CN117574072A - Rail transit system state determining method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a device, equipment and a storage medium for determining a state of a rail transit system, and belongs to the technical field of data processing. The method comprises the following steps: acquiring target track index data of a target track; the target track index data comprises passenger flow data, infrastructure equipment state data, train management data and environment data; normalizing the target track index data to obtain target normalized data; projecting the target normalized data to a target projection vector to obtain a target projection characteristic; and determining the distance between the target projection characteristics and each cluster center in the track traffic system state determination model, and determining the target track state of the target track according to the distance. By the technical scheme, the state of the rail transit system can be accurately and comprehensively determined.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining a state of a rail transit system.
Background
The network security evaluation of the rail transit system is always one of important contents of the rail transit security management. The method for evaluating the safety of the rail transit system network by using a scientific and effective method has very important significance. With the regional development of urban groups, the travel distance of passengers is continuously extended, the scale of a rail transit network with regional rail transit as a core is continuously expanded, the networked safe operation of multi-system fusion is ensured, and greater challenges are brought to managers. The regional track traffic operation security risk evaluation method is established, and the most fundamental purpose is to assist a regional track traffic operation manager to control the state of a network operation process and security risks in real time, comprehensively and accurately, realize early warning and pre-control, enable the security risks to be in a controllable state, prevent the occurrence of the security risks, and guarantee operation security.
At present, related researches on the whole safety level evaluation of regional track traffic networks at home and abroad are less. The research on the wire network is mainly developed from the aspects of wire network structure, service level, social benefit, environmental benefit and the like. At present, a perfect track safety evaluation index system aiming at regional or networked is not established, and the track safety evaluation index system aims at objects such as lines, stations or facility equipment. The method for evaluating the safety risk is single, and a qualitative method, a quantitative method or a combination of the qualitative method and the quantitative method is mostly adopted, for example, a fault tree analysis method is used for analyzing the safety of an engineering system or the accident risk is single, and is not applicable to regional comprehensive risk evaluation; the probability risk assessment method is used for analyzing the occurring risk data and experimental result data and evaluating various risks, but has the problems that the number of uncertain variables is large, so that the calculated amount is large and the like; the monte carlo method is directed to risk events with a certain probability distribution or a large number of samples, but can only solve the determined risk assessment and the simpler risk problem. Most of statistical models cannot solve complex situations with more targets, such as qualitative and quantitative. Along with the increasing depth of research of comprehensive evaluation theory, the evaluation method is more and more complicated and diversified. The emphasis point and the footfall point of each method are different. The safety comprehensive evaluation index is rich, and more quantitative indexes are provided.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for determining a state of a rail transit system, which are used for accurately and comprehensively determining the state of the rail transit system.
According to an aspect of the present invention, there is provided a rail transit system state determining method, the method comprising:
acquiring target track index data of a target track; the target track index data comprises passenger flow data, infrastructure equipment state data, train management data and environment data;
normalizing the target track index data to obtain target normalized data;
projecting the target normalized data to a target projection vector to obtain a target projection characteristic;
and determining the distance between the target projection characteristics and each cluster center in the track traffic system state determination model, and determining the target track state of the target track according to the distance.
According to another aspect of the present invention, there is provided a rail transit system state determining apparatus including:
the target track index data acquisition module is used for acquiring target track index data of the target track; the target track index data comprises passenger flow data, infrastructure equipment state data, train management data and environment data;
The target normalization data determining module is used for carrying out normalization processing on the target track index data to obtain target normalization data;
the target projection characteristic determining module is used for projecting the target normalized data to a target projection vector to obtain a target projection characteristic;
and the target track state determining module is used for determining the distance between the target projection characteristics and each cluster center in the track traffic system state determining model, and determining the target track state of the target track according to the distance.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the rail transit system state determining method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for determining a state of a rail transit system according to any embodiment of the present invention when executed.
