Power distribution network disaster damage situation awareness method and system based on emergency communication under rainfall flood

Abstract

The invention discloses a power distribution network disaster damage situation sensing method and system based on emergency communication under rainfall flood. The method comprises the following steps: establishing a power distribution network disaster damage situation sensing model under urban rainstorm waterlogging; constructing a communication working point site selection model, and determining possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle; based on possible stop points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, considering path constraint and time constraint, determining a scheduling scheme and a transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle, and constructing a vehicle-aircraft cooperative communication recovery model; and based on the constructed power distribution network disaster damage situation perception model under the urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, constructing a power distribution network disaster damage situation perception improvement model under the urban rainstorm waterlogging, and solving the power distribution network disaster damage situation perception improvement model under the urban rainstorm waterlogging to obtain a situation perception improvement strategy. The disaster damage situation sensing capability of the power distribution network is improved, and rapid recovery of the power distribution network after the disaster is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster prevention and mitigation of distribution networks, and specifically relates to a method and system for sensing the damage situation of distribution networks based on emergency communications during rain and floods. Background Art

[0002] In recent years, extreme disasters have become increasingly frequent, seriously affecting the safe operation of power systems. The incidence of rainstorms is particularly significant, and the floods and waterlogging caused by them are one of the main disasters in summer. Waterlogging in local areas caused by long-term, high-intensity heavy rainfall seriously affects the normal operation of distribution network equipment, and rainstorm disasters often cause damage to communication equipment, resulting in false alarms and missed alarms in communication equipment. Considering the impact of rainstorm disasters on communication equipment, establishing a temporary emergency communication recovery strategy is of great significance to the post-disaster recovery of distribution networks.

[0003] At present, some scholars have studied the impact mechanism of extreme weather on distribution network, the method of locating faults of system measurement data, and the location of fault complaint telephones, and there are many solutions to restore communication through drones and emergency communication vehicles. However, the existing methods of judging faults are all based on the situation of complete communication. However, in the case of urban rainstorms and waterlogging disasters, the original communication facilities will also be destroyed, and these methods of locating the fault area based on fault indicators may fail. Power companies often confirm the fault situation of distribution networks through telephone complaints from users. In urban rainstorms and waterlogging, extreme rainstorm disasters can damage communication equipment, greatly affecting the availability and accuracy of user fault complaint channels. At the same time, in order to solve the communication problems of personnel in the disaster-stricken areas and the allocation of materials, the deployment strategy of drones and emergency communication vehicles can also play an important role in the restoration of communication of distribution network fault indication components.

[0004] Therefore, to address this problem, it is urgent to find a method for distribution network disaster damage situation awareness based on emergency communication under urban rain and flood conditions. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for disaster damage situation perception of distribution network based on emergency communication under rain and flood, in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problems of communication problems of personnel in disaster-stricken areas and difficulties in material allocation under urban rainstorm and waterlogging conditions.

[0006] The present invention adopts the following technical solutions:

[0007] A method for sensing the damage situation of a distribution network based on emergency communication during rain and flood, comprising the following steps:

[0008] The system measurement data, user-side feedback information and meteorological data are used as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders. According to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model and taking the maximization of Bayesian posterior probability as the criterion, a distribution network disaster loss situation awareness model under urban rainstorm waterlogging is established.

[0009] Build a communication work point site selection model to determine the possible stop points for mobile drone stations and emergency communication vehicles; based on the possible stop points for mobile drone stations and emergency communication vehicles, consider path constraints and time constraints, determine the scheduling plan and transfer path for mobile drone stations and emergency communication vehicles, and build a vehicle-machine collaborative communication recovery model;

[0010] Based on the constructed distribution network disaster damage situation awareness model under urban rainstorm and waterlogging and the constructed vehicle-machine cooperative communication recovery model, a distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is constructed, and the distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is solved to obtain the situation awareness improvement strategy.

