CN112367637A - Method and device for realizing early warning processing, computer storage medium and terminal - Google Patents
Method and device for realizing early warning processing, computer storage medium and terminal Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, a computer storage medium and a terminal for realizing early warning processing, wherein train operation data corresponding to each calling service are obtained, and the train operation data of which the absolute value of the time difference is greater than a preset time length threshold value are combined into section analysis data for analyzing an operation section by determining the time difference of operation time contained in adjacent train operation data; determining a running mileage section of the train according to the analysis data of each line of each train; by integrating the number of the section data obtained in the preset statistical period and the statistics of the train corresponding to the section data, the automatic early warning of the wireless overtime section is realized for the operation mileage section meeting the early warning condition, and the operation reliability of the high-speed train is improved.
Description
Technical Field
The present disclosure relates to, but not limited to, traffic communication technologies, and in particular, to a method, an apparatus, a computer storage medium, and a terminal for implementing an early warning process.
Background
With the rapid development of big data technology and artificial intelligence technology, more and more related applications are fully applied in the internet field, but are limited by some factors, and in the traditional technical field, the advanced technologies are not fully utilized.
The CTCS-3 (C3 for short) system is a train operation control system which currently supports the highest train operation speed in China, and takes a communication network of a global system for mobile communications (GSM-R, GSM for Railways) for Railways as a vehicle-ground bidirectional safety data transmission channel; the C3 system mainly includes two key components, namely, an Automatic Train Protection (ATP) and a Radio Blocking Center (RBC).
Most of the current railway maintenance level is still at the level of manually discovering faults and manually analyzing the faults, and the faults cannot be predicted, wherein how to predict a wireless timeout section provides guarantee for reliable operation of a C3 system is a problem to be solved.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a method and a device for realizing early warning processing, a computer storage medium and a terminal, which can realize automatic early warning of a wireless overtime section and improve the running reliability of a high-speed train.
The embodiment of the invention provides a method for realizing early warning processing, which comprises the following steps:
for each calling service, acquiring a group of train operation data;
for each group of train operation data, combining two adjacent train operation data with the interval duration being greater than a preset interval duration threshold into a section analysis data for section analysis according to the operation time in the train operation data;
analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to kilometer posts, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
the train operation data are sorted according to the time sequence; the train operation data includes: the running time T and the kilometer mark K of the train; the counting period comprises N preset durations, and N is an integer greater than or equal to 2.
On the other hand, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored in the computer storage medium, and when the computer program is executed by a processor, the method for implementing the early warning processing is implemented.
In another aspect, an embodiment of the present invention further provides a terminal, including: a memory and a processor, the memory having a computer program stored therein; wherein the content of the first and second substances,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by the processor, implements a method of implementing the early warning process as described above.
In another aspect, an embodiment of the present invention further provides an apparatus for implementing early warning processing, where the apparatus includes: the system comprises an acquisition unit, a merging unit, an integrated mileage unit and an early warning unit; wherein the content of the first and second substances,
the acquisition unit is configured to: for each calling service, acquiring a group of train operation data;
the merging unit is set as: for each group of train operation data, according to the operation time in the train operation data, combining two adjacent train operation data with the interval duration larger than the preset interval duration threshold value into one section analysis data for section analysis
The integrated mileage unit is set as: analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to kilometer posts, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
the early warning unit is set as follows: determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
the train operation data are sorted according to the time sequence; the train operation data includes: the running time T and the kilometer mark K of the train; the counting period comprises N preset durations, and N is an integer greater than or equal to 2.
