CN110569997A - charging station operation maintenance method based on multi-dimensional data system - Google Patents
charging station operation maintenance method based on multi-dimensional data system Download PDFInfo
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Abstract
The invention relates to a charging station operation maintenance method based on a multi-dimensional data system, wherein a vertical and horizontal data system comprises a power grid, a charging station, static marking data of a charging user and dynamic measurement data. And generating historical evaluation information and event evaluation information according to the model by combining historical fault data, historical operating data and fault event data of the charging facility equipment in a historical dimension and event dimension system. And inputting the data of mining calculation analysis into a fault discrimination model, and judging that the current equipment is in a state. If similar fault information exists in the event dimension database, the multi-dimensional data analysis can send out corresponding warning to an operation and maintenance team about the monitored fault or early warning event, and the operation and maintenance team is reminded to take a solution to the equipment quickly. If the multidimensional data analysis does not have similar information, the multidimensional data analysis updates original fault information and stores the original fault information in a historical database.
Description
Technical Field
The invention relates to the technical field of operation and maintenance of electric vehicle charging stations, in particular to a charging station operation and maintenance method based on a multi-dimensional data system.
background
Along with the development of electric vehicles, the construction of electric vehicle charging stations is gradually increased. And in the face of charging facilities with wide large-scale distribution and large quantity, huge pressure is brought to operation and maintenance personnel. The charging station comprises a plurality of devices, the devices are relatively new, and operation and maintenance personnel lack corresponding technical means. And the structure is complicated, the operation condition is complicated and changeable, the relation with the power grid is close, and the influence of the user use and the operation environment is large. At present, operation and maintenance mainly depend on manual work, and by experience, specific fault information is difficult to master afterwards, so that the fault handling time is too long, and the research on the operation and maintenance technology of charging facilities in an electric vehicle charging station is in a relatively blank state. A large amount of data are accumulated during operation of the charging station, problems existing in data analysis are found, targeted operation and maintenance are carried out, faults and fault characteristic data can be found quickly, and fault prediction can be carried out. The charging station data analysis can provide a powerful means for the operation and maintenance of the charging station. Therefore, how to implement multidimensional analysis of charging facility data in the electric vehicle charging station and efficient maintenance of charging facility faults becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The invention provides a multi-dimensional data analysis method based on the problems. On the one hand, data in the charging station are classified, and a basis is provided for further data analysis, on the other hand, a multi-dimensional data hierarchy system is formed, and the multi-dimensional data of the charging station are analyzed and stored, so that the maintenance efficiency of charging facilities is improved.
The technical problem of the invention is mainly solved by the following technical scheme:
A charging station operation maintenance method based on a multi-dimensional data system is characterized in that a longitudinal and transverse data system, a historical dimension and event dimension data system of an electric vehicle charging facility are established, data in a charging station are classified, multi-dimensional data of the charging station are analyzed and stored, and fault event early warning, fault event updating and fault solution recommendation are achieved.
The method comprises the following steps:
s1, establishing a longitudinal and transverse data system of the charging facility in the transformer substation;
s2, establishing a historical dimension data system of the charging facility in the transformer substation;
s3, establishing an event dimension data system of the charging facility in the transformer substation;
s4, constructing a multi-dimensional data system by combining the longitudinal and transverse data system, the historical dimension system and the event dimension system;
and S5, combining the multidimensional data system to realize early warning of fault events, updating of fault events and recommendation of fault solutions.
the longitudinal and transverse data system comprises a longitudinal data system and a transverse data system.
the vertical data system comprises: the system comprises power distribution network data, charging station data and charging user data, and is used for monitoring charging equipment data in real time.
In a horizontal data system, the power distribution network data comprises: electrical data and device data.
In the horizontal data system, the charging station data consists of three major parts, namely power distribution system data, charger data and charging station monitoring and management system data.
the charger data comprises: rectifier cabinet data, fill electric pile data, rifle data that charge.
In the horizontal data hierarchy, the charging user data includes: the charging station receives the interactive data of the battery and the vehicle model.
the establishing of the longitudinal and transverse data system comprises the following steps: and the horizontal and vertical system structure database is used as a platform for processing the data of the electric vehicle charging station, transmits the required dynamic data to the system after receiving the request, and then inputs the processed data into the fault judgment model to judge whether the current equipment is in a fault state.
the step S2 includes:
and obtaining a prediction score of the charging equipment to be analyzed according to the average data of the charging equipment to be analyzed and the reference charging equipment, and forming historical dimensional data analysis.
