Emergency data aggregation method, system and device and storage medium

Abstract

The invention belongs to the field of data transmission, and particularly relates to an emergency data convergence method, system and device and a storage medium, and the method comprises the steps: dividing emergency convergence data into a plurality of parts, storing the parts in a plurality of mobile terminals, carrying out the data transmission through the plurality of mobile terminals through employing a distributed decision, and obtaining a plurality of transmission decisions of each terminal at a t moment, the method comprises the following steps: acquiring a plurality of transmission decisions, trimming the plurality of transmission decisions to obtain screening transmission decisions, calculating transmission energy consumption and transmission utility of each screening transmission decision, combining constraint conditions to obtain a Lyapunov drift penalty, and finding the transmission decision of which the Lyapunov drift penalty meets the constraint conditions as a final transmission decision. Therefore, the emergency convergence data is transmitted. When data transmission is carried out in a severe environment, the transmission effectiveness of the system is improved under the condition of limited energy.

Description

Technical Field

[0001] The present invention belongs to the field of data transmission, and in particular relates to an emergency data aggregation method, system, equipment and storage medium. Background Art

[0002] With the continuous development of mobile computing related technologies, the functions and performance of mobile smart devices are constantly improving, and they are being widely used in harsh environments such as emergency management. However, due to the suddenness and uncertainty of emergency events, as well as the harsh environment with limited resources, dynamic networks, and hostile environments, traditional single data terminals often cannot meet the needs of fast and comprehensive information acquisition on site. Therefore, the concept of opportunistic edge computing is proposed, which builds adjacent peer nodes into collaborative groups to share computing resources, storage resources, and communication resources to cope with the impact of harsh environments.

[0003] In the related art, when a collaborative group sends data, it sends it in order according to the received data, and different terminals each transmit part of the data and transmit all the received data.

[0004] Regarding the above-mentioned related technologies, when transmitting data in harsh environments, due to the limited terminals and unstable networks, and the different terminals requiring different energy consumption when transmitting different data, sequential transmission cannot maximize the use of the energy of each terminal to transmit as much data as possible, and cannot prioritize the transmission of important data. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an emergency data aggregation method, system, device and storage medium, which introduces user data preference weights under energy and network constraints, thereby improving the overall data transmission efficiency while giving priority to important user needs.

[0006] An emergency data aggregation method, comprising:

[0007] The number of emergency data aggregated at a single time is obtained, each emergency aggregated data is divided into a plurality of data segments of the same size, the data segments are redundantly copied into a plurality of copies, and the copies are stored in a plurality of terminals to obtain a data segment stored in each terminal;

[0008] Establishing a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtaining a decision function according to the joint decision factor;

[0009] Get the monotonic threshold set;

[0010] According to the decision function and the monotonic threshold set, a plurality of transmission decisions are obtained, wherein the transmission decision is whether each terminal transmits a data segment at time t;

[0011] Setting constraints for all terminals in the aggregation process, wherein the constraints include maximizing transmission utility and minimizing transmission energy consumption;

[0012] Based on the transmission decision, the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD are obtained;

[0013] According to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraints, the Lyapunov drift minus penalty of all terminals is obtained;

[0014] Traversing the monotonic threshold set, selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision;

[0015] The emergency aggregate data is aggregated according to the final transmission decision.

[0016] Among them, emergency data includes personnel location data and available resource data when organizing rescue operations for earthquake and mudslides in deep mountains.

[0017] Optionally, the emergency aggregated data includes personnel location data and available resource data.

[0018] Optionally, calculate the transfer utility including:

[0019] Obtain the preference coefficient of each data segment transmitted at time t and the amount of valid data segments;

[0020] The transmission utility is calculated according to the preference coefficient and the effective data segment amount.

[0021] Optionally, the feature is,

[0022] The decision function is:

[0023]

[0024] Where Y is the channel state-data preference balance parameter of the cooperative group, β r (t) is the preference coefficient of data r at time t, s rij (t) indicates that the jth data segment of the rth data at time t is stored on terminal i, R is the number of data copies that need to be transmitted during one aggregation process, c i (t) is the channel state of mobile terminal i at time t, s(t) is the storage status of R data in the cooperative group at time t, and β(t) is the data preference coefficient;

[0025] The decision function is:

[0026]

[0027] in, is the decision value, is the monotonic threshold.