[0011] Preferably, the distribution network damage situation awareness model under urban rainstorm waterlogging is established as follows:

[0012] Considering the information integrity, a mapping relationship model between RFI fault alarm information and distribution network damage section is established, and the post-disaster distribution network damage situation is determined based on system measurement data;

[0013] A direct failure model of ring network high-voltage switchgear based on meteorological data was established. Based on the benchmark failure rate of ring network high-voltage switchgear, the humidity and temperature of the high-voltage switchgear cabinet were taken as covariates of the indirect failure model to improve the indirect failure model of high-voltage switchgear. The indirect failure probability of the high-voltage switchgear was determined at time t by improving the indirect failure model. The comprehensive failure probability P of the node under heavy rain and waterlogging was obtained according to the independent event probability formula. i (t);

[0014] Locate distribution network faults based on user-side feedback information;

[0015] The system measurement data, meteorological data and user-side feedback information are integrated to comprehensively analyze the post-disaster fault scenario of the distribution network and obtain a distribution network disaster damage situation awareness model under heavy rain and waterlogging.

[0016] Preferably, determining the post-disaster power distribution network damage situation based on system measurement data is specifically as follows:

[0017] According to the distribution network topology, a network description matrix [N] is established, and the element N kl Determined according to the following rules:

[0018] If feeder LS1 is the starting point of RFIk, N kl =1;

[0019] If feeder LS1 is the end point of RFIk, N kl = -1;

[0020] If the feeder LS1 is not the starting point or the end point of RFIk, N kl =0;

[0021] Fault discrimination vector [FS] = [N] [F] T , T-type structure fault discrimination vector [F T ]=[N T ]·[F] T ;

[0022] For the non-T-type section LSk, according to the value of the kth element in the fault discrimination vector [FS], if FS k ≥1, it is determined that there is a fault in segment k, otherwise it is determined that there is no fault in segment k;

[0023] For a T-shaped structure segment, if the corresponding element FS k ≥1, and the corresponding [F T ] element, it is determined that there is a fault in the T segment, otherwise there is no fault.

[0024] Preferably, the comprehensive failure probability of nodes under heavy rain and flooding is P i (t) Specifically:

[0025] P i (t) = 1-(1-P i F (t))(1-P i J (t))

[0026] Among them, P i F (t) is the direct failure probability of the high-voltage switchgear, P i J (t) is the indirect failure probability of the high-voltage switchgear.

[0027] Preferably, locating the distribution network fault based on the user-side feedback information is specifically as follows:

[0028]

[0029] in, is the node path from the power supply end to the node k; is the binary variable of the state of node i at time t, The node i works normally at time t; d kis the binary variable of the feedback result of user k, d k =1 means user k has made a fault complaint; Ω CF A collection of user feedback status;.

[0030] Preferably, the comprehensive failure probability P of the distribution network nodes under heavy rain and flooding is calculated. i (t), the maximum Bayesian posterior probability is established as the objective function to determine the most likely fault scenario under the rainstorm disaster at time t, and the logarithmic function is used to linearize the objective function. The distribution network disaster loss situation awareness model under urban rainstorm waterlogging is described as:

[0031]

[0032] in, For each node in the system, X i is the binary variable of the node i state, p i F is the comprehensive failure probability of node i.

[0033] Preferably, in step S2, the objective function of the vehicle-machine cooperative communication recovery model is as follows:

[0034]

[0035] Among them, F obj1 is the normalized communication recovery time; F obj2 is the normalized probability of damage to the distribution network; β 1 , β 2 are the weights of the two sub-objective functions, p k is a binary decision variable that characterizes whether the discrete point k is selected as the emergency communication working point, and n is the number of discrete points.

[0036] Preferably, the constraints of the vehicle-machine cooperative communication recovery model are specifically:

[0037] Path constraints:

[0038]

[0039]

[0040] Among them, C N represents the set of all work points; N represents all vehicle work points including the warehouse; N0 represents the set of vehicle starting points, N + represents the set of vehicle arrival points; P is composed of<i,j,k> It consists of three points, i represents the starting point of the drone, j represents the working point where the drone reaches and takes communication recovery work; x (i,j) is a set of 0-1 variables; y (i,j,k) is a set of 0-1 variables; ui is a continuous variable;

[0041] Time constraints:

[0042]

[0043]

[0044] t′ k -(t′ j -τ′ ij )≤e+M(1-y ijk )

[0045]