According to the technical scheme, the target track index data of the target track are obtained, then the target track index data are normalized to obtain target normalized data, the target normalized data are projected to the target projection vector to obtain the target projection characteristic, the distance between the target projection characteristic and each cluster center in the track traffic system state determination model is finally determined, and the target track state of the target track is determined according to the distance. According to the technical scheme, the track state of the target track is determined based on the target projection vector and the track traffic system state determination, so that the influence of human factors of expert weighting can be avoided, and the regional track traffic system state can be more accurately and comprehensively assessed.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for determining a state of a rail transit system according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for determining a state of a rail transit system according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a rail transit system status determining device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for determining a state of a rail transit system according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, in the technical scheme of the invention, the processing such as collection, storage, use, processing, transmission, provision and disclosure of the target track index data and the sample track index data and the like all meet the regulations of related laws and regulations and do not violate the popular regulations.
Example 1
Fig. 1 is a flowchart of a method for determining a state of a rail transit system according to a first embodiment of the present invention. The present embodiment may be applied to a case of how to accurately and comprehensively determine a rail transit system state, and the method may be performed by a rail transit system state determining device, which may be implemented in the form of hardware and/or software, and which may be integrated in an electronic device, such as a server, carrying a rail transit system state determining function. As shown in fig. 1, the method includes:
s110, acquiring target track index data of the target track.
In this embodiment, the target track refers to a track related to a track traffic system in a certain area. The target track index data is index data for judging the safety state of the regional track traffic system; optionally, the target track index data includes passenger flow data, infrastructure equipment status data, train management data, and environmental data; the passenger flow data comprise at least one of passenger flow distribution balance, average full load rate and station passenger flow crowding degree; the infrastructure equipment status data includes at least one of failure rate, average failure interval time, failure concentration, failure rate, and average recovery time; the train management data comprises at least one of average train density, train punctuation rate and service reliability; the environmental data includes at least one of extreme weather occurrences, noise, and track line conditions.
It can be understood that the statistical data shows that the passenger flow has a strong correlation with the operation accident, and the distribution state of the passenger flow is also a direct expression of the regional rail transit safety management level, so that the passenger flow distribution balance, the average full load rate and the station passenger flow crowding degree are adopted as passenger flow index data, and the safety state of the regional rail transit system can be judged through the passenger flow data. Infrastructure equipment is the most important component of regional rail transit systems, including systems such as vehicles, signals, power supplies, line facilities and the like; because of the high coupling of regional systems, any system failure can have an important effect on operation safety, and therefore, failure rate, average failure interval time, failure concentration, failure rate and average recovery time are taken as infrastructure equipment state data. The management operation condition (namely train management data) mainly reflects the driving organization level, judges whether a dispatcher directs the safety and the normal operation of a train according to an operation diagram, and the lower organization level can cause the confusion of the operation of a regional rail transit system, so that the overall safety level of the system is reduced; therefore, the average train density, the train positive point rate, and the service reliability are taken as the train management data. The good operation environment (namely, environment data) is the basis of the safe operation of regional rail transit. Statistical analysis of the rail operation accidents shows that the operation accidents caused by extreme weather (heavy rain, strong wind, snow, fog, thunder and lightning and the like) among environmental factors affecting the operation safety are the vast majority. Noise pollution directly endangers the health of passengers and staff, so that the human error rate is obviously increased, and the attention to noise is also required to be increased; in addition, the track is the basis of train driving, once the line shape is determined, the line shape can not be changed no matter whether the line shape is good or bad, and any bad design can become potential traffic safety hazards to influence driving safety and passenger travelling comfort; thus, the number of extreme weather occurrences, noise, and track line conditions are taken as environmental data.
Specifically, the target track index data of the target track may be obtained from a third party channel associated with the regional track traffic system through different interfaces.
S120, carrying out normalization processing on the target track index data to obtain target normalized data.
In this embodiment, the target normalized data refers to data obtained by normalizing target track index data.