[0028] Optionally, according to the decision function and the monotonic threshold set, a number of transmission decisions are obtained, including:

[0029] According to the monotonic threshold set, a threshold to be determined is obtained;

[0030] According to the channel state of the current terminal at time t, the data demand preference of the data segment and the decision function, a decision value is obtained;

[0031] Determining whether the decision value is greater than the threshold to be determined;

[0032] If the decision value is greater than or equal to the threshold to be determined, the current terminal transmits data at time t as the transmission decision;

[0033] If the decision value is less than the threshold to be determined, the current terminal does not transmit data at time t as the transmission decision;

[0034] The monotonic threshold set is traversed to obtain several transmission decisions of the current terminal under different monotonic thresholds.

[0035] Optionally, obtaining the Lyapunov drift minus penalty of all terminals according to the transmission utility, transmission energy consumption when the current terminal transmits the data segment at time t and the transmission utility, transmission energy consumption and constraints of other terminals before time tD includes:

[0036] Obtain the device channel status and decision feedback of other terminals at time tD;

[0037] According to the device channel status and decision feedback at time tD, the energy consumption before time tD is obtained;

[0038] Establishing a virtual queue according to the transmission energy consumption, the energy consumption before time tD, and the initial queue value, and obtaining a Lyapunov function according to the virtual queue;

[0039] According to the virtual queue, the Lyapunov function and the constraint conditions, the Lyapunov drift minus penalty is obtained.

[0040] Optionally, traversing the monotonic threshold set and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision includes:

[0041] Traversing the monotonic threshold set, obtaining the Lyapunov drift minus penalty for different transmission decisions, and calculating the upper bound of the Lyapunov drift minus penalty as an optimization objective function;

[0042] The transmission decision corresponding to the minimum optimization objective function is obtained as the final transmission decision.

[0043] An emergency data aggregation system, comprising:

[0044] An acquisition module, used for acquiring the number of emergency data aggregated at a single time, dividing each emergency aggregated data into a plurality of data segments of the same size, redundantly copying the data segment into a plurality of copies, and storing the data segments in a plurality of mobile terminals, to obtain the data segments stored in each terminal;

[0045] A decision module, used to establish a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtain a decision function according to the joint decision factor;

[0046] An acquisition module, used to acquire a monotonic threshold set;

[0047] A setting module, used to set the constraints of all terminals in the aggregation process, wherein the constraints include maximizing transmission utility and minimizing transmission energy consumption;

[0048] A calculation module, used to obtain the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD based on the transmission decision;

[0049] A constraint module is used to obtain the Lyapunov drift minus penalty of all terminals according to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraint conditions;

[0050] A traversal module, used for traversing the monotonic threshold set, and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision;

[0051] The aggregation module is used to aggregate the emergency aggregation data according to the final transmission decision.

[0052] A terminal device comprises a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, an emergency data aggregation method is adopted.

[0053] A computer-readable storage medium stores a computer program. When the computer program is loaded and executed by a processor, an emergency data aggregation method is adopted.

[0054] The beneficial effects of the present invention are:

[0055] 1. By dividing the emergency converged data into multiple parts and storing them in multiple mobile terminals respectively, the data transmission adopts distributed decision-making through multiple mobile terminals to obtain several transmission decisions of each terminal at time t, and prunes several transmission decisions to obtain screening transmission decisions, calculate the transmission energy consumption and transmission utility of each screening transmission decision, combine the constraints, obtain the Lyapunov drift minus penalty, find the Lyapunov drift minus penalty transmission decision that meets the constraints as the final transmission decision, and transmit the emergency converged data. When data transmission is carried out in harsh environments, the transmission utility of the system is improved under limited energy conditions.