[0046] Among them, τ ij represents the time taken by the vehicle to travel from work point (or warehouse) i to work point j (or warehouse); τ′ ij represents the time taken by the drone to travel from work point (or warehouse) i to work point j (or warehouse); s L Indicates the operation time required for vehicle staff to launch the drone; s R It represents the time required for the vehicle staff to recover and replace the battery of the drone until the drone can complete the launch again; e represents the endurance of the drone; t j is a continuous variable, indicating the time when the vehicle arrives at work point (or warehouse) j; t' j The time when the UAV arrives at j is; t j =t' j =0, indicating the initial time when the vehicle and the drone leave the warehouse;

[0047] Access order constraints:

[0048]

[0049] Among them, p (i,j) is a set of 0-1 variables. When the i workstation is visited before j on the vehicle path, i∈Cj∈{C:j≠i}, p (i,j) =1;

[0050] Additional constraints:

[0051] t0=0

[0052] t'0=0

[0053]

[0054] The distribution network fault location constraints are as follows:

[0055] Continuous time corresponds to discrete timestamps:

[0056]

[0057] Among them, γ i,t is a binary variable indicating whether the working point i is repaired at time t, γ i,t =1 means that the communication of working point i is successfully repaired at time t; Δt is the time interval of the entire model; t is the discrete time parameter; v is the error value;

[0058] Distribution network fault section location constraints:

[0059]

[0060] [Fs t ]=[N][F t ] T =[z 1,t ,z 2,t ,z 3,t ,...,z n,t ] T

[0061]

[0062] in, Recovering an indication vector for segment communication; Client communication recovery indication vector; C is the set of communication working points to be restored; Ω station(i) is the set of RFI points covered by the communication working point i; Ω call(i) is the set of customer service center user points covered by the communication working point i; [N] is the network description matrix of the distribution network; [F] is the true value of the fault indication vector that the master station should receive; [C F ] is the true value of the user-side feedback information vector; [Fs t ] is the fault indication vector generated at time t; is the actual fault vector received by the master station at time t; is the actual user feedback vector received by the master station at time t; is the indicator vector of segment k at time t, There is a fault inside the section; C LS is a collection of sections; is the fault state of node i at time t; is the comprehensive failure probability of node i.

[0063] Preferably, the objective function of the communication working point location selection model is:

[0064]

[0065] Constraints:

[0066]

[0067] Among them, S UAV is a set of discrete point coordinates; S CN is the coordinate set of communication demand points; N c is the transmission capacity; p k is a binary decision variable that characterizes whether the discrete point k is selected as the emergency communication working point; x c,k is a binary variable indicating whether the communication demand point c is covered by the discrete point k; (x c ,y c ),(x k ,y k ) are the location coordinates of the communication demand point c and the discrete point k; d k,c represents the distance between communication node c and discrete point k and the coverage capability of the base station.

[0068] In a second aspect, an embodiment of the present invention provides a distribution network disaster damage situation awareness system based on emergency communication under rain and flood conditions, comprising:

[0069] The construction module takes system measurement data, user-side feedback information and meteorological data as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders. According to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model and taking the maximization of Bayesian posterior probability as the criterion, a distribution network disaster loss situation awareness model under urban rainstorm waterlogging is established;

[0070] The collaborative module builds a communication work point site selection model to determine the possible stop points for the mobile drone station and the emergency communication vehicle. Based on the possible stop points for the mobile drone station and the emergency communication vehicle, the scheduling scheme and transfer path of the mobile drone station and the emergency communication vehicle are determined, and a vehicle-machine collaborative communication recovery model is built, taking into account the path constraints and time constraints.

[0071] The output module constructs a distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging based on the constructed distribution network disaster damage situation awareness model under urban rainstorm and waterlogging and the constructed vehicle-machine cooperative communication recovery model. The distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is solved to obtain the situation awareness improvement strategy.

[0072] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for distribution network disaster damage situation awareness based on emergency communication during rain and flood when executing the computer program.

[0073] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for distribution network disaster damage situation awareness based on emergency communication during rain and floods.

[0074] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for disaster situation awareness of distribution network based on emergency communication during rain and flood when executing the computer program.

[0075] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for distribution network disaster damage situation awareness based on emergency communication during rain and floods.

