Railway dangerous goods transportation emergency material management optimization method

Abstract

The present application relates to the technical field of dangerous goods transportation, in particular to a railway dangerous goods transportation emergency material management optimization method. The present application determines the predicted demand materials of the current disaster point and the initial transportation route thereof, clusters the predicted demand materials of the current disaster point to obtain a material cluster; obtains a safety constraint factor of the material cluster, and divides the material cluster into an ideal cluster and a to-be-processed cluster; adjusts the overlap of the initial transportation routes of different predicted demand materials by using the safety constraint factor, re-clusters the predicted demand materials in the to-be-processed cluster, obtains a new to-be-processed cluster, and adjusts the safety constraint factor of the new to-be-processed cluster until the safety constraint factor meets a preset condition; the to-be-processed cluster meeting the preset condition is the ideal cluster; determines an optimized transportation route according to the initial transportation route of the predicted demand materials in the ideal cluster, and transports the predicted demand materials according to the optimized transportation route. The present application can effectively solve the problem of excessive concentration of dangerous goods in the material transportation process, and improve the efficiency of railway transportation.

Description

Technical Field

[0001] This invention relates to the field of dangerous goods transportation technology, and specifically to an optimized method for the management of emergency supplies for railway dangerous goods transportation. Background Technology

[0002] After natural disasters, accidents, and other disasters occur, a well-planned railway network can quickly transport large quantities of emergency supplies to the affected areas. When multiple disasters occur simultaneously within the railway network, ensuring the timely and effective transportation and dispatch of emergency supplies is of paramount importance.

[0003] Existing methods typically cluster disaster sites by geographical location to form multiple sub-regions, and then plan separate routes for each sub-region to transport emergency supplies to disaster sites within the sub-region. However, since emergency supplies may contain hazardous materials, a large number of hazardous materials may be present on the same route, which may lead to large-scale safety accidents, resulting in poor management of emergency supplies transported by rail. Summary of the Invention

[0004] To address the technical problem of a large quantity of hazardous materials on the same route, the present invention aims to provide an optimized method for the management of emergency supplies for railway hazardous materials transportation. The specific technical solution adopted is as follows:

[0005] This invention proposes an optimized method for the management of emergency supplies in railway hazardous materials transportation, the method comprising:

[0006] Obtain disaster data for historical and current disaster sites, as well as the required supplies and transportation routes for historical disaster sites;

[0007] Based on the differences in disaster data between the current disaster site and historical disaster sites, the predicted demand materials and their initial transportation routes for the current disaster site are selected from the demand materials and transportation routes of historical disaster sites.

[0008] Based on the overlap of the initial transportation routes of different predicted demand materials, the predicted demand materials of all current disaster points are clustered to obtain material clusters; according to the overlap of the initial transportation routes of the predicted demand materials within the material clusters, the difficulty of driving, and the degree of danger of the predicted demand materials, safety constraint factors are obtained, and the material clusters are divided into ideal clusters and unprocessed clusters.

[0009] Using the aforementioned safety constraint factor, the overlap of the initial transportation routes for different predicted demand materials is adjusted, and the predicted demand materials within the cluster to be processed are re-clustered to obtain new clusters to be processed and their aforementioned safety constraint factors, until the safety constraint factors of all new clusters to be processed meet the preset conditions; when the preset conditions are met, the cluster to be processed is recorded as the ideal cluster.

[0010] The optimal transportation route is determined based on the initial transportation route of the predicted demand materials within the ideal cluster, and the predicted demand materials are transported according to the optimal transportation route.

[0011] Furthermore, the selection of the predicted material needs at the current disaster site and its initial transportation route includes:

[0012] Based on the differences in disaster data between the current disaster site and historical disaster sites, the predicted demand for materials at the current disaster site is selected from the demand materials of historical disaster sites.

[0013] Select a transportation route that passes through the current disaster site from the predicted transportation routes of the materials needed at the current disaster site, and record it as the candidate route for the current disaster site;

[0014] Based on the transportation volume of each predicted material demand at the current disaster site and the usage of the route to be tested, obtain the initial transportation route for each predicted material demand at the current disaster site.

[0015] Furthermore, the method for obtaining the material cluster includes:

[0016] The transportation route includes at least two path nodes;

[0017] The distance index is obtained by negatively correlating and normalizing the number of identical path nodes on the two initial transportation routes.

[0018] Add a vertical axis representing the type and quantity of required materials to the two-dimensional coordinate system where the current disaster point is located, and expand it into a three-dimensional coordinate system; map all predicted required materials for all disaster points to the three-dimensional coordinate system for labeling, and obtain the coordinate points of the corresponding predicted required materials.

[0019] Based on the distance index, all coordinate points in the three-dimensional coordinate system are clustered to obtain a cluster, denoted as the material cluster.