The average data of the charging equipment to be analyzed comprises: setting the average number of times of failure of the device to be analyzed before the time pointsetting the average number of overhauls of the equipment to be analyzed before the time pointSetting the average number of times of failure of the device to be analyzed between the time point and the current timesetting the average number of overhauled devices to be analyzed between the time point and the current time
the reference device charging device averaging data comprises: the number DF of faults of the reference charging equipment before the current time point and the number DJ of overhauled reference charging equipment before the current time point are obtained.
The set time is set to 30 days before the current time.
Obtaining a prediction score for a device to be analyzed includes:
Calculating a prediction score of a device to be analyzed by the following formula
Wherein alpha is a fault scoring coefficient and beta is a maintenance scoring coefficient.
Take alpha-2 and beta-1.
And if the prediction score W is larger than or equal to alpha multiplied by DF + beta multiplied by DJ, early warning is taken.
the step S3 includes: according to the severity (S) of the failure mode, the occurrence rate (O) of each fault and the detection difficulty (D) of each fault, calculating the product of the three indexes:
RPN＝S×O×D，
calculating a failure mode with the maximum risk;
The RPN represents the risk priority number, and is a parameter comprehensively weighing the fault severity, the fault occurrence rate and the detection difficulty.
In the step S4, in the above step,
The multi-dimensional data architecture includes charging facility information, historical fault data for individual devices, charging facility real-time operation data, charging facility historical operation data, solutions to charging facility device faults, and charging facility event evaluation information.
In the step S4:
And calculating a prediction score W and an event risk priority RPN of the analysis equipment by combining a historical data dimension and an event data dimension system, inputting data mined, calculated and analyzed by the longitudinal and transverse dimension data system into a fault discrimination model, and judging that the current equipment is in a state.
step S5 includes:
When the charging station breaks down, if similar fault information exists in the multidimensional data system, the multidimensional data system analysis can send out corresponding warning to an operation and maintenance team about the monitored fault or early warning event, and the operation and maintenance team is reminded to quickly take a solution to the equipment; if the multidimensional data system does not have similar information in analysis, updating the original fault information by the multidimensional data system analysis and storing the updated fault information in a historical data dimensional system; and then, the event dimension data system inputs the fault information into a fault discrimination model to divide the fault types, and sends corresponding fault solutions to an operation and maintenance team according to the fault types.
Therefore, compared with the prior art, the invention has the following advantages: 1. the charging station operation and maintenance team can complete the establishment and analysis of the charging station multi-dimensional data architecture, so that the demands of the operation and maintenance team can be responded timely and at low cost easily, and the analysis of the charging station multi-dimensional data architecture is not available at present. 2. The technical scheme of the invention is that the charging station multidimensional data system structure is maintained and managed from the equipment level, the operation maintenance scheme is optimized, and the popularization and the sharing of the field and the industry are easy to realize.
Drawings
FIG. 1 is a multi-dimensional data architecture within a charging station according to an embodiment of the present invention;
FIG. 2 is a vertical data architecture of an embodiment of the present invention;
FIG. 3 is a horizontal data architecture of an embodiment of the present invention;
FIG. 4 is a history dimension architecture of the present invention;
FIG. 5 is a table of a multi-dimensional data structure for a charging station;
Fig. 6 is a flowchart of a charging station operation maintenance method based on a multidimensional data architecture.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
Design of multidimensional data architecture
The multidimensional data system structure design block diagram is shown in fig. 1, the core of the maintenance of the electric vehicle charging station lies in the establishment of the multidimensional data system structure, and through the establishment of the charging station vertical and horizontal system, the vertical and horizontal system structure database stores the relevant operation data of the charging equipment, which is the basis for the system to judge and monitor the faults of the charging facility equipment in the charging station in real time. The reasonable design of the multidimensional data system structure can improve the efficiency of failure solution and plays an important role in improving the economic efficiency of the system. The multi-dimensional data architecture includes charging facility information, historical fault data for individual devices, charging facility real-time operation data, charging facility historical operation data, solutions to charging facility device faults, charging facility event evaluation information. Because the data structure of the electric vehicle charging station is complex, and the electric vehicle charging station has more different devices, and the operation data of each device is various, the multidimensional data system structure mainly researches the transverse dimension, the longitudinal dimension, the historical dimension and the event dimension in order to simplify and improve the analysis efficiency.
