Heavy haul railway flood control disposal method, system, equipment, medium and program

Abstract

The invention relates to a heavy haul railway flood control disposal method, system, device, medium and program, and the method comprises the steps: sending a pre-flood inspection work order to each work area, obtaining a pre-flood inspection result returned for the pre-flood inspection work order, and updating a preset initial work plan into a flood control work plan according to the pre-flood inspection result; sending a flood control work plan to each work area, and obtaining real-time flood control monitoring data returned for the flood control work plan; flood disaster identification and disaster early warning are carried out on the flood control monitoring data to obtain flood disaster early warning work orders, the flood disaster early warning work orders are sent to all work areas, and emergency flood disaster data returned for the flood disaster early warning work orders are obtained; generating an emergency disposal plan set according to the flood early warning work order and the emergency flood data; and personnel matching and flood control scheduling are performed on each work area according to the emergency disposal plan set and the emergency flood disaster data, so that the flood control disposal efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of flood prevention and treatment, and in particular to a method, system, equipment, medium and program for flood prevention and treatment of heavy-duty railways. Background Art

[0002] Heavy-haul railway is a freight railway specially used for transporting large goods. It can carry extremely large loads. When facing the threat of floods, heavy-haul railway will take a series of flood prevention measures to ensure the safety of train operation and the integrity of railway facilities.

[0003] The existing flood prevention and treatment methods for heavy-load railways are mostly based on manual scheduling and management. That is, on-site staff will issue flood warnings for heavy-load railways and hand them over to the management for flood treatment and scheduling. In actual applications, flood prevention and treatment plans based on manual scheduling and management lack feedback on pre-flood inspection results, making it difficult to perform data analysis, prone to flood prevention loopholes, and prone to layer-by-layer decision-making and approval, resulting in low efficiency in flood prevention treatment for heavy-load railways. Summary of the invention

[0004] The present invention provides a method, system, equipment, medium and procedure for flood prevention and treatment of heavy-duty railways, which improves the efficiency of flood prevention and treatment by means of cyclical pre-flood inspections, improves the efficiency of flood warning by means of digital intelligent flood identification, and improves the efficiency of flood prevention and treatment by means of efficient personnel and material allocation strategies.

[0005] In a first aspect, the present disclosure provides a method for flood prevention and disposal of heavy-duty railways, comprising: sending a pre-flood inspection work order to each work area, obtaining a pre-flood inspection result returned for the pre-flood inspection work order, and updating a preset initial work plan into a flood prevention work plan according to the pre-flood inspection result; sending the flood prevention work plan to each work area, and obtaining real-time flood prevention monitoring data returned for the flood prevention work plan; performing flood identification and disaster warning on the flood prevention monitoring data to obtain a flood warning work order, sending the flood warning work order to each work area, and obtaining emergency flood data returned for the flood warning work order; generating an emergency response plan set according to the flood warning work order and the emergency flood data; and performing personnel matching and flood prevention scheduling for each work area according to the emergency response plan set and the emergency flood data.

[0006] In some embodiments, updating the preset initial work plan into a flood control work plan based on the pre-flood inspection results includes: splitting the pre-flood inspection results into a work area inspection result set according to the work area; obtaining a work area maintenance data set corresponding to the work area inspection result set; using the work area maintenance data set to perform difference matching on the work area inspection result set to obtain a work area difference data set; performing flood control hazard analysis on the work area difference data set to obtain a flood control hazard level set; and performing priority updating on each work area work plan in the preset initial work plan according to the flood control hazard level set to obtain a flood control work plan.

[0007] In some embodiments, the flood control monitoring data is subjected to flood identification and disaster warning to obtain a flood warning work order, including: splitting the flood control monitoring data into work area monitoring data sets according to the work area; extracting a work area slope atlas from the work area monitoring data set, performing landslide identification on the work area slope atlas, and obtaining a landslide data set; extracting a flood environment parameter set from the work area monitoring data set, performing data vectorization operations on the flood environment parameter set, and obtaining a flood environment feature set; generating a flood monitoring feature set based on the flood environment feature set and the landslide data set; performing disaster identification on the flood monitoring feature set to obtain a flood data set; and performing flood warning based on the flood data set to obtain a flood warning work order.

[0008] In some embodiments, the landslide identification on the work area slope atlas to obtain a landslide data set includes: filtering and denoising the work area slope atlas to obtain a denoised slope atlas; performing edge detection and edge segmentation on the denoised slope atlas to obtain a segmented slope atlas; extracting a slope feature set from the segmented slope atlas; classifying landslides on the slope feature set to obtain a landslide type set; grading landslides on the slope feature set to obtain a landslide severity level set; and generating a landslide data set based on the landslide type set and the landslide severity level set.

