CN113313352A - Safety monitoring method for hydrogen station, electronic equipment and storage medium - Google Patents
Safety monitoring method for hydrogen station, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a safety monitoring method for a hydrogenation station, which comprises the following steps: determining a current scene of a hydrogen station; acquiring real-time data corresponding to a current scene; filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data; inputting effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene; and carrying out safety monitoring on the hydrogenation station based on the monitoring index. By applying the safety monitoring scheme of the hydrogen station, the data mining of the multi-dimensional collected data of the hydrogen station can be performed by utilizing a big data processing algorithm, the limitations of the type and the data structure of the collected data in the traditional data mining are effectively solved, corresponding real-time data are collected aiming at each use scene, and the monitoring index prediction is performed based on the machine learning model, so that the safety monitoring of the hydrogen station is realized, the efficiency of data analysis on the collected data is improved, and the prediction result of the machine learning model obtained by training is more accurate.
Description
Technical Field
The invention relates to the technical field of hydrogen station monitoring, in particular to a hydrogen station safety monitoring method, electronic equipment and a storage medium.
Background
Data mining is the process of automatically discovering useful information in large databases. In the traditional data mining, the collected data is of a single type, and the data structure is usually a relational data structure. However, in the hydrogen station monitoring system, the collected data is usually diversified for a plurality of station control devices and a plurality of application scenarios such as hydrogen production, hydrogen transportation, hydrogen sale, and the like, and the data structure includes a semi-structural data structure and a non-structural data structure in addition to a relational data structure. Therefore, the traditional data mining method is difficult to adapt to a complex hydrogen station monitoring system and perform multi-dimensional data mining on the acquired data.
Therefore, it is desirable to provide a safety monitoring scheme for a hydrogen refueling station, so as to utilize the collected data to perform safe production, transportation and precise marketing on the hydrogen refueling station.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the safety monitoring scheme for the hydrogen station is provided, so that the hydrogen station is safely produced, transported and accurately marketed by using collected data.
In order to solve the technical problem, the invention provides a safety monitoring method for a hydrogen refueling station, which comprises the following steps:
determining a current scene of a hydrogen station;
acquiring real-time data corresponding to the current scene;
filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data;
inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene;
and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
Optionally, the determining the current scenario of the hydrogen refueling station includes:
and determining the current scene according to the equipment state parameters of the hydrogen station.
Optionally, the scene of the hydrogen station includes one or more of the following: hydrogen production scenes, hydrogen transportation scenes, hydrogen storage scenes and hydrogen sale scenes.
Optionally, the acquiring real-time data corresponding to the current scene includes:
determining target equipment corresponding to the current scene;
and collecting monitoring parameters of the target equipment.
Optionally, the acquiring the monitoring parameter of the target device includes:
and acquiring the pressure value and the temperature value of the target equipment in real time.
Optionally, the filtering out invalid data in the real-time data to obtain valid data includes:
and carrying out data extraction, cleaning, conversion and loading processes on the real-time data to obtain processed effective data.
Optionally, the method further comprises: and constructing a data warehouse of the valid data based on a third paradigm rule.
Optionally, the machine learning model is a monitoring index obtained by training based on multi-dimensional historical collected data and used for predicting the current scene, and the multi-dimensional historical collected data at least includes data in the data warehouse and data extracted from log files.
Optionally, the multi-dimensional historical collected data is obtained based on collected data of a hydrogenation machine, a hydrogen storage tank, a compressor, a vehicle-connected platform, an infrared correlation and an air discharge column.
Optionally, the method further comprises: and generating a data report of the hydrogen filling station based on the monitoring index.
Optionally, further comprising: and judging whether an alarm condition is met or not based on the numerical relation between the monitoring index and a set value, and triggering an alarm if the alarm condition is met.
Optionally, when the current scene is a hydrogen transportation scene, the acquiring real-time data corresponding to the current scene includes:
and collecting vehicle running data, current position information and weather data of the hydrogen transport vehicle.
Optionally, the method further comprises: acquiring hydrogen energy operation data of the hydrogen generation station based on the machine learning model;
and determining the hydrogen production amount of the hydrogen station according to the hydrogen energy operation data.