Design of multidimensional data architecture data table
the multidimensional data architecture data table is shown in FIG. 5.
The variable attributes contained in each data table are as follows:
Firstly, a horizontal and vertical dimension system structure data table.
And establishing the data according to a longitudinal and horizontal data architecture. The data inside the charging station are divided into static mark data and dynamic measurement data, the mark data are used as identifiers of data packets to be correlated with other data, and the dynamic measurement data are transmitted to the system to participate in fault judgment. And the external data are respectively bound into data packets by a power grid, a charging station and a charging user according to a transverse system structure and are recorded into a database of a longitudinal and transverse structure system.
the electric wire netting data can be followed charging station distribution network data reaction, and the distribution network data table includes:
Electrical data (charging station voltage, current, active power, reactive power, grid frequency, power factor)
② equipment data (metering monitoring equipment, transformer model, transformer three-phase winding temperature, main transformer switch cabinet state)
The charging station device data includes:
The above power distribution system data
charger data-
the charger data include:
rectifier cabinet data
(1) Electric quantity (incoming voltage and current, output voltage and current)
(2) state quantity (bus tie contactor state, emergency stop switch state, gate control switch state)
(3) Environmental quantity (rectifier cabinet temperature, rectifier module temperature)
② charging pile data
(1) electric quantity (charging pile output voltage and current)
(2) State quantity (contactor state, gun electromagnetic lock state, discharge contactor state, travel switch state, emergency stop switch state, charge confirmation)
(3) Environmental quantity (internal temperature of charger)
Third charging gun data
(1) Environmental quantity (charging gun head temperature)
(2) Mechanical quantity (insulation gun wire damage)
The charging user data includes:
Receiving interactive data of battery (battery number, battery type, battery capacity, output power, output voltage, output current, residual capacity, working time and battery temperature) by charging station
model of vehicle
And II, a historical dimension system structure data table.
The method comprises the steps of forecasting scores of equipment to be analyzed, historical fault data, historical maintenance conditions and solutions of common equipment faults. And thirdly, an event dimension system structure data table.
event severity score (S), event occurrence score (O), event detection difficulty score (D), event Risk Priority (RPN).
FIG. 1 is a schematic block diagram of a multi-dimensional data architecture according to an embodiment of the present invention. As shown in FIG. 1, a multi-dimensional data architecture 100 according to an embodiment of the invention includes: the longitudinal and transverse dimensions 102 are used for establishing a longitudinal and transverse data architecture of the charging station, and correspond to the specific structure of the charging facility in the charging station; historical dimension 104, which is used for establishing historical dimension data of the charging station according to the historical condition of the charging station; event dimension 106, which establishes event dimension data by studying and analyzing the consequences, severity, frequency of occurrence of faults, and likelihood of failure events of various failure modes. And acquiring and analyzing the analysis snapshots corresponding to the selected multiple dimensions respectively so as to realize the multi-dimensional data analysis of the data analysis object.
vertical and horizontal data architecture building
According to the technical scheme, the charging station longitudinal system and the charging station transverse system can be respectively established by taking the charging station as the center according to the relationship among the analysis objects. The establishment of a transverse and longitudinal data system of the charging station is the basis for real-time monitoring and fault judgment of the system on the equipment. The reasonable establishment of the longitudinal and transverse data system structure can improve the failure solving efficiency and plays an important role in improving the economic efficiency of the system.
As shown in fig. 2, a charging station longitudinal data system, in which a charging station is connected to a power grid and power is supplied by the power grid; connect electric automobile under the charging station, provide the energy for electric automobile. As shown in fig. 3, the charging station horizontal data hierarchy is a horizontal hierarchy in which the charging station data is composed of three major components, namely, power distribution system data, charger data, and charging station monitoring and management system data. The charger also comprises a rectifier cabinet, a charging pile and a charging gun.
