CN110569181A - System capability evaluation method and device and computer equipment - Google Patents
System capability evaluation method and device and computer equipment Download PDFInfo
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- G—PHYSICS
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Abstract
The application relates to a system capacity evaluation method and device and computer equipment. The method comprises the following steps: acquiring original data of a system to be tested; extracting target data from the raw data; mapping the target data into a target format file; screening data of the target format file; performing cluster analysis on the screened data to obtain a plurality of data clusters; evaluating the ability of each data cluster by adopting a BPR model to obtain the ability parameter of each data cluster; and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters. The method comprises the steps of extracting original data to obtain related data parameters for evaluating the system capacity, mapping target data into exchangeable target format files, further screening data to eliminate abnormal data, performing cluster analysis on the screened data, performing capacity evaluation on each data cluster independently, evaluating the overall capacity of the system according to the capacity parameters of each data cluster, and achieving full-automatic evaluation with high reliability.
Description
Technical Field
the invention relates to the technical field of computers, in particular to a system capacity evaluation method and device and computer equipment.
Background
The statements herein merely provide background information related to the present application and may not necessarily constitute prior art.
the research on the problem of calculating and strengthening the transport capacity of the existing railway line relates to the most essential transport capacity and how to transport the railway, so that the method is always a hotspot of academic research, and in the aspect of capacity calculation, an important subject is to accurately and timely calculate and evaluate the capacity utilization conditions of transport stations and sections and the standard operation time of each basic operation, so that the transport organization effect can be confirmed, weak links on the existing equipment and work organization can be found, and further appropriate capacity expansion measures can be taken to achieve the purpose of potential excavation and efficiency improvement.
from the existing research results, the railway basic equipment utilization rate and the operation time standard in China are still in the manual stage or the semi-automatic standard checking method stage combined with the DMIS (dispatching and monitoring system) playback function, and the whole process from data acquisition to checking is not realized. In the more advanced semi-automatic calibration method, a signal microcomputer monitoring system, a train operation monitoring and recording device and the like can be adopted to collect partial data, and a software model can be applied to calculate indexes for measuring the railway transportation capacity such as the throat passing capacity, the arrival and departure line passing capacity and the like. Compared with the traditional manual standard checking method, the semi-automatic standard checking method has the advantages that the efficiency and the accuracy are greatly improved, but the data screening still needs to be carried out manually so as to ensure the data reliability of the railway transportation capacity evaluation.
Disclosure of Invention
Therefore, it is necessary to provide a system capability evaluation method and apparatus, and a computer device, for solving the problem of low efficiency of the semi-automatic calibration method in the conventional technology.
in one aspect, an embodiment of the present application provides a system capability evaluation method, including:
acquiring original data of a system to be tested;
Extracting target data from the raw data;
mapping the target data into a target format file;
screening data of the target format file;
Performing cluster analysis on the screened data to obtain a plurality of data clusters;
evaluating the ability of each data cluster by adopting a BPR model to obtain the ability parameter of each data cluster;
and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
according to the system capacity evaluation method provided by the invention, the original data is extracted to obtain the relevant data parameters for evaluating the system capacity, the target data is mapped into the exchangeable target format file, data screening is further adopted to eliminate abnormal data, the screened data is subjected to cluster analysis, the capacity evaluation is independently carried out on each data cluster, the overall capacity of the system is evaluated according to the capacity parameters of each data cluster, full-automatic evaluation can be realized, and the reliability is high.
in one embodiment, the step of extracting the target data from the raw data comprises:
and extracting target data from the original data according to a preset rule.
in one embodiment, the system capability evaluation method further includes:
preprocessing the target data to generate preprocessed data;
The step of mapping the target data into a target format file comprises:
And converting the preprocessed data into a target format file.
In one embodiment, the system under test is a rail transport system.
In one embodiment, the raw data comprises: the data of the train dispatching command system, the data of the railway transportation management information system and/or the data of the train operation monitoring and recording device.
in one embodiment, the target format file is a second JSON file, and the step of mapping the target data into the target format file includes:
mapping the target data to obtain a first JSON file;
adding a style of a built-in data type in a JSON Schema to elements in each JSON file to generate intermediate data of different data types;
And respectively converting the intermediate data of each type to generate a second JSON file.
In one embodiment, the step of performing cluster analysis on the filtered data to obtain a plurality of data clusters and outlier data comprises:
And performing cluster analysis on the screened data by using a DBSCAN algorithm to generate a plurality of data clusters.