[0056] 2. In order to prevent the terminal from consuming too much energy to transmit all the data, a preference coefficient for the data segment is set. The transmission decision is made based on the preference coefficient and the channel status, so that more important data can be transmitted first during transmission to avoid the inability to transmit important data due to insufficient energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a flow chart of an emergency data aggregation method of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0059] An emergency data aggregation method, such as Figure 1 As shown, including:

[0060] S100, obtaining the number of emergency data aggregated at a single time, dividing each emergency aggregated data into a plurality of data segments of the same size, redundantly copying the data segment into multiple copies, and storing them in multiple terminals to obtain the data segment stored in each terminal.

[0061] Specifically, the collaborative group consists of N The terminal consists of T terminals, and a total of T data aggregation processes are performed. Each aggregation requires the transmission of R data. Use the vector β(t) = (β1(t),...,β r (t).,..,β R (t)) represents the data preference coefficient of data transmission at time t. In order to enhance data storage capacity and improve its reliability, each data is divided into P data segments of the same size and redundantly replicated into multiple copies for distributed storage among multiple mobile terminals. ri (t)=(s ri1 (t),...,s riP (t)) represents the data segment storage status of the rth data on terminal i at time t, srip (t) = 1 means that a copy of the pth data segment of data r at time t is stored in mobile terminal i, otherwise s rip (t) = 0. Use vector s r (t)=(s r1 (t),...,s rN (t)) represents the terminal storage status of data r at time t, s(t)=(s1(t),...,s R (t)) represents the total storage status of all data at time t.

[0062] Emergency aggregation data includes personnel location data and available resource data. For example, when rescuing people from earthquakes and mudslides in the mountains, a group of drone terminals captures information about trapped people. Due to poor signals in the mountains, the network environment is particularly complex and dynamic. At the same time, it is affected by the power of the drone, so there is no way to upload all data to the edge server in real time. Emergency aggregation data includes the location data of trapped people and some of their property data obtained by the drone. Available resource data includes data such as the walkable routes for rescuing people in the mountains, dangerous areas to avoid, and important supplies for trapped people.

[0063] Because each terminal has limited energy and network constraints in the deep mountains, it is necessary to transmit as much important data as possible with limited resources.

[0064] S110 , establishing a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtaining a decision function according to the joint decision factor.

[0065] Specifically, the distributed decision-making is that each terminal decides whether to transmit a data segment at time t.

[0066] Specifically, since the channel state and data demand preference need to be considered simultaneously during the data aggregation process, the joint decision factor g(t)=(g1(t),...,g N (t)), where g i (t) is:

[0067]

[0068] Where Y is the channel state-data preference balance parameter of the cooperative group, β r (t) is the preference coefficient of data r at time t, s rij (t) indicates that the jth data segment of the rth data at time t is stored on terminal i, R is the number of data copies that need to be transmitted during one aggregation process, c i (t) is t Moment mobile terminal iThe channel state s(t) is t time R The storage situation of individual data in the collaborative group, β(t) is the data preference coefficient.

[0069] Where Y ≥ 0, represents the trade-off parameter between the channel state and the maximum preference coefficient. Definition is the decision function of mobile terminal i. Whether terminal i transmits will be completely determined by the joint decision factor. The decision of N terminals is represented by vector In order to maximize the effective data transmission volume of the most urgent data, the maximum preference data decision item stored in the terminal is introduced into the joint decision factor. This enables the mobile terminal to simultaneously i (t)) and data preference to make decisions. When Y = 0, the mobile terminal completely relies on the communication channel status to make decisions. As the value of Y increases, the terminal will make transmission decisions more based on the maximum preference of the stored data.

[0070] Specifically, since each mobile terminal has d i (t)∈{0,1} two decision choices. And each decision factor g i It will be affected by a total of γ channel states and R different preference data. The possible values ​​of the decision factor are |g i |=Rγ. Therefore, the possible The total number of decisions is For m∈{1,...,M}, it represents a distributed strategy specified among M possible strategies.

[0071] S120: Obtain a monotonic threshold set.

[0072] Specifically, since the amount of data and the number of channel states are discrete, the number of monotonic thresholds is also limited, and all monotonic thresholds constitute a monotonic threshold set.

