Emergency rescue scheme acquisition method, device and equipment based on mobile phone signaling

Abstract

The application provides an emergency rescue scheme acquisition method, device and equipment based on mobile phone signaling. The method comprises the following steps: acquiring the number of disaster-stricken populations and the portrait of the disaster-stricken populations in a disaster-stricken area based on the disaster-stricken area identified in a geographic information system, the historical signaling data and the current signaling data of the users in the disaster-stricken area; generating an emergency rescue scheme matched with the number of disaster-stricken populations and the portrait of the disaster-stricken populations in the disaster-stricken area based on a preset emergency plan library and an emergency resource library; and pushing the emergency rescue scheme to a terminal device of an emergency rescue department. The method can assist the emergency rescue department in reasonably allocating rescue personnel and materials and improving the rescue efficiency.

Description

Technical Field

[0001] This application relates to the field of big data analysis, and in particular to a method, apparatus and equipment for obtaining emergency rescue plans based on mobile phone signaling. Background Technology

[0002] With my country's urbanization, population concentration has become increasingly pronounced. In the event of an emergency, timely rescue of affected populations and minimizing casualties are paramount. Improving the nation's ability to acquire, process, and provide services related to on-site information during emergencies is crucial for enhancing the scientific rigor and effectiveness of emergency command and decision-making, and for the rational allocation of national emergency resources.

[0003] In the event of sudden disasters such as earthquakes, typhoons, and floods, a large number of people are affected within the disaster-stricken area. Effectively and quickly determining the population density within the disaster zone and accurately understanding the population composition of the affected area are crucial challenges in emergency management. This information is essential for emergency management departments to develop precise rescue plans. Simultaneously, rapidly notifying relevant government units, social forces, and the public to initiate self-rescue efforts is a key difficulty in current emergency management. Therefore, under my country's current national conditions, it is imperative to quickly grasp the situation of affected people in specific areas, their movement trends, and basic demographic information to create a population profile, enabling precise evacuation and rescue operations during disasters.

[0004] In the current technology, when a major emergency occurs, a population census is used to obtain the number of affected people and their distribution and movement through a hierarchical reporting process. This is necessary to coordinate rescue forces and supplies, but it is impossible to formulate an effective rescue plan, resulting in low rescue efficiency. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for obtaining emergency rescue plans based on mobile phone signaling, which can improve rescue efficiency.

[0006] Firstly, this application provides a method for obtaining emergency rescue solutions based on mobile phone signaling, applied to a server, the method comprising:

[0007] Based on the disaster-stricken areas identified in the geographic information system, the historical signaling data and current signaling data of users within the disaster-stricken areas, the number of disaster-stricken people and their profiles are obtained within the disaster-stricken areas.

[0008] Based on a pre-set emergency plan database and emergency resource database, an emergency rescue plan is generated that matches the number of affected people and their profiles within the disaster-stricken area. The emergency plan database stores multiple emergency response plans for different disaster situations and different affected populations, and the emergency resource database stores inventory information of reserved emergency supplies.

[0009] The emergency rescue plan will be pushed to the terminal equipment of the emergency rescue department.

[0010] In one possible implementation, the method further includes:

[0011] Get the latest disaster response level, casualty data, disaster impact range and secondary disaster situation in real time;

[0012] Based on the disaster response level, the casualty data, the scope of the disaster impact, and the secondary disaster situation, the emergency rescue plan is revised to obtain the revised emergency rescue plan.

[0013] The revised emergency rescue plan will be pushed to the terminal equipment of the emergency rescue department.

[0014] In one possible implementation, the method further includes:

[0015] Based on the historical signaling data and current signaling data of users in the disaster-stricken area, a spatiotemporal tag is established for each user in the disaster-stricken area. The spatiotemporal tag is used to indicate the user's residence and behavioral characteristics in the disaster-stricken area.

[0016] Based on each user's spatiotemporal tag, the resettlement location of the affected users is obtained, and the resettlement location of the affected users is used to allocate emergency supplies when determining the emergency rescue plan.

[0017] In one possible implementation, each user's spatiotemporal tags include a first-level tag, a second-level tag, and a third-level tag;

[0018] The primary tags include stay or travel;

[0019] The secondary label corresponding to the stay includes any one of the following: work or residence; the secondary label corresponding to the travel includes any one of the following: local commuting, local travel, or travel to other places;

[0020] The three-level tags corresponding to the work include the work address and the number of workdays; the three-level tags corresponding to the residence include the residential address and the length of time spent at home; the three-level tags corresponding to the local commute include the commuting destination address, commuting distance, and the number of travel days per month; the three-level tags corresponding to the local trip include the departure time, trip duration, and trip destination address; and the three-level tags corresponding to the trip to other places include the departure time, trip duration, trip destination address, and mode of transportation.

[0021] In one possible implementation, the process of obtaining the number of affected people and their profiles within the disaster-stricken area based on the disaster-stricken area identified in the geographic information system, historical signaling data of users within the disaster-stricken area, and current signaling data includes:

[0022] Based on the terminal signaling data stored by each operator, obtain all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and obtain the current signaling data;

[0023] Based on the information of each user in the current signaling data, obtain the signaling data of each user in a preset historical time period to obtain the historical signaling data;

[0024] The current signaling data and the historical signaling data are cleaned respectively to obtain cleaned current signaling data and cleaned historical signaling data. The cleaning process includes processing missing data, duplicate data and erroneous data.

[0025] Based on the disaster severity classification map of the disaster-stricken area, the cleaned current signaling data, and the cleaned historical signaling data, the number of disaster-stricken people and the disaster-stricken population profile in the disaster-stricken area are obtained.

[0026] In one possible implementation, obtaining the number of affected people and the population profile in the disaster-stricken area based on the disaster severity classification map of the disaster-stricken area, the cleaned current signaling data, and the cleaned historical signaling data includes:

[0027] Based on the cleaned current signaling data and the cleaned historical signaling data, the population of each sub-region in the disaster-stricken area and the user characteristic attributes of each user are obtained; wherein, the characteristic attributes of each user include at least the user's gender and age;

[0028] Based on the disaster severity classification map of the disaster-stricken areas, the disaster severity of each sub-region and the disaster weight coefficient corresponding to each disaster severity are obtained. The disaster weight coefficient is determined in advance based on the disaster type, the area where the incident occurred, and the scope of the disaster.

[0029] The number of affected people in the disaster-stricken area is calculated based on the population of each sub-region in the disaster-stricken area and the disaster weight coefficient corresponding to the degree of disaster in each sub-region.

[0030] Based on the user characteristic attributes of each user in the disaster-stricken area, a user profile is classified to obtain the disaster-stricken population profile.

[0031] In one possible implementation, obtaining the population of each sub-region within the disaster-stricken area based on the cleaned current signaling data and the cleaned historical signaling data includes:

[0032] Based on the cleaned current signaling data and the cleaned historical signaling data, the DBSCAN algorithm and the K-means algorithm are used to cluster users in the disaster-stricken area to obtain multiple disaster-stricken population clusters.

[0033] Based on the multiple affected population clusters, the affected area is divided into multiple sub-regions, and population statistics are performed on the affected population clusters in each sub-region to obtain the population size in each sub-region.

[0034] Secondly, this application provides an emergency rescue plan acquisition device based on mobile phone signaling, comprising:

[0035] The first acquisition module is used to acquire the number of disaster-stricken people and a profile of the disaster-stricken people in the disaster-stricken area based on the disaster-stricken area identified in the geographic information system, the historical signaling data of users within the disaster-stricken area, and the current signaling data.