The method comprises the steps that train operation data corresponding to each calling service are obtained, the time difference value of adjacent train operation data is determined, and the train operation data with the absolute value of the time difference value larger than a preset time threshold value are combined into section analysis data for performing overtime early warning judgment; analyzing the data of the section of each line of each train to determine the mileage section of the train operation; through the statistics of the data of the sections including the mileage sections in the statistical period, the automatic early warning of the wireless overtime sections is realized for the mileage sections meeting the set early warning conditions, and the running reliability of the high-speed train is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flowchart of a method for implementing early warning processing according to an embodiment of the present invention;
fig. 2 is a block diagram of a device for implementing the early warning processing according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for implementing an early warning process according to an embodiment of the present invention, as shown in fig. 1, including:
in an illustrative example, a call service according to an embodiment of the present invention includes: the method comprises the following steps that a vehicle-mounted train overspeed protection (ATP) in a C3 system carries out calling service to a ground Radio Block Center (RBC); the embodiment of the invention can load the data corresponding to the call service according to the day from the communication interface for transmitting the call service in the C3 system. Train operation data may be obtained by one skilled in the art from data packets containing train operation data for the call service as specified by the protocol, including but not limited to: an Application Protocol Data Unit (APDU)136 packet or an APDU129 packet; the train operation data may include one or any combination of the following: train identity information, line information, train running time T and kilometer posts K; the statistical period includes N preset durations. Here, the train identity information may include: a calling mobile subscriber number (MSISDN); the line information may include: line identity number (ID).
In one illustrative example, each set of train operating data acquired in the practice of the present invention is sorted in chronological order within the set.
it should be noted that, the train operation data is frequently generated; in the calling service process, the embodiment of the invention generally distinguishes the train operation data through the sequence number including the N _ R; taking N _ R as an example of the sequence number, the two adjacent train operation data include: when the sequence number N _ R1 is changed to N _ R2, one piece of train operation data with the sequence number of N _ R1 and one piece of train operation data with the sequence number of N _ R2; for the train operation data with the same N _ R, which one is specifically selected for the judgment of the interval duration can be preset by a person skilled in the art.
In an exemplary example, the interval duration of the running data of two adjacent trains can be calculated by the following method:
acquiring train operation time in the two adjacent train operation data;
and calculating the difference of the two obtained running times, and then taking an absolute value as the interval duration.
Here, the merging of the train operation data is only to perform the following simple data arrangement: removing the same part in the two sets of train operation data; reserving different parts according to a preset arrangement sequence; for example, if the train identity information and the route information in the two pieces of train operation data are the same, any one of the two pieces of train operation data is reserved, and if the train operation time T is different from the kilometer sign K, the train operation data is reserved according to a preset arrangement sequence, including but not limited to the following arrangement sequence: the train running time T and the kilometer post K in the front train running data and the train running time T and the kilometer post K in the rear train running data are sequenced; in an exemplary embodiment, the section analysis data may be generated according to other set information arrangements.
In an exemplary embodiment, the interval duration threshold in the embodiment of the present invention may be set by a person skilled in the art according to the timeout duration of the zone warning; for example, if the wireless timeout of the mileage section is greater than 20 seconds based on experience, and the mileage section is considered to have a safety risk, the interval duration threshold may be set to a value less than 20 seconds, for example, 15 seconds.
103, analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to the kilometer post, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
in an exemplary embodiment, the train and route to which the section analysis data belongs may be determined based on train identification information and route information included in the section analysis data.
In an exemplary embodiment, the preset time duration may be determined according to an operation cycle of the high-speed railway; in one illustrative example the preset duration may be set to one day.
In an exemplary example, the section data includes, in addition to the mileage section, the section analysis data for specifying the mileage section, the column part information, the route information, and the like.
104, determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
in an exemplary embodiment, the statistical period includes N preset durations, where N is an integer greater than or equal to 2; for example, N equals 30, which can be set and adjusted empirically by a person skilled in the art.
In an exemplary example, the train corresponding to the section data may be determined according to the train identity information in the section data.
The method and the device for analyzing the train operation data have the advantages that the train operation data corresponding to each calling service are obtained, the time difference value of the operation time contained in the adjacent train operation data is determined, and the train operation data with the absolute value of the time difference value larger than a preset time length threshold value are combined into the section analysis data for analyzing the operation section; determining a running mileage section of the train according to the analysis data of each line of each train; by integrating the number of the section data obtained in the preset statistical period and the statistics of the train corresponding to the section data, the automatic early warning of the wireless overtime section is realized for the operation mileage section meeting the early warning condition, and the operation reliability of the high-speed train is improved.
In an exemplary embodiment, before step 102 is combined into a piece of segment analysis data for segment analysis, the method of the embodiment of the present invention further includes:
and respectively carrying out smoothing treatment on the acquired train operation data of each group.
