CN113691567A - Method and system for encrypting detection data of motor train unit wheel set - Google Patents
Method and system for encrypting detection data of motor train unit wheel set Download PDFInfo
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- CN113691567A CN113691567A CN202111245945.6A CN202111245945A CN113691567A CN 113691567 A CN113691567 A CN 113691567A CN 202111245945 A CN202111245945 A CN 202111245945A CN 113691567 A CN113691567 A CN 113691567A
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Abstract
The embodiment of the invention provides an encryption method and system for motor train unit wheel set detection data, which can search motor train unit wheel set detection data of each motor train unit wheel set area uploaded, carry out hiding feature decision on the motor train unit wheel set detection data according to a hiding feature decision network meeting network deployment requirements to obtain hiding decision features, derive a related encryption decision data set according to the hiding decision features, and carry out encryption processing on detection data items of each related motor train unit wheel set area having a connection relation according to the encryption decision data set. According to the scheme, the hidden feature decision network is used for carrying out hidden feature decision on the detection data of the motor train unit wheel set, and then data encryption is carried out according to the hidden feature decision network, so that an intruder can be effectively prevented from illegally acquiring track terrain scene data through analyzing the detection data of the motor train unit wheel set, and the risk of geographic information leakage is avoided.
Description
Technical Field
The invention relates to the technical field of data encryption, in particular to an encryption method and system for detection data of a motor train unit wheel set.
Background
The motor train unit wheel pair is influenced by the surrounding environment in the operation process, part of characteristics of detection data of the motor train unit wheel pair generated in the influence process can reflect the characteristics of a surrounding environment scene, the characteristics can be understood as characteristic data of hiding data needing to be encrypted, and lawless persons in the related technology reversely deduce the surrounding environment scene based on analysis of the hiding decision characteristics, so that the risk of geographic information leakage is possibly caused.
Disclosure of Invention
In order to overcome at least the above defects in the prior art, the invention aims to provide a method and a system for encrypting detection data of a wheel set of a motor train unit.
In a first aspect, the invention provides a method for encrypting detection data of a wheel set of a motor train unit, which is applied to an encryption system of the detection data of the wheel set of the motor train unit, and the method comprises the following steps:
searching the uploaded motor train unit wheel pair detection data of each motor train unit wheel pair area;
carrying out hiding feature decision on the detection data of the motor train unit wheel set according to a hiding feature decision network meeting the network deployment requirement to obtain hiding decision features;
and deriving a related encryption decision data set according to the concealment decision characteristics, and encrypting the detection data items of each related motor train unit wheel pair region having the relation according to the encryption decision data set.
In a second aspect, an embodiment of the present invention further provides an encryption system for wheel set detection data of a motor train unit, where the encryption system for wheel set detection data of a motor train unit includes a processor and a machine-readable storage medium, where machine-executable instructions are stored in the machine-readable storage medium, and the machine-executable instructions are loaded and executed by the processor to implement the aforementioned encryption method for wheel set detection data of a motor train unit.
According to any one of the aspects, the uploaded motor train unit wheel pair detection data of each motor train unit wheel pair area can be searched, the hiding feature decision is carried out on the motor train unit wheel pair detection data according to the hiding feature decision network meeting the network deployment requirement to obtain the hiding decision feature, the related encryption decision data set is derived according to the hiding decision feature, and the detection data items of the related motor train unit wheel pair areas with the contact relation are encrypted according to the encryption decision data set. According to the scheme, the hidden feature decision network is used for carrying out hidden feature decision on the detection data of the motor train unit wheel set, and then data encryption is carried out according to the hidden feature decision network, so that an intruder can be effectively prevented from illegally acquiring track terrain scene data through analyzing the detection data of the motor train unit wheel set, and the risk of geographic information leakage is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings which are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for encrypting detection data of a motor train unit wheel set according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a structure of an encryption system for motor train unit wheel set detection data for implementing the encryption method for motor train unit wheel set detection data according to the embodiment of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a particular application and its requirements. It will be apparent to those skilled in the art that various modifications to the disclosed embodiments are possible, and that the general principles defined in this disclosure may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present invention is not limited to the described embodiments, but should be accorded the widest scope consistent with the claims.
The terminology used in the description of the invention herein is for the purpose of describing particular example embodiments only and is not intended to limit the scope of the present invention. As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, components, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, and/or groups thereof.
These and other features, aspects, and advantages of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the accompanying drawings, all of which form a part of this specification. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.