example 1: the longitudinal and transverse data architecture provided by the invention is mainly used for providing data support for maintenance of the electric vehicle charging station, and the database of the transverse and longitudinal architecture of the electric vehicle charging station stores relevant operation data of the electric vehicle charging station, as shown in a transverse and longitudinal dimension architecture data table. The database is used as a platform for processing the data of the electric vehicle charging station, and access requests sent by the electric vehicle charging station maintenance system are received every 5 minutes. The landscape architecture database, upon receiving the request, will pass the required dynamic data to the system. The system then preprocesses the returned data, fills in missing values therein and converts the data structure. And then inputting the processed data into a fault discrimination model to judge whether the current equipment is in a fault state. And if the model judges that the abnormality exists, sending corresponding alarm to the user through the client for the monitored fault or early warning event, and reminding operation and maintenance personnel to quickly take a solution to the equipment.
historical dimension data architecture building
In the above technical solution, preferably, the analysis history dimension 104 includes: acquiring historical data; the reference device charging device data includes: the number DF of faults of the reference charging equipment before the current time point and the number DJ of overhauled reference charging equipment before the current time point are obtained.
Acquiring data of a device to be analyzed, wherein the data of the charging device to be analyzed comprises: the method comprises the steps of setting the number of times that the equipment to be analyzed fails before a time point, the number of times that the equipment to be analyzed is overhauled before the time point, the number of times that the charging equipment to be analyzed between the time point and the current time fails and the number of times that the equipment to be analyzed is overhauled between the current time.
calculating average data of the devices to be analyzed according to all the data of the charging devices to be analyzed, wherein the average data of the charging devices of the devices to be analyzed comprises the following steps: setting the average number of times of failure of the device to be analyzed before the time pointAnd the average number of overhauls of the equipment to be analyzed before the set time pointSetting the average number of times of failure of the device to be analyzed between the time point and the current timeAnd the average number of overhauls of the equipment to be analyzed between the present times
According to the technical scheme, the prediction score of the charging equipment to be analyzed is obtained according to the charging equipment data to be analyzed and the reference charging equipment average data, and historical dimensional data analysis is formed.
Alternatively, the set time may be day 30 prior to the current time.
Specifically, obtaining the prediction score of the device to be analyzed according to the device data to be analyzed and the reference device average data includes: calculating a predictive score for the device to be analyzed by the formula:
Wherein alpha is a fault scoring coefficient and beta is a maintenance scoring coefficient. Preferably, α ═ 2 and β ═ 1 are used. In this technical solution, specifically, the performing an early warning on the device according to the prediction score includes: if W is more than or equal to alpha multiplied by DF + beta multiplied by DJ, early warning is adopted.
According to the technical scheme, the relevance of the fault occurrence frequency and the overhaul frequency in different time periods is obtained according to the fault occurrence frequency and the overhaul frequency of the reference charging equipment in different time periods. And predicting the failure times and the overhaul times of the analyzed charging equipment according to the failure times and the overhaul times of the analyzed charging equipment in the known time period, further obtaining a prediction score according to the predicted failure times and overhaul times of the analyzed charging equipment in the future time period, and carrying out early warning according to the prediction score, thereby realizing high-accuracy analysis and early warning.
Event dimension data architecture build
in the above technical solution, preferably, the event dimension 106 establishes event dimension data by researching and analyzing consequences, severity, occurrence frequency of faults and possibility of failure events of various failure modes of the charging facility in the charging station.
the invention specifically comprises the following steps of establishing a charging facility event dimension data system in a charging station:
1. To investigate the event dimension data architecture of a charging station, first the directions that can characterize four potential failure effects of the charging facility of the charging station are defined:
Whether normal use is affected: the definition of normal usage includes main function charging, and edge functions such as billing, timing, etc. that interact directly with the user.
Whether it can be known by the user: whether the user can fill the screening of electric pile in advance through the App. The App should be ensured to report all problem charging piles in real time as much as possible.
Whether to cause damage to the user's property or safety: whether the user uses the problem to fill electric pile can threaten their property or security.
whether user satisfaction will be reduced: the degree to which the user's satisfaction is affected when the user finds a problem by himself.
2. By way of generalization, the severity (S) of failure modes is defined according to the degrees of these four directional problems, and the severity of each failure mode is divided into 5 levels.
3. By analyzing the underlying causes of the failure modes, a rating of the incidence (O) of each fault is defined based on the frequency of failure occurrences. The occurrence rate of each fault is divided into 5 levels.
4. the detection modes applied in practice at present have three types: and manual inspection, user reporting and system automatic reporting. On the premise that the system does not generate False Alarm (False Alarm) and Miss (Miss), the error detection difficulty reported by the system is defined as the lowest, the error detection difficulty which is not reported by the system and is extremely difficult to find manually is defined as the highest. And dividing the detection difficulty (D) of each fault according to the failure detection.
