High-speed railway perimeter intrusion alarm information processing method and system based on multi-sensor technology integration

Abstract

The present invention discloses a high-speed railway perimeter intrusion alarm information processing method and system integrating multi-sensor technology, which relates to the field of data processing, wherein the method comprises: dividing the perimeter of the target high-speed railway according to the perimeter basic data, and obtaining M high-speed railway perimeter areas; performing perimeter intrusion monitoring configuration on the M high-speed railway perimeter areas, and obtaining M perimeter area monitoring submodules; performing perimeter monitoring on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules, and obtaining M perimeter area monitoring data sets; obtaining M perimeter area intrusion identification results; transmitting the M perimeter area intrusion identification results to the high-speed railway perimeter intrusion alarm platform, and performing high-speed railway perimeter intrusion alarm on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform. The method solves the technical problem that the accuracy of the high-speed railway perimeter intrusion alarm in the prior art is insufficient, resulting in poor effect of the high-speed railway perimeter intrusion alarm.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for processing high-speed railway perimeter intrusion alarm information by integrating multiple sensing technologies. Background Art

[0002] With the rapid increase in the mileage of high-speed railways, high-speed railway perimeter intrusion has become an important issue that needs to be urgently addressed for railway safety. In the existing technology, there is a technical problem that the accuracy of high-speed railway perimeter intrusion alarm is insufficient, resulting in poor effect of high-speed railway perimeter intrusion alarm. How to effectively alarm high-speed railway perimeter intrusion has attracted widespread attention. Summary of the invention

[0003] The present application provides a high-speed railway perimeter intrusion alarm information processing method and system integrating multiple sensing technologies. The method solves the technical problem that the accuracy of the high-speed railway perimeter intrusion alarm in the prior art is insufficient, resulting in poor effect of the high-speed railway perimeter intrusion alarm. The method achieves the technical effect of improving the accuracy of the high-speed railway perimeter intrusion alarm, improving the quality of the high-speed railway perimeter intrusion alarm, and thus effectively ensuring the safety of the high-speed railway perimeter.

[0004] In view of the above problems, the present application provides a high-speed railway perimeter intrusion alarm information processing method and system integrating multiple sensor technologies.

[0005] In a first aspect, the present application provides a method for processing high-speed railway perimeter intrusion alarm information by integrating multiple sensor technologies, wherein the method is applied to a high-speed railway perimeter intrusion alarm information processing system by integrating multiple sensor technologies, wherein the system includes a high-speed railway perimeter intrusion alarm platform, and the method includes: obtaining basic perimeter data of a target high-speed railway perimeter; dividing the target high-speed railway perimeter according to the basic perimeter data to obtain M high-speed railway perimeter areas, and M is a positive integer greater than 1; obtaining a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator; based on The multi-dimensional perimeter intrusion monitoring configuration indicators are used to perform perimeter intrusion monitoring configuration on the M high-speed rail perimeter areas to obtain M perimeter area monitoring sub-modules; perimeter monitoring is performed on the M high-speed rail perimeter areas according to the M perimeter area monitoring sub-modules to obtain M perimeter area monitoring data sets; high-speed rail perimeter intrusion identification is performed based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; the M perimeter area intrusion identification results are transmitted to the high-speed rail perimeter intrusion alarm platform, and high-speed rail perimeter intrusion alarms are performed on the M high-speed rail perimeter areas according to the perimeter intrusion alarms in the high-speed rail perimeter intrusion alarm platform.

[0006] In the second aspect, the present application also provides a high-speed railway perimeter intrusion alarm information processing system integrating multiple sensing technologies, wherein the system includes a high-speed railway perimeter intrusion alarm platform, and the system also includes: a perimeter basic data acquisition module, the perimeter basic data acquisition module is used to obtain the perimeter basic data of the target high-speed railway perimeter; a perimeter division module, the perimeter division module is used to divide the target high-speed railway perimeter according to the perimeter basic data, and obtain M high-speed railway perimeter areas, and M is a positive integer greater than 1; a monitoring configuration indicator acquisition module, the monitoring configuration indicator acquisition module is used to obtain multi-dimensional perimeter intrusion monitoring configuration indicators, wherein the multi-dimensional perimeter intrusion monitoring configuration indicators include monitoring-hard configuration indicators and monitoring-soft configuration indicators; a perimeter intrusion monitoring configuration module, the perimeter intrusion monitoring configuration module is used to obtain a perimeter basic data of the target high-speed railway perimeter; a perimeter division module, the perimeter division module is used to divide the perimeter of the target high-speed railway perimeter according to the perimeter basic data, and obtain M high-speed railway perimeter areas, and M is a positive integer greater than 1; a monitoring configuration indicator acquisition module, the monitoring configuration indicator acquisition module is used to obtain a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator; a perimeter intrusion monitoring configuration module According to the multi-dimensional perimeter intrusion monitoring configuration index, perimeter intrusion monitoring configuration is performed on the M high-speed rail perimeter areas to obtain M perimeter area monitoring sub-modules; a perimeter monitoring module, the perimeter monitoring module is used to perform perimeter monitoring on the M high-speed rail perimeter areas according to the M perimeter area monitoring sub-modules to obtain M perimeter area monitoring data sets; a perimeter intrusion identification module, the perimeter intrusion identification module is used to perform high-speed rail perimeter intrusion identification based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; a perimeter intrusion alarm module, the perimeter intrusion alarm module is used to transmit the M perimeter area intrusion identification results to the high-speed rail perimeter intrusion alarm platform, and perform high-speed rail perimeter intrusion alarms on the M high-speed rail perimeter areas according to the perimeter intrusion alarm in the high-speed rail perimeter intrusion alarm platform.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The target high-speed railway perimeter is divided according to the perimeter basic data to obtain M high-speed railway perimeter areas; the perimeter intrusion monitoring configuration is configured for the M high-speed railway perimeter areas according to the multi-dimensional perimeter intrusion monitoring configuration index to obtain M perimeter area monitoring submodules; the perimeter monitoring is performed on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets; the high-speed railway perimeter intrusion identification is performed on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; the M perimeter area intrusion identification results are transmitted to the high-speed railway perimeter intrusion alarm platform, and the high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform. The technical effect of improving the accuracy of the high-speed railway perimeter intrusion alarm and improving the quality of the high-speed railway perimeter intrusion alarm is achieved, thereby effectively ensuring the safety of the high-speed railway perimeter.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention are briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0011] Figure 1 A flowchart of a high-speed railway perimeter intrusion alarm information processing method integrating multiple sensor technologies is provided in this application;

