CN117933708A - Material data early warning analysis method and system based on big data distribution - Google Patents
Material data early warning analysis method and system based on big data distribution Download PDFInfo
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Abstract
The invention relates to a big data distributed material data early warning analysis method and a big data distributed material data early warning analysis system, which are mainly used for data identification analysis and predictive warning in a big data distributed material management scene in a power grid. The number of devices is continuously increased in the current power grid environment, and the traditional device management mode has a plurality of problems including complex application requirements, inadaptation to data acquisition of a distributed environment and difficult processing of mass device information. The invention provides a system comprising material equipment, a material warehouse management terminal, a distributed data embedded point agent, a service scheduling server, an Hbase database and a material risk management server, wherein the system realizes risk early warning of materials through learning and training of a unified data acquisition service interface, the distributed data embedded point agent and a material risk model. The system operation flow comprises the steps of data acquisition, storage, model training, risk assessment and the like, and can adapt to complex power grid environments and material management requirements.
Description
Technical Field
The invention belongs to the field of computer data identification and data prediction representation, and particularly relates to data identification analysis and prediction alarm under a big data distributed material management scene.
Background
In recent years, more and more devices are put into operation in a power grid environment, the warehouse entry, the warehouse exit and the damage conditions of the devices are usually simply carried out according to the flow, and if the abnormal conditions of the devices caused by various factors in the equipment operation are required to be well analyzed and early warned, a plurality of problems exist:
firstly, the application requirements of power grid equipment are complex, the application scenes are various, the purposes and properties of the equipment are different in the distributed environment, the equipment cannot be collected uniformly during data collection, and even if the equipment is adapted through an intermediate interface, the cost is too high.
Secondly, in the existing data acquisition, analysis and early warning modes, the terminal equipment is required to be invaded to install a sensor or directly add an informationized reporting interface, the former is not suitable for a material management scene of a distributed environment of a power grid, the latter is large in workload, the latter cannot be implemented in certain terminals which cannot be added with secondary development, and in a whole, the mode of directly implementing modification at the terminals is not suitable for the actual material management requirement of the distributed environment of the power grid.
Moreover, the cost for classifying management and classifying index monitoring is high when processing the information of the mass equipment, on one hand, each type of classifying label is required to be configured, and on the other hand, various early warning indexes are required to be configured, so that the operation management difficulty is high.
Therefore, how to realize simple and efficient material management risk early warning aiming at material management characteristics in a power grid environment is a technical problem which is more prominent at present.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a big data distributed material data early warning analysis system, which comprises material equipment, a material warehouse management terminal, a distributed data embedded point agent, a service scheduling server, an Hbase database and a material risk management server, and is characterized in that the system mainly comprises the following operation flows:
Step 1: the distributed data embedded point agent provides a unified data acquisition service interface, each material warehouse management terminal device in the power grid distributed environment calls the data acquisition service interface to report the operation information of the material device to the distributed data embedded point agent, the operation information is acquired in real time according to a preset index, and the distributed data embedded point agent sends the data information to the service scheduling server; wherein, the preset index comprises: material number, material description, warehouse-in time and current state;
Step 2: the service scheduling server stores the received data information into the Hbase database; the material risk management server side obtains a material risk model through learning and training in advance according to historical data stored in the Hbase database; the historical data is subjected to learning training, including supervised learning aiming at the material type by taking a material number and a material description index as samples, and unsupervised learning aiming at risk scoring by taking the warehousing time and the warehousing state index as samples and combining with preset evaluation parameters; wherein, the preset evaluation parameter Q is a risk factor corresponding to the current state, t is the current time, and t , is the warehousing time;
Step 3: and the service scheduling server acquires the latest data information according to a preset period, matches the acquired data information based on the material risk model to obtain a preset material management type, scores the risk of the data information based on the material management type, and performs risk early warning according to the risk scoring result.
Further, the material number is a device serial number, the material description includes a material name, the time of storage is the last time of storage of the material, and the current state includes: warehousing, in-operation, reporting and repairing, in-maintenance, damage and loss, and corresponding risk factors are respectively as follows: 0.1, 2, 2.5, 3, 3.5.
Furthermore, the service scheduling server provides a management channel, and the risk factors can be adjusted as required.