[0076] Compared with the prior art, the present invention has at least the following beneficial effects:

[0077] A method for distribution network disaster situation awareness based on emergency communication under rain and floods is proposed. A distribution network disaster situation awareness strategy based on multi-source data fusion technology is established. First, a model of the impact of urban rainstorm meteorological data on distribution network components and equipment is established to obtain the vulnerability curve of distribution network components; secondly, a logical causal relationship between RFI fault current information and the fault section is established to determine the fault location; finally, a logical causal relationship between user-side calls and the fault section is established, and the distribution network fault point is given based on the maximum Bayesian posterior probability model combined with meteorological data, measurement data, and user-side feedback data; an emergency communication recovery strategy based on the vehicle-machine collaboration mode is established. First, a vehicle and drone work point site selection model is established to establish the minimum work points under the premise of covering all communication demand points; secondly, the path planning and time planning constraints of the vehicle-machine collaboration model are established, and combined with the distribution network disaster situation awareness model, the distribution network disaster situation that develops dynamically with rainstorms is obtained.

[0078] Furthermore, a distribution network damage situation awareness model under urban rainstorm and waterlogging is established, which integrates meteorological data, system measurement data and user-side feedback data. Each data source can provide distribution network damage situation information from different time and space dimensions, which is conducive to achieving more accurate grasp of the distribution network damage situation.

[0079] Furthermore, a method for inferring distribution network fault scenarios based on system measurement data is established, which can locate the fault section according to the collected fault indication data.

[0080] Furthermore, in order to determine the only system failure scenario, more evidence is needed as support. By using the comprehensive failure probability of the distribution network nodes under heavy rain and waterlogging, the maximum Bayesian posterior probability is established as the objective function to determine the most likely failure scenario. At the same time, it is noted that the objective function of the optimization problem is nonlinear and cannot be directly solved to obtain its result. The logarithmic function has a monotonically increasing property, so the logarithmic function is used to linearize the target.

[0081] Furthermore, in urban floods, rainwater accumulation often causes road damage. Emergency communication vehicles are limited by road conditions, and drones are limited by their endurance. Neither can complete communication restoration work alone. The vehicle-machine collaborative communication restoration objective function ensures the maximum ability to perceive the damage situation of the distribution network while minimizing the time required for restoration.

[0082] Furthermore, the scheduling constraints of the vehicle-machine collaborative emergency communication recovery strategy include path constraints, time constraints, sequential access constraints, etc. Among them, the path constraint ensures that each communication node is accessed according to the optimal path, avoiding duplication and waste in the path selection of emergency vehicles or drones; the time constraint ensures that the time of vehicles and drones is coordinated, taking into account the time required for the communication recovery process; the sequential access constraint determines the access order of the work points, ensuring the order of the drone path scheduling.

[0083] Furthermore, with regard to the optimal location problem of emergency communication base station working points, as few candidate working points as possible are adopted when the communication coverage area meets all communication demand points.

[0084] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0085] In summary, the present invention performs distribution network damage situation perception based on emergency communications under urban rain and flood disasters. On the one hand, it takes into account information sources of multiple dimensions, directly reflects the operating status of the distribution network through system measurement data, uses user-side feedback information as direct and effective auxiliary information, and obtains the impact results on the distribution equipment through meteorological data, so as to realize the perception of the distribution network operating status when the information is accurate. On the other hand, considering the destruction of the original communication facilities by heavy rain, a recovery strategy for emergency communication vehicles and drone systems is established to achieve a more accurate perception of the post-disaster operating status of the distribution network, dynamically grasp the damage situation of the distribution network, and enhance the perception ability of the damage situation of the distribution network.