Specifically, the maximum value and the minimum value in all index data in the target track index data are determined, then a first difference value between the maximum value and the minimum value is calculated, further, for each index data in the target track index data, a second difference value between the index number and the minimum value is calculated, and the quotient between the second difference value and the first difference value is used as normalized data of the index data.
S130, projecting the target normalized data to a target projection vector to obtain target projection characteristics.
In this embodiment, the target projection vector is an optimal one-dimensional vector for projecting high-dimensional data to the vector to increase the accuracy of subsequent clusters. The target projection feature is a feature obtained by projecting target normalized data onto a target projection vector, and is a one-dimensional feature value.
Specifically, the target normalized data and the target projection vector are multiplied to obtain the target projection characteristic.
S140, determining the distance between the projection characteristics of the target and each cluster center in the track traffic system state determination model, and determining the target track state of the target track according to the distance.
In this embodiment, the rail transit system state determination model is determined in advance based on sample normalization data; optionally, the rail transit system state determining model is a clustering model, and includes a plurality of clustering clusters, wherein each clustering cluster represents a security level of the rail transit system state; the center of each cluster point group is the cluster center.
The target track state refers to a security state level of the regional track traffic system.
Specifically, the distance between the target projection characteristic and each cluster center in the track traffic system state determination model can be calculated, then the cluster point group corresponding to the distance center with the smallest distance is determined, and the system state security level corresponding to the cluster point group is used as the target track state of the target track.
As an alternative of the present invention, the rail transit system state determination model may be determined by: normalizing the sample track index data to obtain sample normalized data; projecting the sample normalized data to a target projection vector to obtain sample projection characteristics; and clustering at least two sample normalized data according to the sample projection characteristics to obtain a rail transit system state determining model.
The sample track index data refers to track index data related to a historical track traffic system; optionally, the sample track index data includes passenger flow data, infrastructure equipment status data, train management data, and environmental data; the passenger flow data comprise at least one of passenger flow distribution balance, average full load rate and station passenger flow crowding degree; the infrastructure equipment status data includes at least one of failure rate, average failure interval time, failure concentration, failure rate, and average recovery time; the train management data comprises at least one of average train density, train punctuation rate and service reliability; the environmental data includes at least one of extreme weather occurrences, noise, and track line conditions.
The sample normalization data is data obtained by normalizing sample orbit data. The sample projection feature is a feature obtained by projecting sample normalized data onto a target projection vector, and is a one-dimensional feature value.
Specifically, for each index data, a maximum value and a minimum value in the index data in all sample track index data are determined, and then a first difference value between the maximum value and the minimum value is calculated; and further, for each index data in the sample track index data, calculating a second difference value between the index number and the minimum value, and taking the quotient between the second difference value and the first difference value as normalized data of the index data. For example, can be determined by the following formula:
（1）
Wherein,sample normalization data of the j index data in the i sample track index data; />Refers to the j index data in the i sample track index data; />，/>The maximum value and the minimum value of the j-th index data, respectively.
Further, multiplying the sample normalized data projection with the target projection vector to obtain a sample projection characteristic; and clustering at least two sample normalized data according to the sample projection characteristics to obtain a rail transit system state determination model, for example, the sample projection characteristics corresponding to the at least two sample normalized data can be clustered to obtain a plurality of clustering clusters, and each clustering cluster represents a security level to obtain the rail transit system state determination model.
It can be appreciated that by projecting such high-dimensional data of the sample track index data into the ground space to determine the track traffic system state determination model, the calculation amount can be reduced, and the influence of the subjective factors on the clustering result can be reduced.
According to the technical scheme, the target track index data of the target track are obtained, then the target track index data are normalized to obtain target normalized data, the target normalized data are projected to the target projection vector to obtain the target projection characteristic, the distance between the target projection characteristic and each cluster center in the track traffic system state determination model is finally determined, and the target track state of the target track is determined according to the distance. According to the technical scheme, the track state of the target track is determined based on the target projection vector and the track traffic system state determination, so that the influence of human factors of expert weighting can be avoided, and the regional track traffic system state can be more accurately and comprehensively assessed.