[0012] Figure 2 This is a structural schematic diagram of a high-speed railway perimeter intrusion alarm information processing system integrating multiple sensor technologies in this application. DETAILED DESCRIPTION

[0013] This application provides a high-speed railway perimeter intrusion alarm information processing method and system integrating multi-sensor technology. It solves the technical problem that the accuracy of the high-speed railway perimeter intrusion alarm in the prior art is insufficient, resulting in poor effect of the high-speed railway perimeter intrusion alarm. It achieves the technical effect of improving the accuracy of the high-speed railway perimeter intrusion alarm, improving the quality of the high-speed railway perimeter intrusion alarm, and thus effectively ensuring the safety of the high-speed railway perimeter.

[0014] Embodiment 1

[0015] Please refer to the attached Figure 1 The present application provides a high-speed railway perimeter intrusion alarm information processing method integrating multiple sensing technologies, wherein the method is applied to a high-speed railway perimeter intrusion alarm information processing system integrating multiple sensing technologies, wherein the system includes a high-speed railway perimeter intrusion alarm platform, and the method specifically includes the following steps:

[0016] Obtain basic perimeter data of the target high-speed railway perimeter;

[0017] Divide the target high-speed railway perimeter into M high-speed railway perimeter areas according to the perimeter basic data, and obtain M high-speed railway perimeter areas, where M is a positive integer greater than 1;

[0018] Connect a high-speed rail perimeter intrusion alarm information processing system integrating multiple sensing technologies in this application, retrieve the perimeter basic data of the target high-speed rail perimeter, and divide the perimeter of the target high-speed rail perimeter according to the perimeter basic data to obtain M high-speed rail perimeter areas. And, M is a positive integer greater than 1. Among them, the target high-speed rail perimeter can be any high-speed rail perimeter for which the intelligent perimeter intrusion alarm is performed using the high-speed rail perimeter intrusion alarm information processing system integrating multiple sensing technologies. The perimeter basic data includes perimeter parameter information such as the geographical location, topography, perimeter area, perimeter length, and perimeter fence height corresponding to the target high-speed rail perimeter.

[0019] Exemplarily, when dividing the target high-speed rail perimeter according to the perimeter basic data, the target high-speed rail perimeter can be divided into multiple areas according to geographical locations, and these multiple areas are recorded as M high-speed rail perimeter areas, where the value of M is the total number of the multiple areas.

[0020] Obtaining a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator;

[0021] Based on the multi-dimensional perimeter intrusion monitoring configuration index, perimeter intrusion monitoring configuration is performed on the M high-speed railway perimeter areas to obtain M perimeter area monitoring submodules;

[0022] Wherein, based on the multi-dimensional perimeter intrusion monitoring configuration index, the perimeter intrusion monitoring configuration is performed on the M high-speed railway perimeter areas to obtain M perimeter area monitoring submodules, including:

[0023] According to the M high-speed rail perimeter areas, an m-th high-speed rail perimeter area is obtained, where m is a positive integer belonging to M;

[0024] According to the perimeter basic data, matching the mth perimeter area basic information;

[0025] The M high-speed rail perimeter areas are extracted in sequence to obtain the m-th high-speed rail perimeter area, and the m-th perimeter area basic information corresponding to the m-th high-speed rail perimeter area is matched according to the perimeter basic data. Among them, the m-th high-speed rail perimeter area is each high-speed rail perimeter area in the M high-speed rail perimeter areas in sequence. The m-th perimeter area basic information is the perimeter parameter information corresponding to the m-th high-speed rail perimeter area in the perimeter basic data.

[0026] Performing a perimeter intrusion feature analysis based on the mth high-speed railway perimeter area to obtain an intrusion feature of the mth perimeter area;

[0027] Wherein, performing perimeter intrusion feature analysis based on the mth high-speed railway perimeter area to obtain the intrusion feature of the mth perimeter area includes:

[0028] Get the preset historical time zone constraints;

[0029] According to the preset historical time zone constraint, retrieve the high-speed rail perimeter intrusion alarm record library of the m-th high-speed rail perimeter area;

[0030] Obtaining a multi-dimensional perimeter intrusion feature analysis index, wherein the multi-dimensional perimeter intrusion feature analysis index includes perimeter intrusion subject features, perimeter intrusion time zone features, and perimeter intrusion space features;

[0031] Based on the multi-dimensional perimeter intrusion feature analysis index, feature recognition is performed on the high-speed railway perimeter intrusion alarm record library to generate a multi-modal perimeter intrusion feature parameter set;

[0032] The multimodal perimeter intrusion feature parameter set is traversed to perform frequency screening to generate the mth perimeter area intrusion feature.

[0033] According to the preset historical time zone constraints, the perimeter intrusion alarm records of the mth high-speed railway perimeter area are collected to obtain a high-speed railway perimeter intrusion alarm record library. The preset historical time zone constraints are historical time ranges pre-set and determined by the high-speed railway perimeter intrusion alarm information processing system integrated with the multi-sensor technology. The high-speed railway perimeter intrusion alarm record library includes multiple perimeter intrusion alarm records corresponding to the mth high-speed railway perimeter area within the preset historical time zone constraints. Each perimeter intrusion alarm record includes data information such as the historical perimeter intrusion subject (people, animals, falling rocks, etc. that cause perimeter intrusion), historical perimeter intrusion time, and historical perimeter intrusion location corresponding to the mth high-speed railway perimeter area within the preset historical time zone constraints.