In one embodiment, the data cluster comprises traffic flow data, train operation technical speed data and train actual operation time data;
Evaluating the capacity of each data cluster by adopting a BPR model, and acquiring the capacity parameter of each data cluster, wherein the capacity parameter comprises the following steps:
Evaluating the capacity of each data cluster according to the following formula to obtain the capacity parameter of each data cluster:
Wherein C is capacity, V is traffic flow data of each data clustering conversion, t0And (4) the technical speed data of train operation, the actual train operation time data in each data cluster of t, and alpha and beta are preset values.
a system capability assessment apparatus comprising:
the device comprises an original data acquisition unit, a data acquisition unit and a data acquisition unit, wherein the original data acquisition unit is used for acquiring original data of a system to be tested;
a target data extraction unit for extracting target data from the original data;
The target format file conversion unit is used for mapping the target data into a target format file;
The data screening unit is used for screening data of the target format file;
The cluster analysis unit is used for carrying out cluster analysis on the screened data to obtain a plurality of data clusters;
the capacity parameter acquiring unit is used for evaluating the capacity of each data cluster by adopting a BPR model and acquiring the capacity parameter of each data cluster;
and the system capability evaluation unit is used for evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the system capability assessment method when executing the program.
drawings
FIG. 1 is a flow diagram illustrating a method for system capability assessment in one embodiment;
FIG. 2 is a schematic diagram showing the structure of a system capability evaluating apparatus according to an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
it will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element and be integral therewith, or intervening elements may also be present. The terms "mounted," "one end," "the other end," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In one aspect, an embodiment of the present application provides a system capability evaluation method, as shown in fig. 1, including:
S10: acquiring original data of a system to be tested;
s20: extracting target data from the raw data;
s30: mapping the target data into a target format file;
S40: screening data of the target format file;
S50: performing cluster analysis on the screened data to obtain a plurality of data clusters;
s60: evaluating the ability of each data cluster by adopting a BPR model to obtain the ability parameter of each data cluster;
s70: and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
the system to be tested can be an application system such as a railway transportation system and a power supply system. The raw data refers to various data directly acquired by a data acquisition device configured by the system to be tested from each operating device. The target data is data capable of reflecting the capability of the system to be tested. The object format file may be a data exchange format file such as a JSON (javascript object notification) file or an XML (Extensible Markup Language) file set according to the data content and type. The specific implementation process of data screening on the target format file can be specifically set according to the application scenario of the system capability evaluation method. For example, part of data in the target format file is calibrated for learning of a pattern recognition model, then the data basic structure of the data in the target format file is analyzed through a pattern recognition method to obtain different clustering data, and meanwhile, outlier data are screened out to obtain screened data. The cluster analysis refers to an analysis process of grouping a set of physical or abstract objects into a plurality of classes composed of similar objects, and correspondingly divides data screened from a target format file into a plurality of data clusters in the application. The capability parameter refers to a parameter capable of reflecting the operation capability of the system to be tested, for example, the capability parameter of the railway transportation system may include train operation state data and the like.
The system capacity evaluation method provided by the embodiment of the application specifically includes the steps that the original data are extracted to obtain relevant data parameters for evaluating the capacity of the system, for example, data such as train running state data and the number of trains entering and exiting from a station in a railway transportation system are significant for evaluating the capacity of the railway transportation system, the train running state data can be extracted from the original data collected by the train running monitoring and recording device, and the number of trains entering and exiting from the station can be extracted from the original data collected by the electric service maintenance terminal. The original data acquired by the system data are mostly data defined by various interfaces, so that after the target data are acquired, the target data are mapped into a target format file which can be exchanged among systems, data format conversion is performed, data screening is further adopted to eliminate abnormal data to obtain effective data, the screened effective data are subjected to cluster analysis, the ability of each cluster data is independently evaluated, the overall ability of the system is evaluated according to the ability parameters of each data cluster obtained after evaluation, full-automatic evaluation can be realized, and the reliability is high.
In one embodiment, the step of extracting the target data from the raw data comprises:
And extracting target data from the original data according to a preset rule. The specific structure of the target format file (for example, the specific structure of the JSON format file) can be designed in advance according to the application scene requirements, and taking the system to be tested as a train system as an example, the target data can be extracted from the data of a train dispatching and commanding system, the data of a railway transportation management information system and/or the data of a train operation monitoring and recording device according to the recording time of the data, the train type or keywords and the like.
in one embodiment, the system capability evaluation method further includes:
preprocessing the target data to generate preprocessed data;
the step of mapping the target data into a target format file comprises:
and converting the preprocessed data into a target format file.
in order to improve the data effectiveness and improve the reliability and accuracy of the system capability evaluation method, in the method, target data is extracted from original data and then preprocessed. Due to errors in data measurement and collection or small-probability abnormity of a computer, abnormal data can exist in massive raw data, and the abnormal data in the raw data need to be removed, so that abnormal data in the raw data can be removed in the preprocessing process. In addition, for some data, parameter correction can be performed on part or all of data points through data analysis methods such as data fitting and the like so as to eliminate the influence of background factors, and therefore information capable of reflecting the capability of the system to be measured (the actual railway transportation system) can be obtained. And further converting the preprocessed data into a target format file for exchanging among systems.