[0020] Further, obtaining the security constraint factor includes:

[0021] Obtain the material safety level and transportation safety index of the predicted demand materials; determine the dangerous materials within the material cluster based on the material safety level; obtain the hazard characteristic parameters of each predicted demand material within the material cluster according to the proportion of dangerous materials in all predicted demand materials, and the material safety level and transportation safety index of each predicted demand material; calculate the sum of the hazard characteristic parameters of all predicted demand materials within the material cluster as the cluster hazard level.

[0022] The cluster overlap of the material cluster is obtained by calculating the mean of the distance index between all predicted demand materials within the material cluster and the predicted demand materials corresponding to the cluster center point.

[0023] Obtain the transportation time of the initial transportation route for the predicted demand materials; take the ratio of the length of the initial transportation route to the transportation time as the transportation speed; average the transportation speed of all predicted demand materials within the material cluster to obtain the overall material speed.

[0024] The cluster hazard level, cluster overlap, and overall material velocity are used as safety constraint factors for the material clusters.

[0025] Further, obtaining the new cluster to be processed and its security constraint factor includes:

[0026] A new safety constraint factor is recalculated using each of the hazardous materials in the cluster to be processed as the cluster center point. The sum of the absolute value of the difference between the new safety constraint factors of any two hazardous materials in the cluster to be processed and the distance index is used as the new distance index for the two hazardous materials.

[0027] Based on the new distance index of different predicted demand materials within the cluster to be processed, all predicted demand materials within the cluster to be processed are re-clustered to obtain new material clusters, and the safety constraint factors of the new material clusters are obtained; new material clusters that do not meet the preset conditions are taken as new clusters to be processed.

[0028] Furthermore, the security constraint factor of the ideal cluster is less than or equal to a preset security threshold, while the security constraint factor of the cluster to be processed is greater than the preset security threshold.

[0029] Furthermore, the preset condition is that the security constraint factor of the new cluster to be processed is less than or equal to a preset security threshold.

[0030] Furthermore, the optimized transportation route passes through the start and end points of the initial transportation routes for all predicted demand materials within each ideal cluster.

[0031] Furthermore, the selection of predicted material needs at the current disaster site includes:

[0032] Obtain the disaster types of historical disaster sites and current disaster sites; standardize the disaster data to obtain standard disaster data;

[0033] For historical disaster points with the same disaster type as the current disaster point, calculate the sum of the absolute values ​​of the differences between the current disaster point and the historical disaster points for the same type of standard disaster data, and use this as the disaster difference degree; obtain the disaster similarity degree based on the distance between the current disaster point and the historical disaster points and the disaster difference degree; select the historical disaster points with the largest disaster similarity degree between the current disaster point and all historical disaster points, and use these as similar disaster points of the current disaster point;

[0034] All the required materials from similar disaster sites at the current disaster site are used as the predicted required materials for the current disaster site.

[0035] Furthermore, obtaining the initial transportation route for each predicted material demand at the current disaster site includes:

[0036] Obtain the quantity of each type of required material and the length of the transportation route for the required materials at historical disaster sites;

[0037] The average quantity of each predicted demand material at the current disaster site is calculated from the quantity of the same demand material at similar disaster sites to obtain the transportation volume of the corresponding predicted demand material; the ratio of the transportation volume of each predicted demand material at the current disaster site to the total transportation volume of all predicted demand materials is taken as the quantity ratio of each predicted demand material.

[0038] The frequency of use of each candidate route is determined by the number of times the start and end points of each candidate route at the current disaster site appear simultaneously in all candidate routes.

[0039] Based on the quantity ratio of each predicted demand material at the current disaster site, and the length and usage frequency of each candidate route for each predicted demand material, the screening index for each candidate route for each predicted demand material at the current disaster site is obtained.

[0040] The maximum value among all candidate routes for each type of predicted material demand at the current disaster site is selected as the initial transportation route for each type of predicted material demand at the current disaster site.

[0041] The present invention has the following beneficial effects:

[0042] In this embodiment of the invention, the predicted demand for materials at the current disaster site and their initial transportation routes are predicted. To reduce transportation costs, predicted demand materials with similar initial transportation routes are arranged on the same transportation route. Based on the overlap of the initial transportation routes of different predicted demand materials, the predicted demand materials are clustered to obtain material clusters. The overlap of the initial transportation routes of the predicted demand materials within a material cluster and the difficulty of transportation are analyzed, successively from the perspectives of the consistency of route spatial distribution and route driving conditions, to determine the probability of risks arising from the transportation of predicted demand materials within the same material cluster on the same route. Combined with the degree of danger of the predicted demand materials, a safety constraint factor is constructed to quantify the risks of predicted demand materials within the same cluster on the same route. The risk level of online transportation determines the rationality of the allocation of predicted demand materials on the same route, and then the material clusters are divided into ideal clusters with lower transportation risks and unprocessed clusters with higher transportation risks. The predicted demand materials within the unprocessed clusters are re-allocated, and the overlap of the initial transportation routes is adjusted using safety constraint factors to increase the possibility of being assigned to different transportation routes. This ensures that the obtained ideal clusters can reduce the mutual influence between the transportation of dangerous goods on high-risk transportation routes and ensure that dangerous goods are not concentrated on the same transportation route. The predicted demand materials within the ideal clusters are transported on optimized transportation routes, which can effectively solve the problem of excessive concentration of dangerous goods in the transportation process and improve the efficiency of railway transportation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the steps of an optimized method for managing emergency supplies for railway hazardous materials transportation, provided in one embodiment of the present invention;

[0045] Figure 2 A flowchart illustrating a method for obtaining an initial transportation route according to an embodiment of the present invention;

[0046] Figure 3 A flowchart illustrating a method for obtaining a safety constraint factor according to an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of a computer device for optimizing the management of emergency supplies for railway hazardous materials transportation, provided as an embodiment of the present invention. Detailed Implementation

[0048] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an optimized method for emergency material management in railway hazardous materials transportation proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] The following description, in conjunction with the accompanying drawings, details a specific scheme for an optimized method of emergency material management for railway hazardous materials transportation provided by the present invention.

[0051] Example 1:

[0052] This invention proposes an optimized method for the management of emergency supplies in railway hazardous materials transportation. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of the steps in an embodiment of the present invention for optimizing the management of emergency supplies for railway hazardous materials transportation. The method includes:

[0053] Step S1: Obtain disaster data for historical disaster sites and current disaster sites, as well as the required materials and transportation routes for historical disaster sites.

[0054] Specifically, railway track maps are retrieved from the railway database, with each location designated as a path node on the map. Locations where disasters occurred within the past five years and material transport was completed are recorded as historical disaster points, while locations where disasters have occurred but material transport has not yet commenced are designated as current disaster points. Disaster data for both historical and current disaster points is obtained, including affected area, number of affected persons, and altitude changes; other types of disaster data may also be included. Simultaneously, each required material for historical disaster points, its transport route, quantity, and the length and duration of the transport route for each required material are retrieved from disaster management agency databases, disaster maps, or OpenStreetMap (OSM) platforms.

[0055] It should be noted that each type of material required at a historical disaster site corresponds to a transportation route. The transportation route has at least a starting point, which is the location of the material inventory, and an ending point, which is the historical disaster site. All locations passed through by the transportation route are path nodes on the transportation route.

[0056] Step S2: Based on the difference between the disaster data of the current disaster point and the historical disaster points, select the predicted demand materials and the initial transportation route of the current disaster point from the demand materials and transportation routes of the historical disaster points.

[0057] In disaster emergency scenarios, disasters are constantly changing, and the disaster data initially collected by the dispatch center may not be comprehensive or accurate enough. Meanwhile, the demand for emergency supplies is time-sensitive. In order to meet the timeliness of material transportation, when considering the transportation of emergency supplies to multiple current disaster points on the railway track map, the similarity of the disaster situation can be analyzed based on the differences between the disaster data of the current disaster point and the historical disaster point. This can predict the materials needed for the current disaster point and the material transportation route, thus obtaining the predicted demand materials and their initial transportation route, enabling material dispatch to be prepared in advance.

[0058] Step S3: Based on the overlap of the initial transportation routes of different predicted demand materials, cluster the predicted demand materials of all current disaster points to obtain material clusters; according to the overlap of the initial transportation routes of the predicted demand materials within the material clusters, the difficulty of travel, and the degree of danger of the predicted demand materials, obtain safety constraint factors, and divide the material clusters into ideal clusters and unprocessed clusters.

[0059] Traditional methods optimize the transportation routes of emergency supplies based solely on factors such as time and distance. However, the emergency supply dispatch process for predicted needs at disaster sites includes hazardous materials. When too many vehicles transporting hazardous materials are concentrated on the same route, any transportation problems could trigger a chain reaction, leading to larger-scale safety accidents. Therefore, in transporting predicted needs to disaster sites, it is crucial to avoid concentrating large quantities of hazardous materials on a single route and ensure the dispersed nature of hazardous material transportation.