[0009] In some embodiments, generating an emergency response plan set based on the flood warning work order and the emergency flood data includes: splitting the flood warning work order into work area warning work order sets according to the work area; matching a warning plan set from a preset warning plan library based on the work area warning work order set; matching an emergency data set corresponding to the work area warning work order set from the emergency flood data; extracting an emergency consumables feature set from the emergency data set; and dynamically adjusting the warning plan set based on the emergency consumables feature set to obtain an emergency response plan set.

[0010] In some embodiments, the personnel matching and flood control scheduling of each work area according to the emergency response plan set and the emergency flood data include: selecting the emergency response plans in the emergency response plan set one by one as the target response plan, and taking the emergency data of the work area corresponding to the target response plan in the emergency flood data as the target emergency data; extracting the response personnel requirements and the emergency response positions from the target response plans respectively; extracting the emergency personnel data and the emergency personnel positions from the target emergency data respectively; matching the capabilities of the emergency personnel data according to the response personnel requirements to obtain emergency response personnel; matching the response personnel positions corresponding to the emergency response personnel from the emergency personnel positions; planning the paths of the emergency response personnel according to the emergency response positions and the response personnel positions to obtain the emergency scheduling path; and scheduling the emergency response personnel for flood control according to the emergency scheduling path.

[0011] In a second aspect, the present disclosure provides a heavy-duty railway flood control and disposal system, including: a pre-flood inspection module, which is used to send a pre-flood inspection work order to each work area, obtain the pre-flood inspection result returned for the pre-flood inspection work order, and update the preset initial work plan into a flood control work plan according to the pre-flood inspection result; a flood control monitoring module, which is used to send the flood control work plan to each work area, and obtain the real-time flood control monitoring data returned for the flood control work plan; a flood warning module, which is used to perform flood identification and disaster warning on the flood control monitoring data, obtain a flood warning work order, send the flood warning work order to each work area, and obtain the emergency flood data returned for the flood warning work order; a plan allocation module, which is used to generate an emergency disposal plan set according to the flood warning work order and the emergency flood data; a flood control scheduling module, which is used to match personnel and perform flood control scheduling for each work area according to the emergency disposal plan set and the emergency flood data.

[0012] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a heavy-load railway flood prevention method described in the above aspect.

[0013] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a heavy-load railway flood prevention method described in the above aspect.

[0014] In a fifth aspect, the present disclosure provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of a heavy-load railway flood prevention method described in the above aspects.

[0015] The present invention provides a method, system, equipment, medium and program for flood prevention and disposal of heavy-duty railways. By sending pre-flood inspection work orders to each work area and obtaining pre-flood inspection results, it is possible to implement pre-flood data collation and efficient flood situation monitoring, thereby improving flood response efficiency. By generating a flood prevention work plan, it is possible to implement primary flood prevention and disposal in combination with real-time feedback data from the work area, thereby improving flood prevention capabilities for key areas.

[0016] By acquiring real-time flood control monitoring data, it is possible to implement real-time data monitoring of railway floods, realize the digitization of flood control, improve the efficiency of flood control information dissemination, and thus improve the efficiency of flood control disposal. By conducting flood identification and disaster warning, it is possible to combine artificial intelligence technology to realize automated flood identification and flood warning, thereby improving the efficiency and accuracy of flood control warning and reducing the danger of personnel going to the accident site for investigation. By acquiring the emergency flood data returned by the flood warning work order, it is possible to realize real-time data collection of flood conditions, thereby improving the flexibility of flood control disposal.

[0017] By generating an emergency response plan set based on the flood warning work order and the emergency flood data, it is possible to propose dynamic adjustments to the current plan through artificial intelligence self-learning, thereby achieving dynamic and cyclical adjustments between auxiliary plans and actual conditions, and improving the efficiency of material planning during flood prevention. By matching personnel and flood control scheduling for each work area based on the emergency response plan set and the emergency flood data, it is possible to achieve flood control scheduling that matches individuals, save scheduling time, and improve the efficiency of flood control. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0019] Figure 1 A schematic diagram of a process for generating a flood prevention work plan according to the first embodiment of the present disclosure is shown.

[0020] Figure 2 A schematic diagram of the process of generating an emergency response plan set in the first embodiment of the present disclosure is shown.

[0021] Figure 3 A schematic diagram of the process of performing printing simulation and real-time process rendering in the first embodiment of the present disclosure is shown.

[0022] Figure 4 A schematic flow chart of a heavy-load railway flood prevention method according to a second embodiment of the present disclosure is shown.