Further, the data acquisition service interface comprises a direct calling mode and a callback processing mode.
Further, the material warehouse management terminal device invoking the data acquisition service interface to report the operation information of the material device to the distributed data embedded point proxy includes: and when the operation state of the material equipment is changed, the material warehouse management terminal equipment extracts the material equipment information and the change state information and calls the data acquisition service interface to implement information reporting.
Further, the material warehouse management terminal device invoking the data acquisition service interface to report the operation information of the material device to the distributed data embedded point proxy includes: and pasting a link two-dimensional code reflecting the preset index information on the material equipment, wherein when the operation state of the material equipment is changed, the material warehouse management terminal equipment reports the two-dimensional code link and the change state information to the distributed data embedded point agent through the data acquisition service interface, and the distributed data embedded point agent accesses the two-dimensional code link through the callback interface and analyzes the access content to obtain the preset index information and then sends the preset index information to the service scheduling service end.
Further, the historical data in the step 2 is the operation information of the time interval from the last 1 month to the last month of the year stored in the Hbase database, and if the current time is 1 month, the operation information of the time interval from the last year to the last year is the operation information of the time interval.
Further, the material risk management server updates a material risk model through learning training according to the historical data stored in the Hbase database on the 1 st day of each month.
Further, the preset period in the step3 is 1 hour.
The application also relates to a material data early warning analysis method based on big data distribution, which is applied to the material data early warning analysis system based on big data distribution, wherein the business scheduling server forms a material operation health status report according to risk early warning by day.
The beneficial technical effects of the invention include:
Firstly, the application takes the indexes of 'material number, material description, warehouse-in time and current state' as the acquisition information, and the information basically covers all the properties of the operational materials and is easy to acquire.
And secondly, the application integrates and reports the basic material information by combining the distributed data embedded point agent through the characteristic of greater flexibility of material warehouse management terminal equipment development, is convenient to process, and has very small invasiveness to the material management situation of the existing power grid distributed environment.
And moreover, the Hbase database is used for storing basic information of materials in a massive distributed environment, classification and early warning output of the materials are realized through a trained material risk model, complex configuration is not needed, and the method is suitable for early warning requirements for material management characteristics in a power grid environment.
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Fig. 1: the system according to an embodiment of the invention constitutes a frame diagram.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
The embodiment of the invention provides a big data distributed material data early warning analysis system, as shown in fig. 1, comprising material equipment, a material warehouse management terminal, a distributed data embedded point agent, a service scheduling server, an Hbase database and a material risk management server, wherein the system is characterized in that the main operation flow comprises:
Step 1: the distributed data embedded point agent provides a unified data acquisition service interface, each material warehouse management terminal device in the power grid distributed environment calls the data acquisition service interface to report the operation information of the material device to the distributed data embedded point agent, the operation information is acquired in real time according to a preset index, and the distributed data embedded point agent sends the data information to the service scheduling server; wherein, the preset index comprises: material number, material description, warehouse entry time and current state.
Preferably, the material number is a device serial number, the material description includes a material name, the time of storage is the last time of storage of the material, and the current state includes: warehouse entry, in operation, in repair, maintenance, damage and loss.
Optionally, the data acquisition service interface includes a direct calling mode and a callback processing mode.
When the direct calling mode is adopted, the material warehouse management terminal equipment calls the data acquisition service interface to report the operation information of the material equipment to the distributed data embedded point proxy, and the operation information comprises the following steps: and when the operation state of the material equipment is changed, the material warehouse management terminal equipment extracts the material equipment information and the change state information and calls the data acquisition service interface to implement information reporting.
Preferably, taking a county power supply office as an example of a notebook computer going out to follow engineering treatment due to the requirement of distribution network fault treatment in a certain day, the county power supply office material warehouse management terminal calls an API interface DeviceStateNotice of the distributed data burial point agent, and interface main body information is as follows:
Wherein Mode is reporting Mode, id is material number, desc is material description, instorageTime is warehouse-in time, curState is current state. The Mode and CurState parameters are enumerated values, mode=1 indicates that the current direct call Mode is the current callback Mode, and mode=2 indicates that the current callback Mode is the current callback Mode; curState in warehouse entry, in shipment, in repair, in maintenance, in damage and loss enumeration values can be set to 1, 2, 3, 4, 5, 6, respectively.