[0034] The multi-dimensional perimeter intrusion feature analysis indicators include perimeter intrusion subject characteristics, perimeter intrusion time zone characteristics and perimeter intrusion space characteristics. The perimeter intrusion subject characteristics refer to the subject that causes the perimeter intrusion (such as people, animals, falling rocks, etc.). The perimeter intrusion time zone characteristics refer to the time when the perimeter intrusion occurs. The perimeter intrusion space characteristics refer to the specific location where the perimeter intrusion occurs. Then, according to the multi-dimensional perimeter intrusion feature analysis indicators, feature recognition is performed on the high-speed rail perimeter intrusion alarm record library, that is, multiple historical perimeter intrusion subjects in the high-speed rail perimeter intrusion alarm record library are recorded as multiple perimeter intrusion subject feature parameters to obtain a perimeter intrusion subject feature parameter set. At the same time, multiple historical perimeter intrusion times in the high-speed rail perimeter intrusion alarm record library are recorded as multiple perimeter intrusion time zone feature parameters to obtain a perimeter intrusion time zone feature parameter set. Multiple historical perimeter intrusion locations in the high-speed rail perimeter intrusion alarm record library are recorded as multiple perimeter intrusion spatial feature parameters to obtain a perimeter intrusion spatial feature parameter set, and the perimeter intrusion main feature parameter set and the perimeter intrusion time zone feature parameter set are combined to obtain a multimodal perimeter intrusion feature parameter set. The multimodal perimeter intrusion feature parameter set includes a perimeter intrusion main feature parameter set, a perimeter intrusion time zone feature parameter set, and a perimeter intrusion spatial feature parameter set. The perimeter intrusion main feature parameter set includes multiple perimeter intrusion main feature parameters. The perimeter intrusion time zone feature parameter set includes multiple perimeter intrusion time zone feature parameters. The perimeter intrusion spatial feature parameter set includes multiple perimeter intrusion spatial feature parameters.

[0035] Furthermore, the perimeter intrusion subject feature parameter set, perimeter intrusion time zone feature parameter set, and perimeter intrusion space feature parameter set in the multimodal perimeter intrusion feature parameter set are respectively screened by frequency to generate the mth perimeter area intrusion feature. The mth perimeter area intrusion feature includes the perimeter intrusion subject feature information, perimeter intrusion time zone feature information, and perimeter intrusion space feature information corresponding to the mth high-speed rail perimeter area.

[0036] Exemplarily, when the perimeter intrusion subject feature parameter set in the multimodal perimeter intrusion feature parameter set is frequency-screened, the number of occurrences of each perimeter intrusion subject feature parameter in the perimeter intrusion subject feature parameter set is recorded as the intrusion subject frequency, and multiple intrusion subject frequencies corresponding to the multiple perimeter intrusion subject feature parameters are obtained. Furthermore, the multiple perimeter intrusion subject feature parameters are sorted in order of the multiple intrusion subject frequencies from large to small, and the first K perimeter intrusion subject feature parameters are recorded as the perimeter intrusion subject feature information corresponding to the mth high-speed rail perimeter area. The K value can be determined by adaptive setting. Preferably, K is 2 / 3 of the number of the multiple perimeter intrusion subject feature parameters. The perimeter intrusion time zone feature information and the perimeter intrusion space feature information are obtained in a similar manner to the perimeter intrusion subject feature information, which will not be repeated here.

[0037] The perimeter intrusion characteristics of the m-th high-speed railway perimeter area are analyzed through multi-dimensional perimeter intrusion characteristic analysis indicators to obtain reliable intrusion characteristics of the m-th perimeter area, thereby improving the adaptability of the monitoring configuration of the m-th high-speed railway perimeter area.

[0038] Based on the multi-dimensional perimeter intrusion monitoring configuration indicators, a perimeter intrusion monitoring configuration model is built;

[0039] Wherein, based on the multi-dimensional perimeter intrusion monitoring configuration indicators, a perimeter intrusion monitoring configuration model is constructed, including:

[0040] Based on the multi-dimensional perimeter intrusion feature analysis index and the multi-dimensional perimeter intrusion monitoring configuration index, the perimeter intrusion monitoring configuration data is backtracked to obtain a plurality of sample perimeter area basic information, a plurality of sample perimeter area intrusion features and a plurality of sample perimeter intrusion monitoring configuration schemes, and the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes have a sample mapping relationship;

[0041] Based on the knowledge graph, a perimeter intrusion monitoring configuration architecture is obtained, wherein the perimeter intrusion monitoring configuration architecture includes a mode layer and a data layer;

[0042] Embedding the sample mapping relationship into the pattern layer to obtain a feature pattern layer;

[0043] According to the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes, a plurality of configuration primary index feature information, a plurality of configuration secondary index feature information and a plurality of monitoring configuration response feature information are obtained;

[0044] Adding the plurality of configuration primary index feature information, the plurality of configuration secondary index feature information and the plurality of monitoring configuration response feature information to the data layer to obtain a feature data layer;

[0045] The characteristic data layer is subjected to data fusion according to the characteristic pattern layer to generate the perimeter intrusion monitoring configuration model.

[0046] Inputting the mth perimeter area basic information and the mth perimeter area intrusion characteristics into the perimeter intrusion monitoring configuration model to obtain the mth perimeter intrusion monitoring configuration solution;

[0047] Based on the mth perimeter intrusion monitoring configuration scheme, the monitoring configuration of the mth high-speed railway perimeter area is executed to obtain the mth perimeter area monitoring submodule, and the mth perimeter area monitoring submodule is added to the M perimeter area monitoring submodules.

[0048] The multi-dimensional perimeter intrusion monitoring configuration indicators include monitoring-hard configuration indicators and monitoring-soft configuration indicators. Monitoring-hard configuration indicators refer to the type of monitoring sensor deployment, the model of monitoring sensor deployment, the location of monitoring sensor deployment, and the number of monitoring sensors deployed. Monitoring-soft configuration indicators refer to the monitoring computing power and monitoring frequency of the monitoring sensor. Then, according to the multi-dimensional perimeter intrusion feature analysis indicators and the multi-dimensional perimeter intrusion monitoring configuration indicators, the historical perimeter intrusion monitoring configuration data is queried to obtain multiple sample perimeter area basic information, multiple sample perimeter area intrusion features, and multiple sample perimeter intrusion monitoring configuration schemes. Among them, each sample perimeter area basic information includes historical perimeter area basic information. Each sample perimeter area intrusion feature includes historical perimeter intrusion subject feature information, historical perimeter intrusion time zone feature information, and historical perimeter intrusion space feature information corresponding to the sample perimeter area basic information. Each sample perimeter intrusion monitoring configuration plan includes the basic information of the sample perimeter area, the historical monitoring-hard configuration indicator information corresponding to the intrusion characteristics of the sample perimeter area (historical monitoring sensor deployment type parameters, historical monitoring sensor deployment model parameters, historical monitoring sensor deployment location parameters, historical monitoring sensor deployment quantity parameters), and historical monitoring-soft configuration indicator information (historical monitoring computing power parameters, historical monitoring frequency parameters). The sample mapping relationship refers to the correspondence between multiple sample perimeter area basic information, multiple sample perimeter area intrusion characteristics, and multiple sample perimeter intrusion monitoring configuration plans.