Flow charts are used in the present invention to illustrate operations performed by systems according to some embodiments of the present invention. It should be understood that the operations in the flow diagrams may be performed out of order. Rather, various steps may be processed in reverse order or simultaneously. Further, one or more other operations may be added to the flowchart. One or more operations may also be deleted from the flowchart.
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments.
Fig. 1 is a schematic flow chart of an encryption method for wheel set detection data of a motor train unit according to an embodiment of the present invention, and the following describes the encryption method for wheel set detection data of the motor train unit in detail.
And step S110, searching the uploaded motor train unit wheel pair detection data of each motor train unit wheel pair area.
And step S120, carrying out hiding characteristic decision on the detection data of the motor train unit wheel set according to a hiding characteristic decision network meeting the network deployment requirement to obtain hiding decision characteristics.
For example, the hidden feature decision network can perform convergence optimization through deep feature learning, namely, neural network training is performed to obtain the hidden feature decision network meeting the network deployment requirement, and the hidden feature decision network meeting the network deployment requirement can perform hidden feature decision on detection data of the motor train unit wheel set. The hidden decision-making characteristics can refer to characteristic data of hidden data which needs to be encrypted and possibly exists in the detection data of the motor train unit wheel set, for example, the motor train unit wheel set can be influenced by the surrounding environment in the operation process, part of characteristics of the detection data of the motor train unit wheel set generated in the influence process can reflect the characteristics of the surrounding environment scene, the characteristics can be understood as the characteristic data of the hidden data which needs to be encrypted, and lawless persons in the related technology reversely deduce the surrounding environment scene based on the analysis of the hidden decision-making characteristics, so that the risk of geographic information leakage can be caused.
And S130, deriving a related encryption decision data set according to the hiding decision characteristic, and encrypting the detection data items of each related motor train unit wheel pair area having a contact relation according to the encryption decision data set.
For example, a target hidden decision feature that satisfies an encryption decision requirement among the hidden decision features may be used as encryption decision data to generate a relevant encryption decision data set, so as to encrypt a detection data item having a relationship with each motor train unit wheel pair region. For example, a detection data item in which each encryption decision data in the encryption decision data set has a relationship is obtained, and encryption processing is performed according to a preset encryption mode corresponding to a characteristic feature of each encryption decision data.
According to the steps, the motor train unit wheel pair detection data of each motor train unit wheel pair area can be searched, the hiding feature decision is carried out on the motor train unit wheel pair detection data according to the hiding feature decision network meeting the network deployment requirement to obtain the hiding decision feature, the related encryption decision data set is derived according to the hiding decision feature, and the detection data items of each motor train unit wheel pair area with the contact relation are encrypted according to the encryption decision data set. According to the scheme, the hidden feature decision network is used for carrying out hidden feature decision on the detection data of the motor train unit wheel set, and then data encryption is carried out according to the hidden feature decision network, so that an intruder can be effectively prevented from illegally acquiring track terrain scene data through analyzing the detection data of the motor train unit wheel set, and the risk of geographic information leakage is avoided.
In some embodiments, step S120 may be implemented as follows.
And S121, deciding a hidden feature space and a hidden feature significant event contained in the detection data of the motor train unit wheel pair according to the hidden feature decision network, and deciding a hidden feature type related to the hidden feature space and a significant event type related to the hidden feature significant event.
And step S122, taking the wheel pair detection data of the motor train unit, the hiding feature space and the related hiding feature types, the hiding feature significant events and the related significant event types as the hiding decision-making features.
For example, the motor train unit wheel set detection data can be transmitted to a hidden feature decision network meeting network deployment requirements, hidden feature categories related to hidden feature spaces contained in the motor train unit wheel set detection data are decided according to hidden feature space decision components contained in the hidden feature decision network, and significant event categories related to hidden feature significant events related to the motor train unit wheel set detection data are decided according to a significant event decision structure contained in the hidden feature decision network.
In some embodiments, the convergence optimization step of the above concealed feature decision network may be referred to as the following step.
Step S101: an example motor train unit wheel set detection dataset is searched, the example motor train unit wheel set detection dataset comprising motor train unit wheel set detection data configured with a hidden feature category and a significant event category.
For example, motor train unit wheel pair detection data uploaded in real time can be searched, and hidden feature categories related to hidden feature space included in the motor train unit wheel pair detection data and salient event categories related to salient events based on the hidden features included in the motor train unit wheel pair detection data can be configured.
The above-described concealed feature types arranged in the detection data of the motor train unit wheelset, the concealed feature types included in the set concealed feature space, and the concealed feature types included in the set detection vector sequence may be expressed according to the label attribute for indicating the concealed feature types.