[0086] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1This is a flow chart of the method for awareness of the disaster loss situation of the distribution network;

[0088] Figure 2 It is a schematic diagram of the line fault and RFI indicator status;

[0089] Figure 3 is a schematic diagram of the disaster situation of the IEEE 33-node test system, where (a) the RFI indicator and user-side feedback are normal, (b) the RFI indicator and user-side have missed reports, and (c) is a false report of the RFI indicator;

[0090] Figure 4 A geographical diagram of the IEEE 33-node test system;

[0091] Figure 5 Results of site selection for IEEE 33-node test system emergency communications;

[0092] Figure 6 Provide a trend chart for the perception of the damage situation in the distribution network;

[0093] Figure 7 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0094] Figure 8 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0096] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0097] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0098] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0099] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0100] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0101] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0102] The present invention provides a method for sensing the damage situation of a distribution network based on emergency communications during rain and floods. The method integrates system measurement information, meteorological data, and user-side feedback information, and sends the remote fault indication device (RFI) and user feedback information to the dispatching control center through the collaboration of unmanned aerial vehicles and emergency communication vehicles to obtain the real-time damage situation of the distribution network.

[0103] See also Figure 1 The present invention provides a method for sensing the damage situation of a distribution network based on emergency communication during rain and flood, comprising the following steps:

[0104] S1. Based on multi-source data fusion technology, a distribution network damage situation awareness model under urban rainstorm and waterlogging is established;

[0105] S101. Considering the information integrity, a mapping relationship model between RFI fault alarm information and the damage section of the distribution network is established to preliminarily determine the damage situation of the distribution network after the disaster.

[0106] Select any node as the reference power source and assign an initial direction to each feeder segment. Define the RFI status indication set as the fault indication vector. The elements in the fault indication vector depend on the direction of the RFI induced fault current. When it is the same as the reference direction, the corresponding element is 1; when it is opposite to the reference direction, the corresponding element is -1; when there is no fault current, the corresponding element is 0.

[0107] In order to obtain the operating status information of the distribution network after a fault occurs, it is necessary to establish a network description matrix [N] according to the distribution network topology. The element N kl Determined according to the following rules:

[0108] If feeder LS1 is the starting point of RFIk, N kl =1;

[0109] If feeder LS1 is the end point of RFIk, N kl = -1;

[0110] If the feeder LS1 is not the starting point or the end point of RFIk, N kl =0.

[0111] Define the fault discrimination vector [FS] = [N] [F] T , T-type structure fault discrimination vector [F T ]=[N T ]·[F] T For the non-T-type section LSk, according to the value of the kth element in the fault discrimination vector [FS], if FS k ≥1, it is determined that there is a fault in segment k, otherwise it is determined that there is no fault in segment k. For a T-shaped structure segment, if its corresponding element FS k ≥1, and its corresponding [F T ] element, it is determined that there is a fault in the T segment, otherwise there is no fault.

[0112] S102. Establish a model of the impact of rainstorm and waterlogging on urban distribution networks, select high-voltage switchgear and lines, analyze the impact of rainstorm and waterlogging on them, and calculate the probability of component failure at different periods after the disaster;

[0113] First, a direct failure model of the ring network high-voltage switch cabinet is established. During the urban rainstorm and flooding process, the time period [0, T] is composed of several Δt. The direct failure rate of the equipment w in this time period is:

[0114]

[0115] Where, d w (t) is the waterlogging depth at the location of the high-voltage switchgear at time t, D w is the designed flood-proof height of the distribution station, D Bw is the cable joint height, ζ is the attenuation coefficient, and γ is the damping coefficient.

[0116] Based on the benchmark failure rate of the ring network high-voltage switchgear, the environmental humidity and temperature inside the high-voltage switchgear are taken as covariates of the indirect failure model to improve the indirect failure model of the high-voltage switchgear:

[0117]

[0118] Among them, β w is the shape parameter; η w is the scale parameter; t e is the time conversion factor; X w,1 , X w,2 is the humidity inside the switch cabinet and the cable joint temperature; α1 and α2 are covariate parameters.

[0119] By improving the indirect failure model, the indirect failure probability of the high-voltage switchgear at time t is obtained:

[0120]

[0121] Where n is the number of switch cabinets equipped at node i.

[0122] It is believed that the direct and indirect effects of extreme rainstorm and waterlogging on distribution equipment are independent of each other. The comprehensive failure probability of nodes under rainstorm and waterlogging is obtained according to the independent event probability formula:

[0123] P i (t) = 1-(1-P i F (t))(1-P i J (t))

[0124] S103, locating distribution network faults based on user-side feedback information;

[0125] The parts of power failure reported by users are as follows:

[0126]

[0127] in, is the node path from the power supply end to the node k; is the binary variable of the state of node i at time t, The node i works normally at time t; d k is the binary variable of the feedback result of user k, d k=1 means user k has made a fault complaint; Ω CF is the user feedback state set; this formula constrains that there must be at least one fault on the distribution path of the faulty user.