[0049] Knowledge graph is a way of expressing data information. Knowledge graph includes pattern layer and data layer. The data layer consists of a series of facts. The pattern layer is built on the data layer and is mainly used to standardize the expression of a series of facts in the data layer. Set the knowledge graph as the perimeter intrusion monitoring configuration architecture. The perimeter intrusion monitoring configuration architecture includes the pattern layer and the data layer.

[0050] Further, the sample mapping relationship is embedded into the pattern layer to obtain a feature pattern layer. Multiple sample perimeter area basic information is set as multiple configuration first-level index feature information, multiple sample perimeter area intrusion features are set as multiple configuration second-level index feature information, and multiple sample perimeter intrusion monitoring configuration schemes are set as multiple monitoring configuration response feature information. Then, multiple configuration first-level index feature information, multiple configuration second-level index feature information, and multiple monitoring configuration response feature information are added to the data layer to obtain a feature data layer.

[0051] Further, data fusion is performed on the feature data layer according to the feature pattern layer, that is, multiple configuration first-level index feature information, multiple configuration second-level index feature information, and multiple monitoring configuration response feature information in the feature data layer are arranged according to the sample mapping relationship in the feature pattern layer to obtain a perimeter intrusion monitoring configuration model. The perimeter intrusion monitoring configuration model includes a feature pattern layer and a feature data layer. The feature pattern layer includes a sample mapping relationship. The feature data layer includes multiple configuration first-level index feature information, multiple configuration second-level index feature information, and multiple monitoring configuration response feature information arranged according to the sample mapping relationship.

[0052] Further, the basic information of the mth perimeter area and the intrusion characteristics of the mth perimeter area are input into the perimeter intrusion monitoring configuration model to obtain the mth perimeter intrusion monitoring configuration scheme. The mth perimeter intrusion monitoring configuration scheme includes monitoring-hard configuration indicator information and monitoring-soft configuration indicator information. The monitoring-hard configuration indicator information includes monitoring sensor deployment type parameters (such as millimeter wave radar, thermal imager, etc.), monitoring sensor deployment model parameters, monitoring sensor deployment location parameters, and monitoring sensor deployment quantity parameters. The monitoring-soft configuration indicator information includes monitoring computing power parameters and monitoring frequency parameters of each monitoring sensor. Then, according to the mth perimeter intrusion monitoring configuration scheme, the sensor deployment and sensor setting are performed on the mth high-speed rail perimeter area to obtain the mth perimeter area monitoring submodule, and the mth perimeter area monitoring submodule is added to the M perimeter area monitoring submodules. The mth perimeter area monitoring submodule includes a plurality of monitoring sensors that have completed the monitoring configuration and communication connection after the sensor deployment and sensor setting are performed on the mth high-speed rail perimeter area according to the mth perimeter intrusion monitoring configuration scheme. The M perimeter area monitoring submodules are constructed in the same manner as the mth perimeter area monitoring submodule, which will not be repeated here.

[0053] Performing perimeter monitoring on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets;

[0054] Perform high-speed rail perimeter intrusion identification based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results;

[0055] Wherein, based on the M perimeter area monitoring data sets, high-speed rail perimeter intrusion identification is performed to obtain M perimeter area intrusion identification results, including:

[0056] According to the M perimeter area monitoring data sets, obtaining an mth perimeter area monitoring data set;

[0057] Through M perimeter area monitoring submodules, real-time perimeter monitoring is performed on M high-speed rail perimeter areas to obtain M perimeter area monitoring data sets, and the M perimeter area monitoring data sets are sequentially extracted to obtain the mth perimeter area monitoring data set. Each perimeter area monitoring data set includes a plurality of real-time monitoring data (such as real-time radar data, real-time thermal imaging data, etc.) corresponding to each high-speed rail perimeter area. The mth perimeter area monitoring data set is each perimeter area monitoring data set in the M perimeter area monitoring data sets in turn.

[0058] Perform simulation fitting based on the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring cloud map;

[0059] The process of performing simulation fitting based on the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring cloud map includes:

[0060] Preprocessing the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring data source;

[0061] Modeling is performed according to the basic information of the mth perimeter area to obtain the mth perimeter area model;

[0062] The m-th perimeter area monitoring data source is rendered to the m-th perimeter area model to obtain the m-th perimeter area monitoring cloud map.

[0063] The mth perimeter area monitoring data source is obtained by preprocessing the mth perimeter area monitoring data set. Exemplarily, when preprocessing the mth perimeter area monitoring data set, data cleaning is performed according to different data types, such as data filtering (deleting data with low quality and large signal interference), data standardization (unit elimination, dimension unification), and data noise reduction are performed on the real-time radar data in the mth perimeter area monitoring data set to obtain cleaned radar data, and image enhancement and image denoising are performed on the real-time thermal imaging data in the mth perimeter area monitoring data set to obtain cleaned thermal imaging data, and the cleaned radar data and cleaned thermal imaging data are added to the mth perimeter area monitoring data source.

[0064] Further, the existing modeling software is used to model the mth perimeter area basic information corresponding to the mth perimeter area monitoring data set, and the mth perimeter area model is obtained. Then, the mth perimeter area monitoring data source is input into the mth perimeter area model to obtain the mth perimeter area monitoring cloud map. The mth perimeter area model is a three-dimensional simulation model of the mth perimeter area basic information corresponding to the mth perimeter area monitoring data set. The mth perimeter area monitoring cloud map includes multi-angle images corresponding to the mth perimeter area model after the mth perimeter area monitoring data source is input into the mth perimeter area model.