[0036] The first processing module is used to generate an emergency rescue plan that matches the number of affected people and their profiles in the disaster-stricken area based on a preset emergency plan library and emergency resource library; wherein, the emergency plan library stores multiple emergency response plans for different disaster situations and different affected groups, and the emergency resource library stores inventory information of reserved emergency supplies.

[0037] The first push module is used to push the emergency rescue plan to the terminal equipment of the emergency rescue department.

[0038] In one possible implementation, the device further includes:

[0039] The second acquisition module is used to acquire the latest disaster response level, casualty data, disaster impact range and secondary disaster situation in real time;

[0040] The second processing module is used to modify the emergency rescue plan based on the disaster response level, the casualty data, the scope of the disaster impact, and the secondary disaster situation, so as to obtain the modified emergency rescue plan.

[0041] The second push module is used to push the revised emergency rescue plan to the terminal equipment of the emergency rescue department.

[0042] In one possible implementation, the device further includes:

[0043] The third acquisition module is used to establish a spatiotemporal tag for each user in the disaster-stricken area based on the historical signaling data and the current signaling data of the users in the disaster-stricken area. The spatiotemporal tag is used to indicate the user's residence and behavioral characteristics in the disaster-stricken area.

[0044] The third processing module is used to obtain the resettlement location of the disaster-stricken users based on the spatiotemporal tags of each user. The resettlement location of the disaster-stricken users is used to allocate emergency supplies when determining the emergency rescue plan.

[0045] In one possible implementation, each user's spatiotemporal tags include a first-level tag, a second-level tag, and a third-level tag;

[0046] The primary tags include stay or travel;

[0047] The secondary label corresponding to the stay includes any one of the following: work or residence; the secondary label corresponding to the travel includes any one of the following: local commuting, local travel, or travel to other places;

[0048] The three-level tags corresponding to the work include the work address and the number of workdays; the three-level tags corresponding to the residence include the residential address and the length of time spent at home; the three-level tags corresponding to the local commute include the commuting destination address, commuting distance, and the number of travel days per month; the three-level tags corresponding to the local trip include the departure time, trip duration, and trip destination address; and the three-level tags corresponding to the trip to other places include the departure time, trip duration, trip destination address, and mode of transportation.

[0049] In one possible implementation, the first acquisition module is specifically used for:

[0050] Based on the terminal signaling data stored by each operator, obtain all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and obtain the current signaling data;

[0051] Based on the information of each user in the current signaling data, obtain the signaling data of each user in a preset historical time period to obtain the historical signaling data;

[0052] The current signaling data and the historical signaling data are cleaned respectively to obtain cleaned current signaling data and cleaned historical signaling data. The cleaning process includes processing missing data, duplicate data and erroneous data.

[0053] Based on the disaster severity classification map of the disaster-stricken area, the cleaned current signaling data, and the cleaned historical signaling data, the number of disaster-stricken people and the disaster-stricken population profile in the disaster-stricken area are obtained.

[0054] In one possible implementation, the first acquisition module is specifically used for:

[0055] Based on the cleaned current signaling data and the cleaned historical signaling data, the population of each sub-region in the disaster-stricken area and the user characteristic attributes of each user are obtained; wherein, the characteristic attributes of each user include at least the user's gender and age;

[0056] Based on the disaster severity classification map of the disaster-stricken areas, the disaster severity of each sub-region and the disaster weight coefficient corresponding to each disaster severity are obtained. The disaster weight coefficient is determined in advance based on the disaster type, the area where the incident occurred, and the scope of the disaster.

[0057] The number of affected people in the disaster-stricken area is calculated based on the population of each sub-region in the disaster-stricken area and the disaster weight coefficient corresponding to the degree of disaster in each sub-region.

[0058] Based on the user characteristic attributes of each user in the disaster-stricken area, a user profile is classified to obtain the disaster-stricken population profile.

[0059] In one possible implementation, the first acquisition module is specifically used for:

[0060] Based on the cleaned current signaling data and the cleaned historical signaling data, the DBSCAN algorithm and the K-means algorithm are used to cluster users in the disaster-stricken area to obtain multiple disaster-stricken population clusters.

[0061] Based on the multiple disaster-affected population clusters, the disaster-affected area is divided into multiple sub-regions, and population statistics are performed on the disaster-affected population clusters in each sub-region to obtain the population size in each sub-region.

[0062] Thirdly, this application provides an electronic device, including: a processor, a memory, and an interaction interface;

[0063] The memory stores computer-executed instructions;

[0064] The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any of the first aspects.

[0065] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described in any of the first aspects.

[0066] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first aspects.

[0067] The emergency rescue plan acquisition method, apparatus, and equipment provided in this application can obtain the number of affected people and their profiles within a disaster-stricken area based on the disaster-stricken area identified in a geographic information system, historical signaling data of users within that area, and current signaling data. Based on a pre-set emergency plan database and emergency resource database, an emergency rescue plan matching the number of affected people and their profiles within the disaster-stricken area can be generated and pushed to the terminal equipment of the emergency rescue department. In this process, a disaster severity classification map of the disaster-stricken area can be used to obtain the number of affected people and their profiles through user signaling data within the disaster-stricken area. For different disaster-stricken areas, combined with the pre-set emergency plan database and emergency resource database, an emergency rescue plan can be generated and pushed to the emergency rescue department to assist in rescue operations and improve rescue efficiency. Attached Figure Description

[0068] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0069] Figure 1 A schematic diagram illustrating the application scenarios provided in the embodiments of this application;

[0070] Figure 2 A flowchart illustrating an emergency rescue plan acquisition method based on mobile phone signaling, provided in an embodiment of this application;

[0071] Figure 3 A flowchart illustrating another method for obtaining an emergency rescue plan based on mobile phone signaling, provided in an embodiment of this application;

[0072] Figure 4 A flowchart illustrating another method for obtaining an emergency rescue plan based on mobile phone signaling, provided in an embodiment of this application;

[0073] Figure 5A detailed flowchart illustrating a method for obtaining an emergency rescue plan based on mobile phone signaling, provided in an embodiment of this application;

[0074] Figure 6 A flowchart illustrating mobile phone signaling data analysis and processing logic provided in this application embodiment;

[0075] Figure 7 A schematic diagram of a disaster-stricken area and multiple disaster-stricken population clusters provided for embodiments of this application;

[0076] Figure 8 This application provides a schematic diagram illustrating a process for obtaining an emergency rescue plan.

[0077] Figure 9 This is a schematic diagram illustrating the generation of an emergency rescue plan model provided in an embodiment of this application.

[0078] Figure 10 A schematic diagram of an emergency rescue plan acquisition device based on mobile phone signaling provided in an embodiment of this application;

[0079] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0080] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0081] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0083] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0084] It should be noted that the emergency rescue plan acquisition method, device and equipment based on mobile phone signaling provided in this application can be used in the field of big data analysis, or in any field other than big data analysis. The application field of the emergency rescue plan acquisition method, device and equipment based on mobile phone signaling provided in this application is not limited.

[0085] To facilitate understanding, the following will be combined with... Figure 1 The application scenarios applicable to the embodiments of this application will be described.

[0086] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. Please refer to [link / reference]. Figure 1 The emergency response plan database can store multiple emergency response plans, while the emergency resource database can store inventory information of reserved emergency supplies. The multiple emergency response plans can include Emergency Response Plan 1, Emergency Response Plan 2, etc., and the inventory information of reserved emergency supplies can include information on items such as tents and drinking water.

[0087] After a major disaster, the system can obtain the number of affected people and their profiles within the affected area. Based on a pre-set emergency plan database and emergency resource database, it can generate emergency rescue plans that match the number of affected people and their profiles within the affected area, and then push these plans to emergency rescue departments to assist them in carrying out rescue operations.