In order to solve the above technical problem, the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.
To solve the above technical problem, the present invention provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor implements the above method.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
by applying the safety monitoring scheme of the hydrogen station, the data mining of the multidimensional collected data of the hydrogen station can be performed by utilizing a big data processing algorithm, the limitations of the type and the data structure of the collected data in the traditional data mining are effectively solved, the corresponding real-time data can be collected aiming at each use scene in the safety monitoring process of the hydrogen station, and the monitoring index prediction of the collected real-time data is performed based on the machine learning model, so that the safety monitoring of the hydrogen station is realized, the efficiency of data analysis of the collected data is effectively improved based on the parallel processing mode of the big data, and the invalid data is removed from the collected real-time data, so that the prediction result of the machine learning model obtained by training is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a first flow chart of a method for monitoring the safety of a hydrogen refueling station according to an embodiment of the present invention;
FIG. 2 is a block diagram of a safety monitoring system for a hydrogen refueling station according to an embodiment of the present invention;
FIG. 3 is a second flowchart of a method for monitoring the safety of a hydrogen refueling station according to an embodiment of the present invention;
FIG. 4 is a third flowchart of a safety monitoring method for a hydrogen refueling station according to an embodiment of the present invention;
FIG. 5 is a fourth flowchart of a safety monitoring method for a hydrogen refueling station according to an embodiment of the present invention;
FIG. 6 is a fifth flowchart of a safety monitoring method for a hydrogen refueling station according to an embodiment of the present invention;
fig. 7 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Data mining is an important way to find useful information from large amounts of data. In the prior art, useful information is often found by adopting a traditional data mining mode, but the type of data collected by the traditional data mining is single, the data structure is usually a relational data structure, and the processing of program codes or algorithms is often inclined. The traditional parallel database technology focuses more on high consistency and fault tolerance, and according to CAP theory, the usability and expansibility of traditional data mining are difficult to guarantee.
Data sources in a big data environment are usually very rich and data types are various, the amount of data to be stored, analyzed and mined is huge, the requirement on data presentation is high, and the requirement on the efficiency and the availability of data processing is also high. In the hydrogen station monitoring system, for a plurality of station control devices and a plurality of application scenarios such as hydrogen production, hydrogen transportation, hydrogen sale and the like, the acquired data are diversified, multi-dimensional data processing is required, and the data structure comprises a semi-structure data structure and a non-structure data structure besides a relational data structure.
Obviously, the traditional data mining method is difficult to adapt to a complex hydrogen station monitoring system and perform multidimensional data mining on the collected data. Therefore, in order to provide a safety monitoring scheme for a hydrogen refueling station to achieve the purpose of utilizing collected data to perform safe production, transportation and accurate marketing on the hydrogen refueling station, the embodiment of the invention provides a safety monitoring method for the hydrogen refueling station.
The hydrogen refueling station safety monitoring method provided by the embodiment of the invention is explained by combining with the figure 1 and the figure 2. As shown in fig. 1, a first flowchart of a hydrogen refueling station safety monitoring method provided in an embodiment of the present invention may include the following steps:
step S101: and determining the current scene of the hydrogen filling station.
In an actual hydrogen station monitoring system, a plurality or plurality of devices may be generally included, and may include, for example, a hydrogen engine, a hydrogen storage tank, a compressor, an infrared correlation, a gas discharge column, an in-vehicle platform, and the like. For convenience of description, the plurality of application scenarios may be generally divided according to different operation contents of the hydrogen refueling station, and it is understood that the types or the numbers of the devices corresponding to the application scenarios are different, and related parameters of the devices are also different.
In one implementation, the scene of the hydrogen station may include one or more of the following: hydrogen production scenes, hydrogen transportation scenes, hydrogen storage scenes and hydrogen sale scenes.
For different application scenarios, the state parameters of the corresponding devices are different, and therefore, the current scenario can be determined according to the device state parameters of the hydrogen filling station.
The equipment state parameters of the hydrogen station may include an equipment name, an equipment number, an equipment type, an equipment grade, an equipment manufacturer, an equipment process type, factory time, a hydrogen station number, and the like.