[0065] Building a high-speed rail perimeter intrusion identification channel, wherein the high-speed rail perimeter intrusion identification channel includes multi-dimensional perimeter intrusion identification indicators, and the multi-dimensional perimeter intrusion identification indicators include perimeter intrusion judgment indicators, perimeter intrusion subject indicators and perimeter intrusion risk indicators;

[0066] Inputting the mth perimeter area monitoring cloud map into the high-speed railway perimeter intrusion identification channel to generate the mth perimeter area intrusion identification result;

[0067] The mth perimeter area intrusion identification result is added to the M perimeter area intrusion identification results.

[0068] The multi-dimensional perimeter intrusion identification index includes perimeter intrusion judgment index (whether there is perimeter intrusion), perimeter intrusion subject index (when there is perimeter intrusion, the subject of perimeter intrusion, such as people, animals, falling rocks, etc.), and perimeter intrusion risk index (when there is perimeter intrusion, the impact of perimeter intrusion). Then, according to the multi-dimensional perimeter intrusion identification index, the high-speed rail perimeter intrusion identification record is collected to obtain multiple perimeter intrusion identification records. Each perimeter intrusion identification record includes a historical perimeter area monitoring cloud map, and historical perimeter intrusion judgment index information, historical perimeter intrusion subject index information and historical perimeter intrusion risk index information corresponding to the historical perimeter area monitoring cloud map. Then, according to the convolutional neural network, the multiple perimeter intrusion identification records are continuously self-trained and learned to a convergence state to obtain a high-speed rail perimeter intrusion identification channel. The convolutional neural network is a type of feedforward neural network that includes convolution calculations and has a deep structure. The convolutional neural network has the ability to represent learning and can perform translation-invariant classification of input information according to its hierarchical structure. The high-speed rail perimeter intrusion identification channel includes an input layer, a hidden layer, and an output layer.

[0069] Further, the mth perimeter area monitoring cloud map is input into the high-speed rail perimeter intrusion identification channel, and the high-speed rail perimeter intrusion identification channel analyzes the mth perimeter area monitoring cloud map according to the multi-dimensional perimeter intrusion identification index, obtains the mth perimeter area intrusion identification result, and adds the mth perimeter area intrusion identification result to the M perimeter area intrusion identification results. Among them, the mth perimeter area intrusion identification result includes the perimeter intrusion judgment index information, perimeter intrusion subject index information and perimeter intrusion risk index information corresponding to the mth perimeter area monitoring cloud map. The perimeter intrusion judgment index information is whether there is perimeter intrusion in the mth high-speed rail perimeter area / there is no perimeter intrusion in the mth high-speed rail perimeter area. The perimeter intrusion subject index information includes the perimeter intrusion subject corresponding to the mth high-speed rail perimeter area. The perimeter intrusion risk index information is used to characterize the corresponding perimeter intrusion impact when there is perimeter intrusion in the mth high-speed rail perimeter area. The higher the perimeter intrusion impact, the greater the corresponding perimeter intrusion risk index information. When the perimeter intrusion judgment index information indicates that there is no perimeter intrusion in the mth high-speed railway perimeter area, the corresponding perimeter intrusion subject index information and perimeter intrusion risk index information are both 0. In addition, the M perimeter area intrusion identification results are the same as the mth perimeter area intrusion identification result, which will not be repeated here.

[0070] Through the high-speed rail perimeter intrusion identification channel, high-speed rail perimeter intrusion identification is performed on M perimeter area monitoring data sets respectively to obtain accurate M perimeter area intrusion identification results, thereby improving the accuracy of high-speed rail perimeter intrusion alarm.

[0071] The intrusion identification results of the M perimeter areas are transmitted to the high-speed railway perimeter intrusion alarm platform, and high-speed railway perimeter intrusion alarms are issued for the M high-speed railway perimeter areas according to the perimeter intrusion alarms in the high-speed railway perimeter intrusion alarm platform.

[0072] The intrusion identification results of the M perimeter areas are transmitted to the high-speed railway perimeter intrusion alarm platform, and the high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform, including:

[0073] Perform feature recognition according to the mth perimeter area intrusion recognition result to obtain the mth perimeter intrusion judgment operator and the mth perimeter intrusion attribute operator;

[0074] The perimeter intrusion alarm includes an intrusion alarm judgment layer and an intrusion alarm level layer;

[0075] Inputting the mth perimeter intrusion judgment operator into the intrusion alarm judgment layer, and generating a perimeter intrusion warning instruction if the mth perimeter intrusion judgment operator satisfies a preset intrusion alarm judgment operator in the intrusion alarm judgment layer;

[0076] According to the perimeter intrusion warning instruction, the mth perimeter intrusion attribute operator is input into the intrusion alarm level layer to obtain the mth perimeter intrusion alarm signal;

[0077] A high-speed railway perimeter intrusion alarm is executed in the mth high-speed railway perimeter area according to the mth perimeter intrusion alarm signal.

[0078] The intrusion identification results of the M perimeter areas are transmitted to the high-speed railway perimeter intrusion alarm platform, and the high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform, thereby improving the effect of the high-speed railway perimeter intrusion alarm.

[0079] Preferably, when a high-speed rail perimeter intrusion alarm is sounded for M high-speed rail perimeter areas according to the perimeter intrusion alarm, feature recognition is performed on the mth perimeter area intrusion identification result among the M perimeter area intrusion identification results, that is, the perimeter intrusion judgment index information in the mth perimeter area intrusion identification result is set as the mth perimeter intrusion judgment operator, and the perimeter intrusion subject index information and the perimeter intrusion risk index information in the mth perimeter area intrusion identification result are set as the mth perimeter intrusion attribute operator.

[0080] Furthermore, the perimeter intrusion alarm includes an intrusion alarm judgment layer and an intrusion alarm level layer. The intrusion alarm judgment layer includes a preset intrusion alarm judgment operator. The preset intrusion alarm judgment operator is the existence of perimeter intrusion. The mth perimeter intrusion judgment operator is input into the intrusion alarm judgment layer. If the mth perimeter intrusion judgment operator satisfies the preset intrusion alarm judgment operator in the intrusion alarm judgment layer (that is, the mth perimeter intrusion judgment operator is consistent with the preset intrusion alarm judgment operator), the high-speed rail perimeter intrusion alarm information processing system integrating multiple sensing technologies automatically generates a perimeter intrusion warning instruction. The perimeter intrusion warning instruction is an instruction information used to characterize the existence of perimeter intrusion and the need for a perimeter intrusion alarm.