[0088] In the current technology, when a major emergency occurs, a population census is used to obtain the number of affected people and their distribution and movement through a hierarchical reporting process. This is necessary to coordinate rescue forces and supplies, but it is impossible to formulate an effective rescue plan, resulting in low rescue efficiency.

[0089] In this embodiment, based on the disaster-stricken area identified in a Geographic Information System (GIS), historical signaling data of users within the disaster-stricken area, and current signaling data, the number of affected people and their profiles within the disaster-stricken area can be obtained. Based on a pre-set emergency plan database and emergency resource database, an emergency rescue plan matching the number of affected people and their profiles within the disaster-stricken area can be generated, and the emergency rescue plan can be pushed to the terminal devices of the emergency rescue department. In this process, the number of affected people and their profiles can be obtained through user signaling data within the disaster-stricken area, and an emergency rescue plan can be generated by combining it with the pre-set emergency plan database and emergency resource database, assisting the emergency rescue department in completing rescue operations and improving rescue efficiency.

[0090] Figure 2 This is a flowchart illustrating a method for obtaining an emergency rescue plan based on mobile phone signaling, provided as an embodiment of this application. Please refer to... Figure 2 The method may include:

[0091] S101. Based on the disaster-stricken areas identified in the geographic information system, historical signaling data of users within the disaster-stricken areas, and current signaling data, obtain the number of disaster-stricken people and their profiles within the disaster-stricken areas.

[0092] The execution subject of this application embodiment can be an electronic device or an emergency rescue plan acquisition device based on mobile phone signaling installed in the electronic device. The emergency rescue plan acquisition device based on mobile phone signaling can be implemented by software or by a combination of software and hardware. For ease of understanding, the following description uses an electronic device as the execution subject.

[0093] In the event of a major natural disaster, electronic devices can obtain the disaster-stricken area identified in the geographic information system (GIS). This disaster-stricken area may contain historical and current signaling data from multiple users. Based on this data, the number of affected people and their profiles within the disaster-stricken area can be determined. Specifically, based on the terminal signaling data stored by each operator, all signaling data detected during the disaster in the disaster-stricken area identified in the GIS can be obtained to generate current signaling data. Based on the information of each user in the current signaling data, signaling data for each user within a preset historical time period can be obtained to generate historical signaling data. The current and historical signaling data are then cleaned to obtain cleaned current and historical signaling data. Finally, based on the disaster severity map of the disaster-stricken area, the cleaned current and historical signaling data can be used to obtain the number of affected people and their profiles within the disaster-stricken area.

[0094] Geographic Information Systems (GIS) are computer-based tools that can analyze and process spatial information. They can be used to integrate disaster impact range models. For example, during an earthquake, seismic intensity models from seismic networks can be accessed to obtain seismic intensity and spatial extent data. During a flood, flood disaster imagery can be accessed, and pre- and post-disaster comparisons can be made using remote sensing imagery to extract the actual impact range of the disaster, or flood inundation models can be used to predict the flood inundation impact range after a certain period.

[0095] Signaling data refers to a series of information generated during mobile phone use, including the phone's location, call logs, SMS records, and internet browsing history. This data is generated through communication between the mobile phone and the base station and can be used to analyze user behavior and movement trends. For example, a current signaling data entry for user A might be: Time: 2020.03.28, 15:30:13″, Longitude: 116.2831″, Latitude: 40.0468″, Location: No. 101, Y Street, X District, N City, M Province.

[0096] Optionally, the core of user profiling is tagging users, and these tag sets can abstract a complete picture of a user's information. The user tag set can include age, mobile phone number, gender, ID number, and mobile phone model, etc. Each tag describes a dimension of the user, and these dimensions are interconnected to form a holistic description of the user. For example, user A's profile could include: age: 28 years old, gender: male.

[0097] Optionally, the system can also retrieve disaster-affected areas identified in the geographic information system, along with a map showing the severity of damage within those areas. For example, in the event of an earthquake, the area within 5 kilometers of the epicenter can be classified as disaster-affected area 1, with a severity level of I, representing severe damage. The area between 5 and 20 kilometers from the epicenter can be classified as disaster-affected area 2, with a severity level of II, representing relatively severe damage. The area beyond 20 kilometers from the epicenter, extending to the furthest point affected by the earthquake waves, can be classified as disaster-affected area 3, with a severity level of III. Level III represents relatively minor damage, and the furthest point affected by the disaster can be 50 kilometers.

[0098] For example, each operator's stored terminals can store multiple terminal signaling data entries. For instance, operator 1's stored terminals can store 100,000 signaling data entries, and operator 2's stored terminals can store 200,000 signaling data entries. Operator 1's stored terminals can retrieve 400 signaling data entries detected during the disaster in the disaster-stricken area identified in the geographic information system, and operator 2's stored terminals can retrieve 600 signaling data entries detected during the disaster in the disaster-stricken area identified in the geographic information system, thus obtaining the current signaling data, which contains 1000 entries. Based on the information of each user in 1000 current signaling data entries, signaling data for each user within a preset historical time period can be obtained, resulting in historical signaling data, which can be 800 entries. The 1000 current signaling data entries and the 800 historical signaling data entries are cleaned separately, resulting in 900 cleaned current signaling data entries and 700 cleaned historical signaling data entries. Based on the division of the disaster area into three disaster areas, namely disaster area 1, disaster area 2, and disaster area 3, the 900 cleaned current signaling data entries and the 700 cleaned historical signaling data entries can be used to obtain the number of disaster-stricken people in disaster area 1 as 400, the number of disaster-stricken people in disaster area 2 as 200, and the number of disaster-stricken people in disaster area 3 as 80, as well as the disaster-stricken population profile in each disaster area.

[0099] S102. Based on the preset emergency plan database and emergency resource database, generate an emergency rescue plan that matches the number of affected people and their profiles in the disaster-stricken area.

[0100] Once the number of affected people and their profiles in the disaster-stricken area are obtained, emergency response plans corresponding to different affected groups can be retrieved from the pre-set emergency response plan database; inventory information of reserved emergency supplies can be retrieved from the emergency resource database; and emergency rescue plans that match the number of affected people and their profiles in the disaster-stricken area can be generated using the emergency response plans and the inventory information of emergency supplies.

[0101] Optionally, the emergency response plan database stores multiple emergency response plans. For example, if the disaster population profile includes 10 elderly people over 80 years old, then Emergency Response Plan 1 could include 2 boxes of drinking water, 10 thermal blankets, 10 stretchers, and 2 rescue personnel for each stretcher. If the disaster population profile includes 20 young adults, then Emergency Response Plan 2 could include 5 boxes of drinking water and 20 thermal blankets.

[0102] Optionally, the emergency resource depot stores inventory information of emergency supplies, which can be retrieved according to the emergency response plan. For example, the emergency resource depot stores inventory information for 100 stretchers, 1000 cases of drinking water, and 600 thermal blankets. According to Emergency Response Plan 1, 2 cases of drinking water, 10 thermal blankets, and 10 stretchers can be retrieved from the emergency resource depot.

[0103] For example, if the disaster-stricken area includes disaster-stricken area 1, disaster-stricken area 2, and disaster-stricken area 3, based on a pre-set emergency response plan database, emergency response plan 1 for disaster-stricken area 1, emergency response plan 2 for disaster-stricken area 2, and emergency response plan 3 for disaster-stricken area 3 can be obtained. Then, by combining these three emergency response plans with the emergency resource database, an emergency rescue plan can be generated that matches the number of affected people and their profiles within the disaster-stricken area.

[0104] S103. Push the emergency rescue plan to the terminal equipment of the emergency rescue department.