[0023] Figure 5 A functional module diagram of a heavy-duty railway flood prevention and disposal system according to a third embodiment of the present disclosure is shown.

[0024] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0027] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Embodiment 1

[0029] Figure 1 The following is a flow chart of a heavy-load railway flood prevention method provided by an embodiment of the present disclosure. Figure 1 As shown, a heavy-load railway flood prevention method comprises:

[0030] S1. Send a pre-flood inspection work order to each work area, obtain the pre-flood inspection result returned by the pre-flood inspection work order, and update the preset initial work plan into a flood control work plan according to the pre-flood inspection result.

[0031] In detail, the work area refers to each work area of ​​a heavy-load railway, and the pre-flood inspection work order includes work orders such as special inspection work order for railway equipment during flood season, inspection work order for emergency materials during flood season, compliance inspection work order for emergency training during flood season, and compliance inspection work order for emergency personnel during flood season. Among them, the special inspection work order for railway equipment during flood season is a work order for inspecting equipment and facilities in the railway, the inspection work order for emergency materials during flood season is a work order for inspecting emergency materials during flood prevention, the compliance inspection work order for emergency training during flood season is a work order for checking the compliance level of flood prevention training, and the compliance inspection work order for emergency personnel during flood season is a work order for checking the compliance level of emergency personnel in flood prevention. The work order is a written document used to record and convey work tasks, requirements and information, and the pre-flood inspection work order can be sent to each work area using an integrated digital platform.

[0032] Specifically, the pre-flood inspection results refer to the inspection results obtained after each work area conducts a pre-flood inspection in accordance with the pre-flood inspection work order, including pre-flood railway equipment operation data, pre-flood emergency material data, and pre-flood emergency personnel capabilities and deployment locations. The flood prevention work plan refers to the flood prevention priority of the protection area of ​​each work area and the work plan related to flood prevention monitoring and personnel deployment.

[0033] In the embodiment of the present invention, refer to Figure 2 As shown, the updating of the preset initial work plan into a flood prevention work plan according to the pre-flood inspection results includes:

[0034] S21, splitting the pre-flood inspection results into work area inspection result sets according to the work areas;

[0035] S22, obtaining a work area maintenance data set corresponding to the work area inspection result set;

[0036] S23, using the work area maintenance data set to perform difference matching on the work area inspection result set to obtain a work area difference data set;

[0037] S24, performing flood control hazard analysis on the work area difference data set to obtain a flood control hazard level set;

[0038] S25. Update the priority of each work area work plan in the preset initial work plan according to the flood control hazard level set to obtain a flood control work plan.

[0039] Specifically, each work area inspection result in the work area inspection result set corresponds to the inspection result corresponding to each work area in the pre-flood inspection result, and each work area historical maintenance data in the work area maintenance data set corresponds to the inspection result of a work area during daily maintenance, and the work area maintenance data set corresponding to the work area inspection result set can be obtained from a preset maintenance database.

[0040] In detail, the difference matching refers to calculating the difference between each numerical value in the work area inspection result and the corresponding work area maintenance data and work area inspection result in the work area maintenance data set and the work area inspection result, and compiling the difference as the work area difference data into a work area difference data set.

[0041] Specifically, the flood control hazard analysis refers to analysis using a hazard analysis model pre-trained in a preset big data platform, and the hazard analysis model is a neural network model whose input is work area difference data and whose output is flood control hazard level.

[0042] In detail, each flood hazard level in the flood hazard level set is used to measure the possibility level of the existence of flood hazards in a work area. The initial work plan contains multiple work area work plans. The work area work plan refers to the work plan related to the flood control priority and flood control measures configuration of the protection section of a single work area. The priority update refers to updating the flood control priority of each work area in the initial work plan according to the size of the flood hazard level.

[0043] In the embodiment of the present invention, by sending pre-flood inspection work orders to each work area and obtaining pre-flood inspection results, pre-flood data collation and efficient flood monitoring can be achieved, thereby improving flood response efficiency. By generating a flood control work plan, primary flood control measures can be implemented in combination with real-time feedback data from the work area, thereby improving flood control capabilities for key areas.

[0044] S2. Send the flood control work plan to each work area, and obtain real-time flood control monitoring data returned for the flood control work plan.

[0045] Specifically, the real-time flood control monitoring data refers to the data obtained by each work area through flood control monitoring in accordance with the flood control work plan. The real-time flood control monitoring data is the data collected by the Internet of Things platform on the railway facilities, equipment and environment. The flood control work plan is also sent to the central large screen, command center, station sub-center, work area operation terminal, on-site command terminal and on-site collaboration terminal.