Example two
Fig. 2 is a flowchart of a method for determining a state of a rail transit system according to a second embodiment of the present invention. The present embodiment describes in detail how to determine the target projection vector on the basis of the above embodiments. As shown in fig. 2, the method includes:
s210, acquiring target track index data of a target track.
S220, carrying out normalization processing on the target track index data to obtain target normalized data.
And S230, projecting the target normalized data to a target projection vector to obtain target projection characteristics.
S240, determining the distance between the projection characteristics of the target and each cluster center in the track traffic system state determination model, and determining the target track state of the target track according to the distance.
Alternatively, the target projection vector may be determined as follows: normalizing the index data of at least two sample tracks to obtain sample normalized data of the sample tracks; according to the sample normalized data and the candidate projection vector, determining candidate projection features corresponding to the sample normalized data; and determining a target projection vector according to the candidate projection characteristics, the sample number and at least two sample orbit index data.
The candidate projection vectors refer to one-dimensional vectors in different directions, and the one-dimensional length of the candidate projection vectors is the same as the index number of the sample orbit index data. The candidate projection feature is a feature obtained by projecting sample normalized data onto a candidate projection vector, and is a one-dimensional feature value.
Specifically, normalization processing is performed on at least two sample track index data respectively to obtain sample normalization data of a sample track, then for each sample normalization data, the sample normalization data and a candidate projection vector are multiplied to obtain a candidate projection feature corresponding to the sample normalization data, for example, the candidate projection feature can be determined by the following formula:
（2）
wherein,representing candidate projection features corresponding to the ith sample track index data (i.e. sample normalization data); m represents the number of sample track index data; n represents the number of indexes in the sample track index data; />A value representing a candidate projection vector corresponding to the j-th index; />Representing candidate projection vectors.
Further, the target projection vector may be determined based on the projection pursuit clustering method from the candidate projection features, the number of samples, and at least two sample trajectory index data.
Alternatively, determining the target projection vector from the candidate projection feature, the number of samples, and the at least two sample trajectory index data includes: constructing a projection index function and a constraint condition corresponding to the candidate projection vector according to the candidate projection characteristics, the sample number and at least two sample orbit index data; and determining a target projection vector from at least two candidate projection vectors according to the projection index function and the constraint condition based on a real number coding acceleration genetic algorithm.
Specifically, the projection vector optimizing problem (determination of the target projection vector) can be understood as the extreme value of a problem under the constraint condition, and a projection index function Q should be established) The judgment is performed, specifically, a projection index function and a constraint condition corresponding to the candidate projection vector can be constructed according to the candidate projection characteristic, the sample number and at least two sample orbit index data based on a preset projection index function construction mode. Wherein, the constraint condition is:。
further, constructing a projection index function corresponding to the candidate projection vector according to the candidate projection characteristics, the sample number and at least two sample orbit index data, including: determining a feature mean of at least two candidate projection features; determining standard deviation of the candidate projection features according to the feature mean, the candidate projection features and the sample number; determining a window radius of the local density according to at least two sample track index data; determining the local density of the candidate projection features according to the distance between the candidate projection values and the radius of the window; and determining a projection index function corresponding to the candidate projection vector according to the standard deviation of the candidate projection feature and the local density of the candidate projection feature. Where local density refers to the number of samples per unit area within a certain range.
Specifically, first, a feature mean of at least two candidate projection features is calculatedThe method comprises the steps of carrying out a first treatment on the surface of the The standard deviation of the candidate projection features may then be determined from the feature mean, the candidate projection features, and the number of samples based on the following formula:
(3)
wherein,representing the standard deviation of the candidate projection features.
