A regional disaster collaborative rescue method adapted to the large emergency response system

Abstract

The present invention relates to the technical field of emergency management, and particularly to a regional disaster collaborative rescue method adapted to the large emergency system, including periodically obtaining environmental information of the disaster area, real-time obtaining network information and rescue target information of the disaster area; analyzing water level data and network information obtained in several cycles to determine disaster rescue difficulty characterization parameters of the disaster area; determining rescue interference types of the disaster area according to the disaster rescue difficulty characterization parameters of the disaster area, and determining rescue methods for the disaster area according to the rescue interference types of the disaster area; the present invention improves rescue efficiency and safety, ensures the accuracy and timeliness of rescue operations in complex environments; and enhances the adaptability and effectiveness of rescue operations by flexibly adjusting the distribution and implementation amount of rescue materials.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and in particular to a regional disaster collaborative rescue method adapted to the large emergency system. Background Art

[0002] In the large emergency disaster area rescue, the existing technologies include unmanned aerial vehicle monitoring, satellite remote sensing, mobile communication systems, and automated rescue robots. Unmanned aerial vehicles provide real-time aerial surveillance, satellite remote sensing is used for large-scale disaster assessment, mobile communication systems ensure smooth information flow, and automated robots can perform tasks in dangerous areas. However, the difficulties faced in rescue include insufficient real-time data processing capabilities, poor durability of equipment in harsh environments, stability problems of communication networks, and insufficient adaptability to complex terrains. Solving these difficulties requires improving the reliability and adaptability of technologies, strengthening the integration and collaboration capabilities of systems, and optimizing data analysis and processing processes to improve rescue efficiency and effectiveness.

[0003] Chinese Patent Publication No.: CN116957303B discloses an emergency response scheduling decision method and system for flood disaster scenarios. The method includes: determining the grid inundation depth within the inundated range according to the rainfall and basic geographical data of the study area at the target moment and determining the grid inundation level according to the inundation level division rules; determining the number of people in the grid according to mobile phone signaling data; determining the number of affected people in the grid according to the grid inundation level and the number of people; merging grids into affected areas according to the affected area division rules; determining the affected points and the number of affected people in the affected areas through the population centroid algorithm; determining the inundation level of the grid where the affected point is located as its inundation level and determining the number of rescue personnel required for the affected point according to the number of affected people; constructing a scheduling decision model based on the defined index function; solving the scheduling decision model through the non-dominated sorting genetic algorithm according to the number of rescue personnel required for the affected point to obtain the optimal scheduling plan. Thus, the emergency response scheduling decision method and system for flood disaster scenarios have the following problems: They do not consider the poor network in the rescue environment, cannot accurately and efficiently calculate a large amount of data, and cannot transmit too much information to the rescue location, thus affecting the rescue of the disaster area. Summary of the Invention

[0004] Therefore, the present invention provides a regional disaster collaborative rescue method adapted to the large emergency system to overcome the problems in the prior art that do not consider the poor network in the rescue environment, cannot accurately and efficiently calculate a large amount of data, and cannot transmit too much information to the rescue location, thus affecting the rescue of the disaster area.

[0005] To achieve the above object, the present invention provides a regional disaster collaborative rescue method adapted to the large emergency system, including:

[0006] Periodically obtain the environmental information of the disaster area, and obtain the network information and rescue target information of the disaster area in real time. The environmental information includes meteorological data, water level data, and soil data. The network information includes packet loss rate and latency. The rescue target information includes the rescue target location and moving speed;

[0007] Analyze the water level data and network information obtained in several cycles to determine the disaster rescue difficulty characterization parameter of the disaster area;

[0008] Determine the rescue interference type of the disaster area according to the disaster rescue difficulty characterization parameter of the disaster area;

[0009] Determine the rescue method for the disaster area according to the rescue interference type of the disaster area, including,

[0010] Shorten the acquisition cycle of environmental information and reduce the amount of information transmitted. Divide the disaster area into several analysis areas, and determine the material allocation amount and rescue implementation amount according to the distribution of each rescue target in the analysis area;

[0011] Or, maintain the current rescue material allocation amount and rescue implementation amount, determine the secondary disaster occurrence tendency parameter of the current disaster area according to the soil data, so as to determine whether to transfer the rescue target.