When the two-dimension code link for describing the material information is available in the material equipment or the linked two-dimension code reflecting the preset index information can be attached to the newly-stored equipment, a callback processing mode can be adopted, namely, when the operation state of the material equipment is changed, the material warehouse management terminal equipment reports the two-dimension code link and the change state information to the distributed data embedded point agent through the data acquisition service interface, and the distributed data embedded point agent accesses the two-dimension code link through the callback interface and analyzes the access content to obtain the preset index information and then sends the preset index information to the service scheduling service end.
Preferably, the callback processing Mode is consistent with the direct calling Mode interface main structure, but at the moment, mode=2, id is null, desc is a two-dimensional code link address, instorageTime and CurState are actual effective values, at the moment, the data embedded point proxy accesses the two-dimensional code link address, analyzes according to the accessed content to obtain effective Id and Desc field information, and sends the effective Id and Desc field information to the service scheduling server.
Step 2: the service scheduling server stores the received data information into the Hbase database; the material risk management server side obtains a material risk model through learning and training in advance according to historical data stored in the Hbase database; the historical data is subjected to learning training, including supervised learning aiming at the material type by taking a material number and a material description index as samples, and unsupervised learning aiming at risk scoring by taking the warehousing time and the warehousing state index as samples and combining with preset evaluation parameters; wherein, the preset evaluation parameterQ is a risk factor corresponding to the current state, t is the current time, and t , is the warehousing time;
Preferably, the corresponding risk factors are respectively: 0.5, 1, 2, 2.5, 3, 3.5.
Forming a classification model based on supervised machine learning, combining a plurality of existing classification label samples, and forming a scoring prediction model based on unsupervised machine learning, combining influencing factor parameters and evaluation parameters, are conventional means in machine learning, and specific implementation components are not further limited herein, and all embodiments capable of achieving expected classification and evaluation effects are listed herein.
According to the application, the warehouse-in time is the last warehouse-in time of the materials, and the recycling of the material equipment is considered.
Hbase is a Hadoop Distributed File System (HDFS) based column storage non-relational database that can store vast amounts of sparse data and provide efficient random read and write capabilities. Therefore, the method has obvious advantages in the aspects of scheduling data writing of the service scheduling server side or reading of training sample data by the material risk management server side.
Preferably, the historical data is the operation information of the time interval from the last 1 month to the last month of the year stored in the Hbase database, and if the current operation information is 1 month, the operation information is the operation information of the time interval from the last year to the last year.
And the material risk management server updates a material risk model through learning training according to the historical data stored in the Hbase database on the 1 st day of each month so as to realize dynamic adjustment of a risk early warning basic strategy.
It can be understood that the more the material state deviates from the conventional, the longer the warehouse-in time, the higher the risk score of the material risk model, and the total feature reflected by the material equipment type attribute and the material equipment mass data can be combined when the material risk model is calculated according to the real-time value prediction.
Step 3: and the service scheduling server acquires the latest data information according to a preset period, matches the acquired data information based on the material risk model to obtain a preset material management type, scores the risk of the data information based on the material management type, and performs risk early warning according to the risk scoring result.
Preferably, the preset period is 1 hour based on the early warning requirement characteristics of material management of the distributed environment of the power grid.
In addition, the application also provides a material data early warning analysis method based on big data distribution, which is applied to the material data early warning analysis system based on big data distribution, wherein the business scheduling server forms a material operation health status report according to risk early warning by day.
The service dispatching server side can be a rear end support for material management risk early warning, so that a management and control system facing operators is formed. The service scheduling server can draw topology hot spots aiming at massive material state information, and realize the whole-flow support from distributed data layout display, acquisition analysis and early warning of material management.
Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The foregoing description of the preferred embodiments of the present invention has been presented for purposes of clarity and understanding, and is not intended to limit the invention to the particular embodiments disclosed, but is intended to cover all modifications, alternatives, and improvements within the spirit and scope of the invention as outlined by the appended claims.