[0073] S130. Obtain several transmission decisions according to the decision function and the monotonic threshold set, wherein the transmission decision is whether each terminal should transmit a data segment at time t.

[0074] According to the decision function and the monotonic threshold set, several transmission decisions are obtained, including:

[0075] S1300. Obtain a threshold to be determined according to the monotonic threshold set.

[0076] S1310. Obtain a decision value according to the channel state of the current terminal at time t, the data demand preference of the data segment, and the decision function.

[0077] S1320: Determine whether the decision value is greater than the threshold to be determined.

[0078] S1330: If the decision value is greater than or equal to the threshold to be determined, the current terminal transmits data at time t as the transmission decision.

[0079] S1340: If the decision value is less than the threshold to be determined, the current terminal does not transmit data at time t as the transmission decision.

[0080] S1350 , traverse the monotonic threshold set to obtain several transmission decisions of the current terminal under different monotonic thresholds.

[0081] Specifically, for a terminal, if the monotonic threshold is determined, then for the terminal, the transmission decision is determined, and data is transmitted when the value is greater than the monotonic threshold, and data is not transmitted when the value is less than the monotonic threshold.

[0082] The threshold to be determined is one of the monotonic thresholds in the monotonic threshold set. When traversing, the monotonic thresholds in the monotonic threshold set can be sorted from small to large, and then traversed one by one to obtain several transmission decisions of the current terminal under different monotonic thresholds.

[0083] The current terminal is any terminal among all terminals.

[0084] For the optimization problem of maximizing system utility under given energy consumption constraints, for all i∈{1,...,N}, the optimal strategy is Not dependent on variable g i (t) decreases and decreases, so there is the following formula:

[0085]

[0086] in, is the decision value, is the monotonic threshold.

[0087] When the function g i (t) higher than When the time threshold is reached, the transmission is selected, otherwise it will not participate in the transmission. At this time, the decision pruning is polynomial in complexity. The computational complexity is reduced, all transmission decisions with decision values ​​less than the monotonic threshold are excluded, and transmission decisions with decision values ​​greater than the monotonic threshold are retained as screening transmission decisions.

[0088] Since the amount of data and the number of channel states are discrete, the value of the joint decision factor for the pruned strategy is discrete and finite. The problem changes from the selection of the optimal decision strategy to the selection of the monotonic threshold, that is, finding the optimal monotonic threshold to maximize the utility while satisfying the energy consumption constraint.

[0089] S140: Setting constraints for all terminals in the aggregation process, where the constraints include maximizing transmission utility and minimizing transmission energy consumption.

[0090] Specifically, the calculation of transmission utility includes:

[0091] S1400. Obtain the preference coefficient of each data segment transmitted at time t and the amount of valid data segments according to the screening transmission decision.

[0092] S1411. Calculate the transmission utility based on the preference coefficient and the amount of valid data segments.

[0093] Specifically, the preference coefficient of a data segment is . During data transmission, due to harsh environments and terminal energy constraints, not all data may be transmitted. Therefore, important data is optimized for transmission. The size of the preference coefficient of a data segment represents the priority of the importance of the data segment. The limited amount of data segments is due to the use of redundant data storage. When multiple terminals transmit the same copy, there is only one data segment that is effectively transmitted. Therefore, the amount of valid data segments of data r collected in the data aggregation process t is calculated as follows:

[0094]

[0095] Among them, u r (t) is the effective data segment of the rth data transmitted at time t, d(t) is the transmission decision, s r (t) is the storage status of the rth data in the collaborative group at time t, and P is the maximum number of data segments that each data can be divided into.

[0096] Taking into account the different preferences of each data, the total joint utility of the system is modeled as the weighted sum of the effective data segment utilities of each data according to their preference coefficients, which is defined as follows:

[0097]

[0098] βr(t) for t The preference coefficient of the moment data r, β max is the largest preference coefficient among all the data.