[0081] Further, according to the perimeter intrusion warning instruction, the mth perimeter intrusion attribute operator is input into the intrusion alarm level layer, the mth perimeter intrusion alarm signal is obtained, and the high-speed rail perimeter intrusion alarm is performed on the mth high-speed rail perimeter area according to the mth perimeter intrusion alarm signal. The intrusion alarm level layer includes a preset intrusion alarm level table. The preset intrusion alarm level table includes a plurality of preset perimeter intrusion attribute operators, and a plurality of preset perimeter intrusion alarm level signals corresponding to the plurality of preset perimeter intrusion attribute operators. Each preset perimeter intrusion attribute operator includes preset perimeter intrusion subject indicator information and preset perimeter intrusion risk indicator information. The mth perimeter intrusion alarm signal includes the preset perimeter intrusion alarm level signal corresponding to the mth perimeter intrusion attribute operator.

[0082] In summary, the high-speed railway perimeter intrusion alarm information processing method provided by the present application that integrates multiple sensor technologies has the following technical effects:

[0083] The target high-speed railway perimeter is divided according to the perimeter basic data to obtain M high-speed railway perimeter areas; the perimeter intrusion monitoring configuration is configured for the M high-speed railway perimeter areas according to the multi-dimensional perimeter intrusion monitoring configuration index to obtain M perimeter area monitoring submodules; the perimeter monitoring is performed on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets; the high-speed railway perimeter intrusion identification is performed on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; the M perimeter area intrusion identification results are transmitted to the high-speed railway perimeter intrusion alarm platform, and the high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform. The technical effect of improving the accuracy of the high-speed railway perimeter intrusion alarm and improving the quality of the high-speed railway perimeter intrusion alarm is achieved, thereby effectively ensuring the safety of the high-speed railway perimeter.

[0084] Embodiment 2

[0085] Based on the high-speed railway perimeter intrusion alarm information processing method integrated with a multi-sensor technology in the above-mentioned embodiment, the present invention also provides a high-speed railway perimeter intrusion alarm information processing system integrated with a multi-sensor technology, the system includes a high-speed railway perimeter intrusion alarm platform, please refer to the attached Figure 2 , the system further comprises:

[0086] A perimeter basic data acquisition module, wherein the perimeter basic data acquisition module is used to obtain perimeter basic data of the target high-speed railway perimeter;

[0087] A perimeter division module, the perimeter division module is used to divide the target high-speed railway perimeter according to the perimeter basic data to obtain M high-speed railway perimeter areas, and M is a positive integer greater than 1;

[0088] A monitoring configuration indicator acquisition module, wherein the monitoring configuration indicator acquisition module is used to obtain a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator;

[0089] A perimeter intrusion monitoring configuration module, the perimeter intrusion monitoring configuration module is used to perform perimeter intrusion monitoring configuration on the M high-speed railway perimeter areas based on the multi-dimensional perimeter intrusion monitoring configuration index to obtain M perimeter area monitoring submodules;

[0090] A perimeter monitoring module, the perimeter monitoring module is used to perform perimeter monitoring on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets;

[0091] A perimeter intrusion identification module, the perimeter intrusion identification module is used to perform high-speed rail perimeter intrusion identification based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results;

[0092] A perimeter intrusion alarm module is used to transmit the intrusion identification results of the M perimeter areas to the high-speed railway perimeter intrusion alarm platform, and to perform high-speed railway perimeter intrusion alarms for the M high-speed railway perimeter areas according to the perimeter intrusion alarms in the high-speed railway perimeter intrusion alarm platform.

[0093] Furthermore, the perimeter intrusion monitoring configuration module is also used to:

[0094] According to the M high-speed rail perimeter areas, an m-th high-speed rail perimeter area is obtained, where m is a positive integer belonging to M;

[0095] According to the perimeter basic data, matching the mth perimeter area basic information;

[0096] Performing a perimeter intrusion feature analysis based on the mth high-speed railway perimeter area to obtain an intrusion feature of the mth perimeter area;

[0097] Based on the multi-dimensional perimeter intrusion monitoring configuration indicators, a perimeter intrusion monitoring configuration model is built;

[0098] Inputting the mth perimeter area basic information and the mth perimeter area intrusion characteristics into the perimeter intrusion monitoring configuration model to obtain the mth perimeter intrusion monitoring configuration solution;

[0099] Based on the mth perimeter intrusion monitoring configuration scheme, the monitoring configuration of the mth high-speed railway perimeter area is executed to obtain the mth perimeter area monitoring submodule, and the mth perimeter area monitoring submodule is added to the M perimeter area monitoring submodules.

[0100] Furthermore, the perimeter intrusion monitoring configuration module is also used to:

[0101] Get the preset historical time zone constraints;

[0102] According to the preset historical time zone constraint, retrieve the high-speed rail perimeter intrusion alarm record library of the m-th high-speed rail perimeter area;

[0103] Obtaining a multi-dimensional perimeter intrusion feature analysis index, wherein the multi-dimensional perimeter intrusion feature analysis index includes perimeter intrusion subject features, perimeter intrusion time zone features, and perimeter intrusion space features;

[0104] Based on the multi-dimensional perimeter intrusion feature analysis index, feature recognition is performed on the high-speed railway perimeter intrusion alarm record library to generate a multi-modal perimeter intrusion feature parameter set;

[0105] The multimodal perimeter intrusion feature parameter set is traversed to perform frequency screening to generate the mth perimeter area intrusion feature.

[0106] Furthermore, the perimeter intrusion monitoring configuration module is also used to:

[0107] Based on the multi-dimensional perimeter intrusion feature analysis index and the multi-dimensional perimeter intrusion monitoring configuration index, the perimeter intrusion monitoring configuration data is backtracked to obtain a plurality of sample perimeter area basic information, a plurality of sample perimeter area intrusion features and a plurality of sample perimeter intrusion monitoring configuration schemes, and the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes have a sample mapping relationship;

[0108] Based on the knowledge graph, a perimeter intrusion monitoring configuration architecture is obtained, wherein the perimeter intrusion monitoring configuration architecture includes a mode layer and a data layer;

[0109] Embedding the sample mapping relationship into the pattern layer to obtain a feature pattern layer;

[0110] According to the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes, a plurality of configuration primary index feature information, a plurality of configuration secondary index feature information and a plurality of monitoring configuration response feature information are obtained;

[0111] Adding the plurality of configuration primary index feature information, the plurality of configuration secondary index feature information and the plurality of monitoring configuration response feature information to the data layer to obtain a feature data layer;

[0112] The characteristic data layer is subjected to data fusion according to the characteristic pattern layer to generate the perimeter intrusion monitoring configuration model.