Table 1 event dimension data architecture scoring criteria:
5. as shown in FIG. 4, by combining three indexes of severity (S), incidence (O) and detection difficulty (D), the product of the three indexes is calculated to calculate the failure mode with the highest risk, wherein the Risk Priority Number (RPN) is a parameter comprehensively weighing the severity of the fault, the incidence of the fault and the detection difficulty. The method provides visual indication for maintenance and determines the direction for product debugging and optimization. And obtaining an event dimension data architecture analysis method. The formula is as follows:
RPN＝S×O×D
is a risk priority number; s is the severity of the failure mode, O is the occurrence rate of each fault, and D is the detection difficulty of each fault.
Charging station operation maintenance method flow based on multidimensional data system
the invention provides a charging station operation maintenance method based on a multi-dimensional data system. As shown in fig. 6, the data architecture is established in the longitudinal and transverse directions through the electric vehicle charging facility. The charging station longitudinal and transverse data are divided into static marking data and dynamic measurement data, the marking data are used as identifiers of data packets to be correlated with other data, the dynamic measurement data are transmitted to a system, then the system preprocesses the returned data, missing values in the returned data are filled, and a data structure is converted. And then inputting the processed data into a fault discrimination model to judge whether the current equipment is in a fault state. The proposed history dimension and event dimension system is combined. And fault event early warning and fault event updating are realized.
A prediction score and an event Risk Priority Number (RPN) of the device to be analyzed are calculated. And combines historical fault data, historical maintenance conditions, and solutions for common equipment failures for electric vehicle charging facilities. The historical fault data comprises the possible positions of the abnormity of the electric automobile charging facility, data indexes and fault occurrence time. The historical dimension database compares the real-time data with the existing fault information in the historical database, and if similar fault information exists in the database, the historical database sends the solution of the fault information to an operation and maintenance team, so that the operation and maintenance team can maintain the equipment in a targeted manner. If the historical database does not have similar information, the event database updates the original fault information and stores the fault information into the historical database. And then the event database inputs the fault information into a fault discrimination model to divide the fault types, and sends corresponding fault solutions to an operation and maintenance team according to the fault types.
the basic fault discrimination model in the event database is initially provided by the equipment manufacturer vendor and stored in the event database for fault discrimination of fault information uploaded by the owner. The distinguishing effect of the model can be evaluated by operation and maintenance professionals, and the event database can update the model according to the evaluation information of the professionals, so that a better distinguishing effect is achieved.
And updating the fault solution in the event database according to the feedback of the operation and maintenance team. After receiving the failure solution, the operation and maintenance team may also share the failure specific information with the failure solution if the operation and maintenance team successfully maintains the device. If the operation and maintenance team encounters similar faults, the operation and maintenance team can quickly maintain the operation and maintenance team by inquiring the event database.
the technical scheme of the invention is described in detail in the above with reference to the attached drawings, and can be realized through the technical scheme of the invention:
The technical scheme of the invention is that a charging station operation and maintenance team can complete the establishment and analysis of a charging station multi-dimensional data system structure, so that the requirements of the operation and maintenance team can be responded with low cost in time; and there is no multidimensional data architecture analysis for charging stations today.
the technical scheme of the invention is that the charging station multidimensional data system structure is maintained and managed from the equipment level, the operation maintenance scheme is optimized, and the popularization and the sharing of the field and the industry are easy to realize.
while the best mode for carrying out the invention has been described in detail and illustrated in the accompanying drawings, it is to be understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the scope of the invention should be determined by the appended claims and any changes or modifications which fall within the true spirit and scope of the invention should be construed as broadly described herein.
Claims (20)
1. A charging station operation maintenance method based on a multi-dimensional data system is characterized by comprising the following steps: the charging station internal data are classified by establishing a charging station longitudinal and transverse data system, a historical dimension data system and an event dimension data system, and multi-dimension data of the charging station are analyzed and stored, so that fault event early warning, fault event updating and fault solution recommendation are realized.
2. method according to claim 1, characterized in that it comprises the following steps:
S1, establishing a charging station longitudinal and transverse data system;
S2, establishing a charging station historical dimension data system;
S3, establishing a charging station event dimension data system;
S4, constructing a multi-dimensional data system by combining the longitudinal and transverse data system, the historical dimension system and the event dimension system;
And S5, combining the multidimensional data system to realize early warning of fault events, updating of fault events and recommendation of fault solutions.