[0105] After obtaining emergency rescue plans based on the pre-set emergency plan database and emergency resource database, the emergency rescue plans can be pushed to the terminal devices of the emergency rescue department.

[0106] Optionally, an emergency rescue department refers to a team organization engaged in emergency rescue and relief missions. The system can divide rescue areas based on the number of affected people and their profiles. It determines the corresponding rescue teams and numbers for each rescue area based on the affected population. Rescue information and team divisions are shared with collaborating units at the disaster site to avoid blind rescue efforts and effectively carry out rescue operations for the affected population. It can also analyze and allocate disaster relief supplies based on the number of people at resettlement sites, the type of disaster, and disaster mobilization to assist in the rescue of affected people.

[0107] Optionally, the terminal device can be a tablet computer, laptop, mobile phone, or other device specifically designed for emergency rescue departments.

[0108] For example, an emergency rescue plan that combines three emergency response plans and an emergency resource database to generate a plan that matches the number of affected people and their profiles in the disaster area can be pushed to a dedicated tablet computer of the emergency rescue department.

[0109] In this embodiment, based on the disaster-stricken area identified in the geographic information system, historical signaling data of users within the disaster-stricken area, and current signaling data, the number of affected people and their profiles within the disaster-stricken area can be obtained. Based on a pre-set emergency plan database and emergency resource database, an emergency rescue plan matching the number of affected people and their profiles within the disaster-stricken area can be generated, and the emergency rescue plan can be pushed to the terminal devices of the emergency rescue department. In the above process, a disaster severity classification map of the disaster-stricken area can be combined with user signaling data within the disaster-stricken area to obtain the number of affected people and their profiles. For different disaster-stricken areas, emergency response plans can be matched with the pre-set emergency plan database, and emergency material inventory information can be obtained from the emergency resource database to obtain the emergency rescue plan, which is then pushed to the emergency rescue department to assist the emergency rescue department in completing the rescue and improve rescue efficiency.

[0110] Because the number of people affected by major natural disasters changes over time, emergency response plans also need to be updated accordingly. Below, in... Figure 2 Based on the illustrated embodiments, combined with Figure 3 The above-mentioned method for obtaining emergency rescue solutions based on mobile phone signaling will be further explained.

[0111] Figure 3 This is a flowchart illustrating another method for obtaining an emergency rescue plan based on mobile phone signaling, provided as an embodiment of this application. Please refer to... Figure 3 The method may include:

[0112] S201. Obtain the latest disaster response level, casualty data, disaster impact range, and secondary disaster situation in real time.

[0113] When a major natural disaster occurs, the scope of the impact and the effects it causes are difficult to fully quantify in a short period of time. Therefore, when emergency rescue departments obtain emergency rescue plans and organize rescue forces to prepare for rescue, they need to obtain the latest disaster response level, casualty data, scope of disaster impact, and secondary disaster situation in real time, based on the time elapsed since the major natural disaster occurred.

[0114] Secondary disasters refer to disasters induced by primary disasters. Many major natural disasters, especially high-level and high-intensity natural disasters, may trigger a series of other disasters in succession. The first disaster to occur and take effect is called the primary disaster.

[0115] For example, a magnitude 4 earthquake occurred, with a disaster response level of Level III, resulting in 2 casualties and affecting 10 towns. While the emergency rescue department received the emergency rescue plan and organized rescue forces, a magnitude 6 earthquake occurred, with a disaster response level of Level I, resulting in 8 deaths and injuries, as well as secondary disasters such as collapsed buildings. 6 people were trapped, and the disaster affected 10 towns.

[0116] S202. Based on the disaster response level, casualty data, disaster impact range, and secondary disaster situation, the emergency rescue plan is revised to obtain the revised emergency rescue plan.

[0117] When the latest disaster response level, casualty data, disaster impact range and secondary disaster situation are obtained in real time, it is necessary to revise the emergency rescue plan formed during the occurrence of major natural disasters in order to meet the latest disaster needs and obtain the revised emergency rescue plan.

[0118] For example, after an earthquake, the affected areas include affected area 1 and affected area 2. The resulting emergency rescue plan 1 includes two parts: Emergency Response Plan 1 and Emergency Response Plan 2. Emergency Response Plan 1 involves dispatching a 10-person rescue team and five stretchers to affected area 1. Emergency Response Plan 2 involves dispatching a 20-person rescue team and ten stretchers to affected area 2. While preparing to implement Emergency Response Plan 1, a magnitude 6 earthquake occurs, raising the disaster response level from Level III to Level I, creating affected area 3, causing 8 deaths and injuries, and secondary disasters such as collapsed buildings, with 6 people trapped. Electronic equipment can obtain the latest disaster response level, casualty data, disaster impact range, and secondary disaster information in real time. This allows for the modification of Emergency Response Plan 1, resulting in a modified Emergency Response Plan 2. The modified Emergency Response Plan 2 can then be supplemented with Emergency Response Plan 3, which could involve dispatching a 30-person rescue team and ten stretchers to affected area 3.

[0119] S203. Push the revised emergency rescue plan to the terminal equipment of the emergency rescue department.

[0120] For example, the revised emergency response plan 2 can be pushed to a dedicated tablet computer of the emergency response department. The revised emergency response plan 2 can include emergency response plan 1, emergency response plan 2, and emergency response plan 3.

[0121] In this embodiment, the latest disaster response level, casualty data, disaster impact range, and secondary disaster situation can be obtained in real time. Based on the disaster response level, casualty data, disaster impact range, and secondary disaster situation, the emergency rescue plan is revised to obtain a revised emergency rescue plan, which is then pushed to the terminal equipment of the emergency rescue department. During this process, the emergency rescue department can obtain the revised emergency rescue plan and allocate rescue teams and materials corresponding to different disaster-stricken areas according to the revised plan, effectively carrying out rescue operations for the affected population and improving rescue efficiency.

[0122] Once the revised emergency relief plan is obtained, emergency supplies can be allocated based on the resettlement locations of the affected population. Below, in... Figure 3 Based on the illustrated embodiments, combined with Figure 4 This paper further explains the above-mentioned method for obtaining emergency rescue solutions based on mobile phone signaling.

[0123] Figure 4 This is a flowchart illustrating another method for obtaining an emergency rescue plan based on mobile phone signaling, provided as an embodiment of this application. Please refer to... Figure 4 The method may include:

[0124] S301. Based on the historical signaling data and current signaling data of users in the disaster-stricken area, establish a spatiotemporal label for each user in the disaster-stricken area.

[0125] Electronic devices can acquire historical and current signaling data of users in disaster-stricken areas. Based on the signaling data, they can analyze the location of people and signaling anomalies before and after the disaster, and establish spatiotemporal tags for each user in the disaster-stricken area. These spatiotemporal tags are used to indicate the user's residence and behavioral characteristics in the disaster-stricken area.

[0126] In one alternative implementation, each user's spatiotemporal tags include primary tags, secondary tags, and tertiary tags;

[0127] The primary tags include stay or travel;

[0128] The secondary labels for residency include any of the following: work or residence; the secondary labels for travel include any of the following: local commuting, local travel, or travel to other places.

[0129] The third-level tags for work include work address and number of workdays; the third-level tags for residence include residential address and length of time spent at home; the third-level tags for local commuting include commuting destination address, commuting distance, and number of travel days per month; the third-level tags for local travel include departure time, travel duration, and destination address; and the third-level tags for out-of-town travel include departure time, travel duration, destination address, and mode of transportation. Specific tags are shown in Table 1.