And determining the hiding feature types related to the hiding feature space contained in each motor train unit wheel pair detection data and the significant event types related to the hiding feature significant events contained in each motor train unit wheel pair detection data, and forming an example motor train unit wheel pair detection data set by all the uploaded motor train unit wheel pair detection data. After the concealed feature space, the detection vector sequence and the significant event vector sequence are set according to the scheme, network convergence optimization can be carried out on the detection data set based on the deployed exemplary motor train unit wheel.
Step S102: a network configuration component that deploys a concealed feature decision network for predicting concealed decision features, the concealed feature decision network comprising a significant event decision structure based on a plurality of decision feature functions and a focused function.
For example, the vector mining component, the suppressed feature space-migration mining component, the suppressed feature focusing component, and the suppressed feature decision component are functionally associated to obtain the suppressed feature space decision component. And performing function connection on the significant event focusing assembly, the significant wheel pair scene focusing assembly and the focusing decision assembly and the significant event type decision assembly to obtain a significant event decision structure. Wherein the focus decision component comprises a plurality of decision feature functions and a focus-on-focus function. Both the suppressed feature space decision component and the significant event decision structure are linked to a convergence evaluation component.
Step S103: and carrying out convergence optimization on the hidden feature decision network according to the detection data of the motor train unit wheel set covered by the detection data set of the motor train unit wheel set.
In some embodiments, the convergence optimization process for the suppressed feature decision network is as follows.
(1) And searching the wheel set detection data of the motor train unit in a preset data range from the wheel set detection data set of the motor train unit.
The concealed feature decision network can be transmitted to a plurality of motor train unit wheel pair detection data. And searching the wheel set detection data of the motor train unit in a preset data range from the wheel set detection data set of the motor train unit. The predetermined data range is the convergence optimization upper limit number supported by the concealed feature decision network.
(2) And respectively transmitting the detection data of the wheel set of the motor train unit to a hidden feature space decision component and a significant event decision structure, and then executing the following steps.
And transmitting the detection data of the motor train unit wheel set to a vector mining component contained in the hidden feature space decision component, and simultaneously transmitting the detection data of the motor train unit wheel set to a significant event focusing component, a significant wheel set scene focusing component and a focusing decision component in a significant event decision structure. And synchronously carrying out hidden feature space deep training and hidden feature significant event deep training on the detection data of the motor train unit wheel set according to the hidden feature space decision component and the significant event decision structure, wherein the hidden feature space and hidden feature significant event deep training efficiency of the detection data of the motor train unit wheel set is higher. And in the obvious event decision structure, hidden characteristic obvious event depth training is synchronously performed on the wheel set detection data of the motor train unit according to the obvious event focusing assembly, the obvious wheel set scene focusing assembly and the focusing decision assembly, so that the decision efficiency of the hidden characteristic obvious event is improved.
(3) And deciding the hiding feature type related to the hiding feature space contained in the detection data of the motor train unit wheel set according to the hiding feature space decision component.
For example, a wheel set detection vector sequence of each wheel set detection periodic data in the motor train unit wheel set detection data can be mined according to a vector mining component. Most of the wheel set detection vector sequences of the hidden feature space are useless feature information, so that wheel set detection periodic data, of which the wheel set detection vector sequences are the designated wheel set detection vector sequences, are removed from the wheel set detection data of the motor train unit after the wheel set detection vector sequences are configured, and the useless feature information in the wheel set detection data of the motor train unit is filtered.
Next, the hidden feature part included in the motor train unit wheel set detection data after removal can be mined according to the hidden feature space-migration mining component, wherein the hidden feature part includes the relationship between the hidden feature space included in the data features of the motor train unit wheel set detection data and the hidden feature space.
After the concealed feature portion is extracted, the concealed feature type related to the concealed feature portion is determined according to the concealed feature focusing component. In step S101, a concealment feature space is set in a synchronized configuration when the motor train unit wheelset detection data is added. And according to the extracted hiding feature part, setting whether the hiding feature space covers the hiding feature category related to the hiding feature part according to the focusing of the hiding feature focusing component. If so, the category of the concealed feature related to the concealed feature part is searched from the set concealed feature space. The suppressed feature category to which the suppressed feature portion relates is then decided upon by a suppressed feature decision component.