[0113] Furthermore, the perimeter intrusion identification module is also used for:

[0114] According to the M perimeter area monitoring data sets, obtaining an mth perimeter area monitoring data set;

[0115] Perform simulation fitting based on the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring cloud map;

[0116] Building a high-speed rail perimeter intrusion identification channel, wherein the high-speed rail perimeter intrusion identification channel includes multi-dimensional perimeter intrusion identification indicators, and the multi-dimensional perimeter intrusion identification indicators include perimeter intrusion judgment indicators, perimeter intrusion subject indicators and perimeter intrusion risk indicators;

[0117] Inputting the mth perimeter area monitoring cloud map into the high-speed railway perimeter intrusion identification channel to generate the mth perimeter area intrusion identification result;

[0118] The mth perimeter area intrusion identification result is added to the M perimeter area intrusion identification results.

[0119] Furthermore, the perimeter intrusion identification module is also used for:

[0120] Preprocessing the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring data source;

[0121] Modeling is performed according to the basic information of the mth perimeter area to obtain the mth perimeter area model;

[0122] The m-th perimeter area monitoring data source is rendered to the m-th perimeter area model to obtain the m-th perimeter area monitoring cloud map.

[0123] Furthermore, the perimeter intrusion alarm module is also used for:

[0124] Perform feature recognition according to the mth perimeter area intrusion recognition result to obtain the mth perimeter intrusion judgment operator and the mth perimeter intrusion attribute operator;

[0125] The perimeter intrusion alarm includes an intrusion alarm judgment layer and an intrusion alarm level layer;

[0126] Inputting the mth perimeter intrusion judgment operator into the intrusion alarm judgment layer, and generating a perimeter intrusion warning instruction if the mth perimeter intrusion judgment operator satisfies a preset intrusion alarm judgment operator in the intrusion alarm judgment layer;

[0127] According to the perimeter intrusion warning instruction, the mth perimeter intrusion attribute operator is input into the intrusion alarm level layer to obtain the mth perimeter intrusion alarm signal;

[0128] A high-speed railway perimeter intrusion alarm is executed in the mth high-speed railway perimeter area according to the mth perimeter intrusion alarm signal.

[0129] A high-speed rail perimeter intrusion alarm information processing system integrating multiple sensor technologies provided in an embodiment of the present invention can execute a high-speed rail perimeter intrusion alarm information processing method integrating multiple sensor technologies provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0130] The modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0131] The present application provides a high-speed railway perimeter intrusion alarm information processing method integrating multi-sensor technology, wherein the method is applied to a high-speed railway perimeter intrusion alarm information processing system integrating multi-sensor technology, and the method includes: dividing the perimeter of the target high-speed railway according to the perimeter basic data to obtain M high-speed railway perimeter areas; configuring the perimeter intrusion monitoring of the M high-speed railway perimeter areas according to the multi-dimensional perimeter intrusion monitoring configuration index to obtain M perimeter area monitoring submodules; performing perimeter monitoring of the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets; performing high-speed railway perimeter intrusion identification on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; transmitting the M perimeter area intrusion identification results to the high-speed railway perimeter intrusion alarm platform, and performing high-speed railway perimeter intrusion alarm on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform. The technical problem that the accuracy of the high-speed railway perimeter intrusion alarm in the prior art is insufficient, resulting in poor effect of the high-speed railway perimeter intrusion alarm, is solved. The technical effect of improving the accuracy of high-speed railway perimeter intrusion alarms and improving the quality of high-speed railway perimeter intrusion alarms has been achieved, thereby effectively ensuring the safety of high-speed railway perimeters.