[0130] Table 1

[0131]

[0132] It should be noted that each user's spatiotemporal tags can also include fourth-level tags in addition to first-level, second-level, and third-level tags. The fourth-level tags can be: the street corresponding to the work location (third-level tag), the street corresponding to the residence (third-level tag), the time period corresponding to travel duration (less than 30 minutes, 30-60 minutes, 60-120 minutes, and greater than 120 minutes) (third-level tag), and the specific travel mode (fourth-level tag) such as airplane, rail, or road.

[0133] For example, if user A and user B exist within disaster-stricken area 1, then a spatiotemporal label 1 for user A can be established based on user A's historical and current signaling data; similarly, a spatiotemporal label 2 for user B can be established based on user B's historical and current signaling data. Spatiotemporal label 1 can be defined as: user A's travel origin at location A, departure time at 8:00 AM, destination at location B, and stop point at location C during the disaster period. Spatiotemporal label 2 can be defined as: user B's travel origin at location A, destination at location B, and stop point at location D during the disaster period.

[0134] S302. Obtain the resettlement location of the affected users based on the spatiotemporal tags of each user.

[0135] After establishing spatiotemporal tags for each user within the disaster-stricken area, the user's transfer trajectory can be obtained based on these tags. The transfer trajectory includes the user's starting point, stopping point, and destination. The resettlement location of the affected users can then be determined based on this trajectory. This resettlement location is used to allocate emergency supplies when determining the emergency relief plan.

[0136] For example, in disaster-stricken area 1, there are users A and B. Based on user A's spatiotemporal tag 1 and user B's spatiotemporal tag 2, the resettlement location of users A and B can be determined as location B. When determining the emergency rescue plan, emergency supplies can be allocated based on the resettlement locations of users A and B.

[0137] In this embodiment, a spatiotemporal tag is established for each user in the disaster-stricken area based on historical and current signaling data. The resettlement location of each user is then obtained based on their spatiotemporal tag. During this process, emergency rescue departments can obtain the resettlement locations of affected users within the disaster area and allocate emergency supplies based on these locations when determining emergency rescue plans. This effectively facilitates rescue efforts for the affected population and improves rescue efficiency.

[0138] Below, in Figure 2 Based on the illustrated embodiments, combined with Figure 5 This paper provides a detailed explanation of the above-mentioned method for obtaining emergency rescue solutions based on mobile phone signaling.

[0139] Figure 5 For a detailed flowchart illustrating an emergency rescue plan acquisition method based on mobile phone signaling provided in this application embodiment, please refer to [link / reference]. Figure 5 The method may include:

[0140] S401. Based on the terminal signaling data stored by each operator, obtain all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and obtain the current signaling data.

[0141] Mobile phones automatically send relevant data to the base station system at regular intervals. By parsing the signaling, the location data of all mobile phones in the target area and the timestamp of each data upload can be obtained. Different operators can store signaling data in the mobile communication system to obtain terminal signaling data. Electronic devices can obtain the current signaling data by comparing the terminal signaling data stored by each operator with all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster.

[0142] For example, the terminal stored by operator 1 can store 500 million signaling data entries, and the terminal stored by operator 2 can store 700 million signaling data entries. It can retrieve 500 signaling data entries from the terminal stored by operator 1 that were detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and it can retrieve 700 signaling data entries from the terminal stored by operator 2 that were detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, to obtain the current signaling data, which is 1200 entries.

[0143] S402. Based on the information of each user in the current signaling data, obtain the signaling data of each user within a preset historical time period to obtain historical signaling data.

[0144] After obtaining the current signaling data, it can be parsed to retrieve user information such as the user's unique identifier, timestamp, upload time, longitude, and latitude. Based on the user's unique identifier in the current signaling data, signaling data for each user within a preset historical time period can be obtained, thus generating historical signaling data.

[0145] For example, after obtaining the current 1200 signaling data entries, these entries can be parsed to retrieve information such as the user's unique identifier, timestamp, upload time, longitude, and latitude for each entry. Based on the user's unique identifier in each current signaling data entry, the signaling data for each user at 8:00 AM the day before the earthquake can be retrieved, resulting in historical signaling data. The historical signaling data contains 1100 entries.

[0146] S403. Clean the current signaling data and the historical signaling data respectively to obtain the cleaned current signaling data and the cleaned historical signaling data.

[0147] Due to transmission errors, system malfunctions, and other reasons, the signaling data collected by the terminals stored by operators will generate a large amount of invalid data. This data will affect the judgment of the number of people affected by the disaster and the population profile of the affected population. Therefore, it is necessary to clean the current signaling data and the historical signaling data separately. The cleaning process includes processing missing data, duplicate data, and erroneous data to obtain the cleaned current signaling data and the cleaned historical signaling data.

[0148] Missing Data: When operators store signaling data collected and recorded by terminals, data loss sometimes occurs. If the missing data is a user identification code, it can be supplemented using context information. If the missing data is other information that cannot be supplemented, the signaling data containing the missing information can be deleted.

[0149] Duplicate data: Signaling data may contain consecutive data entries with identical field attributes and content. This is called duplicate data, primarily caused by errors in the signaling acquisition system. For duplicate data, we choose to retain the first record and delete the others.

[0150] Error data: Some data in the signaling data is outside the normal range. For example, the date is not within the specified date range, or the latitude and longitude coordinates are not within the appropriate range. We choose to delete this part of the signaling data.

[0151] Optionally, the characteristics of signaling data are mainly reflected in several aspects such as spatiotemporal continuity, information correlation, storage redundancy, and real-time efficiency. The logic diagram for the analysis and processing of signaling data can be as follows: Figure 6 As shown.

[0152] Figure 6 A flowchart illustrating a mobile phone signaling data analysis and processing logic provided in an embodiment of this application. Please refer to [link / reference]. Figure 6 The basic data layer can include a user signaling data table and a base station location data table. The user signaling data table can store multiple signaling data entries, and the base station data table can store the locations and numbers of multiple base stations.

[0153] On one hand, signaling trajectory data can be obtained by parsing user signaling in the user signaling data table. Further data processing of the signaling trajectory data yields basic signaling data. By organizing and summarizing the basic signaling data, user dwell time data and user signaling data tables by state can be obtained. These user dwell time data and user signaling data tables by state can be used for signaling data modeling.

[0154] On the other hand, signaling data can be tagged, stored according to different categories, and used to complete data modeling. With the continuous development of signaling data, it can also be applied at the application layer to assist in completing population statistics in disaster-stricken areas, creating population profiles, and analyzing disaster-related relocation.

[0155] For example, by processing the current 1200 signaling data entries and the historical 1100 signaling data entries for missing, duplicate, and erroneous data respectively, we can obtain the cleaned current signaling data and the cleaned historical signaling data. The cleaned current signaling data can be 1100 entries, and the cleaned historical signaling data can be 1000 entries.

[0156] S404. Based on the cleaned current signaling data and the cleaned historical signaling data, obtain the population of each sub-region in the disaster area and the user characteristic attributes of each user.

[0157] To improve the efficiency of calculating the population in disaster areas, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means algorithms can be used to cluster users within the disaster-stricken area based on cleaned current signaling data and cleaned historical signaling data, resulting in multiple disaster-affected population clusters. Based on these clusters, the disaster-stricken area is then divided into multiple sub-regions. Population statistics are then performed on the disaster-affected population clusters within each sub-region to obtain the population size and user characteristic attributes for each user. Each user's characteristic attributes include at least their gender and age.

[0158] The DBSCAN algorithm can identify discrete points in spatial data and is a typical density-based clustering algorithm. The clustering idea of ​​the DBSCAN algorithm can be summarized as dividing a dataset into sets with different densities based on density reachability relationships.