When the motor train unit wheel set detection data is added in step S101, a set detection vector sequence including a large number of wheel set detection periodic vector sequences and concealed feature categories related to the motor train unit wheel set detection data is also generated. If the concealed feature types related to the concealed feature parts are not obtained in the set concealed feature space, according to the motor train unit wheel set detection data after the wheel set detection periodic data of the specified wheel set detection vector sequence are removed, the wheel set detection periodic vector sequence related to the motor train unit wheel set detection data is excavated according to the vector excavating unit. And focusing and setting whether the relevance between the wheel set detection periodic vector sequence including the wheel set detection data of the motor train unit wheel set detection data in the detection vector sequence is not less than a preset relevance wheel set detection periodic vector sequence according to the hidden feature focusing assembly. If the train wheel set detection data is determined to be the hidden feature type relevant to the hidden feature part of the train wheel set detection data, determining the hidden feature type relevant to the hidden feature part of the train wheel set detection data, wherein the relevance degree of the train wheel set detection data is not smaller than the preset relevance degree, and deciding the hidden feature type relevant to the hidden feature part according to a hidden feature decision component. If the association degree between the wheel set detection periodic vector sequence of the motor train unit wheel set detection data and the wheel set detection periodic vector sequence is not smaller than the preset association degree wheel set detection periodic vector sequence, configuring the hidden feature types related to the hidden feature parts of the motor train unit wheel set detection data into preset hidden feature types, and deciding the hidden feature types related to the hidden feature parts according to a hidden feature decision component.
(4) And deciding the salient event category related to the hidden characteristic salient event related to the detection data of the motor train unit wheel set according to the salient event decision structure.
Since the hidden characteristic significant event is generally continuous detection data, a continuous wheel set detection data part can be extracted from the motor train unit wheel set detection data. And searching a first hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set according to the extracted continuous wheel set detection data part and a set significant event vector sequence and a significant event focusing component. The method comprises the steps of acquiring a significance vector of a detection data part of a continuous wheel set according to a significance event focusing component in a set significance event vector sequence, and taking a significance event category related to the significance vector as an extracted significance event category related to the detection data part of the continuous wheel set. And the first hidden characteristic significant event decision content comprises the significant event category partially related to the detection data by the aid of the persistent wheel.
In this embodiment, second hidden feature significant event decision content related to the detection data of the motor train unit wheel set is searched for according to the detection data of the motor train unit wheel set and the set significant event frequency vector sequence and according to the significant wheel set scene focusing component. The method comprises the steps of acquiring a significant event detection vector in the motor train unit wheel set detection data in a set significant event frequency vector sequence according to a significant wheel set scene focusing assembly, searching a significant event category related to the significant event detection vector from the set significant event frequency vector sequence, and taking the significant event category as the significant event category related to the motor train unit wheel set detection data. Wherein the second concealed feature significant event decision content comprises the significant event category related to the significant event detection vector.
In this embodiment, the third hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set is searched according to the focusing decision component. For example, the wheel set detection periodic data configured with the hidden feature type in the wheel set detection data of the motor train unit is separated from other detection vectors by using a first target wheel set detection vector sequence, and the wheel set detection periodic data configured with the significant event type is separated from other detection vectors by using a second target wheel set detection vector sequence, wherein the first target wheel set detection vector sequence and the second target wheel set detection vector sequence are different wheel set detection vectors. And separating the wheel set detection periodic data of the hidden feature space and the hidden feature significant event from other detection vectors according to a specific wheel set detection vector so as to facilitate a subsequent focusing decision-making component to call the wheel set detection data of the motor train unit.
And carrying out vector mining on the detection data of the motor train unit wheel set according to a plurality of decision characteristic functions in the focusing decision component to obtain a related periodic vector sequence. And determining a concentrated focusing function value related to each wheel pair detection periodic vector sequence in the periodic vector sequence according to a concentrated focusing function in the focusing decision component. And calculating corresponding attribution values of the hidden characteristic significant events contained in the motor train unit wheel set detection data corresponding to each significant event type according to the wheel set detection periodic vector sequence and the concentrated focusing function values related to the wheel set detection periodic vector sequence and the decision characteristic subfunctions contained in the decision characteristic functions. And taking the significant event category with the highest corresponding attribution value as third hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set. The third suppressed feature significant event decision content includes the significant event category with the highest corresponding attribution value.
And the focusing decision-making component is transmitted to a wheel set detection periodic data detection vector sequence in the wheel set detection data of the motor train unit, the hiding characteristic significant events in the wheel set detection periodic data detection vector sequence are output as corresponding attribution values of all significant event categories, and the significant event category with the highest corresponding attribution value is used as third hiding characteristic significant event decision-making content related to the wheel set detection data of the motor train unit. The third suppressed feature significant event decision content includes the significant event category with the highest corresponding attribution value.