According to the embodiment of the invention, the train operation data is subjected to smoothing processing, so that the train operation data can be prevented from jumping.
In an exemplary example, the train operation information in the embodiment of the present invention further includes a train operation speed S, and the smoothing processing on the acquired train operation data includes:
the kilometer mark K of the train operation data sequenced at the front in the two adjacent train operation data1Kilometer K equal to the sorted train operating data2And then, setting:
the kilometer post of the sequenced train operation data is equal to K1+(-1)iS1*(T2-T1)；
The speed S1 of the front-ranked train operation data is equal to the speed S of the rear-ranked train operation data2；
Time T of the preceding train operating data1Equal to the time T of the sequenced train operation data2；
The kilometer mark K of the train operation data sequenced at the front in the two adjacent train operation data1Kilometer mark K of train operation data after sequencing2When the train running data are not equal, keeping the train running data unchanged;
the subscript 1 is used for identifying the train operation data sequenced at the front, and the subscript 2 is used for identifying the train operation data sequenced at the back; when the train moves downwards, i is even number, and when the train moves upwards, i is odd number. Here, the train descending means a running direction in which the kilometer post is continuously increased when the train runs; the ascending of the train refers to the running direction of the train with the kilometer post continuously reduced when the train runs.
It should be noted that, in the above-mentioned optional example of the embodiment of the present invention, the rule for smoothing the train operation data is performed, and the embodiment of the present invention may adopt other rules to smooth the train operation data.
In one illustrative example, step 103 determines a mileage section for a train continuously running in a line based on kilometer posts, comprising:
analyzing data of sections within a preset time length of each line of each train, and determining whether the mileage of the trains has intersection or not according to kilometers in the section analysis data;
and when the intersection of the mileage of the train is determined, determining the mileage sections of the train continuously running in the line according to the kilometer posts in the section analysis data with the intersection.
In an exemplary embodiment, determining whether the mileage of the trains intersects according to the kilometer posts in the section analysis data includes, for the section analysis data within the preset duration of each route of each train, selecting two of the section analysis data for each time to perform the following comparison to determine whether the mileage of the trains intersects:
the method comprises the steps that a large kilometer post in selected first section analysis data is larger than a small kilometer post in selected second section analysis data and smaller than a large kilometer post in selected second section analysis data, the small kilometer post in the selected first section analysis data is smaller than the small kilometer post in the selected second section analysis data, and the fact that the running mileage of a train has an intersection is determined; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen the above sections are analyzed for public in the dataThe magnitude relationship of the milestones can be represented by the following formula: k1n<K1m<K2n<K2m；
The larger kilometer post in the selected second section analysis data is smaller than the larger kilometer post in the selected first section analysis data and larger than the smaller kilometer post in the selected first section analysis data, the smaller kilometer post in the selected first section analysis data is larger than the smaller kilometer post in the selected second section analysis data, and the running mileage of the train is determined to have intersection; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1m<K1n<K2m<K2n；
The larger kilometer post in the selected first section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected second section analysis data, the smaller kilometer post in the selected second section analysis data is larger than the smaller kilometer post in the selected first section analysis data, and the running mileage of the train is determined to have intersection; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1n<K1m<K2m<K2n；
And determining that the mileage of the train has intersection when the larger kilometer post in the selected second section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected first section analysis data, and the smaller kilometer post in the selected first section analysis data is larger than the smaller kilometer post in the selected second section analysis data. Assuming the first segment analysis numberAccording to the kilometer scale of K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1m<K1n<K2n<K2m。
In one illustrative example, the starting kilometer scale for the mileage section in an embodiment of the present invention is: analyzing the minimum kilometer post in data of all sections with intersection in the mileage of the train;
in one illustrative example, the ending kilometer scale of the mileage section in an embodiment of the present invention is: the maximum kilometer post in all sections of analysis data where the mileage of the train runs intersects.