[0099] During the aggregation process, the mobile terminal transmits all the data segments of the required data it stores, so the energy consumption penalty can be determined by the channel state. i (t) is the energy consumption penalty of terminal i in the aggregation process t, and the transmission energy consumption is calculated as follows:

[0100]

[0101] Among them, d i (t) is the transmission decision of terminal i at time t, ci (t) is the channel status of terminal i.

[0102] S150. Based on the transmission decision, obtain the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD.

[0103] Specifically, since distributed decision-making is adopted, that is, each terminal decides whether to transmit data or not, the terminals cannot obtain the data information to be transmitted at time t. Therefore, a delay time D is set, and the energy consumption and utility of the data transmitted by other terminals before time tD are used as the energy consumption and transmission utility at the current moment, so as to estimate the energy consumption and transmission utility of all terminals.

[0104] S160. Obtain the Lyapunov drift minus penalty of all terminals according to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and constraints.

[0105] S1600: Obtain device channel status and decision feedback of other terminals at time tD.

[0106] S1610: Obtain energy consumption before time tD according to the device channel state and decision feedback at time tD.

[0107] S1620 establishes a virtual queue according to the transmission energy consumption, the energy consumption before time tD, and the initial queue value, and obtains the Lyapunov function according to the virtual queue.

[0108] S1630. Obtain a Lyapunov drift minus penalty according to the virtual queue, the Lyapunov function and the constraints.

[0109] Specifically, in each convergence process, the mobile terminal selects a strategy in m∈{1,...,M}, D≥0 represents the feedback delay of the system, and all mobile devices will receive the channel state c(t) and decision feedback d(t) of each device before time D at the end of the upload process.

[0110] The energy consumption constraint is converted into a stability problem of a virtual queue. For each mobile terminal, a virtual queue Q(t) = (Q1(t), ..., Q N (t)), where Q(0) = 0, and e i (-1)=...=e i (-D) = 0. Each mobile terminal will update Q(t) at the end of the tth upload process, and the update formula is:

[0111] Q i(t+1)=max{Q i (t)+e i (tD)-C,0}

[0112] Among them, Q i (t+1) is the sequence queue value at the next moment, Q i (t) is the virtual queue value at the current moment, e i (tD) is the transmission energy consumption before time D, and C is the average energy consumption of the terminal.

[0113] In a data aggregation process, the mobile terminal cannot obtain the channel status and decision of other terminals. Therefore, an approximate method is used. Each terminal will know the channel status c(tD) and decision d(tD) before the delay time D, and use a window of size W, a positive integer, to sample the energy consumption and utility in the previous process and approximately estimate the current energy consumption and utility:

[0114]

[0115] If the virtual queue is stable (i.e. ), then the energy cost constraint is satisfied. The Lyapunov function is defined as follows:

[0116]

[0117] Specifically, the constraints are to minimize transmission energy consumption and maximize transmission utility, which can be expressed by the following formula:

[0118]

[0119] C is the average energy consumption constraint of the mobile terminal.

[0120] The Lyapunov function represents a scalar measure of the queue Q(t). If the value of L(t) is small, the virtual queue of the mobile device has strong stability, that is, the energy cost constraint is satisfied. In order to keep the virtual queue stable when the delay is D, the Lyapunov drift is defined:

[0121] Δ(t+D)=L(t+D+1)-L(t+D)

[0122] Among them, L(t+D+1) represents the value of the Lyapunov function at time t+D+1, L(t+D) represents the value of the Lyapunov function at time t+D, Δ(t+D) represents the change of the system in time period D, and the smaller the value of Δ(t+D), the more stable the system is.

[0123] The purpose of this drift is to avoid the congestion of the Lyapunov function. Minimizing the above Lyapunov drift can maintain the stability of the queue. Given that the system has a feedback delay at time D, the stability of the queue at the current moment can only be obtained after time D, so the Lyapunov drift at time D is used.

[0124] In order to maximize utility while minimizing energy consumption, the optimization objective is transformed into minimizing the upper bound of the Lyapunov drift minus penalty expression E{Δ(t+D)-Vu(t)|Q(t)}. In order to control the trade-off between queue stability (i.e., energy consumption) and utility, the parameter V is introduced. As the value of V increases, the cooperative group will gradually change from being more inclined to maintain low energy consumption to being more inclined to transmit greater effective system utility.