And parallelly processing the wheel set detection data of the motor train unit according to a significant event focusing component, a significant wheel set scene focusing component and a focusing decision component to obtain a first hidden characteristic significant event decision content, a second hidden characteristic significant event decision content and a third hidden characteristic significant event decision content, and then determining the significant event category related to the hidden characteristic significant event related to the wheel set detection data of the motor train unit according to the first hidden characteristic significant event decision content, the second hidden characteristic significant event decision content and the third hidden characteristic significant event decision content. For example, the significance coefficient of each significant event category in the first, second and third concealment-feature significant event decision contents in all significant event categories is respectively determined; if the significance coefficients of the significant event categories are different, taking the significant event category with the highest significance coefficient as the significant event category related to the hidden feature significant event related to the detection data of the motor train unit wheel set; and if the significance coefficients of the significant event categories are the same, configuring the hidden feature significant events related to the detection data of the motor train unit wheel set into preset significant event categories.
(5) And determining a convergence evaluation value of the convergence optimization process according to the determined hiding feature type and the significant event type and the convergence evaluation component.
After the concealed feature type corresponding to the concealed feature space and the significant event type related to the concealed feature significant event in the detection data of the motor train unit wheel pair obtained according to the embodiment are obtained, the determined concealed feature type and the determined significant event type are transmitted to the convergence evaluation component.
In this embodiment, based on the concealed feature decision network, the concealed feature decision network configures a centralized focusing function in the concealed feature significant event deep training to converge a plurality of decision feature functions, thereby improving the network convergence effect. The method has the advantages that when the decision of the hidden characteristic significant events is carried out by adding the centralized focusing function to the decision characteristic functions, the hidden characteristic significant events are deeply trained, and the decision learning effect of the hidden characteristic significant events is improved. In the aspect of the hidden feature space depth training, wheel set detection periodic data of useless features are removed, and the hidden feature space depth training is carried out according to the set hidden feature space, so that the network convergence effect is further improved.
In some embodiments, reference is further made to the following steps.
And step S140, traversing and calling the encryption decision characteristic sequence of each motor train unit wheel pair area in the target operation stage.
Step S150, search a plurality of virtual response feature fields triggered by the encryption decision feature sequence under the virtual configuration accessor.
And step S160, performing abnormal ciphertext traceability on the plurality of virtual response characteristic fields to obtain a plurality of reference abnormal ciphertexts linked with the encryption decision characteristic sequence.
Step S170, traversing each reference abnormal ciphertext based on a plurality of reference abnormal ciphertexts, according to the virtual response interception information of the encryption decision feature sequence in the plurality of virtual response feature fields and the abnormal source tracing field of the reference abnormal ciphertext in the plurality of virtual response feature fields, determining the abnormal probability parameters of the encryption decision feature sequence and the reference abnormal ciphertext in the plurality of virtual response feature fields, and obtaining the abnormal probability parameter distribution related to the reference abnormal ciphertext.
And step S180, determining an abnormal ciphertext knowledge space of the encryption decision characteristic sequence according to the abnormal probability parameter distribution corresponding to each of the plurality of reference abnormal ciphertexts.
And step S190, searching a target abnormity repairing instruction corresponding to the abnormity ciphertext knowledge space based on a pre-developed abnormity repairing instruction set according to the abnormity ciphertext knowledge space.
In some embodiments, the exception source tracing field of the reference exception ciphertext may also be determined, for example, the exception source tracing may be performed on the reference exception ciphertext to obtain exception field response information of the reference exception ciphertext related to the virtual configuration accessor, and the obtained exception field response information of the exception ciphertext may be used as the exception source tracing field of the reference exception ciphertext in the plurality of virtual response feature fields.
For example, in some embodiments, the specific implementation of step S170 may be as follows.
Step S171, traversing each of the plurality of virtual response characteristic fields, and determining an abnormal ciphertext field as a target abnormal description field according to the abnormal field response information related to the reference abnormal node of the reference abnormal ciphertext, based on the currently traversed virtual response characteristic field.
Step S172, according to the target anomaly description field, determining a significant virtual response feature field corresponding to a significant virtual response activity of the encryption decision feature sequence in the traversed virtual response feature field, and using the significant virtual response feature field and a contact response feature field of the significant virtual response activity as a significant encryption decision field, where the significant virtual response activity is obtained according to virtual response interception information of the encryption decision feature sequence in the traversed virtual response feature field in a target encryption decision stage before the current encryption decision stage, and indicates a current virtual response position of the encryption decision feature sequence in the traversed virtual response feature field.
Step S173, determining the anomaly probability parameters of the encryption decision feature sequence and the reference anomaly ciphertext in the traversed virtual response feature field according to the target anomaly description field and the significant encryption decision field.