[0128] S104. Determine a plan for post-disaster failure scenarios of the distribution network.

[0129] Assume that the decision variable composed of the fault status of each node is x; Assume that the system measurement and user complaint telephone are accurate information, recorded as b0; Assume that the mapping relationship between the system fault status and the surviving measurement is f, then solving the disaster loss situation of the urban distribution network is to solve a set of equations:

[0130]

[0131] in, represents the state set of the distribution system nodes; c is the current system measurement data, which is a set of parameters with values ​​of ±1 / 0, given by the remote fault indicator. d is the current user-side feedback result, which is a set of parameters with values ​​of 0 / 1.

[0132] It is more convenient to deduce the fault section through system measurement information and user-side feedback information, and f(x)=b0 is equivalently expressed as:

[0133] x=g1(c)

[0134] x=g2(d)

[0135] Specifically, it is expressed as:

[0136] [Fs]=[N][F] T =[z1,z2,z3,...,z n ] T

[0137]

[0138] in, is the fault binary variable of segment k at time t, Indicates that there is a fault in segment k; C LS is a numbered set of non-T structure segments; It is a collection of T-shaped structure section numbers.

[0139] The comprehensive failure probability P of distribution network nodes under heavy rain and flooding i (t), the maximum Bayesian posterior probability is established as the objective function to determine the most likely failure scenario under the rainstorm disaster at time t, and the logarithmic function is used to linearize the objective function. The model is described as:

[0140]

[0141] S2, constructing the objective function and constraint conditions of the distribution network disaster loss situation awareness model under urban rainstorm waterlogging established in step S1;

[0142] S201, determining the model objective function;

[0143] The objective function of recovery is to ensure the maximum awareness of the distribution network damage situation while minimizing the time taken for communication recovery.

[0144] f sum =β 1 F obj1 +β 2 F obj2

[0145]

[0146] Among them, the objective function F obj1 is the normalized communication recovery time; F obj2 is the normalized probability of damage to the distribution network; β 1 , β 2 are the weight values ​​of the two sub-objective functions. In the subsequent examples, β is set 1 =0.9,β 1 =0.1.

[0147] S202, determining a communication work point location model;

[0148] The objective function of the vehicle-machine cooperative emergency communication working point is:

[0149]

[0150] Constraints:

[0151]

[0152]

[0153] Among them, S UAV is a set of discrete point coordinates; S CN is the coordinate set of communication demand points; N c is the transmission capacity; p k is a binary decision variable that represents whether the discrete point k is selected as the emergency communication working point. If point k is selected as the emergency communication working point, p k =1, if point k is not an emergency communication working point, p k =0;x c,kis a binary variable indicating whether the communication demand point c is covered by the discrete point k. If the communication demand point c is within the coverage of the discrete point k, the value is 1; if the communication demand point c is not within the coverage of the discrete point k, the value is 0; (x c ,y c ),(x k ,y k ) are the location coordinates of the communication demand point c and the discrete point k; d k,c represents the distance between communication node c and discrete point k and the coverage capability of the base station.

[0154] S203, establishing vehicle-machine collaborative planning constraints;

[0155] The scheduling problem of the vehicle-machine collaborative recovery emergency communication strategy includes path constraints, time constraints, and loop constraints.

[0156] Path constraints:

[0157]

[0158]

[0159] Among them, C N = {1,2,...c} represents the set of all work points (excluding the warehouse); N = {0,1,...,c+1} represents all vehicle work points including the warehouse, with the starting point being 0 and the ending point being c+1; N0 = {0,1,...,c} represents the set of vehicle starting points, and the corresponding N + ={1,2,...,c+1} represents the set of vehicle arrival points; P is composed of<i,j,k> It consists of three points, i represents the set of starting points of the drone, that is j represents the working point j∈{C':j≠i} where the UAV reaches and takes the communication recovery action, k∈{N + :k≠j,k≠i,τ ij '+τ jk '≤e}; x (i,j) is a set of 0-1 variables, and the vehicle travels from i∈N0 to j∈N + , and i≠j, x (i,j) =1;y (i,j,k) is a set of 0-1 variables. The drone starts from i∈N0 to j∈C and returns to k∈{N + :(i,j,k)∈P}, y (i,j,k) =1;u i is a continuous variable, indicating the position of work point i in the vehicle access path, and 1≤u i ≤c+2 to eliminate subloops.