Secondly, determining the window radius of the local density according to at least two sample track index data, wherein the window radius can be determined by the following method: the window radius R of the local density needs to be determined in advance, at present, R is not selected by a known unified method, but the influence on the final clustering result is extremely large, and a large number of samples can be classified into the same dot group due to the overlarge value, so that the clustering effect can not be achieved; too small values can cause too many clusters, and fewer samples are in the clusters, so that the reliability of the clustering result is also affected. The invention utilizes a K-means clustering method to determine the radius of a local density window, and the specific thinking is as follows:
k-means clustering is carried out on n samples subjected to clustering, and n is divided into K types (dot groups) under the assumption that each dot group contains the number of samples ofAnd (2) andin descending orderIs marked as，In descending order with sequence number kValue of thenWherein, the method comprises the steps of, wherein,. The value of R should satisfy each sample between different clusters The minimum value should be greater than R, while samples within the same clusterSo for a certain pointIn the case of a dough, satisfiesWill not be counted in the category, the specific number isSo willAfter descending order, the p-thThe value of (2) can be taken as a reasonable value of R, namely. Wherein,representing distances between candidate projection features, i.e.。
Thirdly, determining the local density of the candidate projection features according to the distance between the candidate projection values and the window radius, wherein the local density can be determined by the following formula:
(4)
wherein,representing local densities of candidate projection features; />Representing a unit step function whenWhen it is, its value is 1; when->When it is, its value is 0.
Fourth, according to the standard deviation of the candidate projection feature and the local density of the candidate projection feature, determining a projection index function corresponding to the candidate projection vector, and specifically multiplying the standard deviation of the candidate projection feature and the local density of the candidate projection feature to be used as the projection index function corresponding to the candidate projection vector. For example, the following formula:
(5)
after determining the projection index function and the constraint condition, a target projection vector may be determined from the at least two candidate projection vectors based on the projection index function and the constraint condition based on a real number encoding acceleration genetic algorithm. The basic original and calculation steps are as follows:
Candidate projection directionsInfluence of projection index function Q #) Thus, the target projection vectorMay be equivalent to solving the maximum of the projected index function:
objective function:
(6)
constraint conditions:
(7)
after the evaluation problem is converted into the mathematical problem, the variable number to be optimized is M, and according to the application condition of the method and the complexity of data in the evaluation, the initial parent population number N can be set, and the optimization problem is set as follows:
(8)
step 1: and (5) real number coding. Unlike binary coding of standard genetic algorithm, RAGA adopts real number coding, and linear transformation is carried out by formula (9)Corresponding to interval [0,1 ]]Real +.>Corresponding to the genes in the standard genetic algorithm, and sequentially connecting the genes corresponding to all the optimized variables to form the code of the optimal solution +.>Called chromosome, at this time +.>The value range of (2) is [0,1 ]]。
（9）
Step 2: and initializing parent population. Randomly generated N groups of lie intervals [0,1 ]]Random number onObtaining an optimized variable +.>Is a parent population of (c). Will->Bringing the objective function into the objective function to obtain the objective function value->。
（10）
Step 3: and (5) evaluating the adaptability of the parent population. Definition of fitness function valuesNamely objective function value->The fitness values are arranged in order from large to small.
Step 4: and (5) selecting. Selection of generation 1 of the sub-generation population using roulette wheelThe selection probability of the parent population is expressed as a formula (10), and the selection probability is related to the fitness value, so that individuals with low fitness values are eliminated, and the optimizing process is completed.
Step 5: crossing. The individuals in the population are crossed with a certain probability, and a pair of father-generation individual genes are selected to cross with the probability shown in the formula (11) to generate the 2 nd generation population
（11）
Step 6: variation. Any few genes on the chromosome of each parent individual are overturned with smaller probability to obtain the 3 rd generation population。
Step 7: and (3) repeating the steps of selecting, crossing and mutating to obtain 3N child individuals, selecting child individuals with fitness value rows corresponding to the previous N child individuals for next iteration, and repeating the steps of selecting, crossing and mutating until the iteration times are reached.
Step 8: the optimal variable interval corresponding to the excellent child population generated after each cycle operation is continuously contracted and gradually approaches to the optimal variable until the optimal individual in the latest generation of child population is output after the iteration times are completed, and the optimal variable is the optimal variable, namely the target projection vector.