In practical application, a person skilled in the art may first determine each currently operating device, further obtain a state parameter of each device, and then determine an application scenario of the hydrogen station based on the current state parameter of each device. It should be noted that, a person skilled in the art needs to preset a range of the state parameter of the device corresponding to each application scenario, so that the corresponding scenario can be determined according to the corresponding relationship.
Step S102: and acquiring real-time data corresponding to the current scene.
In one example, when the current scene is a hydrogen transportation scene, the real-time data corresponding to the current scene may include vehicle travel data, current position information, and weather data of the hydrogen transportation vehicle. It should be noted that, the above example is only a specific implementation manner of the present invention, and for a hydrogen transportation scenario, the corresponding real-time data needs to be set by a person skilled in the art according to a specific device, and is not limited to the above data.
In addition, the present invention does not limit the real-time data corresponding to each application scenario, and those skilled in the art need to set the real-time data according to the specific situation of the station control device of the hydrogen filling station. In addition, the acquired real-time data can also comprise vehicle networking data, hydrogen storage tank data and hydrogen selling data besides the equipment state parameters.
The vehicle networking data can be vehicle registration information, a unique vehicle identification code, positioning information, accumulated travel mileage, a vehicle running state, a charging state and the like; the data of the hydrogen storage tank can be hydrogen storage amount, hydrogen storage time, pressure of the hydrogen storage tank, temperature of the hydrogen storage tank, inlet and outlet pressure of a compressor and running time of the compressor; the hydrogen selling data specifically includes equipment number, starting time, ending time, hydrogenation duration, hydrogenation amount, hydrogenation unit price, hydrogenation amount, card number, license plate number, payment mode, hydrogenation state, subsidy unit price, subsidy total price and the like. It should be noted that the specific content of the real-time data listed above is only an example, and is not a limitation to the present invention, and those skilled in the art need to make specific settings according to the specific devices in the station control detection system of the hydrogen refueling station. According to the invention, a large amount of invalid data can be filtered through the current scene, so that the calculation amount of data processing can be reduced, and the data calculation speed can be improved.
In an implementation manner, the target device corresponding to the current scene may be determined first, and then the monitoring parameters of the target device may be collected. In addition, the present invention does not limit the specific status parameters of the target device, for example, the monitoring parameters of the target device may include: and acquiring the pressure value and the temperature value of the target equipment in real time.
Step S103: and filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data.
In the monitoring process of the hydrogen station, the acquired real-time data is not all valid data, and the valid data refers to data capable of reflecting monitoring information of the station control equipment of the hydrogen station. By the steps, invalid data can be filtered from the collected real-time data, so that the data processing amount is reduced, and the speed of data processing and calculation is increased.
In one implementation, the processed valid data may be obtained by performing data extraction, cleaning, conversion, and loading processes on the real-time data. Specifically, the processing can be performed by using an ELT (Extract-Transform-Load) tool provided in the prior art, and common ETL tools include five types, i.e., a SEDWA, a button, an oracle golden gate, an informatics, and a talent, and real-time data can be transmitted from the acquisition end to the destination end through the ETL tool.
In one embodiment, the data processing speed can be increased by a parallel processing technique of MapReduce, which is a distributed computing framework that processes massive data sets at the upper TB level in parallel in a reliable and fault-tolerant manner. The MapReduce realizes the parallel processing of large data through a large number of cheap servers, has low requirement on data consistency, has the outstanding advantages of expansibility and usability, and is particularly suitable for the mixed processing of massive structured, semi-structured and unstructured data. MapReduce can perform distributed processing on traditional query, decomposition and data analysis, distribute processing tasks to different processing nodes and have stronger parallel processing capacity.
Step S104: and inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene.
In an embodiment of the present invention, the machine learning model is a monitoring index obtained by training based on multi-dimensional historical collected data and used for predicting the current scene, where the multi-dimensional historical collected data at least includes data in the data warehouse and data extracted from log files.
Specifically, the multi-dimensional historical collected data is obtained based on collected data of a hydrogenation machine, a hydrogen storage tank, a compressor, a vehicle-connected platform, an infrared correlation and an air discharge column. It should be noted that, the specific description in step S102 may be referred to for the collected data of each device, and details are not described here.