[0046] In detail, the real-time flood control monitoring data includes monitoring data of each work area, and the monitoring data includes rainfall data collected by a rain sensor, ambient temperature collected by a temperature sensor, ambient humidity collected by a humidity sensor, water level data collected by a water level sensor, water flow velocity collected by a flow velocity sensor, and slope pictures collected by photographing the slope and slope protection with a camera.

[0047] In the embodiment of the present invention, by acquiring real-time flood control monitoring data, real-time data monitoring of railway floods can be achieved, flood control can be digitized, the efficiency of dissemination of flood control information is improved, and thus the efficiency of flood control disposal is improved.

[0048] S3. Perform flood identification and disaster warning on the flood control monitoring data to obtain a flood warning work order, send the flood warning work order to each work area, and obtain emergency flood data returned by the flood warning work order.

[0049] In detail, the flood warning work order refers to a work order for disaster warning in areas where floods may occur or are about to occur, and the flood warning work order includes different warning plans for specific types and levels of floods.

[0050] In an embodiment of the present invention, the flood control monitoring data is subjected to flood identification and disaster warning to obtain a flood warning work order, including: splitting the flood control monitoring data into a work area monitoring data set according to the work area; extracting a work area slope atlas from the work area monitoring data set, and performing landslide identification on the work area slope atlas to obtain a landslide data set; extracting a flood environment parameter set from the work area monitoring data set, and performing data vectorization operations on the flood environment parameter set to obtain a flood environment feature set; generating a flood monitoring feature set based on the flood environment feature set and the landslide data set; performing disaster identification on the flood monitoring feature set to obtain a flood data set; and performing flood warning based on the flood data set to obtain a flood warning work order.

[0051] In detail, each work area monitoring data in the work area monitoring data set corresponds to the monitoring data corresponding to a work area in the flood control monitoring data, and each work area slope image in the work area slope atlas corresponds to a photographed image of the slope and slope protection of a work area in the flood control monitoring data.

[0052] Specifically, each flood environmental parameter in the flood environmental parameter set includes rainfall data, ambient temperature, ambient humidity, water level data, water flow velocity and other data of the corresponding work area, and each flood monitoring feature in the flood monitoring feature set includes the corresponding flood environmental features in the flood environmental feature set and the landslide data in the landslide data set.

[0053] In detail, a convolutional neural network model trained with a large number of flood monitoring features labeled with flood types and flood hazard levels can be used to perform disaster identification on the flood monitoring feature set to obtain a flood data set. The flood warning refers to judging whether a flood has occurred in the corresponding work area based on the flood types and flood hazard levels in each flood data in the flood data set. If so, a corresponding work area warning work order is generated, and all work area warning work orders are used as flood warning work orders, wherein the flood types include reservoir breaches, urban floods, and mountain torrents, and the flood hazard levels include extremely serious floods, serious floods, relatively large floods, and general floods.

[0054] Specifically, the landslide identification is performed on the work area slope atlas to obtain a landslide data set, including: filtering and denoising the work area slope atlas to obtain a denoised slope atlas; edge detection and edge segmentation are performed on the denoised slope atlas to obtain a segmented slope atlas; a slope feature set is extracted from the segmented slope atlas; landslide classification is performed on the slope feature set to obtain a landslide type set; landslide classification is performed on the slope feature set to obtain a landslide severity level set; and a landslide data set is generated according to the landslide type set and the landslide severity level set.

[0055] In detail, the filtering and denoising method may be a Gaussian filter or a median filter, etc., the edge detection may be performed using a Canny algorithm, and the edge segmentation may be performed using a region growing or threshold segmentation method.

[0056] Specifically, each slope feature in the slope feature set corresponds to geometric features such as contour, edge length, area and shape of each segmented slope image in the segmented slope atlas; texture features such as roughness and directionality; and color features such as color distribution.

[0057] In detail, a convolutional neural network model or a support vector machine model trained with a large number of slope features labeled with landslide types and landslide severity levels can be used to classify and grade landslides on the slope feature set, wherein the landslide types in the landslide type set include collapse, landslide, mud-rock flow and foundation settlement, and the landslide severity levels in the landslide severity level set include minor landslide, moderate landslide and catastrophic landslide.

[0058] Specifically, sending the flood warning work order to each work area refers to sending the warning work order corresponding to the work area in the flood warning work order to each work area, and the emergency flood data is composed of emergency data of multiple work areas that have received the work area warning work order. The emergency data refers to the inspection data of the emergency inspection of the corresponding work area by each work area after receiving the corresponding warning work order, and the inspection data includes emergency railway equipment data, emergency material data, and emergency personnel capabilities and deployment location data.