[0012] Further, the acquisition cycles of the meteorological data, the water level data, and the soil data are the same;

[0013] Among them, the meteorological data includes wind speed and precipitation.

[0014] Further, determine the disaster rescue difficulty characterization parameter of the disaster area according to the water level information in several cycles and the network information within the corresponding time. The disaster rescue difficulty characterization parameter is determined according to formula (1),

[0015] ,

[0016] In formula (1), K is the disaster rescue difficulty characterization parameter, α is the water level change influence coefficient, n is the total number of acquisition cycles, ti is the latency in the i-th acquisition cycle, t0 is the average latency, βn is the packet loss rate in the n-th cycle, i is an integer greater than 0, and α takes a value of 1 or 2.

[0017] Further, determine the rescue interference type of the disaster area according to the disaster rescue difficulty characterization parameter of the disaster area, including,

[0018] If the disaster rescue difficulty characterization parameter is greater than or equal to the standard difficulty characterization parameter, the rescue interference type of the disaster area is the strong rescue interference type;

[0019] If the disaster rescue difficulty characterization parameter is less than the standard difficulty characterization parameter, the rescue interference type in the disaster area is the weak rescue interference type.

[0020] Furthermore, select the corresponding rescue method according to the rescue interference type in the disaster area, including,

[0021] If the rescue interference type in the disaster area is the strong rescue interference type, the rescue method is to shorten the acquisition cycle of environmental information and reduce the amount of information transmitted, divide the disaster area into several analysis areas, and determine the material allocation amount and rescue implementation amount according to the distribution of each rescue target in the analysis area;

[0022] If the rescue interference type in the disaster area is the weak rescue interference type, the rescue method is to maintain the current rescue material allocation amount and rescue implementation amount, determine the secondary disaster occurrence tendency parameter of the current disaster area according to the soil data, so as to determine whether to transfer the rescue target.

[0023] Furthermore, the shortening of the acquisition cycle of environmental information includes,

[0024] Calculate the difference between the disaster rescue difficulty characterization parameter of the current disaster area and the standard difficulty characterization parameter, denoted as the difficulty difference;

[0025] Determine the adjustment amount of the acquisition cycle according to the difficulty difference;

[0026] Among them, the adjustment amount of the acquisition cycle is positively correlated with the difficulty difference.

[0027] Furthermore, dividing the disaster area into several analysis areas includes,

[0028] Equally divide the disaster area into several monitoring areas;

[0029] Identify the number of rescue targets in each monitoring area, determine the monitoring area type according to the number of rescue targets, and the monitoring area type includes the temporary stability monitoring area and the emergency rescue monitoring area;

[0030] Merge the monitoring areas corresponding to the temporary stability area type and denote them as the temporary stability analysis area, and denote the emergency rescue monitoring area as the emergency rescue analysis area;

[0031] Among them, the types of the analysis areas include the temporary stability analysis area and the emergency rescue analysis area.

[0032] Furthermore, confirm the material allocation amount and rescue implementation amount according to the distribution of each rescue target in the analysis area, including,

[0033] If the type of the analysis area is the temporary stability analysis area, determine the material allocation amount according to the total number of rescue targets;

[0034] If the type of the analysis area is an emergency rescue analysis area, the increase amount of the rescue implementation quantity is determined according to the rescue target information.

[0035] Furthermore, the secondary disaster occurrence tendency parameter is determined according to the soil stability coefficient and the pore water pressure in the soil data, and the secondary disaster occurrence tendency parameter is determined according to formula (2).

[0036] ,

[0037] In formula (2), D is the secondary disaster occurrence tendency parameter, p1 is the pore water pressure, p0 is the standard pore water pressure, γ is the soil stability coefficient, and the value of γ is 1 or 2.