[0159] The K-means algorithm is a classic partitioning-based clustering algorithm whose cluster centers are greatly affected by noise points. Given a dataset, the goal of the K-means algorithm is to divide the dataset into multiple classes, so that the similarity within each class is high and the similarity between classes is low.

[0160] Figure 7 This is a schematic diagram illustrating a disaster-stricken area and multiple clusters of affected people, provided as an embodiment of this application. Please refer to... Figure 7 The central origin represents the core location of a major natural disaster, an exceptionally heavy rainstorm. The area within 2 kilometers of the rainstorm core can be divided into disaster area 1, with a disaster level of I. The area within 2 to 5 kilometers of the rainstorm core can be divided into disaster area 2, with a disaster level of II. The area beyond 5 kilometers of the rainstorm core, extending to the farthest point affected by the volcano, can be divided into disaster area 3, with a disaster level of III.

[0161] The DBSCAN and K-means algorithms were used to cluster users within the disaster-stricken area, resulting in multiple disaster-affected population clusters. Specifically, the disaster-affected population clusters in disaster-affected area 1 were A1 and A2, those in disaster-affected area 2 were B1 and B2, and those in disaster-affected area 2 were C1, C2, and C3.

[0162] Based on multiple affected population clusters, the disaster-stricken area is divided into multiple sub-regions. Specifically, the sub-regions of disaster-stricken area 1 can be sub-region 11 and sub-region 12, where sub-region 11 contains affected population cluster A1 and sub-region 12 contains affected population cluster A2; the sub-regions of disaster-stricken area 2 can be sub-region 21 and sub-region 22, where sub-region 21 contains affected population cluster B1 and sub-region 22 contains affected population cluster B2; and the sub-regions of disaster-stricken area 3 can be sub-regions 31, 32, and 33, where sub-region 31 contains affected population cluster C1, sub-region 32 contains affected population cluster C2, and sub-region 33 contains affected population cluster C3.

[0163] When multiple sub-regions are obtained, population statistics can be performed on the affected population clusters within each sub-region to obtain the population size and user characteristics of each user in each sub-region. For example, sub-region 11 has a population of 50 people, including user 1, user 2, ..., and user 50. User 1's user characteristics could be 25 years old and male.

[0164] S405. Based on the disaster severity classification map of the disaster-stricken areas, obtain the disaster severity of each sub-region and the disaster weight coefficient corresponding to each disaster severity.

[0165] Since the number of people affected by major natural disasters is related to their location at the time of the disaster, a conceptual model of the affected population under emergency conditions can be introduced to calculate the number of affected people. In practice, the degree of disaster in each sub-region and the disaster weight coefficient corresponding to each degree of disaster can be obtained from the disaster severity map of the disaster-affected area. The number of affected people can then be calculated using the population of each sub-region and the disaster weight coefficient.

[0166] Optionally, the disaster weighting coefficient is affected by constraints such as disaster type, incident area, and disaster impact range (such as earthquake intensity level and range). The methods for determining the weight include subjective weighting and objective weighting.

[0167] The conceptual model of the disaster-affected population under emergency conditions can be: A = f(l, p). Where A represents the actual number of people affected by the disaster, l represents the location of the people after clustering of the signaling read when the disaster occurs, and p represents the probability that the people at that location are affected by the disaster.

[0168] For example, in the event of a severe rainstorm disaster, the disaster severity level of affected area 1 is Level I, with a disaster weighting coefficient of 0.8; the disaster severity level of affected area 2 is Level II, with a disaster weighting coefficient of 0.6; and the disaster severity level of affected area 3 is Level III, with a disaster weighting coefficient of 0.4. Sub-area 11 has a population of 50, sub-area 12 has a population of 100, sub-area 21 has a population of 50, sub-area 22 has a population of 100, sub-area 31 has a population of 50, sub-area 32 has a population of 100, and sub-area 33 has a population of 150. Therefore, the actual number of people affected is calculated to be 330.

[0169] S406. Based on the user characteristic attributes of each user in the disaster-stricken area, classify the user profiles to obtain the disaster-stricken population profiles.

[0170] By statistically analyzing the disaster-affected population clusters in each sub-region and obtaining the user characteristic attributes of each user in each sub-region, a population profile of the user can be created based on the user characteristic attributes, thus obtaining a disaster-affected population profile.

[0171] For example, sub-region 11 has a population of 50, including user 1, user 2, ..., and user 50. User 1's user characteristics could be 25 years old and male, while user 2's user characteristics could be 90 years old and female. Therefore, we can obtain disaster-affected population profile 1 based on user 1's user characteristics, and disaster-affected population profile 2 based on user 2's user characteristics.

[0172] S407. Based on the preset emergency plan database and emergency resource database, generate an emergency rescue plan that matches the number of affected people and their profiles in the disaster-stricken area.

[0173] It should be noted that the specific execution process of step S407 can be found in the specific execution process of step S102, and will not be repeated here.

[0174] S408. Push the emergency rescue plan to the terminal equipment of the emergency rescue department.

[0175] It should be noted that the specific execution process of step S408 can be found in the specific execution process of step S103, and will not be repeated here.

[0176] In this embodiment, based on the terminal signaling data stored by each operator, all signaling data detected during the disaster in the disaster-stricken area identified in the geographic information system can be obtained to obtain current signaling data. Based on the information of each user in the current signaling data, signaling data for each user within a preset historical time period can be obtained to obtain historical signaling data. The current and historical signaling data are cleaned separately to obtain cleaned current and historical signaling data. Based on the cleaned current and historical signaling data, the population size of each sub-region in the disaster-stricken area and the user characteristic attributes of each user are obtained. According to the disaster severity classification map in the disaster-stricken area, the disaster severity of each sub-region and the disaster weight coefficient corresponding to each severity are obtained. Based on the user characteristic attributes of each user in the disaster-stricken area, a user profile is classified to obtain a disaster-stricken population profile. Based on a preset emergency plan library and emergency resource library, an emergency rescue plan matching the disaster-stricken population size and disaster-stricken population profile in the disaster-stricken area is generated, and the emergency rescue plan is pushed to the terminal equipment of the emergency rescue department. In the above process, the population size in the disaster area and the user characteristic attributes of each user can be obtained based on the current signaling data and historical signaling data to obtain a profile of the affected population. Combined with the preset emergency plan library and emergency resource library, an emergency rescue plan can be generated to assist the emergency rescue department in completing the rescue and improve the rescue efficiency.

[0177] Based on any of the above embodiments, the following, in conjunction with Figure 8 The above-mentioned method for obtaining emergency rescue solutions based on mobile phone signaling will be further explained.

[0178] Figure 8 This is a schematic diagram illustrating a process for obtaining an emergency rescue plan, provided as an embodiment of this application. Please refer to... Figure 8 The process of obtaining an emergency response plan may include:

[0179] S501. Identification of disaster-stricken areas in catastrophic scenarios based on geographic information systems.

[0180] S502, Extraction of fences in disaster-stricken areas.

[0181] S503, Analysis of the affected population within the fenced area.

[0182] In one specific implementation, the analysis of the disaster-affected population within the fenced area may include: the number of disaster-affected people, the distribution of disaster-affected people, and the analysis of disaster-affected population profiles.

[0183] S504. Generate an emergency relief plan for the affected population.

[0184] S505, dispatch of rescue forces, dispatch of rescue supplies.

[0185] Emergency rescue departments can use the generated emergency rescue plan for the affected population to dispatch rescue forces and supplies.

[0186] S506. Analysis of personnel transfer and resettlement.

[0187] It should be noted that after completing the analysis of the affected population within the fenced area, a population relocation and resettlement analysis can also be conducted to generate an emergency rescue plan for the affected population, including strategies for the allocation of personnel relocation and resettlement materials, to assist emergency rescue departments in distributing emergency supplies to the affected population.