According to the technical scheme, the target track index data of the target track are obtained, then the target track index data are normalized to obtain target normalized data, the target normalized data are projected to the target projection vector to obtain the target projection characteristic, the distance between the target projection characteristic and each cluster center in the track traffic system state determination model is finally determined, and the target track state of the target track is determined according to the distance. According to the technical scheme, the track state of the target track is determined based on the target projection vector and the track traffic system state determination, so that the influence of human factors of expert weighting can be avoided, and the regional track traffic system state can be more accurately and comprehensively assessed.
Example III
Fig. 3 is a schematic structural diagram of a rail transit system status determining device according to a third embodiment of the present invention. The present embodiment may be applicable to a case of how to accurately and comprehensively determine a rail transit system state, and the rail transit system state determining apparatus may be implemented in the form of hardware and/or software, and may be integrated in an electronic device, such as a server, that carries a rail transit system state determining function. As shown in fig. 3, the apparatus includes:
a target track index data acquisition module 310, configured to acquire target track index data of a target track; the target track index data includes passenger flow data, infrastructure equipment status data, train management data, and environmental data;
the target normalized data determining module 320 is configured to normalize the target track index data to obtain target normalized data;
the target projection feature determining module 330 is configured to project the target normalized data to a target projection vector to obtain a target projection feature;
the target track state determining module 340 is configured to determine a distance between the target projection feature and each cluster center in the track traffic system state determining model, and determine a target track state of the target track according to the distance.
According to the technical scheme, the target track index data of the target track are obtained, then the target track index data are normalized to obtain target normalized data, the target normalized data are projected to the target projection vector to obtain the target projection characteristic, the distance between the target projection characteristic and each cluster center in the track traffic system state determination model is finally determined, and the target track state of the target track is determined according to the distance. According to the technical scheme, the track state of the target track is determined based on the target projection vector and the track traffic system state determination, so that the influence of human factors of expert weighting can be avoided, and the regional track traffic system state can be more accurately and comprehensively assessed.
Optionally, the apparatus further includes a system state determination model obtaining module configured to:
normalizing the sample track index data to obtain sample normalized data;
projecting the sample normalized data to a target projection vector to obtain sample projection characteristics;
and clustering at least two sample normalized data according to the sample projection characteristics to obtain a rail transit system state determining model.
Optionally, the apparatus further includes a target projection vector determination module, which includes:
the sample normalization data determining unit is used for carrying out normalization processing on at least two sample track index data to obtain sample normalization data of a sample track;
the candidate projection feature determining unit is used for determining candidate projection features corresponding to the sample normalized data according to the sample normalized data and the candidate projection vectors;
and the target projection vector determining unit is used for determining a target projection vector according to the candidate projection characteristics, the sample number and at least two sample orbit index data.
Optionally, the target projection vector determining unit includes:
the index function determining subunit is used for constructing a projection index function and a constraint condition corresponding to the candidate projection vector according to the candidate projection characteristics, the sample number and at least two sample orbit index data;
and the target projection vector determining subunit is used for determining the target projection vector from at least two candidate projection vectors according to the projection index function and the constraint condition based on the real number coding acceleration genetic algorithm.
Optionally, the index function determining subunit is specifically configured to:
determining a feature mean of at least two candidate projection features;
Determining standard deviation of the candidate projection features according to the feature mean, the candidate projection features and the sample number;
determining a window radius of the local density according to at least two sample track index data;
determining the local density of the candidate projection features according to the distance between the candidate projection values and the radius of the window;
and determining a projection index function corresponding to the candidate projection vector according to the standard deviation of the candidate projection feature and the local density of the candidate projection feature.
Optionally, the passenger flow data comprises at least one of passenger flow distribution balance, average full load rate and station passenger flow crowding degree; the infrastructure equipment status data includes at least one of failure rate, average failure interval time, failure concentration, failure rate, and average recovery time; the train management data comprises at least one of average train density, train punctuation rate and service reliability; the environmental data includes at least one of extreme weather occurrences, noise, and track line conditions.