As shown in fig. 2, the machine learning model obtained by pre-training may be constructed based on a database of data sources, a real-time data stream, and a real-time file, and the data sources for constructing the machine learning model are not limited in the embodiment of the present invention, and may be determined by those skilled in the art according to the specific situation in the practical application.
In one implementation, the machine learning model may be constructed according to the following expression:
where p is the number of autoregressive terms, q is the number of moving average terms, and d is the difference order made to become a stationary sequence.
Step S105: and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
The operation indexes of the station control equipment of the hydrogen filling station can be predicted by utilizing the machine learning model which is constructed in advance, and then safety monitoring can be carried out according to the predicted indexes, so that the accident is prevented, and the safety of the hydrogen filling station is effectively improved.
Therefore, by applying the safety monitoring scheme of the hydrogen station, the data mining of the multidimensional collected data of the hydrogen station can be performed by utilizing a big data processing algorithm, the limitation of the type and the data structure of the collected data in the traditional data mining is effectively solved, the corresponding real-time data can be collected aiming at each use scene in the safety monitoring process of the hydrogen station, the monitoring index prediction of the collected real-time data is performed on the basis of a machine learning model, the safety monitoring of the hydrogen station is realized, the efficiency of data analysis of the collected data is effectively improved by a parallel processing mode based on the big data, and the invalid data is removed from the collected real-time data, so that the prediction result of the machine learning model obtained by training is more accurate.
As shown in fig. 3, a second flowchart of a hydrogen refueling station safety monitoring method provided in the embodiment of the present invention may include the following steps:
step S201: and determining the current scene of the hydrogen filling station.
Step S202: and acquiring real-time data corresponding to the current scene.
Step S203: and filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data.
Step S204: and constructing a data warehouse of the valid data based on a third paradigm rule.
The Third equation (Third Normal Form, 3rd NF) means that all data elements in the table must be mutually independent and have no other functional relationship, as well as being uniquely identified by the primary key. That is, for a data structure that satisfies 2nd NF, there may be some dependency of some data elements on other non-key data elements in the table that must be eliminated.
It should be noted that, the third paradigm for building the data warehouse is a specific way provided by the embodiment of the present invention, and may be built in other ways, and the present invention does not need to limit the specific way for building the data warehouse.
Step S205: and inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene.
Step S206: and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
It should be noted that steps S201 to S203, step S205 and step S206 in the method embodiment shown in fig. 3 are similar to steps S101 to S105 in the method embodiment shown in fig. 1, and specific contents of the method embodiment shown in fig. 1 may be referred to for relevant points, which are not described herein again.
Therefore, the method embodiment shown in fig. 3 has all the advantages of the method embodiment shown in fig. 1, and also constructs a data warehouse of effective data, and since the accuracy of the monitoring index predicted by the machine learning model directly determines the reliability of the safety monitoring of the hydrogen refueling station, the machine learning is performed by using the data in the data warehouse constructed by the effective data, so that the true degree of the constructed machine learning model is greatly improved, and the accuracy of the monitoring index prediction is favorably ensured.
As shown in fig. 4, a third flowchart of a hydrogen refueling station safety monitoring method provided in the embodiment of the present invention may include the following steps:
step S301: and determining the current scene of the hydrogen filling station.
Step S302: and acquiring real-time data corresponding to the current scene.
Step S303: and filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data.
Step S304: and inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene.
Step S305: and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
It should be noted that steps S301 to S505 in the method embodiment shown in fig. 4 are similar to steps S101 to S105 in the method embodiment shown in fig. 1, and specific contents of the method embodiment shown in fig. 1 may be referred to for relevant points, which are not described herein again.
Step S306: and generating a data report of the hydrogen filling station based on the monitoring index.
Therefore, the method embodiment shown in fig. 4 has all the advantages of the method embodiment shown in fig. 1, and can also generate a data report of the hydrogen refueling station based on the monitoring index, and output data in the safety monitoring system of the hydrogen refueling station, so that data analysis and mining can be further performed by combining the generated data report; in addition, the monitoring indexes can be counted or displayed in a chart view mode, so that the safety monitoring system of the hydrogen station can be managed intuitively.