In one illustrative example, step 104 determines whether to perform a wireless timeout warning for the mileage section, including:
determining the number of the section data with the same line information and mileage sections and the train corresponding to the section data according to the number of the section data and the train corresponding to the section data, which are obtained by integrating the section data in a preset statistical period;
and when the number of the section data with the same line information and mileage sections is larger than a preset number threshold value and the section data corresponds to more than two trains, performing wireless overtime section early warning on the mileage sections.
In an exemplary embodiment, the wireless timeout period warning for the mileage period according to an embodiment of the present invention includes:
when the number of the section data with the same line information and mileage section is about a preset number threshold, determining the proportion of the section data corresponding to each train in the section data with the same line information and mileage section, and when the proportion of the section data corresponding to each train is less than a preset percentage threshold, performing wireless overtime section early warning on the mileage section;
in an illustrative example, a method of an embodiment of the present invention further includes: and when the proportion of the section data corresponding to one train is greater than a preset percentage threshold value, carrying out safety early warning on the train.
In an exemplary embodiment, the percentage threshold in embodiments of the present invention may be set empirically by one skilled in the art.
Fig. 2 is a block diagram of a structure of a device for implementing early warning processing according to an embodiment of the present invention, as shown in fig. 2, including: the system comprises an acquisition unit, a merging unit, an integrated mileage unit and an early warning unit; wherein the content of the first and second substances,
the acquisition unit is configured to: for each calling service, acquiring a group of train operation data;
the merging unit is set as: for each group of train operation data, according to the operation time in the train operation data, combining two adjacent train operation data with the interval duration larger than the preset interval duration threshold value into one section analysis data for section analysis
The integrated mileage unit is set as: analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to kilometer posts, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
the early warning unit is set as follows: determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
the train operation data are sorted according to the time sequence; the train operation data includes: the running time T and the kilometer mark K of the train; the statistical period comprises N preset durations, wherein N is an integer greater than or equal to 2.
The method and the device for analyzing the train operation data have the advantages that the train operation data corresponding to the calling service are obtained, the time difference value of the operation time contained in the adjacent train operation data is determined, and the train operation data with the absolute value of the time difference value larger than a preset time length threshold value are combined into the section analysis data for analyzing the operation section; determining a running mileage section of the train according to the analysis data of each line of each train; by integrating the number of the section data obtained in the preset statistical period and the statistics of the train corresponding to the section data, the automatic early warning of the wireless overtime section is realized for the operation mileage section meeting the early warning condition, and the operation reliability of the high-speed train is improved.
In an exemplary embodiment, the apparatus of the present invention further includes a smoothing unit configured to:
and respectively carrying out smoothing treatment on the acquired train operation data of each group.
In an exemplary embodiment, the smoothing unit according to the embodiment of the present invention is configured to: the kilometer mark K of the train operation data sequenced at the front in the two adjacent train operation data1Kilometer K equal to the sorted train operating data2And then, setting:
the kilometer post of the sequenced train operation data is equal to K1+(-1)iS1*(T2-T1)；
Speed S of the preceding train operating data1Equal to the speed S of the sequenced train operation data2；
Time T of the preceding train operating data1Equal to the time T of the sequenced train operation data2；
The kilometer mark K of the train operation data sequenced at the front in the two adjacent train operation data1Kilometer mark K of train operation data after sequencing2When the train running data are not equal, keeping the train running data unchanged;
the subscript 1 is used for identifying the train operation data sequenced at the front, and the subscript 2 is used for identifying the train operation data sequenced at the back; when the train moves downwards, i is even number, and when the train moves upwards, i is odd number.
In an exemplary embodiment, an integrated mileage unit according to an embodiment of the present invention is configured to determine a mileage section in which a train continuously runs in a line according to a kilometer post, and includes:
analyzing data of sections within a preset time length of each line of each train, and determining whether the mileage of the trains has intersection or not according to kilometers in the section analysis data;
and when the intersection of the mileage of the train is determined, determining the mileage sections of the train continuously running in the line according to the kilometer posts in the section analysis data with the intersection.
