T of each real-time monitoring data 2 Corresponding to the similarity of the 1 st, 2 nd, 3 nd, … … th and N operation and maintenance parameter items.
5. The method for analyzing device status data based on an energy management platform according to claim 1, wherein: the step S400 includes:
step S401: setting alarm threshold F for each operation and maintenance parameter item 1 Closing deviceThreshold F 2 Satisfy F 2 ＞F 1 Will monitor the data acquisition period T from each real time 2 Judging the similarity results of all the collected operation and maintenance parameters;
step S402: for each real-time monitoring data acquisition period T 2 Is higher than the data alarm threshold F in the data feature similarity set V 1 Alarming the operation data items of the equipment;
step S403: for each real-time monitoring data acquisition period T 2 Is higher than the data focus threshold F in the data feature similarity set V of (2) 2 And (3) carrying out frequency statistics on the operation and maintenance data items.
6. The method for analyzing device status data based on an energy management platform according to claim 5, wherein: the system adjusts the attention threshold F by the correction value beta 2 The specific correction method is as follows:
step S501: recording a plurality of real-time monitoring data acquisition periods T 2 The number of times that one operation and maintenance parameter has an over-alarming is an alarming frequency A, and the number of times that the similarity of the characteristics of the data is smaller than a data concern threshold F2 is a concern frequency B;
step S502: the correction value β is the proportion of a to B, i.e., β=a/b×100%;
step S503: setting decision threshold rho and theta, satisfying condition 0 < 2rho < theta < 1 < 2θ, and maintaining the existing F when rho < beta < θ 2 Unchanged, the correction step S504 is performed when β is less than or equal to ρ, and the correction step S505 is performed when β > θ;
step S504: when beta is less than or equal to rho, F 2 Is a correction value F of (2) 2 * Is that
Step S505: when beta > theta, F 2 Is a correction value F of (2) 2 * Is that
Step S506: executing a plurality of real-time monitoring data acquisition periods T 2 Is used for calculating a correction value F after judgment 2 * Correction value F 2 * Substitute for corresponding F 2 The value is used as a decision threshold for the operation and maintenance parameter attention threshold.
7. The equipment state data analysis system for implementing the equipment state data analysis method based on the energy management platform according to any one of claims 1-6, wherein the system comprises a historical monitoring data acquisition module, a real-time monitoring data acquisition and screening module, a similarity comparison module and an early warning classification judgment module, the historical monitoring data acquisition module is used for acquiring historical monitoring data of sensors for monitoring various operation and maintenance parameters of target equipment, the real-time monitoring data acquisition and screening module is used for acquiring and screening real-time monitoring data of the sensors, the similarity comparison module is used for comparing data features extracted from all real-time monitoring data sets with data features extracted from corresponding second historical monitoring databases, and the early warning classification judgment module is used for expanding each real-time monitoring data acquisition period T according to the similarity comparison result of the data features and combining different early warning thresholds 2 Early warning and grading judgment.
8. The device state data analysis system of claim 7, wherein the real-time monitoring data acquisition and screening module comprises a monitoring data acquisition and sorting unit and a monitoring data selection unit, the monitoring data acquisition and sorting unit is used for sequentially arranging each item of real-time monitoring data according to acquisition time, and the monitoring data selection unit is used for selecting the real-time monitoring data meeting the judgment requirement.
9. The system according to claim 7, wherein the early warning classification judging module includes a value judging unit for judging a threshold section in which the similarity degree value is located, a data counting unit for counting the number of judgment results in the threshold section, and a threshold correcting unit for correcting the threshold.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117192269A (en) * | 2023-09-20 | 2023-12-08 | 珠海高新区铭越科技有限公司 | Big data monitoring and early warning system and method for electric room environment control box |
| CN117314244A (en) * | 2023-10-07 | 2023-12-29 | 中节能（石家庄）环保能源有限公司 | Process flow data supervision system and method based on data analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117192269A (en) * | 2023-09-20 | 2023-12-08 | 珠海高新区铭越科技有限公司 | Big data monitoring and early warning system and method for electric room environment control box |
| CN117192269B (en) * | 2023-09-20 | 2024-04-05 | 珠海高新区铭越科技有限公司 | Big data monitoring and early warning system and method for electric room environment control box |
| CN117314244A (en) * | 2023-10-07 | 2023-12-29 | 中节能（石家庄）环保能源有限公司 | Process flow data supervision system and method based on data analysis |
| CN117314244B (en) * | 2023-10-07 | 2024-03-19 | 中节能（石家庄）环保能源有限公司 | Process flow data supervision system and method based on data analysis |
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