In one embodiment, the system under test is a rail transport system. The system capacity evaluation method is particularly suitable for railway transportation systems. The railway transportation system adopting the system capacity evaluation method can realize the beneficial effect of the system capacity evaluation method.
In one embodiment, the raw data comprises: the data of the train dispatching command system, the data of the railway transportation management information system and/or the data of the train operation monitoring and recording device. When the system to be tested is a railway transportation system, the original data may include part or all of data of a Train Dispatching Command System (TDCS), a railway Transportation Management Information System (TMIS) and/or a train operation monitoring and recording device (LKJ), and the original data may also include data acquired by an electric service maintenance terminal.
in one embodiment, the target format file is a second JSON file, and the step of mapping the target data into the target format file includes:
mapping the target data to obtain a first JSON file;
Adding a style of a built-in data type in a JSON Schema to elements in each JSON file to generate intermediate data of different data types;
And respectively converting the intermediate data of each type to generate a second JSON file.
the schema refers to a collection of database objects, i.e. tables, indexes, views, storage procedures, etc. that are commonly referred to as database objects. The implementation process of the system capability evaluation method provided by the embodiment of the application is better described. The system under test is taken as a railway transportation system for illustration. For example, the preprocessed white/red light band open (for short, the amount of light turned on) state data (the preprocessed target data) of a certain section collected by the train dispatching and commanding system TDCS can be mapped to obtain a first JSON file:
{
"name": open light amount change history [ last tone ]
“Change_ID”:“……”,
“ADID”:“……”,
“Name”:“……”,
“State”:“……”,
“Time”:“……”
]}
}
The ChangeID field may indicate a change number of the change of the amount of lighting, the ADID field indicates an AD number, which may be used to indicate the cumulative number of changes of the State of the element (i.e., the amount of lighting), the Name field indicates the Name of a particular element of the segment (e.g., a switch, a stock road, etc.), and the State field indicates the State of the amount of lighting at the time indicated by time.
for another example, data representing a certain train operation record can be extracted from original data of a train operation monitoring and recording device (LKJ), and then the data of the train operation record is mapped to obtain a first JSON file after preprocessing:
{
"name" train operation record [ "map
“recordDate”:“……”,
“trainID”:“……”,
“fileID”:“……”,
“serialNum”:“……”,
“locomotiveName”:“……”,
“lastStation”:“……”,
“thisStation”:“……”,
“upOrDown”:“……”,
“stopTime”:“……”,
“startTime”:“……”,
“dwellTime”:“……”,
“movetime”:“……”,
“moveTimeType”:“……”,
“inStationType”:“……”,
“trainType”:“……”,
“trainLength”:“……”
]}
}
Wherein, the recordDate field indicates a recording date of the train, the train id indicates a train number of the train, the fileID indicates a train number, the serialNum indicates a train serial number of the same train number, the locomativename indicates a locomotive type, the lastStation indicates a front station name, the thissstation indicates a home station name, the updown indicates an up or down line, the stopTime indicates a stop time, the startTime indicates a departure time, the dwellTime indicates a stop time at the station (e.g., in minutes), the movetime indicates a section running time (e.g., in minutes), the moveTimeType indicates a train arrival and departure type, the instanttype indicates a type of the train via the home station, i.e., a type of an operation of the train at the home station (such as passage, ending, and starting), the trainType indicates a train type, and the trainLength indicates a train length.
Furthermore, the JSON element in each of the first JSON files can be added with a style of a built-in data type in the JSON Schema to obtain a usable second JSON file. The built-in data types of the JSON Schema can be divided into basic types and derivative types, and have unified standards. And converting the data in each JSON file according to the data type. The data conversion format in the present embodiment includes: (1) reconstructing, e.g., changing, the data structure form (including splitting data tables and merging data tables, etc.) to facilitate clearer and faster access to the desired data; (2) substitution, such as replacing NULL with "NULL" or "Unknown", or replacing whole words with abbreviations, etc.; (3) data type conversion; (4) date/time format conversion, etc.