And step S174, obtaining the abnormal probability parameter distribution related to the reference abnormal ciphertext according to the abnormal probability parameters corresponding to the traversed plurality of virtual response characteristic fields.
Further, for example, in some embodiments, the specific implementation of step S170 can be referred to the following implementation.
Step S175, respectively traversing each of the plurality of virtual response feature fields, converting the plurality of abnormal ciphertext fields in the abnormal field response information related to the reference abnormal node of the reference abnormal ciphertext to an abnormal evaluation attribute based on the currently traversed virtual response feature field according to the model weight information of the abnormal evaluation model, obtaining a related target abnormal ciphertext field cluster, and converting the virtual response interception information of the encryption decision feature sequence to the abnormal evaluation attribute, so as to obtain a related abnormal evaluation attribute feature.
And step S176, analyzing the reference abnormal ciphertext according to the target abnormal ciphertext field cluster, and determining a reference abnormal node for expressing the reference abnormal ciphertext in the traversed virtual response feature field under the abnormal evaluation attribute.
And step S177, determining the abnormal probability parameters of the encryption decision characteristic sequence and the reference abnormal ciphertext according to the reference abnormal node and the abnormal evaluation attribute characteristic.
Step S178, obtaining the distribution of the abnormal probability parameters related to the reference abnormal ciphertext according to the abnormal probability parameters related to traversing the plurality of virtual response feature fields.
Fig. 2 illustrates a hardware structure of the motor train unit wheel pair detection data encryption system 100 for implementing the above-described motor train unit wheel pair detection data encryption method according to an embodiment of the present invention, and as shown in fig. 2, the motor train unit wheel pair detection data encryption system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.
In some embodiments, the encryption system 100 for the motor train unit wheel pair detection data may be a single server or a server group. The server group may be centralized or distributed (for example, the encryption system 100 for the wheel set detection data of the motor train unit may be a distributed system). In some embodiments, the encryption system 100 for the train wheel set detection data may be local or remote. For example, the encryption system 100 of the motor train unit wheel pair detection data may access information and/or data stored in the machine-readable storage medium 120 via a network. As another example, the encryption system 100 for motor train unit wheelset detection data may be directly connected to the machine-readable storage medium 120 to access stored information and/or data. In some embodiments, the encryption system 100 for the motor train unit wheel set detection data may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, the like, or any combination thereof.
Machine-readable storage medium 120 may store data and/or instructions. In some embodiments, the machine-readable storage medium 120 may store the data from an external terminal. In some embodiments, the machine-readable storage medium 120 may store data and/or instructions used by the encryption system 100 for motor train unit wheel pair detection data to perform or use to perform the exemplary methods described herein. In some embodiments, the machine-readable storage medium 120 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read and write memories can include Random Access Memory (RAM). Exemplary RAM may include active random access memory (DRAM), double data rate synchronous active random access memory (DDR SDRAM), passive random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM), among others. Exemplary read-only memories may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (perrom), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the machine-readable storage medium 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In a specific implementation process, the processors 110 execute the computer executable instructions stored in the machine readable storage medium 120, so that the processors 110 may execute the method for encrypting the wheel pair detection data of the motor train unit according to the above method embodiment, the processors 110, the machine readable storage medium 120, and the communication unit 140 are connected through the bus 130, and the processors 110 may be configured to control the transceiving action of the communication unit 140.
For a specific implementation process of the processor 110, reference may be made to each method embodiment executed by the encryption system 100 for wheel pair detection data of a motor train unit, which has similar implementation principles and technical effects, and details are not described herein again.
In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium is preset with computer executable instructions, and when a processor executes the computer executable instructions, the method for encrypting the wheel set detection data of the motor train unit is realized.
It should be understood that the foregoing description is for purposes of illustration only and is not intended to limit the scope of the present disclosure. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the description of the invention. However, such modifications and variations do not depart from the scope of the present invention.
While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art in view of this disclosure that the above disclosure is intended to be exemplary only and is not intended to limit the invention. Various modifications, improvements and adaptations of the present invention may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.
Also, the present invention has been described using specific terms to describe embodiments of the invention. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiments of the invention is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some of the features, structures, or characteristics of one or more embodiments of the present invention may be combined as suitable.
Moreover, those skilled in the art will recognize that aspects of the present invention may be illustrated and described in terms of several patentable species or situations, including any new and useful process, machine, article, or material combination, or any new and useful modification thereof. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module", or "system". Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media, with computer-readable program code embodied therein.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination thereof.