[0059] In the embodiment of the present invention, by performing flood identification and disaster warning, it is possible to combine artificial intelligence technology to realize automated flood identification and flood warning, thereby improving the efficiency and accuracy of flood prevention and warning, and reducing the danger of personnel going to the accident site to investigate. By obtaining the emergency flood data returned by the flood warning work order, real-time data collection of flood conditions can be realized, thereby improving the flexibility of flood prevention and disposal.

[0060] S4. Generate an emergency response plan set based on the flood warning work order and the emergency flood data.

[0061] In detail, each emergency response plan in the emergency response plan set refers to a plan for emergency flood prevention and control in each work area where a flood has occurred. The content of the emergency response plan set includes the number of personnel with specific capabilities to be deployed to the scene, the location of the work site, the use of flood control materials, the construction of flood control works, etc.

[0062] In the embodiment of the present invention, refer to Figure 3 As shown, the generating of the emergency response plan set according to the flood warning work order and the emergency flood data includes:

[0063] S31, splitting the flood warning work order into work area warning work order sets according to the work area;

[0064] S32, matching an early warning plan set from a preset early warning plan library according to the early warning work order set of the work area;

[0065] S33, matching the emergency data set corresponding to the work area warning work order set from the emergency flood data;

[0066] S34, extracting a feature set of emergency consumables from the emergency data set;

[0067] S35. Dynamically adjust the warning plan set according to the emergency consumables feature set to obtain an emergency response plan set.

[0068] In detail, each work area warning work order in the work area warning work order set is a warning work order for one work area, and the warning plan library contains different warning plans for different types of warning work orders, and the warning plans are obtained in advance by the staff through brainstorming.

[0069] Specifically, each emergency data in the emergency data set refers to the inspection data of the emergency inspection of the corresponding work area after each work area receives the corresponding early warning work order, and the inspection data includes emergency railway equipment data, emergency material data, and emergency personnel capabilities and deployment location data.

[0070] In detail, each emergency consumables feature in the emergency consumables feature set corresponds to the feature of material consumption of an emergency data in the emergency data set. The emergency consumables feature can be calculated by using a long and short time series neural network combined with the pre-flood emergency material data in the pre-flood inspection results corresponding to the emergency data. The pre-flood emergency material data generates an emergency material inventory electronic calendar by synchronizing the database with the data, and vividly displays the changing relationship of the emergency materials in the form of an electronic calendar. The corresponding database in the pre-flood emergency material data has the ability to manage all currently registered emergency material data, including material name, category, code, warehousing time, inventory management unit, inventory cycle, purpose, specialty, warehouse, management unit, consumption unit, consumption purpose, and outbound time.

[0071] Specifically, the trained neural network model can be used to dynamically adjust the warning plan set according to the emergency consumables feature set to obtain an emergency response plan set, wherein the input of the neural network model is the emergency consumables characteristics and the corresponding warning plan, and the output is the adjusted warning plan evaluated manually.

[0072] In an embodiment of the present invention, by generating an emergency response plan set based on the flood warning work order and the emergency flood data, it is possible to propose dynamic adjustments to the current plan through artificial intelligence self-learning, thereby achieving dynamic cyclic adjustment of auxiliary plans and actual conditions, and improving the efficiency of material planning during flood prevention.

[0073] S5. Matching personnel and conducting flood control dispatch for each work area according to the emergency response plan set and the emergency flood disaster data.

[0074] In an embodiment of the present invention, personnel matching and flood control scheduling for each work area based on the emergency response plan set and the emergency flood data include: selecting the emergency response plans in the emergency response plan set one by one as the target response plan, and using the emergency data of the work area corresponding to the target response plan in the emergency flood data as the target emergency data; extracting the response personnel requirements and the emergency response positions from the target response plans respectively; extracting the emergency personnel data and the emergency personnel positions from the target emergency data respectively; matching the capabilities of the emergency personnel data according to the response personnel requirements to obtain emergency response personnel; matching the response personnel positions corresponding to the emergency response personnel from the emergency personnel positions; planning the paths of the emergency response personnel according to the emergency response positions and the response personnel positions to obtain the emergency scheduling path; and performing flood control scheduling for the emergency response personnel according to the emergency scheduling path.

[0075] In detail, the response personnel demand refers to the number of emergency personnel who need to go to the scene and the capabilities of the emergency personnel configured in the target response plan, and the emergency response location refers to the specific location of the work area site where flood control is required in the target response plan.