[0060] To reduce transportation costs, predicted demand materials with similar initial transportation routes are typically grouped onto the same route. Based on the overlap of the initial transportation routes of different predicted demand materials, predicted demand materials at all current disaster sites can be clustered to form material clusters. Within the same material cluster, there may be multiple predicted demand materials of the same type, but these predicted demand materials have different starting or ending points on their initial transportation routes. The overlap of the initial transportation routes of predicted demand materials within a material cluster and the difficulty of transportation are analyzed, considering both the consistency of route spatial distribution and route travel conditions, to determine the likelihood of risks associated with transporting predicted demand materials within the same cluster on the same route. Combined with the degree of danger of the predicted demand materials, a safety constraint factor is constructed to quantify the risk level of transporting predicted demand materials within the same cluster on the same transportation route, thus determining the rationality of dividing predicted demand materials on the same route. Subsequently, the safety constraint factor is used to divide the material clusters into ideal clusters with lower material transportation risks and unprocessed clusters with higher material transportation risks.

[0061] In this embodiment of the invention, the security constraint factor of the ideal cluster is less than or equal to a preset security threshold, and the security constraint factor of the cluster to be processed is greater than the preset security threshold.

[0062] In one implementation of this invention, the preset security threshold is set to 0.7.

[0063] Step S4: Using the safety constraint factor, adjust the overlap of the initial transportation routes of different predicted demand materials, re-cluster the predicted demand materials within the cluster to be processed, obtain new clusters to be processed and their safety constraint factors, until the safety constraint factors of all new clusters to be processed meet the preset conditions; when the preset conditions are met, the cluster to be processed is recorded as the ideal cluster.

[0064] The risk of safety accidents is high when transporting predicted demand materials within the same cluster along the same route. Therefore, it is necessary to reclassify these materials to reduce the mutual impact between hazardous material transport routes on high-risk routes. Safety constraint factors are used to adjust the overlap of initial transport routes. By increasing the overlap of routes with higher transport risks, the likelihood of materials being assigned to different routes is expanded, ensuring that hazardous materials are not concentrated on the same route and reducing over-concentration, thereby mitigating safety hazards during material transportation.

[0065] Step S5: Determine the optimized transportation route based on the initial transportation route of the predicted demand materials within the ideal cluster, and transport the predicted demand materials according to the optimized transportation route.

[0066] The risk of safety accidents when transporting predicted demand materials within an ideal cluster on the same transport route is low. Based on the initial transport route of the predicted demand materials within the ideal cluster, an optimized transport route is determined. All predicted demand materials within the ideal cluster are transported on the optimized transport route, which effectively solves the problem of excessive concentration of dangerous goods during the transport of materials and improves the efficiency of railway transportation.

[0067] In one implementation of this invention, the Floyd algorithm is used to generate an initial transportation route that passes through the start and end points of all predicted demand materials within each ideal cluster, as the optimized transportation route. Preferably, in some possible implementations of this invention, the method for obtaining the initial transportation route is described in [reference needed]. Figure 2 The diagram illustrates a flowchart of a method for obtaining an initial transportation route according to an embodiment of the present invention, the method comprising:

[0068] Step S210: Based on the difference between the disaster data of the current disaster point and the historical disaster points, select the predicted demand materials for the current disaster point from the demand materials of the historical disaster points.

[0069] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the predicted demand materials includes: obtaining the disaster types of historical disaster points and the current disaster point; standardizing the disaster data to obtain standard disaster data; for historical disaster points with the same disaster type as the current disaster point, calculating the sum of the absolute values ​​of the differences between the current disaster point and the historical disaster points of the same type as the disaster difference degree; obtaining the disaster similarity degree based on the distance between the current disaster point and the historical disaster point and the disaster difference degree; selecting several historical disaster points with the largest disaster similarity degree between the current disaster point and all historical disaster points as similar disaster points of the current disaster point; and using all the demand materials of the similar disaster points of the current disaster point as the predicted demand materials of the current disaster point.

[0070] The closer the disaster data of the current disaster point is to historical disaster points, and the smaller the disaster difference, the closer the disaster situation of the current disaster point is to that of the historical disaster point. Conversely, the smaller the distance between the current disaster point and historical disaster points, the greater the reliability of the similarity between their disaster situations. Therefore, both the distance between the current disaster point and historical disaster points and the degree of disaster difference are negatively and positively correlated with disaster similarity. In this embodiment of the invention, the product of the distance between the current disaster point and historical disaster points and the degree of disaster difference is negatively correlated to obtain the disaster similarity.

[0071] It should be noted that in this embodiment of the invention, the data to be processed is used as the exponent of an exponential function with the natural constant as the base to achieve a negative correlation mapping. Alternatively, methods such as taking the reciprocal can be used, and no limitation is made here. Since different types of disaster data have different dimensions, the disaster data must be standardized before analysis. This embodiment of the invention uses range standardization for standardization. Other embodiments may use other methods for standardization, such as Z-score standardization, decimal scaling standardization, and sum-normalization.

[0072] In one implementation of this invention, the number of similar disaster points to the current disaster point is set to 5.

[0073] Step S220: Select a transportation route that passes through the current disaster point from the transportation routes of the predicted demand materials at the current disaster point, and record it as the candidate route for the current disaster point.