The rail transit system state determining device provided by the embodiment of the invention can execute the rail transit system state determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device implementing a method for determining a state of a rail transit system according to an embodiment of the present invention; fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the rail transit system state determination method.
In some embodiments, the rail transit system status determination method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the rail transit system state determining method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the rail transit system state determination method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for determining a state of a rail transit system, comprising:
acquiring target track index data of a target track; the target track index data comprises passenger flow data, infrastructure equipment state data, train management data and environment data;
normalizing the target track index data to obtain target normalized data;
projecting the target normalized data to a target projection vector to obtain a target projection characteristic;
And determining the distance between the target projection characteristics and each cluster center in the track traffic system state determination model, and determining the target track state of the target track according to the distance.
2. The method as recited in claim 1, further comprising:
normalizing the sample track index data to obtain sample normalized data;
projecting the sample normalized data to the target projection vector to obtain a sample projection characteristic;
and clustering at least two sample normalized data according to the sample projection characteristics to obtain a rail transit system state determining model.
3. The method according to claim 1 or 2, further comprising:
normalizing the index data of at least two sample tracks to obtain sample normalized data of the sample tracks;
according to the sample normalized data and the candidate projection vector, determining candidate projection features corresponding to the sample normalized data;
and determining a target projection vector according to the candidate projection characteristics, the sample number and the at least two sample orbit index data.
4. A method according to claim 3, wherein determining a target projection vector from the candidate projection features, the number of samples and the at least two sample trajectory index data comprises:
Constructing a projection index function and a constraint condition corresponding to the candidate projection vector according to the candidate projection characteristics, the sample number and the at least two sample orbit index data;
and determining a target projection vector from at least two candidate projection vectors according to the projection index function and the constraint condition based on a real number coding acceleration genetic algorithm.
5. The method of claim 4, wherein constructing a projection index function corresponding to a candidate projection vector from the candidate projection features, the number of samples, and the at least two sample trajectory index data comprises:
determining a feature mean of at least two candidate projection features;
determining standard deviation of the candidate projection features according to the feature mean, the candidate projection features and the sample number;
determining a window radius of the local density according to the at least two sample track index data;
determining the local density of the candidate projection features according to the distance between the candidate projection values and the window radius;
and determining a projection index function corresponding to the candidate projection vector according to the standard deviation of the candidate projection feature and the local density of the candidate projection feature.
6. The method of claim 1, wherein the traffic data comprises at least one of traffic distribution balance, average full rate, and station traffic congestion level; the infrastructure equipment status data includes at least one of failure rate, average failure interval time, failure concentration, failure rate, and average recovery time; the train management data comprises at least one of average train density, train punctuation rate and service reliability; the environmental data includes at least one of extreme weather occurrences, noise, and track line conditions.
7. A rail transit system state determining apparatus, characterized by comprising:
the target track index data acquisition module is used for acquiring target track index data of the target track; the target track index data comprises passenger flow data, infrastructure equipment state data, train management data and environment data;
the target normalization data determining module is used for carrying out normalization processing on the target track index data to obtain target normalization data;
the target projection characteristic determining module is used for projecting the target normalized data to a target projection vector to obtain a target projection characteristic;
And the target track state determining module is used for determining the distance between the target projection characteristics and each cluster center in the track traffic system state determining model, and determining the target track state of the target track according to the distance.
8. The apparatus of claim 7, further comprising a system state determination model derivation module configured to:
normalizing the sample track index data to obtain sample normalized data;
projecting the sample normalized data to the target projection vector to obtain a sample projection characteristic;
and clustering at least two sample normalized data according to the sample projection characteristics to obtain a rail transit system state determining model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the rail transit system state determination method of any of claims 1-6.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the rail transit system state determination method of any one of claims 1-6.
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