[0076] Specifically, the emergency personnel data refers to the relevant data of the emergency personnel deployed in the corresponding work area in the target emergency data. The emergency personnel data has the ability to manage the data of currently registered emergency response personnel and emergency joint defense units, including basic information of personnel, key capabilities of personnel, experience in handling emergencies of personnel, basic information of joint defense units, locations assigned to joint defense units, scope of joint defense units, emergency handling capabilities of joint defense units, experience in handling emergencies of joint defense units and other information. The emergency personnel position refers to the deployment position of the emergency personnel corresponding to the emergency personnel data.

[0077] In detail, the capability matching can be performed by using a relationship graph docking method. The path planning refers to determining the upper road crossing position closest to the location of the disposal personnel, and planning a path from the upper road crossing position to the emergency disposal position.

[0078] In the embodiment of the present invention, by matching personnel and performing flood control scheduling for each work area according to the emergency response plan set and the emergency flood disaster data, it is possible to achieve flood control scheduling that matches individuals, save scheduling time, and improve the efficiency of flood control.

[0079] Embodiment 2

[0080] On the basis of the above embodiments, in order to more clearly understand the present disclosure, refer to Figure 4 As shown, a second embodiment is used below to further explain the situation in which more accurate flood disaster identification is required in the first embodiment of the present disclosure.

[0081] S41, sending a pre-flood inspection work order to each work area, obtaining the pre-flood inspection result returned for the pre-flood inspection work order, and updating the preset initial work plan into a flood control work plan according to the pre-flood inspection result.

[0082] S42: Send the flood control work plan to each work area, and obtain real-time flood control monitoring data returned for the flood control work plan.

[0083] S43, accurately identifying floods and issuing disaster warnings on the flood control monitoring data, obtaining a flood warning work order, sending the flood warning work order to each work area, and obtaining emergency flood data returned by the flood warning work order.

[0084] In detail, the flood warning work order refers to a work order for disaster warning in areas where floods may occur or are about to occur, and the flood warning work order includes different warning plans for specific types and levels of floods.

[0085] In an embodiment of the present invention, the accurate flood identification and disaster warning of the flood control monitoring data to obtain a flood warning work order includes: splitting the flood control monitoring data into a work area monitoring data set according to the work area; extracting a flood environment parameter set from the work area monitoring data set, performing data vectorization operation on the flood environment parameter set to obtain a flood environment feature set; extracting a work area slope atlas from the work area monitoring data set, performing landslide identification on the work area slope atlas according to the flood environment feature set to obtain a landslide data set; generating a flood monitoring feature set according to the flood environment feature set and the landslide data set; performing disaster identification on the flood monitoring feature set to obtain a flood data set; performing flood warning according to the flood data set to obtain a flood warning work order.

[0086] In detail, each work area monitoring data in the work area monitoring data set corresponds to the monitoring data corresponding to a work area in the flood control monitoring data, and each work area slope image in the work area slope atlas corresponds to a photographed image of the slope and slope protection of a work area in the flood control monitoring data.

[0087] Specifically, each flood environmental parameter in the flood environmental parameter set includes rainfall data, ambient temperature, ambient humidity, water level data, water flow velocity and other data of the corresponding work area, and each flood monitoring feature in the flood monitoring feature set includes the corresponding flood environmental features in the flood environmental feature set and the landslide data in the landslide data set.

[0088] In detail, a convolutional neural network model trained with a large number of flood monitoring features labeled with flood types and flood hazard levels can be used to perform disaster identification on the flood monitoring feature set to obtain a flood data set. The flood warning refers to judging whether a flood has occurred in the corresponding work area based on the flood types and flood hazard levels in each flood data in the flood data set. If so, a corresponding work area warning work order is generated, and all work area warning work orders are used as flood warning work orders, wherein the flood types include reservoir breaches, urban floods, and mountain torrents, and the flood hazard levels include extremely serious floods, serious floods, relatively large floods, and general floods.

[0089] Specifically, the landslide identification is performed on the work area slope atlas according to the red flood environment feature set to obtain a landslide data set, including: filtering and denoising the work area slope atlas to obtain a denoised slope atlas; edge detection and edge segmentation are performed on the denoised slope atlas to obtain a segmented slope atlas; a slope image set is extracted from the segmented slope atlas; a slope geological feature set is extracted from the flood environment feature set; a slope feature set is generated according to the slope image feature set and the slope geological feature set; landslide classification is performed on the slope feature set to obtain a landslide type set; landslide classification is performed on the slope feature set to obtain a landslide severity level set; and a landslide data set is generated according to the landslide type set and the landslide severity level set.