[0074] It should be noted that the transportation route passing through the current disaster point indicates that the route from the starting point of the transportation route, i.e. the material storage point, to the current disaster point is passable and can be used as a route for transporting materials to the current disaster point.

[0075] Step S230: Based on the transportation volume of each predicted material demand at the current disaster point and the usage of the route to be tested, obtain the initial transportation route for each predicted material demand at the current disaster point.

[0076] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the initial transportation route includes: obtaining the quantity of each type of required material and the length of the transportation route for the required materials at historical disaster points; averaging the quantity of each type of predicted required material at the current disaster point with the same type of required material at similar disaster points to obtain the transportation volume of the corresponding predicted required material; taking the ratio of the transportation volume of each type of predicted required material at the current disaster point to the total transportation volume of all predicted required materials as the quantity ratio of each type of predicted required material; taking the number of times the start and end points of each candidate route at the current disaster point appear simultaneously in all candidate routes as the usage frequency of each candidate route; obtaining the screening index for each candidate route for each type of predicted required material at the current disaster point based on the quantity ratio of each type of predicted required material at the current disaster point, and the length and usage frequency of each candidate route for each type of predicted required material; selecting the candidate route corresponding to the maximum value among all candidate routes for each type of predicted required material at the current disaster point as the initial transportation route for each type of predicted required material at the current disaster point.

[0077] It should be noted that, in order to meet emergency needs in the shortest possible time, routes capable of carrying more supplies should be selected; shorter routes are preferred to minimize delays during the critical rescue period. Furthermore, usage frequency represents a priori selection of historical transportation routes; selecting routes with higher frequency of occurrence can effectively mitigate unknown risks and ensure transportation stability. Therefore, the quantity ratio and usage frequency are positively correlated with the screening criteria, while the length of the candidate route is negatively correlated with the screening criteria. In this embodiment of the invention, the product of the quantity ratio of each predicted demand material at the current disaster site and the usage frequency of each candidate route for each predicted demand material is calculated. The ratio of this product to the length of each candidate route for each predicted demand material is used as the screening criterion for each candidate route for each predicted demand material at the current disaster site. The higher the screening criterion, the greater the likelihood that the candidate route will be the initial transportation route for each predicted demand material.

[0078] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining material clusters includes: the transportation route includes at least two path nodes; the number of identical path nodes on the two initial transportation routes is negatively correlated and normalized to obtain a distance index; a vertical axis representing the number of required material types is added to the two-dimensional coordinate system where the current disaster point is located, expanding it into a three-dimensional coordinate system; all predicted required materials for all disaster points are mapped to the three-dimensional coordinate system for labeling to obtain the coordinate points of the corresponding predicted required materials; and all coordinate points in the three-dimensional coordinate system are clustered based on the distance index to obtain clusters, which are denoted as material clusters.

[0079] It should be noted that the more identical path nodes there are on two initial transportation routes, the greater the overlap between the two routes. To reduce transportation costs, the predicted demand materials corresponding to the two initial transportation routes should be transported on the same route as much as possible, and the distance between them should be as small as possible when clustering the predicted demand materials. Therefore, the number of identical path nodes and the distance index are negatively correlated. In this embodiment of the invention, the data to be processed is used as the exponent of an exponential function with the natural constant as the base, to achieve negative correlation and normalization processing.

[0080] In one implementation of this invention, the DBSCAN algorithm is selected to cluster all coordinate points in the three-dimensional coordinate system. Alternatively, K-means clustering algorithm and hierarchical clustering algorithm can be used.

[0081] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the security constraint factor is described in [reference needed]. Figure 3 The diagram illustrates a flowchart of a method for obtaining a security constraint factor according to an embodiment of the present invention, the method comprising:

[0082] Step S310: Obtain the material safety level and transportation safety index of the predicted demand materials; determine the dangerous materials in the material cluster based on the material safety level; obtain the hazard characteristic parameters of each predicted demand material in the material cluster according to the proportion of dangerous materials in the material cluster among all predicted demand materials, and the material safety level and transportation safety index of each predicted demand material; calculate the sum of the hazard characteristic parameters of all predicted demand materials in the material cluster as the cluster hazard level.