As shown in fig. 5, a fourth flowchart of a hydrogen refueling station safety monitoring method provided in the embodiment of the present invention may include the following steps:
step S401: and determining the current scene of the hydrogen filling station.
Step S402: and acquiring real-time data corresponding to the current scene.
Step S403: and filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data.
Step S404: and inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene.
Step S405: and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
It should be noted that steps S401 to S405 in the method embodiment shown in fig. 5 are similar to steps S101 to S105 in the method embodiment shown in fig. 1, and specific contents of the method embodiment shown in fig. 1 may be referred to for relevant points, which are not described herein again.
Step S406: and judging whether an alarm condition is met or not based on the numerical relation between the monitoring index and a set value, and triggering an alarm if the alarm condition is met.
The invention presets the set value corresponding to each monitoring index, and further can determine whether to trigger the alarm by judging the numerical relation between the monitoring index predicted by the machine learning model and the preset set value. For example, assuming that the pressure set value of the hydrogen storage tank of the hydrogen refueling station is 20Mpa and an alarm condition is met when the monitoring index is greater than the set value, if the pressure monitoring index predicted by the machine learning model is 30Mpa, an alarm is triggered.
In one implementation, the specific value of the set value may be determined according to an empirical value, and of course, there may be other manners.
Therefore, the method embodiment shown in fig. 5 has all the advantages of the method embodiment shown in fig. 1, and can perform early warning based on the numerical relationship between the monitoring index and the set value, and the safety of the safety monitoring system of the hydrogen refueling station can be effectively ensured by monitoring each monitoring index in each application scene, which is very important for the hydrogen refueling station.
As shown in fig. 6, a fifth flowchart of a hydrogen refueling station safety monitoring method provided in the embodiment of the present invention may include the following steps:
step S501: and determining the current scene of the hydrogen filling station.
Step S502: and acquiring real-time data corresponding to the current scene.
Step S503: and filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data.
Step S504: and inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene.
Step S505: and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
It should be noted that steps S501 to S505 in the method embodiment shown in fig. 6 are similar to steps S101 to S105 in the method embodiment shown in fig. 1, and specific contents of the method embodiment shown in fig. 1 may be referred to for relevant points, which are not described herein again.
Step S506: and acquiring hydrogen energy operation data of the hydrogen generation station based on the machine learning model.
It can be understood that, in the process of constructing the machine learning model, the hydrogen selling data may be used as training data, and further, the hydrogen energy operation data of the hydrogen refueling station may be output after machine learning, and specific content of the hydrogen selling data may refer to related content of the method embodiment shown in fig. 1, which is not described herein again.
Step S507: and determining the hydrogen production amount of the hydrogen station according to the hydrogen energy operation data.
It can be understood that the operation of the hydrogen station can be effectively guided by analyzing the hydrogen energy operation data, and the station control operation comprises hydrogen storage amount, hydrogen selling amount, personnel management and the like.
In a specific example, the hydrogen energy operation data can be analyzed by utilizing a big data analysis technology to obtain the hydrogen selling condition (week, month, quarter, year) of each hydrogen adding station in different time dimensions; calculating the use loss conversion cost of the equipment by combining the collected equipment state parameters such as pressure values and temperature values corresponding to each application scene; then, the hydrogen storage amount, the number of hydrogenation vehicles and the weather condition are subjected to statistical analysis, the hydrogenation amount prediction condition for each different time period is finally obtained, and operation guidance is further performed according to the hydrogenation amount prediction condition, for example, the hydrogen amount is determined.
Therefore, in addition to the overall beneficial effects of the method embodiment shown in fig. 1, the method embodiment shown in fig. 6 can also determine the hydrogen production amount of the hydrogen refueling station according to the hydrogen energy operation data, so that the hydrogen amount can be determined according to the hydrogen production amount prediction conditions in different time periods, and the price of hydrogen sale can be formulated to adjust the operation conditions, for example, the price fluctuation condition can be determined according to the hydrogen peak or the hydrogen peak.