In one illustrative example, the integrated mileage unit is configured to determine whether there is an intersection between the mileage of the train runs based on the kilometer posts in the section analysis data, and includes: analyzing data of sections within a preset time length of each line of each train, selecting two of the sections each time for comparison to determine whether the mileage of the trains has intersection or not:
the method comprises the steps that a large kilometer post in selected first section analysis data is larger than a small kilometer post in selected second section analysis data and smaller than a large kilometer post in selected second section analysis data, the small kilometer post in the selected first section analysis data is smaller than the small kilometer post in the selected second section analysis data, and the fact that the running mileage of a train has an intersection is determined; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1n<K1m<K2n<K2m；
The larger kilometer post in the selected second section analysis data is smaller than the larger kilometer post in the selected first section analysis data and larger than the smaller kilometer post in the selected first section analysis data, the smaller kilometer post in the selected first section analysis data is larger than the smaller kilometer post in the selected second section analysis data, and the running mileage of the train is determined to have intersection; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThe relationship between the size of the kilometer post in the section analysis dataTo be expressed by the following formula: k1m<K1n<K2m<K2n；
The larger kilometer post in the selected first section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected second section analysis data, the smaller kilometer post in the selected second section analysis data is larger than the smaller kilometer post in the selected first section analysis data, and the running mileage of the train is determined to have intersection; suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1n<K1m<K2m<K2n；
And determining that the mileage of the train has intersection when the larger kilometer post in the selected second section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected first section analysis data, and the smaller kilometer post in the selected first section analysis data is larger than the smaller kilometer post in the selected second section analysis data. Suppose that the kilometer in the first segment of analysis data is labeled K1nAnd K2n，K1nLess than K2nAnd the kilometers in the second section of analysis data are marked as K1mAnd K2m，K1mLess than K2mThen, the relationship between the magnitudes of the kilometers in the section analysis data can be represented by the following formula: k1m<K1n<K2n<K2m。
In one illustrative example, the starting kilometer scale for the mileage section in an embodiment of the present invention is: analyzing the minimum kilometer post in data of all sections with intersection in the mileage of the train;
in one illustrative example, the ending kilometer scale of the mileage section in an embodiment of the present invention is: the maximum kilometer post in all sections of analysis data where the mileage of the train runs intersects.
In an exemplary embodiment, the early warning unit in the embodiment of the present invention is configured to:
determining the number of the section data with the same line information and mileage sections and the train corresponding to the section data according to the number of the section data and the train corresponding to the section data, which are obtained by integrating the section data in a preset statistical period;
and when the number of the section data with the same line information and mileage sections is larger than a preset number threshold value and the section data corresponds to more than two trains, performing wireless overtime section early warning on the mileage sections.
The embodiment of the invention also provides a computer storage medium, wherein a computer program is stored in the computer storage medium, and the method for realizing the early warning processing is realized when the computer program is executed by the processor.
An embodiment of the present invention further provides a terminal, including: a memory and a processor, the memory having stored therein a computer program; wherein the content of the first and second substances,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by a processor, implements a method of implementing the early warning process as described above.
"one of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art. "
Claims (10)
1. A method of implementing early warning processing, comprising:
for each calling service, acquiring a group of train operation data;
for each group of train operation data, combining two adjacent train operation data with the interval duration being greater than a preset interval duration threshold into a section analysis data for section analysis according to the operation time in the train operation data;
analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to kilometer posts, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
the train operation data are sorted according to the time sequence; the train operation data includes: the running time T and the kilometer mark K of the train; the counting period comprises N preset durations, and N is an integer greater than or equal to 2.
2. The method of claim 1, wherein prior to said merging into a piece of segment analysis data for segment analysis, the method further comprises:
and respectively carrying out smoothing treatment on the acquired train operation data of each group.
3. The method according to claim 2, wherein the train operation information further includes a speed S of train operation, and the performing smoothing includes:
the kilometer sign K of the train operation data ranked at the front in the two adjacent train operation data1Kilometer mark K equal to the sequenced train operation data2And then, setting:
the kilometer post of the sequenced train operation data is equal to K1+(-1)iS1*(T2-T1)；
Speed S of the train operation data sorted in the front1Equal to the speed S of the sequenced train operation data2；
Time T of the train operation data sequenced at the front1Equal to the time T of the sequenced train operation data2；
The kilometer sign K of the train operation data ranked at the front in the two adjacent train operation data1And the kilometer post K of the train operation data after the sequence2When the train running data are not equal, keeping the train running data unchanged;
the subscript 1 is used for identifying the operation data of the front-ranked train, and the subscript 2 is used for identifying the operation data of the rear-ranked train; when the train moves downwards, i is an even number, and when the train moves upwards, i is an odd number.