For example, the above-mentioned "open light change history" element may correspond to a built-in data type of JSON Schema mapped as follows:
{
"name": open light amount change history [ last tone ]
“Change_ID”:[{value:“……”,type:“int”}],
“ADID”:[{value:“……”,type:“int”}],
“Name”:[{value:“……”,type:“string”}],
“State”:[{value:“……”,type:“string”}],
“Time”:[{value:“……”,type:“datetime”}]
]}
}
for another example, the above-mentioned train operation record element may be mapped to the following form corresponding to the built-in data type of JSON Schema:
{
"name" train operation record [ "map
“recordDate”:[{value:“……”,type:“datetime”}],
“trainID”:[{value:“……”,type:“int”}],
“fileID”:[{value:“……”,type:“int”}],
“serialNum”:[{value:“……”,type:“int”}],
“locomotiveName”:[{value:“……”,type:“string”}],
“lastStation”:[{value:“……”,type:“string”}],
“thisStation”:[{value:“……”,type:“string”}],
“upOrDown”:[{value:“……”,type:“string”}],
“stopTime”:[{value:“……”,type:“datetime”}],
“startTime”:[{value:“……”,type:“datetime”}],
“dwellTime”:[{value:“……”,type:“int”}],
“movetime”:[{value:“……”,type:“int”}],
“moveTimeType”:[{value:“……”,type:“string”}],
“inStationType”:[{value:“……”,type:“string”}],
“trainType”:[{value:“……”,type:“string”}],
“trainLength”:[{value:“……”,type:“int”}]
]}
}
in one embodiment, the step of performing cluster analysis on the filtered data to obtain a plurality of data clusters and outlier data comprises:
and performing cluster analysis on the screened data by using a DBSCAN algorithm to generate a plurality of data clusters.
The DBSCAN algorithm is a density reachable relations based clustering method with noise, which divides areas with sufficient density into clusters and finds arbitrarily shaped clusters in a spatial database with noise, which defines clusters as the largest set of density-connected points. The algorithm is used for cluster analysis, the number of data clusters does not need to be specified in advance, and the number of the final data clusters is uncertain. The screened data can be divided into three types of data points by using the DBSCAN algorithm, wherein one type of data points is a core point: points in excess of the number MinPts are contained within the radius Eps. The other is a boundary point: the number of points within the radius Eps is less than MinPts, but falls within the neighborhood of the core point. Yet another is outliers: points that are neither core points nor boundary points.
The basic process of using the DBSCAN algorithm to perform cluster analysis on the screened data to generate a plurality of data clusters can be expressed as follows:
in one embodiment, the data cluster comprises traffic flow data, train operation technical speed data and train actual operation time data;
Evaluating the capacity of each data cluster by adopting a BPR model, and acquiring the capacity parameter of each data cluster, wherein the capacity parameter comprises the following steps:
Evaluating the capacity of each data cluster according to the following formula to obtain the capacity parameter of each data cluster:
wherein C is capacity, V is traffic flow data of each data clustering conversion, t0and (4) the technical speed data of train operation, the actual train operation time data in each data cluster of t, and alpha and beta are preset values.
alpha and beta can be calibrated empirically or enumerated using a grid search method. The traffic flow is the number of passing vehicles/corresponding operating time. According to the formula, a Bayesian optimization method can be adopted to select the data with the best matching degree as the clustering capability of different data. Then, the capability of the railway transportation system can be integrally evaluated according to the capability parameter C of different data clusters. For example, a plurality of calculation models in the data processing system can respectively calculate the throat passing capacity, the arrival line passing capacity and the departure line passing capacity of each station of interest, and correct the operation diagram parameters according to the statistical model to obtain the transportation capacity of the station of interest. The capability evaluation model of the system to be tested may be a formula for adding the capability parameters of different data clusters and the specific gravities corresponding to the capability parameters.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
A system capability evaluating apparatus, as shown in fig. 2, the apparatus comprising:
The device comprises an original data acquisition unit 1 for acquiring original data of a system to be tested;
a target data acquisition unit 2 for extracting target data from the raw data;
A target format file conversion unit 3, configured to map target data into a target format file;
The data screening unit 4 is used for screening data of the target format file;
the cluster analysis unit 5 is used for carrying out cluster analysis on the screened data to obtain a plurality of data clusters;
the capacity parameter acquiring unit 6 is used for evaluating the capacity of each data cluster by adopting a BPR model and acquiring the capacity parameter of each data cluster;
and the system capability evaluation unit 7 is used for evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
for specific limitations of the system capability evaluation device, reference may be made to the above limitations of the system capability evaluation method, which is not described herein again. The respective modules in the system capability evaluating apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
specifically, the original data of the system to be tested is obtained through an original data obtaining unit 1, the original data obtaining unit 1 sends the obtained original data to a target format file conversion unit 2, then a target data obtaining unit 3 extracts target data from the original data and maps the target data into a target format file, a data screening unit 4 is used for screening the target format file, a cluster analysis unit 5 conducts cluster analysis on the screened data to obtain a plurality of data clusters, then a capacity parameter obtaining unit 6 evaluates the capacity of each data cluster by adopting a BPR model to obtain capacity parameters of each data cluster, and finally a system capacity evaluating unit 7 evaluates the overall capacity of the system to be tested according to the capacity parameters of different data clusters.