[0188] It should be noted that the process of obtaining an emergency rescue plan provided in this application embodiment can refer to the emergency rescue plan acquisition method based on mobile phone signaling provided in the above embodiment.

[0189] Figure 9 This is a schematic diagram illustrating the generation of an emergency rescue plan model provided in an embodiment of this application. Please refer to [link / reference]. Figure 9 The process of generating an emergency rescue plan model includes:

[0190] S601, Emergency Rescue Plan Framework Generation.

[0191] S602, Attribute Matching.

[0192] Optionally, attribute matching refers to whether the emergency plans in the preset emergency plan library meet the current number of affected people and affected areas.

[0193] S603, Data Query.

[0194] Basic data on disaster-stricken areas, such as village and town data, infrastructure data, urban lifeline data, rescue team data, and relief material data, can be queried using geographic information systems to conduct disaster damage analysis. The emergency resource database can be adaptively adjusted as the disaster-affected population database changes, filling in the corresponding data attributes of the disaster emergency rescue plan framework.

[0195] S604, Multi-source data fusion analysis.

[0196] S605, Emergency Rescue Plan Generation.

[0197] S606, dispatch of rescue forces, dispatch of rescue supplies.

[0198] Based on this, a complete emergency rescue plan is constructed, and the available resources and materials are analyzed through the terminal equipment of the emergency rescue department to achieve rapid rescue of disaster victims.

[0199] S607, Follow-up on the progress of the incident.

[0200] S608. Determine whether the event handling process has ended.

[0201] The development of an emergency response plan is a dynamic process that requires continuous revision and improvement based on the handling and development of the emergency, thus supporting the ongoing response. Therefore, emergency response departments need to continuously monitor the execution of the emergency response plan and determine whether the emergency response has concluded. If yes, the emergency response is considered complete; otherwise, proceed with step S609.

[0202] S609, Contingency Plan Matching.

[0203] The primary basis for generating emergency rescue plans is the emergency plan database. Feature extraction and screening of emergency rescue plans, along with digital and intelligent response matching, are preliminary steps in generating emergency rescue plans. Furthermore, throughout the rescue process, plans need to be continuously revised and updated based on factors such as the event's response level, number of casualties, scope of impact, and secondary disasters.

[0204] During the revision of emergency rescue plans, data analysis reports can be used to calculate and present the data indicators that emergency rescue departments are concerned about. These reports mainly cover the characteristics of disaster victims in the region, the analysis of the disaster situation, and the changes in the location trajectories of different groups. Based on the behavioral characteristics of people, reports can be used to predict suspected casualties, abnormal gatherings, and other situations, reminding emergency rescue departments to pay close attention. The main data indicators involved are shown in Table 2.

[0205] Table 2

[0206]

[0207] Figure 10 This is a schematic diagram of an emergency rescue plan acquisition device based on mobile phone signaling, provided as an embodiment of this application. Please refer to... Figure 10 An emergency rescue plan acquisition device 10 based on mobile phone signaling includes:

[0208] The first acquisition module 11 is used to acquire the number of disaster-stricken people and the disaster-stricken population profile in the disaster-stricken area based on the disaster-stricken area identified in the geographic information system, the historical signaling data of users within the disaster-stricken area and the current signaling data.

[0209] The first processing module 12 is used to generate an emergency rescue plan that matches the number of affected people and the profile of the affected people in the disaster area based on a preset emergency plan library and emergency resource library; wherein, the emergency plan library stores multiple emergency response plans for different disaster situations and different affected people, and the emergency resource library stores inventory information of reserved emergency supplies.

[0210] The first push module 13 is used to push the emergency rescue plan to the terminal equipment of the emergency rescue department.

[0211] The emergency rescue solution acquisition device based on mobile phone signaling provided in this application embodiment can execute the technical solution shown in the above embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0212] In one possible implementation, the device 10 further includes:

[0213] The second acquisition module 14 is used to acquire the latest disaster response level, casualty data, disaster impact range and secondary disaster situation in real time;

[0214] The second processing module 15 is used to modify the emergency rescue plan based on the disaster response level, the casualty data, the scope of the disaster impact, and the secondary disaster situation, so as to obtain the modified emergency rescue plan.

[0215] The second push module 16 is used to push the revised emergency rescue plan to the terminal device of the emergency rescue department.

[0216] In one possible implementation, the device 10 further includes:

[0217] The third acquisition module 17 is used to establish a spatiotemporal tag for each user in the disaster-stricken area based on the historical signaling data and the current signaling data of the users in the disaster-stricken area. The spatiotemporal tag is used to indicate the user's residence and behavioral characteristics in the disaster-stricken area.

[0218] The third processing module 18 is used to obtain the resettlement location of the disaster-stricken users based on the spatiotemporal tags of each user. The resettlement location of the disaster-stricken users is used to allocate emergency supplies when determining the emergency rescue plan.

[0219] In one possible implementation, each user's spatiotemporal label includes a first-level label, a second-level label, and a third-level label;

[0220] The primary tags include stay or travel;

[0221] The secondary label corresponding to the stay includes any one of the following: work or residence; the secondary label corresponding to the travel includes any one of the following: local commuting, local travel, or travel to other places;

[0222] The three-level tags corresponding to the work include the work address and the number of workdays; the three-level tags corresponding to the residence include the residence address and the length of time spent at home; the three-level tags corresponding to the local commute include the commuting destination address, commuting distance, and the number of travel days per month; the three-level tags corresponding to the local trip include the departure time, travel duration, and travel destination address; and the three-level tags corresponding to the trip to other places include the departure time, travel duration, travel destination address, and travel mode.

[0223] In one possible implementation, the first acquisition module 11 is specifically used for:

[0224] Based on the terminal signaling data stored by each operator, obtain all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and obtain the current signaling data;

[0225] Based on the information of each user in the current signaling data, obtain the signaling data of each user in a preset historical time period to obtain the historical signaling data;

[0226] The current signaling data and the historical signaling data are cleaned respectively to obtain cleaned current signaling data and cleaned historical signaling data. The cleaning process includes processing missing data, duplicate data and erroneous data.

[0227] Based on the disaster severity classification map of the disaster-stricken area, the cleaned current signaling data, and the cleaned historical signaling data, the number of disaster-stricken people and the disaster-stricken population profile in the disaster-stricken area are obtained.

[0228] In one possible implementation, the first acquisition module 11 is specifically used for:

[0229] Based on the cleaned current signaling data and the cleaned historical signaling data, the population of each sub-region in the disaster-stricken area and the user characteristic attributes of each user are obtained; wherein, the characteristic attributes of each user include at least the user's gender and age;

[0230] Based on the disaster severity classification map of the disaster-stricken areas, the disaster severity of each sub-region and the disaster weight coefficient corresponding to each disaster severity are obtained. The disaster weight coefficient is determined in advance based on the disaster type, the area where the incident occurred, and the scope of the disaster.

[0231] The number of affected people in the disaster-stricken area is calculated based on the population of each sub-region in the disaster-stricken area and the disaster weight coefficient corresponding to the degree of disaster in each sub-region.

[0232] Based on the user characteristic attributes of each user in the disaster-stricken area, a user profile is classified to obtain the disaster-stricken population profile.

[0233] In one possible implementation, the first acquisition module 11 is specifically used for:

[0234] Based on the cleaned current signaling data and the cleaned historical signaling data, the DBSCAN algorithm and the K-means algorithm are used to cluster users in the disaster-stricken area to obtain multiple disaster-stricken population clusters.