[0125] Therefore, the following formula is obtained:

[0126] For a given V>0, each upload process has:

[0127]

[0128] The left side of the inequality represents the Lyapunov drift minus penalty, and the right side of the inequality is the upper bound of the Lyapunov drift minus penalty. Substitute all the screening transmission decisions at time t into the above formula, and select the smallest upper bound of the Lyapunov drift minus penalty. The transmission decision corresponding to the smallest upper bound of the Lyapunov drift minus penalty is taken as the final transmission decision. At this time, the transmission energy consumption is the smallest and the transmission utility is the largest.

[0129] S170, traversing the monotonic threshold set, and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision.

[0130] S1700, traversing the monotonic threshold set to obtain the Lyapunov drift minus penalty for different transmission decisions, and calculating the upper bound of the Lyapunov drift minus penalty as the optimization objective function.

[0131] S1710. Obtain a transmission decision corresponding to the minimum optimization objective function as a final transmission decision.

[0132] Specifically, when the upper bound of the Lyapunov drift minus penalty is the smallest, that is, when the energy consumption is the smallest and the transmission utility is the largest, the transmission decision corresponding to the smallest optimization objective function is obtained as the final transmission decision.

[0133] S180. Aggregate the emergency data according to the final transmission decision.

[0134] An emergency data aggregation system, comprising:

[0135] An acquisition module, used for acquiring the number of emergency data aggregated at a single time, dividing each emergency aggregated data into a plurality of data segments of the same size, redundantly copying the data segment into a plurality of copies, and storing the data segments in a plurality of mobile terminals, to obtain the data segments stored in each terminal;

[0136] A decision module, used to establish a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtain a decision function according to the joint decision factor;

[0137] An acquisition module, used to acquire a monotonic threshold set;

[0138] A setting module, used to set the constraints of all terminals in the aggregation process, wherein the constraints include maximizing transmission utility and minimizing transmission energy consumption;

[0139] A calculation module, used to obtain the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD based on the transmission decision;

[0140] A constraint module is used to obtain the Lyapunov drift minus penalty of all terminals according to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraint conditions;

[0141] A traversal module, used for traversing the monotonic threshold set, and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision;

[0142] The aggregation module is used to aggregate the emergency aggregation data according to the final transmission decision.

[0143] An embodiment of the present application also discloses a terminal device, including a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, an emergency data aggregation method is adopted.

[0144] The terminal device may be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device may also include input and output devices, a network access device and a bus.

[0145] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0146] Among them, the memory can be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device, or it can be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the terminal device, etc., and the memory can also be a combination of an internal storage unit and an external storage device of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0147] Among them, through this terminal device, an emergency data aggregation method in the above embodiment is stored in the memory of the terminal device, and is loaded and executed on the processor of the terminal device for easy use.

[0148] An embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, an emergency data aggregation method in the above embodiment is adopted.

[0149] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0150] Among them, through this computer-readable storage medium, an emergency data aggregation method in the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0151] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as above, which are not provided in detail for the sake of simplicity.

[0152] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0153] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the protection scope of the present application.

[0154] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for converging emergency data, characterized in that include: The number of emergency data aggregated at a single time is obtained, each emergency aggregated data is divided into a plurality of data segments of the same size, the data segments are redundantly copied into a plurality of copies, and the copies are stored in a plurality of terminals to obtain a data segment stored in each terminal; Establishing a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtaining a decision function according to the joint decision factor; Get the monotonic threshold set; According to the decision function and the monotonic threshold set, a plurality of transmission decisions are obtained, wherein the transmission decision is whether each terminal transmits a data segment at time t; Setting constraints for all terminals in the aggregation process, wherein the constraints include maximizing transmission utility and minimizing transmission energy consumption; Based on the transmission decision, the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD are obtained; According to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraints, the Lyapunov drift minus penalty of all terminals is obtained; Traversing the monotonic threshold set, selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision; The emergency aggregate data is aggregated according to the final transmission decision.