Computer program code required for operation of various portions of the present invention may be written in any one or more of a variety of programming languages, including a subject oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, an active programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are described, the use of letters or other designations herein is not intended to limit the order of the processes and methods of the invention unless otherwise indicated by the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the invention. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. Similarly, it should be noted that in the preceding description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments.
Claims (10)
1. A method for encrypting wheel set detection data of a motor train unit is characterized by being applied to an encryption system of the wheel set detection data of the motor train unit, and the method comprises the following steps:
searching the uploaded motor train unit wheel pair detection data of each motor train unit wheel pair area;
carrying out hiding feature decision on the detection data of the motor train unit wheel set according to a hiding feature decision network meeting the network deployment requirement to obtain hiding decision features;
and deriving a related encryption decision data set according to the concealment decision characteristics, and encrypting the detection data items of each related motor train unit wheel pair region having the relation according to the encryption decision data set.
2. The method for encrypting the wheel set detection data of the motor train unit according to claim 1, wherein the step of performing the hidden feature decision on the wheel set detection data of the motor train unit according to the hidden feature decision network meeting the network deployment requirement to obtain the hidden decision feature comprises the following steps:
according to the hidden feature decision network, deciding a hidden feature space and a hidden feature significant event contained in the detection data of the motor train unit wheel set, and deciding a hidden feature category related to the hidden feature space and a significant event category related to the hidden feature significant event;
and taking the wheel set detection data of the motor train unit, the hidden feature space and the related hidden feature types, the hidden feature significant events and the related significant event types as the hidden decision-making features.
3. The method for encrypting the motor train unit wheel pair detection data according to claim 1, further comprising the following steps of:
searching an example motor train unit wheel set detection data set, wherein the example motor train unit wheel set detection data set comprises motor train unit wheel set detection data with a concealed characteristic category and a significant event category;
deploying a network configuration component for a concealed feature decision network for predicting concealed decision features, the concealed feature decision network comprising a significant event decision structure based on a plurality of decision feature functions and a focused function;
and carrying out convergence optimization on the hidden feature decision network according to the detection data of the motor train unit wheel set covered by the detection data set of the motor train unit wheel set.
4. The method for encrypting the motor train unit wheel set detection data as claimed in claim 3, wherein the network configuration component for deploying the concealed feature decision network for predicting the concealed decision feature comprises:
performing functional relation on the vector mining component, the hidden feature space-migration mining component, the hidden feature focusing component and the hidden feature decision component to obtain a hidden feature space decision component;
the method comprises the steps that a significant event focusing component, a significant wheel pair scene focusing component and a focusing decision component are in functional connection with a significant event type decision component to obtain a significant event decision structure; the focus decision component comprises the plurality of decision feature functions and the centralized focus function;
cascading both the concealed feature space decision component and the significant event decision structure with a convergence evaluation component;
the convergence optimization of the hidden feature decision network according to the motor train unit wheel set detection data covered by the example motor train unit wheel set detection data set comprises the following steps:
searching the wheel set detection data of the motor train unit in a preset data range from the wheel set detection data set of the motor train unit of the example;
respectively transmitting the detection data of the wheel set of the motor train unit to the hidden feature space decision component and the significant event decision structure;
according to the hidden feature space decision component, deciding hidden feature types related to the hidden feature space contained in the detection data of the motor train unit wheel set;
deciding the salient event type related to the hiding characteristic salient event related to the detection data of the motor train unit wheel set according to the salient event decision structure;
and determining a convergence evaluation value of the convergence optimization process according to the convergence evaluation component according to the determined concealed feature type and the significant event type.
5. The method for encrypting the motor train unit wheel set detection data according to claim 4, wherein the step of deciding the hiding feature type related to the hiding feature space contained in the motor train unit wheel set detection data according to the hiding feature space decision component comprises the following steps:
mining a wheel set detection vector sequence of each wheel set detection periodic data in the motor train unit wheel set detection data according to the vector mining component;
removing wheel set detection periodic data with wheel set detection vector sequences as specified wheel set detection vector sequences from the motor train unit wheel set detection data;
mining the removed hidden characteristic part contained in the detection data of the motor train unit wheel set according to the hidden characteristic space-migration mining component;
according to the hiding feature part, whether a hiding feature space covers the hiding feature category related to the hiding feature part or not is set according to the focusing of the hiding feature focusing component;
if yes, searching the hiding feature category related to the hiding feature part from the set hiding feature space;
if not, obtaining a wheel set detection periodic vector sequence related to the wheel set detection data of the motor train unit according to the removed wheel set detection data of the motor train unit;
setting whether the relevance degree between the detection vector sequence and the wheel set detection periodic vector sequence is not less than a preset relevance degree wheel set detection periodic vector sequence or not according to the focusing of the concealed feature focusing assembly;
if the detection result is positive, determining the concealed feature category related to the detection periodic vector sequence by the wheel with the degree of association not less than the preset degree of association as the concealed feature category related to the concealed feature part;
if not, configuring the hiding feature category related to the hiding feature part into a preset hiding feature category;
the category of the concealed features to which the concealed feature portion relates is determined according to the concealed feature decision component.