[0090] In detail, the filtering and denoising method may be a Gaussian filter or a median filter, etc., the edge detection may be performed using a Canny algorithm, and the edge segmentation may be performed using a region growing or threshold segmentation method.

[0091] Specifically, each slope feature in the slope image feature set corresponds to geometric features such as contour, edge length, area and shape of each segmented slope image in the segmented slope atlas; texture features such as roughness and directionality, and color features such as color distribution. The slope geological feature set refers to geological features such as soil moisture and foundation settlement velocity in the flood environment feature set.

[0092] In detail, a convolutional neural network model or a support vector machine model trained with a large number of slope features labeled with landslide types and landslide severity levels can be used to classify and grade landslides on the slope feature set, wherein the landslide types in the landslide type set include collapse, landslide, mud-rock flow and foundation settlement, and the landslide severity levels in the landslide severity level set include minor landslide, moderate landslide and catastrophic landslide.

[0093] Specifically, sending the flood warning work order to each work area refers to sending the warning work order corresponding to the work area in the flood warning work order to each work area, and the emergency flood data is composed of emergency data of multiple work areas that have received the work area warning work order. The emergency data refers to the inspection data of the emergency inspection of the corresponding work area by each work area after receiving the corresponding warning work order, and the inspection data includes emergency railway equipment data, emergency material data, and emergency personnel capabilities and deployment location data.

[0094] In the embodiment of the present invention, by performing flood identification and disaster warning, it is possible to combine artificial intelligence technology to realize automated flood identification and flood warning, thereby improving the efficiency and accuracy of flood prevention and warning, and reducing the danger of personnel going to the accident site to investigate. By obtaining the emergency flood data returned by the flood warning work order, real-time data collection of flood conditions can be realized, thereby improving the flexibility of flood prevention and disposal.

[0095] S44: Generate an emergency response plan set according to the flood warning work order and the emergency flood data.

[0096] S45. Matching personnel and conducting flood control dispatch for each work area according to the emergency response plan set and the emergency flood disaster data.

[0097] Embodiment 3

[0098] Based on the above embodiments, Figure 5 This is a functional module diagram of a heavy-duty railway flood prevention and disposal system provided by an embodiment of the present disclosure. Figure 5 As shown, a heavy-load railway flood prevention and disposal system comprises:

[0099] The heavy-duty railway flood control disposal system 500 described in this embodiment can be installed in an electronic device. According to the functions to be implemented, the heavy-duty railway flood control disposal system 500 can include a pre-flood inspection module 501, a flood control monitoring module 502, a flood warning module 503, a plan allocation module 504 and a flood control scheduling module 505. The module described in the present disclosure can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0100] In this embodiment, the functions of each module / unit are as follows:

[0101] The pre-flood inspection module 501 is used to send a pre-flood inspection work order to each work area, obtain the pre-flood inspection result returned by the pre-flood inspection work order, and update the preset initial work plan into a flood control work plan according to the pre-flood inspection result;

[0102] The flood control monitoring module 502 is used to send the flood control work plan to each work area and obtain real-time flood control monitoring data returned for the flood control work plan;

[0103] The flood warning module 503 is used to perform flood identification and disaster warning on the flood control monitoring data, obtain a flood warning work order, send the flood warning work order to each work area, and obtain emergency flood data returned by the flood warning work order;

[0104] The plan allocation module 504 is used to generate an emergency response plan set according to the flood warning work order and the emergency flood data;

[0105] The flood control scheduling module 505 is used to match personnel and perform flood control scheduling for each work area according to the emergency response plan set and the emergency flood disaster data.

[0106] In detail, each module described in the heavy-load railway flood control system 500 described in the embodiment of the present disclosure adopts the same technical means as the heavy-load railway flood control method described in Example 1 when in use, and can produce the same technical effects, which will not be repeated here.

[0107] Embodiment 4

[0108] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0109] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0110] In some implementations of this embodiment, a computer program product is provided, including a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0111] The processor may include, but is not limited to, one or more processors or microprocessors, etc. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the methods in the above embodiments.

[0112] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0113] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0114] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0115] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0116] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0117] In the embodiments provided in the present disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0119] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A method for flood prevention of heavy-load railways, characterized in that: include: Sending a pre-flood inspection work order to each work area, obtaining the pre-flood inspection results returned for the pre-flood inspection work order, and updating the preset initial work plan into a flood control work plan according to the pre-flood inspection results; Sending the flood control work plan to each work area, and obtaining real-time flood control monitoring data returned for the flood control work plan; Perform flood identification and disaster warning on the flood control monitoring data, obtain a flood warning work order, send the flood warning work order to each work area, and obtain emergency flood data returned by the flood warning work order; Generate an emergency response plan set according to the flood warning work order and the emergency flood data; Personnel matching and flood control scheduling are performed for each work area according to the emergency response plan set and the emergency flood disaster data.