3. The method of claim 1 or 2, wherein the vertical and horizontal data systems comprise a vertical data hierarchy and a horizontal data hierarchy.
4. the method of claim 3, wherein the vertical data hierarchy comprises: the system comprises power distribution network data, charging station data and charging user data, and is used for monitoring charging equipment data in real time.
5. the method of claim 4,
In a horizontal data system, the power distribution network data comprises: electrical data and device data.
6. The method of claim 4,
in the horizontal data system, the charging station data consists of three major parts, namely power distribution system data, charger data and charging station monitoring and management system data.
7. The method of claim 5,
The charger data comprises: rectifier cabinet data, fill electric pile data, rifle data that charge.
8. the method of claim 4,
In the horizontal data hierarchy, the charging user data includes: the charging station receives the interactive data of the battery and the vehicle model.
9. The method of claim 2,
The horizontal and vertical architecture establishes a horizontal and vertical architecture database which is used as a platform for processing the data of the electric vehicle charging station, the horizontal and vertical architecture database transmits required dynamic data to a system after receiving a request, and then the processed data is input into a fault discrimination model to judge whether the current equipment is in a fault state.
10. The method of claim 2, wherein: the step S2 includes:
And acquiring historical data, and acquiring a prediction score of the charging equipment to be analyzed according to the average data of the charging equipment to be analyzed and the reference charging equipment to form historical dimensional data analysis.
11. The method of claim 10,
The average data of the charging equipment to be analyzed comprises: setting the average number of times of failure of the device to be analyzed before the time pointSetting the average number of overhauls of the equipment to be analyzed before the time pointSetting the average number of times of failure of the device to be analyzed between the time point and the current timesetting the average number of overhauled devices to be analyzed between the time point and the current time
12. The method of claim 11,
The reference device charging device averaging data comprises: the number DF of faults of the reference charging equipment before the current time point and the number DJ of overhauled reference charging equipment before the current time point are obtained.
13. the method of claim 11 or 12,
the set time is set to 30 days before the current time.
14. The method of claim 13,
Obtaining a prediction score for a device to be analyzed includes:
Calculating a prediction score of a device to be analyzed by the following formula
Wherein alpha is a fault scoring coefficient and beta is a maintenance scoring coefficient.
15. The method of claim 14,
take alpha-2 and beta-1.
16. The method of claim 14 or 15,
And if the prediction score W is larger than or equal to alpha multiplied by DF + beta multiplied by DJ, early warning is taken.
17. The method of claim 2,
The step S3 includes: acquiring the severity S of the failure mode of the charging station facility, the occurrence rate O of each fault and the detection difficulty D of each fault, and calculating the product of the three indexes:
RPN＝S×O×D，
Calculating a failure mode with the maximum risk;
The RPN represents the risk priority number, and is a parameter comprehensively weighing the fault severity, the fault occurrence rate and the detection difficulty.
18. the method of claim 17,
In the step S4, in the above step,
The multi-dimensional data architecture includes charging facility information, historical fault data for individual devices, charging facility real-time operation data, charging facility historical operation data, solutions to charging facility device faults, and charging facility event evaluation information.
19. The method of claim 18,
In the step S4:
And calculating a prediction score W and an event risk priority RPN of the analysis equipment by combining a historical data dimension and an event data dimension system, inputting data mined, calculated and analyzed by the longitudinal and transverse dimension data system into a fault discrimination model, and judging that the current equipment is in a state.
20. The method as set forth in claim 19, wherein:
Step S5 includes:
When the charging station breaks down, if similar fault information exists in the multidimensional data system, the multidimensional data system analysis can send out corresponding warning to an operation and maintenance team about the monitored fault or early warning event, and the operation and maintenance team is reminded to take a solution to the equipment; if the multidimensional data system does not have similar information, the multidimensional data system analyzes and updates the original fault information and stores the updated fault information into a historical data dimensional system; and then, the event dimension data system inputs the fault information into a fault discrimination model to divide the fault types, and sends corresponding fault solutions to an operation and maintenance team according to the fault types.
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| CN116579762B (en) * | 2023-04-14 | 2023-10-20 | 广州林旺空调工程有限公司 | Intelligent operation and maintenance platform for cooling tower |
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Application publication date: 20191213 |