To solve the above technical problem, the present invention provides a computer device, as shown in fig. 7, including a memory 610, a processor 620, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described above.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer device may include, but is not limited to, a processor 620, a memory 610. Those skilled in the art will appreciate that fig. 7 is merely an example of a computing device and is not intended to be limiting and may include more or fewer components than those shown, or some of the components may be combined, or different components, e.g., the computing device may also include input output devices, network access devices, buses, etc.
The Processor 620 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 610 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 610 may also be an external storage device of a computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device. Further, the memory 610 may also include both an internal storage unit and an external storage device of the computer device. The memory 610 is used for storing the computer program and other programs and data required by the computer device. The memory 610 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the present application further provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the memory in the foregoing embodiment; or it may be a computer-readable storage medium that exists separately and is not incorporated into a computer device. The computer-readable storage medium stores one or more computer programs which, when executed by a processor, implement the methods described above.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory 610, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a described condition or event is detected" may be interpreted, depending on the context, to mean "upon determining" or "in response to determining" or "upon detecting a described condition or event" or "in response to detecting a described condition or event".
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (15)
1. A safety monitoring method for a hydrogen refueling station is characterized by comprising the following steps:
determining a current scene of a hydrogen station;
acquiring real-time data corresponding to the current scene;
filtering invalid data in the real-time data based on a big data processing algorithm to obtain valid data;
inputting the effective data into a machine learning model obtained by pre-training, and predicting to obtain a monitoring index corresponding to the current scene;
and carrying out safety monitoring on the hydrogen filling station based on the monitoring index.
2. The hydrogen station safety monitoring method according to claim 1, wherein the determining the current scene of the hydrogen station comprises:
and determining the current scene according to the equipment state parameters of the hydrogen station.
3. The hydrogen refueling station safety monitoring method according to claim 1, wherein the scene of the hydrogen refueling station comprises one or more of the following: hydrogen production scenes, hydrogen transportation scenes, hydrogen storage scenes and hydrogen sale scenes.
4. The hydrogen refueling station safety monitoring method according to claim 1, wherein the collecting real-time data corresponding to the current scene comprises:
determining target equipment corresponding to the current scene;
and collecting monitoring parameters of the target equipment.
5. The hydrogen refueling station safety monitoring method according to claim 4, wherein the collecting of the monitoring parameters of the target equipment comprises:
and acquiring the pressure value and the temperature value of the target equipment in real time.
6. The hydrogen refueling station safety monitoring method according to claim 1, wherein the filtering out invalid data from the real-time data to obtain valid data comprises:
and carrying out data extraction, cleaning, conversion and loading processes on the real-time data to obtain processed effective data.
7. The hydrogen station safety monitoring method according to claim 1, further comprising:
and constructing a data warehouse of the valid data based on a third paradigm rule.
8. The hydrogen refueling station safety monitoring method according to claim 7, wherein the machine learning model is a monitoring index obtained by training based on multi-dimensional historical collected data and used for predicting the current scene, and the multi-dimensional historical collected data at least comprises data in the data warehouse and data extracted from log files.
9. The hydrogen station safety monitoring method according to claim 8, wherein the multi-dimensional historical collected data is obtained based on collected data of a hydrogen hydrogenation machine, a hydrogen storage tank, a compressor, a vehicle-connected platform, an infrared correlation and a gas discharge column.
10. The hydrogen station safety monitoring method according to claim 1, further comprising:
and generating a data report of the hydrogen filling station based on the monitoring index.
11. The hydrogen station safety monitoring method according to claim 1, further comprising:
and judging whether an alarm condition is met or not based on the numerical relation between the monitoring index and a set value, and triggering an alarm if the alarm condition is met.
12. The hydrogen refueling station safety monitoring method according to claim 8, wherein when the current scene is a hydrogen transportation scene, the collecting real-time data corresponding to the current scene comprises:
and collecting vehicle running data, current position information and weather data of the hydrogen transport vehicle.
13. The hydrogen station safety monitoring method according to claim 1, further comprising:
acquiring hydrogen energy operation data of the hydrogen generation station based on the machine learning model;
and determining the hydrogen production amount of the hydrogen station according to the hydrogen energy operation data.
14. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 13 when executing the computer program.
15. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 13.
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Cited By (1)
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