Claims (10)
1. The utility model provides a material data early warning analysis system based on big data distribution formula, includes material equipment, material bin management terminal, distributed data buries some agency, business scheduling service end, hbase database and material risk management service end, its characterized in that, the main operational flow of system includes:
Step 1: the distributed data embedded point agent provides a unified data acquisition service interface, each material warehouse management terminal device in the power grid distributed environment calls the data acquisition service interface to report the operation information of the material device to the distributed data embedded point agent, the operation information is acquired in real time according to a preset index, and the distributed data embedded point agent sends the data information to the service scheduling server; wherein, the preset index comprises: material number, material description, warehouse-in time and current state;
Step 2: the service scheduling server stores the received data information into the Hbase database; the material risk management server side obtains a material risk model through learning and training in advance according to historical data stored in the Hbase database; the historical data is subjected to learning training, including supervised learning aiming at the material type by taking a material number and a material description index as samples, and unsupervised learning aiming at risk scoring by taking the warehousing time and the warehousing state index as samples and combining with preset evaluation parameters; wherein, the preset evaluation parameter Q is a risk factor corresponding to the current state, t is the current time, and t , is the warehousing time;
Step 3: and the service scheduling server acquires the latest data information according to a preset period, matches the acquired data information based on the material risk model to obtain a preset material management type, scores the risk of the data information based on the material management type, and performs risk early warning according to the risk scoring result.
2. The big data distributed material data early warning analysis system according to claim 1, wherein the material number is a device serial number, the material description includes a material name, the time of entry is the last time of entry of the material, and the current state includes: warehousing, in-operation, reporting and repairing, in-maintenance, damage and loss, and corresponding risk factors are respectively as follows: 0.1, 2, 2.5, 3, 3.5.
3. The big data distributed material data based early warning analysis system according to claim 2, wherein the business scheduling server provides a management channel, and the risk factors can be adjusted as required.
4. The big data distributed material data based early warning analysis system according to claim 1, wherein the data acquisition service interface comprises a direct calling mode and a callback processing mode.
5. The big data distributed material data early warning analysis system according to claim 1, wherein the material warehouse management terminal device invoking the data acquisition service interface to report the operation information of the material device to the distributed data burial point agent comprises: and when the operation state of the material equipment is changed, the material warehouse management terminal equipment extracts the material equipment information and the change state information and calls the data acquisition service interface to implement information reporting.
6. The big data distributed material data early warning analysis system according to claim 1, wherein the material warehouse management terminal device invoking the data acquisition service interface to report the operation information of the material device to the distributed data burial point agent comprises: and pasting a link two-dimensional code reflecting the preset index information on the material equipment, wherein when the operation state of the material equipment is changed, the material warehouse management terminal equipment reports the two-dimensional code link and the change state information to the distributed data embedded point agent through the data acquisition service interface, and the distributed data embedded point agent accesses the two-dimensional code link through the callback interface and analyzes the access content to obtain the preset index information and then sends the preset index information to the service scheduling service end.
7. The distributed material data early warning analysis system according to claim 1, wherein the historical data in the step 2 is the operation information of the time interval from the last 1 month to the present month stored in the Hbase database, and if the current time is 1 month, the operation information is the operation information of the time interval from the last year to the last year.
8. The big data distributed material data based early warning analysis system according to claim 1, wherein the material risk management server updates a material risk model through learning training on day 1 of each month according to the historical data stored in the Hbase database.
9. The system for pre-warning and analyzing the material data based on the big data distribution type according to claim 1, wherein the preset period in the step 3 is 1 hour.
10. The big data distributed-based material data early warning analysis method is applied to the big data distributed-based material data early warning analysis system according to any one of claims 1-9, and is characterized in that a business scheduling server forms a material operation health status report according to risk early warning according to days.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106327127A (en) * | 2016-08-26 | 2017-01-11 | 国网山东省电力公司滨州市滨城区供电公司 | Power distribution equipment material warehousing management and monitoring system |
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN114971501A (en) * | 2022-07-29 | 2022-08-30 | 国网天津市电力公司物资公司 | A characteristic analysis-based monitoring and analysis system for power materials in and out of storage |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327127A (en) * | 2016-08-26 | 2017-01-11 | 国网山东省电力公司滨州市滨城区供电公司 | Power distribution equipment material warehousing management and monitoring system |
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN114971501A (en) * | 2022-07-29 | 2022-08-30 | 国网天津市电力公司物资公司 | A characteristic analysis-based monitoring and analysis system for power materials in and out of storage |
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