2. The heavy-load railway flood prevention method according to claim 1 is characterized in that: The updating of the preset initial work plan into a flood prevention work plan according to the pre-flood inspection results includes: Splitting the pre-flood inspection results into work area inspection result sets according to the work areas; Obtain a work area maintenance data set corresponding to the work area inspection result set; Using the work area maintenance data set to perform difference matching on the work area inspection result set, to obtain a work area difference data set; Performing flood control hazard analysis on the work area difference data set to obtain a flood control hazard level set; The priority of each work area work plan in the preset initial work plan is updated according to the flood control hazard level set to obtain a flood control work plan.

3. The heavy-load railway flood prevention method according to claim 1, characterized in that: The flood prevention monitoring data is used to perform flood identification and disaster warning to obtain a flood warning work order, including: Splitting the flood control monitoring data into work area monitoring data sets according to the work areas; Extracting a work area slope atlas from the work area monitoring data set, performing landslide identification on the work area slope atlas to obtain a landslide data set; Extracting a flood environment parameter set from the work area monitoring data set, performing data vectorization operation on the flood environment parameter set, and obtaining a flood disaster environment feature set; Generate a flood monitoring feature set according to the flood environment feature set and the landslide data set; Performing disaster identification on the flood monitoring feature set to obtain a flood data set; A flood warning is performed according to the flood data set to obtain a flood warning work order.

4. The heavy-load railway flood prevention method according to claim 3 is characterized in that: The step of identifying landslides on the slope atlas of the work area to obtain a landslide data set includes: Filtering and denoising the work area slope atlas to obtain a denoised slope atlas; Performing edge detection and edge segmentation on the denoised slope atlas to obtain a segmented slope atlas; Extracting a slope feature set from the segmented slope atlas; Performing landslide classification on the slope feature set to obtain a landslide type set; Performing landslide classification on the slope feature set to obtain a landslide severity grade set; A landslide data set is generated according to the landslide type set and the landslide severity level set.

5. The heavy-load railway flood prevention method according to claim 1, characterized in that: The generating of an emergency response plan set according to the flood warning work order and the emergency flood data includes: Splitting the flood warning work order into a work area warning work order set according to the work area; According to the work area warning work order set, a warning plan set is matched from a preset warning plan library; Matching the emergency data set corresponding to the work area warning work order set from the emergency flood data; Extracting an emergency consumables feature set from the emergency data set; The early warning plan set is dynamically adjusted according to the emergency consumables feature set to obtain an emergency response plan set.

6. The heavy-load railway flood prevention method according to claim 1, characterized in that: The matching of personnel and flood control dispatching for each work area according to the emergency response plan set and the emergency flood disaster data includes: Selecting emergency response plans in the emergency response plan set one by one as target response plans, and using emergency data of the work area corresponding to the target response plan in the emergency flood disaster data as target emergency data; Extracting disposal personnel requirements and emergency disposal locations from the target disposal plan respectively; extracting emergency personnel data and emergency personnel locations from the target emergency data respectively; According to the needs of the emergency personnel, the emergency personnel data is matched with the capabilities to obtain the emergency personnel; Matching the emergency personnel positions corresponding to the emergency personnel from the emergency personnel positions; Planning a path for the emergency response personnel according to the emergency response location and the location of the response personnel to obtain an emergency dispatch path; The emergency response personnel are dispatched for flood prevention according to the emergency dispatch path.

7. A heavy-load railway flood prevention and disposal system, characterized in that: include: A pre-flood inspection module is used to send a pre-flood inspection work order to each work area, obtain the pre-flood inspection results returned for the pre-flood inspection work order, and update the preset initial work plan into a flood control work plan according to the pre-flood inspection results; A flood control monitoring module, used to send the flood control work plan to each work area and obtain real-time flood control monitoring data returned for the flood control work plan; A flood warning module is used to perform flood identification and disaster warning on the flood control monitoring data, obtain a flood warning work order, send the flood warning work order to each work area, and obtain emergency flood data returned by the flood warning work order; A plan allocation module, used to generate an emergency response plan set according to the flood warning work order and the emergency flood data; The flood control scheduling module is used to match personnel and perform flood control scheduling for each work area according to the emergency response plan set and the emergency flood disaster data.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the heavy-load railway flood prevention method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the heavy-load railway flood prevention method described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program is executed by a processor, the steps of the heavy-load railway flood prevention method described in any one of claims 1 to 6 are implemented.

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