[0160] Time constraints:

[0161]

[0162]

[0163] t′ k -(t′ j -τ′ ij )≤e+M(1-y ijk )

[0164]

[0165] Among them, τ ij represents the time taken by a vehicle to travel from work point (or warehouse) i to work point j (or warehouse), and τ′ ij represents the time taken by the drone to travel from work point (or warehouse) i to work point j (or warehouse), and defines τ 0,c+1 ≡0;s L Indicates the operation time required for vehicle staff to launch the drone; s R It represents the time required for the vehicle staff to recover and replace the battery of the drone until the drone can complete the launch again; e represents the endurance of the drone; t j is a continuous variable, indicating the time when the vehicle arrives at work point (or warehouse) j; t' j The time when the UAV arrives at j is; t j =t' j =0, indicating the initial time when the vehicle and the drone leave the warehouse.

[0166] Access order constraints:

[0167]

[0168] Among them, p (i,j) is a set of 0-1 variables. When the i workstation is visited before j on the vehicle path, i∈Cj∈{C:j≠i}, p (i,j) =1.

[0169] Additional constraints:

[0170] t0=0

[0171] t'0=0

[0172]

[0173] S204. Determine distribution network fault location constraints, including: correspondence between continuous time and discrete time stamps and distribution network fault section location constraints.

[0174] Continuous time corresponds to discrete timestamps:

[0175]

[0176] Among them, γ i,t is a binary variable indicating whether the working point i is repaired at time t, γ i,t =1 means that the communication of working point i is successfully repaired at time t; Δt is the time interval of the entire model; t is the discrete time parameter; ε is the error value.

[0177] Distribution network fault section location constraints:

[0178]

[0179] [Fs t ]=[N][F t ] T =[z 1,t ,z 2,t ,z 3,t ,...,z n,t ] T

[0180]

[0181] in, Recovering an indication vector for segment communication; Client communication recovery indication vector; C is the set of communication working points to be restored; Ω station(i) is the set of RFI points covered by the communication working point i. For example, the restoration of the communication working point 1 will restore the communication between RFI2 and RFI5. Then Ω station(1) =[2,5]; similarly, Ω call(i) is the set of customer service center user points covered by communication work point i. For example, the recovery of communication work point 1 will enable customer service center node 2 and customer service center node 5 to recover information. Then Ω call(1) =[2,5]; [N] is the network description matrix of the distribution network; [F] is the true value of the fault indication vector that the master station should receive; [C F ] is the true value of the user-side feedback information vector; [Fs t ] is the fault indication vector generated at time t, which is used to determine the state of the distribution network section at time t, and its elements are z n,t ; F i,t T is the actual fault vector received by the master station at time t; C i,t T is the actual user feedback vector received by the master station at time t; is the indicator vector of segment k at time t, There is a fault inside the section; C LS is a collection of sections; is the fault state of node i at time t; is the comprehensive failure probability of node i.

[0182] S3. Based on the objective function and constraints constructed in step S2, a model for improving the situational awareness of distribution network damage under urban rainstorms and waterlogging is constructed, and the commercial solver Gurobi is used to solve the model. The obtained results are used to provide a reference for the rapid recovery of the distribution network, and provide a reference for fault location for subsequent distribution network recovery decisions, saving repair time.

[0183] In another embodiment of the present invention, a distribution network disaster damage situation awareness system based on emergency communication during rain and flood is provided. The system can be used to implement the above-mentioned distribution network disaster damage situation awareness method based on emergency communication during rain and flood. Specifically, the distribution network disaster damage situation awareness system based on emergency communication during rain and flood includes a construction module, a coordination module and an output module.