[0038] Furthermore, it is determined whether to transfer the rescue target according to the secondary disaster occurrence tendency parameter of the current disaster area, including:

[0039] If the secondary disaster occurrence tendency parameter is greater than or equal to the standard secondary disaster occurrence tendency parameter, it is determined to transfer the rescue target in the current disaster area;

[0040] If the secondary disaster occurrence tendency parameter is less than the standard secondary disaster occurrence tendency parameter, it is determined not to transfer the rescue target in the current disaster area.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: by periodically acquiring the environmental information of the disaster area, acquiring the network information and the rescue target information in real time, analyzing and determining the disaster rescue difficulty characterization parameter, and then determining the rescue interference type according to this parameter and selecting the corresponding rescue method, the present invention can effectively cope with the situation of poor network environment and large data transmission volume. By shortening the information acquisition cycle, reducing the information transmission volume, reasonably dividing the analysis area, and predicting the secondary disaster risk according to the soil data, the dynamic adjustment and optimization of the rescue operation are realized; the present invention improves the rescue efficiency and safety, and ensures the accuracy and timeliness of the rescue operation in a complex environment; by flexibly adjusting the rescue material distribution and implementation quantity, the adaptability and effectiveness of the rescue operation are enhanced; and in the case of poor network environment, the accuracy of information transmission is ensured by increasing the information sending frequency, thereby providing solid data support for rescue command.

[0042] Further, in the present invention, during the rescue of a disaster area, the rescue methods vary under different environmental conditions. When the water level rises significantly and the network signal is poor, it is easy to encounter danger during the rescue. On the contrary, the rescue is safer and more efficient in the opposite situation. Therefore, by determining the disaster rescue difficulty characterization parameter of the disaster area to characterize the magnitude of the rescue difficulty, it provides data support for selecting an appropriate rescue method for the disaster area, facilitating the subsequent selection of a more efficient and safe rescue method adaptively.

[0043] Further, in the present invention, in actual situations, considering that the disaster area with a larger disaster rescue difficulty characterization parameter has a more complex rescue situation and a harsh rescue environment, at this time, increasing the acquisition frequency of environmental information facilitates better monitoring of the environment in the rescue area, providing a data basis for timely risk avoidance and ensuring the safety of personnel without a rescue target. In addition, in response to the above situation, flexibly adjusting the arrangement of rescue personnel and the distribution of rescue supplies facilitates better improving the rescue efficiency and ensuring the rescue quality.

[0044] Further, in the present invention, for the disaster area with a relatively low disaster rescue difficulty characterization parameter, it is selected to maintain the current rescue state and continue the rescue. At this time, the environmental impact is small, and ensuring the current rescue status can achieve efficient rescue. At this time, it should be noted that monitor whether the soil in the area where the rescue target is placed meets the requirements and whether there is a risk of collapse, reducing the detection and analysis of other data, and ensuring that sufficient safety protection can be provided in the case of a not very good network environment. Description of the Drawings

[0045] Figure 1 It is a flowchart of the regional disaster collaborative rescue method adapted to the large emergency system in the embodiment of the present invention;

[0046] Figure 2 It is a logic diagram for determining the rescue interference type of the disaster area in the embodiment of the present invention;

[0047] Figure 3 It is a logic diagram for confirming the material distribution quantity and the rescue implementation quantity in the embodiment of the present invention;

[0048] Figure 4 It is a logic diagram for determining whether to transfer the rescue target in the embodiment of the present invention. Detailed Embodiment

[0049] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0051] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention.

[0052] Please refer to Figure 1 as shown, which is a flowchart of the regional disaster collaborative rescue method adapted to the large emergency system in the embodiment of the present invention; the present invention provides a regional disaster collaborative rescue method adapted to the large emergency system, including:

[0053] Step S1, periodically obtain the environmental information of the disaster area, and real-time obtain the network information and rescue target information of the disaster area. The environmental information includes meteorological data, water level data, and soil data. The network information includes packet loss rate and delay. The rescue target information includes the rescue target location and moving speed;

[0054] Step S2, analyze the water level data and network information obtained in several cycles, and determine the disaster rescue difficulty characterization parameter of the disaster area;

[0055] Step S3, determine the rescue interference type of the disaster area according to the disaster rescue difficulty characterization parameter of the disaster area;

[0056] Step S4, determine the rescue method for the disaster area according to the rescue interference type of the disaster area, including,

[0057] Shorten the acquisition cycle of environmental information and reduce the amount of information transmitted, divide the disaster area into several analysis areas, and determine the material distribution amount and rescue implementation amount according to the distribution of each rescue target in the analysis area;

[0058] Or, maintain the current rescue material distribution amount and rescue implementation amount, determine the secondary disaster occurrence tendency parameter of the current disaster area according to the soil data, so as to determine whether to transfer the rescue target.