6. The method for encrypting the motor train unit wheel set detection data according to claim 4, wherein the deciding the significant event category related to the concealment feature significant event related to the motor train unit wheel set detection data according to the significant event decision structure comprises:
extracting a continuous wheel pair detection data part from the wheel pair detection data of the motor train unit;
searching a first hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set according to the continuous wheel set detection data part and a set significant event vector sequence and the significant event focusing component;
searching second hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set according to the detection data of the motor train unit wheel set and a set significant event frequency vector sequence and a significant wheel set scene focusing assembly;
searching third hidden characteristic significant event decision content related to the detection data of the motor train unit wheel pair according to the focusing decision component;
and determining the significant event category related to the hidden characteristic significant event related to the detection data of the motor train unit wheel set according to the significant event category decision component and the first hidden characteristic significant event decision content, the second hidden characteristic significant event decision content and the third hidden characteristic significant event decision content.
7. The method for encrypting the motor train unit wheel set detection data according to claim 6, wherein the step of searching for a third concealed significant event decision content related to the motor train unit wheel set detection data according to the focus decision module comprises the steps of:
separating wheel set detection periodic data configured with the hidden characteristic types in the wheel set detection data of the motor train unit from other detection vectors by using a first target wheel set detection vector sequence, and separating wheel set detection periodic data configured with the significant event types from other detection vectors by using a second target wheel set detection vector sequence;
vector mining is carried out on the detection data of the motor train unit wheel set according to the decision characteristic functions in the focusing decision component to obtain a related periodic vector sequence;
determining a concentrated focusing function value related to each wheel pair detection periodic vector sequence in the periodic vector sequence according to the concentrated focusing function in the focusing decision component;
according to the wheel set detection periodic vector sequence and the concentrated focusing function value related to the wheel set detection periodic vector sequence, calculating corresponding attribution values of the hidden characteristic significant events contained in the wheel set detection data of the motor train unit corresponding to the significant event types according to the decision characteristic subfunctions contained in the decision characteristic functions;
and taking the significant event category with the highest corresponding attribution value as third hidden characteristic significant event decision content related to the detection data of the motor train unit wheel set.
8. The method for encrypting the motor train unit wheel pair detection data according to claim 1, further comprising the following steps of:
traversing and calling an encryption decision characteristic sequence of each motor train unit wheel pair area in a target operation stage;
searching a plurality of virtual response characteristic fields triggered by the encryption decision characteristic sequence under a virtual configuration accessor;
performing abnormal ciphertext traceability on the virtual response characteristic fields to obtain a plurality of reference abnormal ciphertexts linked with the encryption decision characteristic sequence;
traversing each reference abnormal ciphertext in a plurality of reference abnormal ciphertexts, determining abnormal probability parameters of the encryption decision characteristic sequence and the reference abnormal ciphertext in the virtual response characteristic fields according to the virtual response interception information of the encryption decision characteristic sequence in the virtual response characteristic fields and the abnormal source tracing field of the reference abnormal ciphertext in the virtual response characteristic fields, and obtaining the abnormal probability parameter distribution related to the reference abnormal ciphertext;
determining an abnormal ciphertext knowledge space of the encryption decision characteristic sequence according to abnormal probability parameter distribution corresponding to each of the plurality of reference abnormal ciphertexts;
and searching a target abnormality repairing instruction corresponding to the abnormal ciphertext knowledge space based on a pre-developed abnormality repairing instruction set according to the abnormal ciphertext knowledge space.
9. The method for encrypting the motor train unit wheel pair detection data according to claim 8, further comprising a step of determining an anomaly tracing field of the reference anomaly ciphertext, wherein the step comprises:
and tracing the abnormal field of the reference abnormal ciphertext to obtain the related abnormal field response information of the reference abnormal ciphertext under the virtual configuration accessor, and taking the obtained abnormal field response information of the abnormal ciphertext as the abnormal tracing field of the reference abnormal ciphertext in the plurality of virtual response characteristic fields.
10. An encryption system for motor train unit wheel pair detection data, which is characterized by comprising a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to realize the encryption method for motor train unit wheel pair detection data according to any one of claims 1 to 9.
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