in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing style data of built-in data types of JSON scheme. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a system capability assessment method.
those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
S10: acquiring original data of a system to be tested;
S20: extracting target data from the raw data;
s30: mapping the target data into a target format file;
s40: screening data of the target format file;
s50: performing cluster analysis on the screened data to obtain a plurality of data clusters;
S60: evaluating the ability of each data cluster by adopting a BPR model to obtain the ability parameter of each data cluster;
s70: and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
the computer device provided by the embodiment of the application can execute other steps in the system capability evaluation method besides the steps, and achieves the beneficial effects achieved by the system capability evaluation method.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
s10: acquiring original data of a system to be tested;
s20: extracting target data from the raw data;
s30: mapping the target data into a target format file;
S40: screening data of the target format file;
S50: performing cluster analysis on the screened data to obtain a plurality of data clusters;
S60: evaluating the ability of each data cluster by adopting a BPR model to obtain the ability parameter of each data cluster;
S70: and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
it will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A system capability assessment method, comprising:
acquiring original data of a system to be tested;
extracting target data from the raw data;
mapping the target data into a target format file;
Screening data of the target format file;
Performing cluster analysis on the screened data to obtain a plurality of data clusters;
evaluating the capacity of each data cluster by adopting a BPR model to obtain a capacity parameter of each data cluster;
and evaluating the overall capability of the system to be tested according to the capability parameters of different data clusters.
2. The system capability assessment method according to claim 1, wherein the step of extracting target data from said raw data comprises:
and extracting the target data from the original data according to a preset rule.
3. The system capability assessment method according to claim 1, further comprising the steps of:
Preprocessing the target data to generate preprocessed data;
the step of mapping the target data into a target format file comprises:
And converting the preprocessed data into a target format file.
4. the system capability evaluation method according to any one of claims 1 to 3, wherein the system under test is a railway transportation system.
5. the system capability assessment method according to claim 4, wherein said raw data comprises: the data of the train dispatching command system, the data of the railway transportation management information system and/or the data of the train operation monitoring and recording device.
6. the system capability evaluation method according to claim 1, 2, 3 or 5, wherein the target format file is a second JSON file, and the step of mapping the target data into a target format file comprises:
Mapping the target data to obtain a first JSON file;
adding a style of a built-in data type in the JSON Schema to elements in each JSON file to generate intermediate data of different data types;
and respectively converting the intermediate data of each type to generate the second JSON file.
7. the system capability assessment method according to claim 6, wherein the step of performing cluster analysis on the screened data to obtain a plurality of data clusters and outlier data comprises:
and performing cluster analysis on the screened data by using a DBSCAN algorithm to generate a plurality of data clusters.
8. the system capacity evaluation method according to claim 4 or 5, wherein the data cluster includes traffic data, train operation technical speed data, and train actual operation time data;
Evaluating the capability of each data cluster by adopting a BPR model, wherein the step of obtaining the capability parameter of each data cluster comprises the following steps:
evaluating the capacity of each data cluster according to the following formula to obtain the capacity parameter of each data cluster:
wherein C is the capacity and V is each ofTraffic data, t, converted from data clusters0And (4) the technical speed data of train operation, the actual train operation time data in each data cluster of t, and alpha and beta are preset values.
9. A system capability assessment apparatus, comprising:
the device comprises an original data acquisition unit, a data acquisition unit and a data acquisition unit, wherein the original data acquisition unit is used for acquiring original data of a system to be tested;
A target data acquisition unit for extracting target data from the raw data;
A target format file conversion unit, configured to map the target data into a target format file;
the data screening unit is used for screening the data of the target format file;
the cluster analysis unit is used for carrying out cluster analysis on the screened data to obtain a plurality of data clusters;
the capacity parameter acquiring unit is used for evaluating the capacity of each data cluster by adopting a BPR model and acquiring the capacity parameter of each data cluster;
and the system capacity evaluation unit is used for evaluating the overall capacity of the system to be tested according to the capacity parameters of different data clusters.
10. 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 steps of the system capability assessment method according to any one of claims 1-8 are implemented when the program is executed by the processor.
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