[0059] In implementation, the network information can be obtained by any network detection device, which is the prior art and will not be specifically limited.

[0060] The present invention can effectively cope with the situation of poor network environment and large data transmission volume by periodically obtaining the environmental information of the disaster area, obtaining the network information and rescue target information in real time, analyzing and determining the parameters characterizing the difficulty of disaster rescue, and then determining the type of rescue interference according to this parameter and selecting the corresponding rescue method. By shortening the information acquisition cycle, reducing the amount of information transmission, reasonably dividing the analysis area, and predicting the secondary disaster risk based on soil data, the dynamic adjustment and optimization of rescue operations are realized; the present invention improves the rescue efficiency and safety, and ensures the accuracy and timeliness of rescue operations in complex environments; by flexibly adjusting the distribution and implementation amount of rescue materials, the adaptability and effectiveness of rescue operations are enhanced; and in the case of poor network environment, the accuracy of information transmission is ensured by increasing the information sending frequency, thus providing solid data support for rescue command.

[0061] Specifically, the cycles for obtaining the meteorological data, the water level data, and the soil data are the same;

[0062] Among them, the meteorological data includes wind speed and precipitation.

[0063] In implementation, the wind speed can be obtained through meteorological satellites and ground meteorological stations. Meteorological satellites can provide wind speed information over a large range, while ground meteorological stations can provide more accurate local wind speed data;

[0064] The precipitation data can be collected in real time through a sensor network installed in key areas and transmitted to the cloud platform through wireless communication technology for remote monitoring;

[0065] The water level data can be obtained through sensors;

[0066] The soil data can be obtained through pore water pressure gauges and ground penetrating radars. The above acquisition methods are existing technologies and are not specifically limited.

[0067] Please refer to Figure 2 As shown, it is a logic diagram for determining the type of rescue interference in the disaster area in an embodiment of the present invention. The parameters characterizing the difficulty of disaster rescue in the disaster area are determined according to the water level information in several cycles and the network information within the corresponding time. The parameters characterizing the difficulty of disaster rescue are determined according to formula (1),

[0068] ,

[0069] In formula (1), K is the parameter characterizing the difficulty of disaster rescue, α is the influence coefficient of water level change, n is the total number of acquisition cycles, ti is the delay in the i-th acquisition cycle, t0 is the average delay, βn is the packet loss rate in the n-th cycle, i is an integer greater than 0, and α takes a value of 1 or 2.

[0070] In implementation, the packet loss rate is obtained using network monitoring tools such as Wireshark, NetFlow, Nagios, etc., which can capture and analyze network traffic to calculate the packet loss rate, and β is less than 1;

[0071] The total number of cycles is not less than 3, preferably 6.

[0072] If the change difference of the water level within a single information acquisition cycle exceeds the preset maximum allowable water level change difference or the changed water level exceeds the safety water level of the current location, α takes the value of 2; if the change difference of the water level within a single information acquisition cycle does not exceed the preset maximum allowable water level change difference and the changed water level does not exceed the safety water level of the current location, α takes the value of 1;

[0073] The preset maximum allowable water level change difference is 0.9 times the maximum water level change amount in the historical data where no secondary disasters occurred; the safety water level is determined according to the maximum value of the water level records in the historical data where no secondary disasters occurred.

[0074] Specifically, the rescue interference type of the disaster area is determined according to the disaster rescue difficulty characterization parameter of the disaster area, including,

[0075] If the disaster rescue difficulty characterization parameter is greater than or equal to the standard difficulty characterization parameter, the rescue interference type of the disaster area is the strong rescue interference type;

[0076] If the disaster rescue difficulty characterization parameter is less than the standard difficulty characterization parameter, the rescue interference type of the disaster area is the weak rescue interference type.

[0077] In implementation, the standard difficulty characterization parameter is selected within the range of [0.7, 0.9].

[0078] In the present invention, when rescuing in a disaster area, the rescue methods are different under different environmental conditions. In the case where the water level rises significantly and the network signal is poor, it is easy to be dangerous during the rescue. On the contrary, the rescue is safer and more efficient. Therefore, by determining the disaster rescue difficulty characterization parameter of the disaster area to characterize the size of the rescue difficulty, it provides data support for selecting a suitable rescue method for the disaster area, facilitating the subsequent adaptive selection of a more efficient and safe rescue method.