[0083] It should be noted that, according to the safety level classification section of the "Regulations on the Administration of the Transport of Dangerous Goods," this embodiment of the invention will classify the predicted demand materials into safety levels, specifically: if the predicted demand material is of general hazard, the safety level is Level 1; if the predicted demand material is a corrosive substance, the safety level is Level 2; if the predicted demand material is a toxic or radioactive substance, the safety level is Level 3; if the predicted demand material is a flammable substance, the safety level is Level 4; and if the predicted demand material is an explosive substance or a highly toxic chemical, the safety level is Level 5. The higher the safety level of the predicted demand material, the higher its hazard. Based on the UN's "Recommendations on the Transport of Dangerous Goods" or the "General Technical Conditions for Packaging for the Transport of Dangerous Goods," the maximum values ​​of the temperature and humidity limits for the predicted demand material are obtained. The highest temperature and highest humidity of the predicted demand material at the current disaster site on the day of transport are also obtained. The absolute values ​​of the difference between the highest temperature of the day and the maximum value of the temperature limit for the predicted demand material, and the absolute values ​​of the difference between the highest humidity and the maximum value of the humidity limit for the predicted demand material are calculated. The reciprocal of the product of these two absolute values ​​is used as the transport safety index. The greater the difference between the highest temperature, highest humidity, and the maximum values ​​of temperature and humidity limits on a given day, the safer the transportation conditions for the predicted demand materials will be, and the lower the transportation safety index will be.

[0084] The higher the proportion of hazardous materials within a material cluster, the greater the likelihood of danger occurring during transportation of the predicted demand materials within the cluster. Conversely, higher material safety levels and transportation safety indicators for predicted demand materials indicate greater material risk during transportation and a higher probability of accidents. Therefore, the proportion of hazardous materials within a material cluster in all predicted demand materials, as well as the material safety levels and transportation safety indicators of each predicted demand material, are positively correlated with the hazard characteristic parameters. In this embodiment of the invention, the proportion of hazardous materials within a material cluster in all predicted demand materials, and the product of the material safety level and transportation safety indicator for each predicted demand material, are used as the hazard characteristic parameters for each predicted demand material within the material cluster. A higher cluster hazard level indicates a greater likelihood of risk during transportation of predicted demand materials within the cluster along the same route.

[0085] Step S320: Calculate the mean of the distance indices between all predicted demand materials within a material cluster and the predicted demand materials corresponding to the cluster center point, and obtain the cluster overlap degree of the material cluster.

[0086] It should be noted that if the distance index between the predicted demand materials and the cluster center point within a material cluster is smaller, the cluster overlap is smaller, the spatial distribution of the initial transportation routes of the predicted demand materials within the material cluster is closer, and the initial transportation routes of the predicted demand materials are more consistent, then the possibility of risks arising from the transportation of the predicted demand materials within the material cluster on the same route is greater.

[0087] Step S330: Obtain the transportation time of the initial transportation route for the predicted demand materials; take the ratio of the length of the initial transportation route to the transportation time as the transportation speed; average the transportation speeds of all predicted demand materials within the material cluster to obtain the overall material speed.

[0088] It should be noted that the lower the transport speed of the predicted demand materials within a material cluster, the greater the difficulty in transporting the predicted demand materials within the material cluster, and the greater the possibility of risks.

[0089] Step S340: Use the cluster hazard level, cluster overlap level and overall material speed as the safety constraint factor for the material cluster.

[0090] It should be noted that the higher the cluster hazard and the lower the cluster overlap and overall material speed, the greater the likelihood of risk in transporting the predicted demand materials within the cluster along the same route, and the larger the safety constraint factor. Therefore, cluster hazard is positively correlated with the safety constraint factor, while cluster overlap and overall material speed are negatively correlated with the safety constraint factor. In this embodiment of the invention, the ratio obtained by using the cluster hazard as the numerator and the product of cluster overlap and overall material speed as the denominator is used as the safety constraint factor.

[0091] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining a new cluster to be processed includes: recalculating a new safety constraint factor with each hazardous material in the cluster as the cluster center point; using the sum of the absolute value of the difference between the new safety constraint factors corresponding to any two hazardous materials in the cluster and the distance index as the new distance index for the two hazardous materials; re-clustering all predicted demand materials in the cluster based on the new distance index of different predicted demand materials in the cluster to obtain a new material cluster, and obtaining the safety constraint factor of the new material cluster; and taking the new material cluster that does not meet the preset conditions as a new cluster to be processed. Wherein, not meeting the preset conditions means that the safety constraint factor of the new material cluster is greater than a preset safety threshold.

[0092] It should be noted that the method for obtaining the safety constraint factor of the new cluster to be processed is the same as that for the material cluster. In the process of recalculating the new safety constraint factor using hazardous materials as cluster centers, only the cluster center point in step S320 of the safety constraint factor calculation process needs to be replaced with the hazardous materials; all other aspects remain unchanged. To increase the likelihood that hazardous materials within the cluster to be processed will be assigned to different new material clusters, a distance index between different hazardous materials needs to be added. The larger the absolute value of the difference between the new safety constraint factors of two hazardous materials, the higher the risk of them being in the same cluster, and the stronger the need for isolation during transportation. By adding this distance index, hazardous materials with greater risk differences are assigned to different clusters, reducing the risk of high-risk materials clustering together. During the process of re-clustering the predicted demand materials within the cluster to be processed, it is necessary to use the new distance index between pairs of hazardous materials and the distance index between other pairs of predicted demand materials.