[0235] Based on the multiple affected population clusters, the affected area is divided into multiple sub-regions, and population statistics are performed on the affected population clusters in each sub-region to obtain the population size in each sub-region.

[0236] The emergency rescue solution acquisition device based on mobile phone signaling provided in this application embodiment can execute the technical solution shown in the above embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0237] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Please refer to... Figure 11 The electronic device 20 may include a processor 21, a memory 22, and an interface 24. Exemplarily, the processor 21, the memory 22, and the interface 24 are interconnected via a bus 23.

[0238] The memory 22 stores computer-executed instructions;

[0239] The processor 21 executes the computer execution instructions stored in the memory 22, causing the processor 31 to perform the method provided in the above method embodiments.

[0240] Figure 11The electronic device shown can be a server for obtaining emergency rescue solutions based on mobile phone signaling.

[0241] Accordingly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method provided in any of the above method embodiments.

[0242] Accordingly, embodiments of this application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the method provided in any of the above method embodiments.

[0243] All or part of the steps in the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above-described method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0244] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0245] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0246] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0247] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0248] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0249] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for obtaining emergency rescue plans based on mobile phone signaling, characterized in that, Applied to a server, the method includes: Based on the disaster-stricken areas identified in the geographic information system, the historical signaling data and current signaling data of users within the disaster-stricken areas, the number of disaster-stricken people and their profiles are obtained within the disaster-stricken areas. Based on the historical signaling data and current signaling data of users in the disaster-stricken area, a spatiotemporal tag is established for each user in the disaster-stricken area. The spatiotemporal tag is used to indicate the user's residence and behavioral characteristics in the disaster-stricken area. Each user's spatiotemporal tag includes a primary tag, a secondary tag, and a tertiary tag. The primary tag includes residence or travel. The secondary tag corresponding to residence includes any one of the following: work or residence. The secondary tag corresponding to travel includes any one of the following: local commuting, local travel, or out-of-town travel. The tertiary tag corresponding to work includes work address and number of workdays. The tertiary tag corresponding to residence includes residence address and length of time at home. The tertiary tag corresponding to local commuting includes commuting destination address, commuting distance, and number of travel days per month. The tertiary tag corresponding to local travel includes travel time, travel duration, and travel destination address. The tertiary tag corresponding to out-of-town travel includes travel time, travel duration, travel destination address, and travel mode. Based on the spatiotemporal tags of each user, the resettlement location of the affected users is obtained, and the resettlement location of the affected users is used to allocate emergency supplies when determining the emergency rescue plan. Based on a pre-set emergency plan database and emergency resource database, an emergency rescue plan is generated that matches the number of affected people and their profiles within the disaster-stricken area. The emergency plan database stores multiple emergency response plans for different disaster situations and different affected populations, and the emergency resource database stores inventory information of reserved emergency supplies. The emergency rescue plan will be pushed to the terminal equipment of the emergency rescue department.

2. The method according to claim 1, characterized in that, The method further includes: Get the latest disaster response level, casualty data, disaster impact range and secondary disaster situation in real time; Based on the disaster response level, the casualty data, the scope of the disaster impact, and the secondary disaster situation, the emergency rescue plan is revised to obtain the revised emergency rescue plan. The revised emergency rescue plan will be pushed to the terminal equipment of the emergency rescue department.

3. The method according to claim 1 or 2, characterized in that, The method involves obtaining the number of affected people and their profiles within the disaster-stricken area based on the disaster-stricken area identified in the geographic information system, historical signaling data of users within the disaster-stricken area, and current signaling data. Based on the terminal signaling data stored by each operator, obtain all signaling data detected in the disaster-stricken area identified in the geographic information system at the time of the disaster, and obtain the current signaling data; Based on the information of each user in the current signaling data, obtain the signaling data of each user in a preset historical time period to obtain the historical signaling data; The current signaling data and the historical signaling data are cleaned respectively to obtain cleaned current signaling data and cleaned historical signaling data. The cleaning process includes processing missing data, duplicate data and erroneous data. Based on the disaster severity classification map of the disaster-stricken area, the cleaned current signaling data, and the cleaned historical signaling data, the number of disaster-stricken people and the disaster-stricken population profile in the disaster-stricken area are obtained.

4. The method according to claim 3, characterized in that, The step of obtaining the number of affected people and the population profile in the disaster-stricken area based on the disaster severity classification map, the cleaned current signaling data, and the cleaned historical signaling data includes: Based on the cleaned current signaling data and the cleaned historical signaling data, the population of each sub-region in the disaster-stricken area and the user characteristic attributes of each user are obtained; wherein, the characteristic attributes of each user include at least the user's gender and age; Based on the disaster severity classification map of the disaster-stricken areas, the disaster severity of each sub-region and the disaster weight coefficient corresponding to each disaster severity are obtained. The disaster weight coefficient is determined in advance based on the disaster type, the area where the incident occurred, and the scope of the disaster. The number of affected people in the disaster-stricken area is calculated based on the population of each sub-region in the disaster-stricken area and the disaster weight coefficient corresponding to the degree of disaster in each sub-region. Based on the user characteristic attributes of each user in the disaster-stricken area, a user profile is classified to obtain the disaster-stricken population profile.

5. The method according to claim 4, characterized in that, The step of obtaining the population of each sub-region in the disaster-stricken area based on the cleaned current signaling data and the cleaned historical signaling data includes: Based on the cleaned current signaling data and the cleaned historical signaling data, the DBSCAN algorithm and the K-means algorithm are used to cluster users in the disaster-stricken area to obtain multiple disaster-stricken population clusters. Based on the multiple affected population clusters, the affected area is divided into multiple sub-regions, and population statistics are performed on the affected population clusters in each sub-region to obtain the population size in each sub-region.

6. An emergency rescue plan acquisition device based on mobile phone signaling, characterized in that, include: The first acquisition module is used to acquire the number of disaster-stricken people and a profile of the disaster-stricken people in the disaster-stricken area based on the disaster-stricken area identified in the geographic information system, the historical signaling data of users within the disaster-stricken area, and the current signaling data. The third acquisition module is used to establish a spatiotemporal tag for each user in the disaster-stricken area based on the historical signaling data and the current signaling data of the users in the disaster-stricken area. The spatiotemporal tag is used to indicate the user's residence and behavioral characteristics in the disaster-stricken area. Each user's spatiotemporal tag includes a primary tag, a secondary tag, and a tertiary tag. The primary tag includes residence or travel. The secondary tag corresponding to residence includes any one of the following: work or residence. The secondary tag corresponding to travel includes any one of the following: local commuting, local travel, or out-of-town travel. The tertiary tag corresponding to work includes work address and number of workdays. The tertiary tag corresponding to residence includes residence address and length of time at home. The tertiary tag corresponding to local commuting includes commuting destination address, commuting distance, and number of travel days per month. The tertiary tag corresponding to local travel includes travel time, travel duration, and travel destination address. The tertiary tag corresponding to out-of-town travel includes travel time, travel duration, travel destination address, and travel mode. The third processing module is used to obtain the resettlement location of the disaster-stricken users based on the spatiotemporal tags of each user. The resettlement location of the disaster-stricken users is used to allocate emergency supplies when determining the emergency rescue plan. The first processing module is used to generate an emergency rescue plan that matches the number of affected people and their profiles in the disaster-stricken area based on a preset emergency plan library and emergency resource library; wherein, the emergency plan library stores multiple emergency response plans for different disaster situations and different affected groups, and the emergency resource library stores inventory information of reserved emergency supplies. The first push module is used to push the emergency rescue plan to the terminal equipment of the emergency rescue department.

7. An electronic device, characterized in that, include: Processor, memory, and interaction interface; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, are used to implement the method described in any one of claims 1 to 5.

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