[0079] Specifically, the corresponding rescue method is selected according to the rescue interference type of the disaster area, including,

[0080] If the rescue interference type of the disaster area is the strong rescue interference type, the rescue method is to shorten the information acquisition cycle of the environment and reduce the amount of information transmitted, divide the disaster area into several analysis areas, and determine the material distribution amount and rescue implementation amount according to the distribution of each rescue target in the analysis area;

[0081] If the rescue interference type in the disaster area is a weak rescue interference type, the rescue method is to maintain the current distribution volume of rescue supplies and the implementation volume of rescue, and determine the secondary disaster occurrence tendency parameter of the current disaster area according to the soil data to determine whether to transfer the rescue target.

[0082] Reduce the amount of information transmitted. The purpose is to transmit information to the command area more efficiently, and the accuracy of information transmission can be ensured by increasing the frequency of information sending. Because in this case, the network environment is poor and the packet loss rate of information transmission is high, which is likely to cause inaccurate command of rescue due to the transmitted information content. Therefore, reducing the amount of information transmitted each time can reduce data loss, and increasing the transmission frequency can ensure the timeliness and accuracy of rescue information transmission. Preferably, the sending frequency is increased to 2 times the original.

[0083] Specifically, shortening the acquisition cycle of environmental information includes,

[0084] Calculate the difference between the disaster rescue difficulty characterization parameter of the current disaster area and the standard difficulty characterization parameter, denoted as the difficulty difference;

[0085] Determine the adjustment amount of the acquisition cycle according to the difficulty difference;

[0086] Among them, the adjustment amount of the acquisition cycle is positively correlated with the difficulty difference.

[0087] In implementation, the adjustment amount of the acquisition cycle △T = (K1 - K0) / K0 × T0, where K1 is the disaster rescue difficulty characterization parameter, K0 is the standard difficulty characterization parameter, T0 is the initial acquisition cycle, the initial acquisition cycle is 10 minutes, and the adjusted acquisition cycle T’ = T0 - △T.

[0088] Please refer to Figure 3 As shown, it is the logic diagram for confirming the distribution volume of supplies and the implementation volume of rescue in the embodiment of the present invention. Dividing the disaster area into several analysis areas includes,

[0089] Equally divide the disaster area into several monitoring areas;

[0090] Identify the number of rescue targets in each monitoring area, and determine the monitoring area type according to the number of rescue targets. The monitoring area type includes a temporary stability monitoring area and an emergency rescue monitoring area type;

[0091] Merge the monitoring areas corresponding to the temporary stability area type and denote them as the temporary stability analysis area, and denote the emergency rescue area type as the emergency rescue analysis area;

[0092] Among them, the types of the analysis areas include a temporary stability analysis area and an emergency rescue analysis area.

[0093] In implementation, the rescue target is a person. During the disaster rescue process, real-time monitoring images of the disaster area can be obtained through a mobile device, and the disaster area can be divided. This is the prior art and will not be specifically limited.

[0094] The monitored area is a square area with a length and width of not less than 5 meters, preferably 10 meters.

[0095] In a single monitored area, if the number of rescue targets is greater than or equal to 10, then it is determined that the monitored area is a temporary stability monitoring area; if the number of rescue targets is less than 10, then it is determined that the monitored area is an emergency rescue monitoring area.

[0096] Specifically, according to the distribution of each rescue target in the analysis area, the material distribution quantity and the rescue implementation quantity are confirmed, including:

[0097] If the type of the analysis area is a temporary stability analysis area, the material distribution quantity is determined according to the total number of rescue targets.

[0098] If the type of the analysis area is an emergency rescue analysis area, the increased quantity of the rescue implementation quantity is determined according to the rescue target information.

[0099] In implementation, for a temporary stability analysis area, the material distribution quantity corresponding to a single rescue target is determined according to the material distribution quantity corresponding to the number of rescue targets in the historical rescue record, and then the total material distribution quantity of the current analysis area is determined by combining the total number of rescue targets in the temporary stability analysis area with the material distribution quantity corresponding to a single rescue target.