[0132] Although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A high-speed railway perimeter intrusion alarm information processing method integrating multiple sensor technologies, characterized in that: The method is applied to a high-speed railway perimeter intrusion alarm information processing system integrating multiple sensor technologies, the system comprising a high-speed railway perimeter intrusion alarm platform, and the method comprises: Obtain basic perimeter data of the target high-speed railway perimeter; Divide the target high-speed railway perimeter into M high-speed railway perimeter areas according to the perimeter basic data, and obtain M high-speed railway perimeter areas, where M is a positive integer greater than 1; Obtaining a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator; Based on the multi-dimensional perimeter intrusion monitoring configuration index, perimeter intrusion monitoring configuration is performed on the M high-speed railway perimeter areas to obtain M perimeter area monitoring submodules; Performing perimeter monitoring on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets; Perform high-speed rail perimeter intrusion identification based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; The intrusion identification results of the M perimeter areas are transmitted to the high-speed railway perimeter intrusion alarm platform, and a high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform; Based on the multi-dimensional perimeter intrusion monitoring configuration index, perimeter intrusion monitoring configuration is performed on the M high-speed railway perimeter areas to obtain M perimeter area monitoring submodules, including: According to the M high-speed rail perimeter areas, an m-th high-speed rail perimeter area is obtained, where m is a positive integer belonging to M; According to the perimeter basic data, matching the mth perimeter area basic information; Performing a perimeter intrusion feature analysis based on the mth high-speed railway perimeter area to obtain an intrusion feature of the mth perimeter area; Based on the multi-dimensional perimeter intrusion monitoring configuration indicators, a perimeter intrusion monitoring configuration model is built; Inputting the mth perimeter area basic information and the mth perimeter area intrusion characteristics into the perimeter intrusion monitoring configuration model to obtain the mth perimeter intrusion monitoring configuration solution; Execute the monitoring configuration of the mth high-speed railway perimeter area based on the mth perimeter intrusion monitoring configuration scheme, obtain the mth perimeter area monitoring submodule, and add the mth perimeter area monitoring submodule to the M perimeter area monitoring submodules; Based on the mth high-speed railway perimeter area, perimeter intrusion feature analysis is performed to obtain the mth perimeter area intrusion feature, including: Get the preset historical time zone constraints; According to the preset historical time zone constraint, retrieve the high-speed rail perimeter intrusion alarm record library of the m-th high-speed rail perimeter area; Obtaining a multi-dimensional perimeter intrusion feature analysis index, wherein the multi-dimensional perimeter intrusion feature analysis index includes perimeter intrusion subject features, perimeter intrusion time zone features, and perimeter intrusion space features; Based on the multi-dimensional perimeter intrusion feature analysis index, feature recognition is performed on the high-speed railway perimeter intrusion alarm record library to generate a multi-modal perimeter intrusion feature parameter set; Traversing the multimodal perimeter intrusion feature parameter set to perform frequency screening to generate the mth perimeter area intrusion feature; Based on the multi-dimensional perimeter intrusion monitoring configuration indicators, a perimeter intrusion monitoring configuration model is constructed, including: Based on the multi-dimensional perimeter intrusion feature analysis index and the multi-dimensional perimeter intrusion monitoring configuration index, the perimeter intrusion monitoring configuration data is backtracked to obtain a plurality of sample perimeter area basic information, a plurality of sample perimeter area intrusion features and a plurality of sample perimeter intrusion monitoring configuration schemes, and the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes have a sample mapping relationship; Based on the knowledge graph, a perimeter intrusion monitoring configuration architecture is obtained, wherein the perimeter intrusion monitoring configuration architecture includes a mode layer and a data layer; Embedding the sample mapping relationship into the pattern layer to obtain a feature pattern layer; According to the plurality of sample perimeter area basic information, the plurality of sample perimeter area intrusion features and the plurality of sample perimeter intrusion monitoring configuration schemes, a plurality of configuration primary index feature information, a plurality of configuration secondary index feature information and a plurality of monitoring configuration response feature information are obtained; Adding the plurality of configuration primary index feature information, the plurality of configuration secondary index feature information and the plurality of monitoring configuration response feature information to the data layer to obtain a feature data layer; Performing data fusion on the feature data layer according to the feature pattern layer to generate the perimeter intrusion monitoring configuration model; Based on the M perimeter area monitoring data sets, high-speed rail perimeter intrusion identification is performed to obtain M perimeter area intrusion identification results, including: According to the M perimeter area monitoring data sets, obtaining an mth perimeter area monitoring data set; Perform simulation fitting based on the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring cloud map; Building a high-speed rail perimeter intrusion identification channel, wherein the high-speed rail perimeter intrusion identification channel includes multi-dimensional perimeter intrusion identification indicators, and the multi-dimensional perimeter intrusion identification indicators include perimeter intrusion judgment indicators, perimeter intrusion subject indicators and perimeter intrusion risk indicators; Inputting the mth perimeter area monitoring cloud map into the high-speed railway perimeter intrusion identification channel to generate the mth perimeter area intrusion identification result; Adding the mth perimeter area intrusion identification result to the M perimeter area intrusion identification results; The intrusion identification results of the M perimeter areas are transmitted to the high-speed railway perimeter intrusion alarm platform, and a high-speed railway perimeter intrusion alarm is performed on the M high-speed railway perimeter areas according to the perimeter intrusion alarm in the high-speed railway perimeter intrusion alarm platform, including: Perform feature recognition according to the mth perimeter area intrusion recognition result to obtain the mth perimeter intrusion judgment operator and the mth perimeter intrusion attribute operator; The perimeter intrusion alarm includes an intrusion alarm judgment layer and an intrusion alarm level layer; Inputting the mth perimeter intrusion judgment operator into the intrusion alarm judgment layer, and generating a perimeter intrusion warning instruction if the mth perimeter intrusion judgment operator satisfies a preset intrusion alarm judgment operator in the intrusion alarm judgment layer; According to the perimeter intrusion warning instruction, the mth perimeter intrusion attribute operator is input into the intrusion alarm level layer to obtain the mth perimeter intrusion alarm signal; A high-speed railway perimeter intrusion alarm is executed in the mth high-speed railway perimeter area according to the mth perimeter intrusion alarm signal.

2. The method according to claim 1, characterized in that Performing simulation fitting based on the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring cloud map includes: Preprocessing the mth perimeter area monitoring data set to obtain the mth perimeter area monitoring data source; Modeling is performed according to the basic information of the mth perimeter area to obtain the mth perimeter area model; The m-th perimeter area monitoring data source is rendered to the m-th perimeter area model to obtain the m-th perimeter area monitoring cloud map.

3. A high-speed railway perimeter intrusion alarm information processing system integrating multiple sensor technologies, characterized in that: The system is used to execute the method according to any one of claims 1 to 2, the system comprises a high-speed railway perimeter intrusion alarm platform, and the system further comprises: A perimeter basic data acquisition module, wherein the perimeter basic data acquisition module is used to obtain perimeter basic data of the target high-speed railway perimeter; A perimeter division module, the perimeter division module is used to divide the target high-speed railway perimeter according to the perimeter basic data to obtain M high-speed railway perimeter areas, and M is a positive integer greater than 1; A monitoring configuration indicator acquisition module, wherein the monitoring configuration indicator acquisition module is used to obtain a multi-dimensional perimeter intrusion monitoring configuration indicator, wherein the multi-dimensional perimeter intrusion monitoring configuration indicator includes a monitoring-hard configuration indicator and a monitoring-soft configuration indicator; A perimeter intrusion monitoring configuration module, the perimeter intrusion monitoring configuration module is used to perform perimeter intrusion monitoring configuration on the M high-speed railway perimeter areas based on the multi-dimensional perimeter intrusion monitoring configuration index to obtain M perimeter area monitoring submodules; A perimeter monitoring module, the perimeter monitoring module is used to perform perimeter monitoring on the M high-speed railway perimeter areas according to the M perimeter area monitoring submodules to obtain M perimeter area monitoring data sets; A perimeter intrusion identification module, the perimeter intrusion identification module is used to perform high-speed rail perimeter intrusion identification based on the M perimeter area monitoring data sets to obtain M perimeter area intrusion identification results; A perimeter intrusion alarm module is used to transmit the intrusion identification results of the M perimeter areas to the high-speed railway perimeter intrusion alarm platform, and to perform high-speed railway perimeter intrusion alarms for the M high-speed railway perimeter areas according to the perimeter intrusion alarms in the high-speed railway perimeter intrusion alarm platform.

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