[0093] As an example, the predicted demand materials are clustered using a new distance index between pairs of predicted demand materials within cluster C1, resulting in two new material clusters, C21 and C22. When obtaining the safety constraint factors for these new material clusters, C21 and C22, the new distance index D1 between pairs of hazardous materials within each new material cluster is used for calculation, sequentially yielding the safety constraint factors SCF21 and SCF22 for C21 and C22. If SCF21 meets a preset condition (i.e., SCF21 is less than or equal to a preset safety threshold), while SCF22 does not meet the preset condition (i.e., SCF22 is greater than the preset safety threshold), it indicates that the risk of transporting predicted demand materials within material cluster C21 along the same route is relatively low, and they can be used for material transportation. Conversely, the risk of transporting predicted demand materials within material cluster C22 along the same route is relatively high, requiring re-clustering of the predicted demand materials within material cluster C22. By adjusting the distance index D1 between pairs of hazardous materials within material cluster C22 using SCF22, a new distance index D2 is obtained. This new distance index D2 is then used to cluster the predicted demand materials within material cluster C22, resulting in new material clusters C31 and C32. The calculation of the safety constraint factors for both clusters requires the new distance index D2 between pairs of hazardous materials within material cluster C22. Assuming that the safety constraint factors for material clusters C31 and C32 both meet the preset conditions, the ideal clusters include: C21, C31, and C32.

[0094] In this embodiment of the invention, the preset condition is that the security constraint factor of the new cluster to be processed is less than or equal to a preset security threshold.

[0095] This invention is now complete.

[0096] Example 2:

[0097] This invention also presents a schematic diagram of a computer device for optimizing the management of emergency supplies for railway hazardous materials transportation. Please refer to [link / reference needed]. Figure 4 The computer device includes a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer device can execute any of the aforementioned methods for optimizing the management of emergency supplies for railway dangerous goods transportation.

[0098] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the railway dangerous goods transportation emergency material management optimization method provided in embodiments of this application.

[0099] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0100] When each module is divided according to its function, the device may also include a communication module, a signal analysis module, a complexity analysis module, and a positioning module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0101] It should be understood that the device provided in this embodiment is used to execute the above-described method for optimizing the management of emergency supplies for railway dangerous goods transportation, and therefore can achieve the same effect as the above-described implementation method.

[0102] When using integrated units, the device may include a processing module and a storage module. When applied to a workpiece, the processing module can be used to control and manage the workpiece's operations. The storage module can be used to support the execution of program code by the workpiece.

[0103] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits contained in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0104] Example 3:

[0105] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the railway dangerous goods transportation emergency material management optimization method provided in the above embodiment.

[0106] Example 4:

[0107] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to realize the railway dangerous goods transportation emergency material management optimization method provided in the above embodiment.

[0108] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0109] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0110] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An optimized method for the management of emergency supplies for railway hazardous materials transportation, characterized in that, The method includes: Obtain disaster data for historical and current disaster sites, as well as the required supplies and transportation routes for historical disaster sites; Based on the differences in disaster data between the current disaster site and historical disaster sites, the predicted demand materials and their initial transportation routes for the current disaster site are selected from the demand materials and transportation routes of historical disaster sites. Based on the overlap of the initial transportation routes of different predicted demand materials, the predicted demand materials of all current disaster points are clustered to obtain material clusters; according to the overlap of the initial transportation routes of the predicted demand materials within the material clusters, the difficulty of driving, and the degree of danger of the predicted demand materials, safety constraint factors are obtained, and the material clusters are divided into ideal clusters and unprocessed clusters. Using the aforementioned safety constraint factor, the overlap of the initial transportation routes for different predicted demand materials is adjusted, and the predicted demand materials within the cluster to be processed are re-clustered to obtain new clusters to be processed and their aforementioned safety constraint factors, until the safety constraint factors of all new clusters to be processed meet the preset conditions; when the preset conditions are met, the cluster to be processed is recorded as the ideal cluster. The optimal transportation route is determined based on the initial transportation route of the predicted demand materials within the ideal cluster, and the predicted demand materials are transported according to the optimal transportation route.

2. The method for optimizing the management of emergency supplies for railway hazardous materials transportation according to claim 1, characterized in that, The selection of the predicted material needs of the current disaster site and its initial transportation route includes: Based on the differences in disaster data between the current disaster site and historical disaster sites, the predicted demand for materials at the current disaster site is selected from the demand materials of historical disaster sites. Select a transportation route that passes through the current disaster site from the predicted transportation routes of the materials needed at the current disaster site, and record it as the candidate route for the current disaster site; Based on the transportation volume of each predicted material demand at the current disaster site and the usage of the route to be tested, obtain the initial transportation route for each predicted material demand at the current disaster site.