[0100] For an emergency rescue analysis area, the rescue implementation quantity corresponding to a single rescue target is determined according to the rescue personnel requirement corresponding to the number of single targets in the historical rescue record. The total number of rescue targets in the emergency rescue analysis area is combined with the rescue implementation quantity corresponding to a single rescue target, and then the total number of actual rescue targets is subtracted to determine.

[0101] In the present invention, in actual situations, considering that the disaster area with a larger disaster rescue difficulty characterization parameter has a more complex rescue situation and a harsh rescue environment, at this time, increasing the acquisition frequency of environmental information is convenient for better monitoring the environment of the rescue area, providing a data basis for timely avoiding danger and ensuring the safety of personnel without rescue targets. In addition, in view of the above situation, flexibly adjusting the arrangement of rescue personnel and the distribution of rescue materials is convenient for better improving the rescue efficiency and ensuring the rescue quality.

[0102] Please refer to Figure 4 As shown, it is a logic diagram for determining whether to transfer rescue targets in an embodiment of the present invention. The secondary disaster occurrence tendency parameter is determined according to the soil stability coefficient and pore water pressure in the soil data. The secondary disaster occurrence tendency parameter is determined according to formula (2).

[0103] ,

[0104] In formula (2), D is the parameter indicating the tendency of secondary disasters, p1 is the pore water pressure, p0 is the standard pore water pressure, γ is the soil stability coefficient, and γ takes the value of 1 or 2.

[0105] In implementation, the standard pore water pressure is determined as 0.9 times the maximum pore water pressure without landslides in historical records;

[0106] If the ground penetrating radar detects more than three consecutive cracks and the cracks are expanding or there are more than three large cavities, then it is determined that γ takes the value of 2. If the ground penetrating radar does not detect more than three consecutive cracks or more than three large cavities, then it is determined that γ takes the value of 1; the standard volume of the large cavity is determined according to the volume of the largest cavity existing in the area without collapses in historical rescue data;

[0107] It can be understood that high pore water pressure indicates soil saturation, indicating high soil fluidity and a greater landslide risk; if there are multiple large cracks or large cavities, it indicates a risk of collapse.

[0108] Specifically, determining whether to transfer the rescue target according to the parameter indicating the tendency of secondary disasters in the current disaster area includes,

[0109] If the parameter indicating the tendency of secondary disasters is greater than or equal to the standard parameter indicating the tendency of secondary disasters, then determine to transfer the rescue target within the current disaster area;

[0110] If the parameter indicating the tendency of secondary disasters is less than the standard parameter indicating the tendency of secondary disasters, then determine not to transfer the rescue target within the current disaster area.

[0111] In the present invention, for a disaster area with a relatively low parameter characterizing the difficulty of disaster rescue, choose to maintain the current rescue state and continue the rescue. At this time, the environmental impact is small, and ensuring the current rescue status can achieve efficient rescue. At this time, it should be noted to monitor whether the soil in the area where the rescue target is placed meets the requirements and whether there is a risk of collapse, reduce the detection and analysis of other data, and ensure that sufficient safety protection can be provided in a situation where the network environment is not very good.

[0112] When the regional disaster collaborative rescue method of the present invention adapted to the large emergency system is implemented in the form of software functional units and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0113] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0114] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A regional disaster collaborative rescue method adapted to a large emergency system, characterized in that: include: Periodically obtain environmental information of the disaster area, and obtain network information and rescue target information of the disaster area in real time. Environmental information includes meteorological data, water level data, and soil data. Network information includes packet loss rate and delay. Rescue target information includes rescue target location and moving speed. Analyze water level data and network information obtained over several periods to determine the parameters characterizing the difficulty of disaster relief in the disaster area; Determining the type of rescue interference in the disaster area according to a parameter characterizing the difficulty of disaster rescue in the disaster area; Determining a rescue method for the disaster area according to the type of rescue interference in the disaster area, including shortening the acquisition cycle of environmental information and reducing the amount of information transmitted, dividing the disaster area into a number of analysis areas, and determining the amount of material distribution and the amount of rescue implementation according to the distribution of each rescue target in the analysis area; Or, maintain the current distribution of relief materials and the amount of rescue implemented, determine the secondary disaster tendency parameters in the current disaster area based on soil data, and determine whether to shift the rescue target; The disaster rescue difficulty characterization parameter of the disaster area is determined based on the water level information of several periods and the network information in the corresponding time. The disaster rescue difficulty characterization parameter is determined according to formula (1): ， In formula (1), K is the parameter representing the difficulty of disaster rescue, α is the influence coefficient of water level change, n is the total number of acquisition cycles, ti is the delay in the i-th acquisition cycle, t0 is the average delay, βn is the packet loss rate in the n-th cycle, i is an integer greater than 0, and α is 1 or 2; The secondary disaster occurrence tendency parameters are determined based on the soil stability coefficient and pore water pressure in the soil data. The secondary disaster occurrence tendency parameters are determined according to formula (2): ， In formula (2), D is the secondary disaster occurrence tendency parameter, p1 is the pore water pressure, p0 is the standard pore water pressure, γ is the soil stability coefficient, and γ takes the value of 1 or 2.