2. The emergency data aggregation method according to claim 1, characterized in that: The emergency aggregate data includes personnel location data and available resource data.

3. The emergency data aggregation method according to claim 1, characterized in that: Calculating the transfer utility includes: Obtain the preference coefficient of each data segment transmitted at time t and the amount of valid data segments; The transmission utility is calculated according to the preference coefficient and the effective data segment amount.

4. The emergency data aggregation method according to claim 1, characterized in that: The joint decision factor is expressed as: Where Y is the channel state-data preference balance parameter of the cooperative group, β r (t) is the preference coefficient of data r at time t, s rij (t) indicates that the jth data segment of the rth data at time t is stored on terminal i, R is the number of data copies that need to be transmitted during one aggregation process, c i (t) is the channel state of mobile terminal i at time t, s (t) is the storage status of R data in the collaborative group at time t, β(t) is the data preference coefficient; The decision function is: in, is the decision value, is the monotonic threshold.

5. The emergency data aggregation method according to claim 1, characterized in that: According to the decision function and the monotonic threshold set, several transmission decisions are obtained, including: According to the monotonic threshold set, a threshold to be determined is obtained; According to the channel state of the current terminal at time t, the data demand preference of the data segment and the decision function, a decision value is obtained; Determining whether the decision value is greater than the threshold to be determined; If the decision value is greater than or equal to the threshold to be determined, the current terminal transmits data at time t as the transmission decision; If the decision value is less than the threshold to be determined, the current terminal does not transmit data at time t as the transmission decision; The monotonic threshold set is traversed to obtain several transmission decisions of the current terminal under different monotonic thresholds.

6. The emergency data aggregation method according to claim 1, characterized in that: The Lyapunov drift minus penalty of all terminals is obtained according to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraints, including: Obtain the device channel status and decision feedback of other terminals at time tD; According to the device channel status and decision feedback at time tD, the energy consumption before time tD is obtained; Establishing a virtual queue according to the transmission energy consumption, the energy consumption before time tD, and the initial queue value, and obtaining a Lyapunov function according to the virtual queue; According to the virtual queue, the Lyapunov function and the constraint conditions, the Lyapunov drift minus penalty is obtained.

7. The emergency data aggregation method according to claim 1, characterized in that: The traversing the monotonic threshold set and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision comprises: Traversing the monotonic threshold set, obtaining the Lyapunov drift minus penalty for different transmission decisions, and calculating the upper bound of the Lyapunov drift minus penalty as an optimization objective function; The transmission decision corresponding to the minimum optimization objective function is obtained as the final transmission decision.

8. An emergency data aggregation system, characterized in that include: An acquisition module, used for acquiring the number of emergency data aggregated at a single time, dividing each emergency aggregated data into a plurality of data segments of the same size, redundantly copying the data segment into a plurality of copies, and storing the data segments in a plurality of mobile terminals, to obtain the data segments stored in each terminal; A decision module, used to establish a joint decision factor according to the channel state of each terminal at time t and the data demand preference of each data segment, and obtain a decision function according to the joint decision factor; An acquisition module, used to acquire a monotonic threshold set; A setting module, used to set the constraints of all terminals in the aggregation process, wherein the constraints include maximizing transmission utility and minimizing transmission energy consumption; A calculation module, used to obtain the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD based on the transmission decision; A constraint module is used to obtain the Lyapunov drift minus penalty of all terminals according to the transmission utility and transmission energy consumption of the current terminal when transmitting the data segment at time t and the transmission utility and transmission energy consumption of other terminals before time tD and the constraint conditions; A traversal module, used for traversing the monotonic threshold set, and selecting the transmission decision whose Lyapunov drift minus penalty satisfies the constraint condition as the final transmission decision; The aggregation module is used to aggregate the emergency aggregation data according to the final transmission decision.

9. A terminal device, comprising a memory and a processor, characterized in that: The memory stores a computer program that can be run on the processor, and when the processor loads and executes the computer program, the convergence method according to any one of claims 1 to 7 is adopted.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is loaded and executed by a processor, the convergence method according to any one of claims 1 to 7 is adopted.

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