4. The method of claim 1, wherein determining the range section for the train to continuously operate in the line based on kilometer posts comprises:
analyzing data of sections within a preset time length of each line of each train, and determining whether the mileage of the trains has intersection or not according to kilometers in the section analysis data;
and when the intersection of the mileage of the train is determined, determining the mileage sections of the train continuously running in the line according to the kilometer posts in the section analysis data with the intersection.
5. The method of claim 4, wherein the determining whether the mileage of the trains intersects with each other according to the kilometer posts in the section analysis data includes comparing the section analysis data of each line of each train within a preset time period, and selecting two of the section analysis data for each time to determine whether the mileage of the trains intersects with each other:
selecting a first section of analysis data with a larger kilometer post, a second section of analysis data with a smaller kilometer post, and determining that the mileage of the train has an intersection;
selecting a second section of analysis data with a larger kilometer post, a first section of analysis data with a larger kilometer post, a second section of analysis data with a smaller kilometer post, a first section of analysis data with a smaller kilometer post, and a second section of analysis data with a smaller kilometer post, wherein the second section of analysis data with a smaller kilometer post is selected, and the mileage of the trains is determined to have intersection;
determining that the mileage of the train has an intersection when the larger kilometer post in the selected first section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected second section analysis data, and the smaller kilometer post in the selected second section analysis data is larger than the smaller kilometer post in the selected first section analysis data;
and determining that the mileage of the train has intersection when the larger kilometer post in the selected second section analysis data is larger than the larger kilometer post and the smaller kilometer post in the selected first section analysis data, and the smaller kilometer post in the selected first section analysis data is larger than the smaller kilometer post in the selected second section analysis data.
6. The method according to claim 4 or 5, characterized in that:
the starting kilometer scale of the mileage section is: analyzing the minimum kilometer post in data of all sections with intersection in the mileage of the train;
the end kilometer scale of the mileage section is: the maximum kilometer post in all sections of analysis data where the mileage of the train runs intersects.
7. The method according to any one of claims 1 to 5, wherein the determining whether to perform wireless timeout warning on the mileage section comprises:
determining the number of the section data with the same line information and the same mileage section and the train corresponding to the section data according to the number of the section data and the train corresponding to the section data which are integrated and obtained in a preset statistical period;
and when the number of the section data with the same line information and the same mileage section is larger than a preset number threshold value and the section data corresponds to more than two trains, performing wireless overtime section early warning on the mileage section.
8. A computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing a method of implementing an early warning process as claimed in any one of claims 1 to 7.
9. A terminal, comprising: a memory and a processor, the memory having a computer program stored therein; wherein the content of the first and second substances,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by the processor, implements a method of implementing an early warning process as claimed in any one of claims 1 to 9.
10. An apparatus for implementing early warning processing, comprising: the system comprises an acquisition unit, a merging unit, an integrated mileage unit and an early warning unit; wherein the content of the first and second substances,
the acquisition unit is configured to: for each calling service, acquiring a group of train operation data;
the merging unit is set as: for each group of train operation data, according to the operation time in the train operation data, combining two adjacent train operation data with the interval duration larger than the preset interval duration threshold value into one section analysis data for section analysis
The integrated mileage unit is set as: analyzing data of sections within a preset time length of each line of each train, determining a mileage section of the train continuously running in the line according to kilometer posts, and integrating the determined mileage section and the section analysis data for determining the mileage section into one section data;
the early warning unit is set as follows: determining whether to perform wireless overtime early warning on the mileage section according to the number of section data obtained by integration in a preset statistical period and the statistics of the train corresponding to the section data;
the train operation data are sorted according to the time sequence; the train operation data includes: the running time T and the kilometer mark K of the train; the counting period comprises N preset durations, and N is an integer greater than or equal to 2.
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