2. The regional disaster collaborative rescue method adapted to the large emergency system according to claim 1 is characterized in that: The meteorological data, the water level data and the soil data are acquired at the same period; The meteorological data include wind speed and precipitation.

3. The regional disaster collaborative rescue method adapted to the large emergency system according to claim 2 is characterized in that: Determining the rescue interference type of the disaster area according to the disaster rescue difficulty characterization parameter of the disaster area includes: If the disaster rescue difficulty characterization parameter is greater than or equal to the standard difficulty characterization parameter, the rescue interference type in the disaster area is a strong rescue interference type; If the disaster rescue difficulty characterization parameter is less than the standard difficulty characterization parameter, the rescue interference type in the disaster area is a weak rescue interference type.

4. The regional disaster collaborative rescue method adapted to a large emergency system according to claim 3 is characterized in that: Selecting a corresponding rescue method according to the rescue interference type of the disaster area, including, if the rescue interference type of the disaster area is a strong rescue interference type, the rescue method is to shorten the environmental information acquisition cycle and reduce the amount of information transmitted, divide the disaster area into several analysis areas, and determine the material distribution amount and the rescue implementation amount according to the distribution of each rescue target in the analysis area; If the rescue interference type in the disaster area is a weak rescue interference type, the rescue method is to maintain the current rescue material allocation and rescue implementation volume, and determine the secondary disaster occurrence tendency parameters in the current disaster area based on soil data to determine whether to transfer the rescue target.

5. The regional disaster collaborative rescue method adapted to a large emergency system according to claim 4 is characterized in that: The shortening of the environmental information acquisition cycle includes calculating the difference between the disaster rescue difficulty characterization parameter of the current disaster area and the standard difficulty characterization parameter, which is recorded as the difficulty difference; Determining an adjustment amount of the acquisition period according to the difficulty difference; The adjustment amount of the acquisition period is positively correlated with the difficulty difference.

6. The regional disaster collaborative rescue method adapted to a large emergency system according to claim 5 is characterized in that: Dividing the disaster area into a number of analysis areas includes dividing the disaster area into a number of monitoring areas; Identify the number of rescue targets in each monitoring area, and determine the type of monitoring area based on the number of rescue targets. The types of monitoring areas include temporary stability monitoring areas and emergency rescue monitoring areas. The monitoring areas corresponding to the temporary stability area type are merged and recorded as temporary stability analysis areas, and the emergency rescue monitoring areas are recorded as emergency rescue analysis areas; The types of analysis areas include temporary stability analysis areas and emergency rescue analysis areas.

7. The regional disaster collaborative rescue method adapted to a large emergency system according to claim 6 is characterized in that: Confirm the amount of materials to be distributed and the amount of rescue to be implemented based on the distribution of each rescue target in the analysis area. Including, if the type of analysis area is a temporary stabilization analysis area, determining the amount of material distribution based on the total number of rescue targets; If the type of the analysis area is an emergency rescue analysis area, the increase in the rescue implementation amount is determined based on the rescue target information.

8. The regional disaster collaborative rescue method adapted to a large emergency system according to claim 7 is characterized in that: Determining whether to transfer the rescue target according to the secondary disaster occurrence tendency parameter of the current disaster area, including: if the secondary disaster occurrence tendency parameter is greater than or equal to the standard secondary disaster occurrence tendency parameter, determining to transfer the rescue target in the current disaster area; If the secondary disaster occurrence tendency parameter is less than the standard secondary disaster occurrence tendency parameter, it is determined not to transfer the rescue target in the current disaster area.

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