3. The method for optimizing the management of emergency supplies for railway hazardous materials transportation according to claim 1, characterized in that, The method for obtaining the material cluster includes: The transportation route includes at least two path nodes; The distance index is obtained by negatively correlating and normalizing the number of identical path nodes on the two initial transportation routes. Add a vertical axis representing the type and quantity of required materials to the two-dimensional coordinate system where the current disaster point is located, and expand it into a three-dimensional coordinate system; map all predicted required materials for all disaster points to the three-dimensional coordinate system for labeling, and obtain the coordinate points of the corresponding predicted required materials. Based on the distance index, all coordinate points in the three-dimensional coordinate system are clustered to obtain a cluster, denoted as the material cluster.

4. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 3, characterized in that, The acquisition of security constraint factors includes: Obtain the material safety level and transportation safety index of the predicted demand materials; determine the dangerous materials within the material cluster based on the material safety level; obtain the hazard characteristic parameters of each predicted demand material within the material cluster according to the proportion of dangerous materials in all predicted demand materials, and the material safety level and transportation safety index of each predicted demand material; calculate the sum of the hazard characteristic parameters of all predicted demand materials within the material cluster as the cluster hazard level. The cluster overlap of the material cluster is obtained by calculating the mean of the distance index between all predicted demand materials within the material cluster and the predicted demand materials corresponding to the cluster center point. Obtain the transportation time of the initial transportation route for the predicted demand materials; take the ratio of the length of the initial transportation route to the transportation time as the transportation speed; average the transportation speed of all predicted demand materials within the material cluster to obtain the overall material speed. The cluster hazard level, cluster overlap, and overall material velocity are used as safety constraint factors for the material clusters.

5. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 4, characterized in that, The process of obtaining the new cluster to be processed and its security constraint factor includes: A new safety constraint factor is recalculated using each of the hazardous materials in the cluster to be processed as the cluster center point. The sum of the absolute value of the difference between the new safety constraint factors of any two hazardous materials in the cluster to be processed and the distance index is used as the new distance index for the two hazardous materials. Based on the new distance index of different predicted demand materials within the cluster to be processed, all predicted demand materials within the cluster to be processed are re-clustered to obtain new material clusters, and the safety constraint factors of the new material clusters are obtained; new material clusters that do not meet the preset conditions are taken as new clusters to be processed.

6. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 1, characterized in that, The security constraint factor of the ideal cluster is less than or equal to the preset security threshold, while the security constraint factor of the cluster to be processed is greater than the preset security threshold.

7. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 1, characterized in that, The preset condition is that the security constraint factor of the new cluster to be processed is less than or equal to the preset security threshold.

8. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 1, characterized in that, The optimized transportation route passes through the start and end points of the initial transportation routes for all predicted demand materials within each ideal cluster.

9. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 2, characterized in that, The selected predicted material needs for the current disaster site include: Obtain the disaster types of historical disaster sites and current disaster sites; standardize the disaster data to obtain standard disaster data; For historical disaster points with the same disaster type as the current disaster point, calculate the sum of the absolute values ​​of the differences between the current disaster point and the historical disaster points for the same type of standard disaster data, and use this as the disaster difference degree; obtain the disaster similarity degree based on the distance between the current disaster point and the historical disaster points and the disaster difference degree; select the historical disaster points with the largest disaster similarity degree between the current disaster point and all historical disaster points, and use these as similar disaster points of the current disaster point; All the required materials from similar disaster sites at the current disaster site are used as the predicted required materials for the current disaster site.

10. The method for optimizing the management of emergency supplies for railway dangerous goods transportation according to claim 9, characterized in that, The process of obtaining the initial transportation route for each predicted material demand at the current disaster site includes: Obtain the quantity of each type of required material and the length of the transportation route for the required materials at historical disaster sites; The average quantity of each predicted demand material at the current disaster site is calculated from the quantity of the same demand material at similar disaster sites to obtain the transportation volume of the corresponding predicted demand material; the ratio of the transportation volume of each predicted demand material at the current disaster site to the total transportation volume of all predicted demand materials is taken as the quantity ratio of each predicted demand material. The frequency of use of each candidate route is determined by the number of times the start and end points of each candidate route at the current disaster site appear simultaneously in all candidate routes. Based on the quantity ratio of each predicted demand material at the current disaster site, and the length and usage frequency of each candidate route for each predicted demand material, the screening index for each candidate route for each predicted demand material at the current disaster site is obtained. The maximum value among all candidate routes for each type of predicted material demand at the current disaster site is selected as the initial transportation route for each type of predicted material demand at the current disaster site.

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