[0184] Among them, the construction module takes system measurement data, user-side feedback information and meteorological data as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders. According to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model and taking the maximization of the Bayesian posterior probability as the criterion, a distribution network disaster loss situation awareness model under urban rainstorm waterlogging is established;

[0185] The collaborative module builds a communication work point site selection model to determine the possible stop points for the mobile drone station and the emergency communication vehicle. Based on the possible stop points for the mobile drone station and the emergency communication vehicle, the scheduling scheme and transfer path of the mobile drone station and the emergency communication vehicle are determined, and a vehicle-machine collaborative communication recovery model is built, taking into account the path constraints and time constraints.

[0186] The output module constructs a distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging based on the constructed distribution network disaster damage situation awareness model under urban rainstorm and waterlogging and the constructed vehicle-machine cooperative communication recovery model. The distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is solved to obtain the situation awareness improvement strategy.

[0187] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distribution network disaster loss situation awareness method based on emergency communication under rain and flood, including:

[0188] The system measurement data, user-side feedback information and meteorological data are used as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders; according to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model, the maximization of the Bayesian posterior probability is used as the criterion to establish a distribution network disaster damage situation awareness model under urban rainstorms and waterlogging; a communication work point site selection model is constructed to determine the possible stop points of mobile drone stations and emergency communication vehicles; based on the possible stop points of mobile drone stations and emergency communication vehicles, the path constraints and time constraints are considered to determine the scheduling plan and transfer path of mobile drone stations and emergency communication vehicles, and a vehicle-machine collaborative communication recovery model is constructed; based on the constructed distribution network disaster damage situation awareness model under urban rainstorms and waterlogging and the constructed vehicle-machine collaborative communication recovery model, a distribution network disaster damage situation awareness improvement model under urban rainstorms and waterlogging is constructed, and the distribution network disaster damage situation awareness improvement model under urban rainstorms and waterlogging is solved to obtain a situation awareness improvement strategy.

[0189] See also Figure 7, the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the distribution network disaster loss situation awareness system based on emergency communication under rain and flood in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0190] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 7 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0191] The processor 61 may be a central processing unit (CPU), or 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. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0192] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0193] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0194] See also Figure 8 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0195] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0196] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0197] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0198] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0199] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0200] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0201] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for sensing the damage situation of a distribution network based on emergency communication under rain and flood in the above embodiment; the processor may load and execute the following steps:

[0202] The system measurement data, user-side feedback information and meteorological data are used as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders; according to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model, the maximization of the Bayesian posterior probability is used as the criterion to establish a distribution network disaster damage situation awareness model under urban rainstorms and waterlogging; a communication work point site selection model is constructed to determine the possible stop points of mobile drone stations and emergency communication vehicles; based on the possible stop points of mobile drone stations and emergency communication vehicles, the path constraints and time constraints are considered to determine the scheduling plan and transfer path of mobile drone stations and emergency communication vehicles, and a vehicle-machine collaborative communication recovery model is constructed; based on the constructed distribution network disaster damage situation awareness model under urban rainstorms and waterlogging and the constructed vehicle-machine collaborative communication recovery model, a distribution network disaster damage situation awareness improvement model under urban rainstorms and waterlogging is constructed, and the distribution network disaster damage situation awareness improvement model under urban rainstorms and waterlogging is solved to obtain a situation awareness improvement strategy.

[0203] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0204] Taking the IEEE 33-node test system as an example, a verification example is designed to verify the effectiveness of the proposed method.

[0205] See also Figure 2 After a rainstorm disaster occurs in a city, the line fault location and fault indicator data are as follows: Figure 2 As shown; assuming that all fault indicators are working normally, RFI gives indication alarm information according to the fault current information, [F] = [10 0 -1-1 0 0].

[0206] First, the network description matrix [N] of the distribution system is obtained:

[0207]

[0208] Then the fault discrimination matrix in this scenario is:

[0209] [FS] = [-1 2 -1 0 0 1 -1 0]

[0210] According to the judgment rules, Section 2 and Section 6 are possible fault areas, and both sections are T-structure sections, which require further judgment.

[0211] For the 2, 4, and 6 sections of the T-type structure, select the corresponding rows in the network description matrix to obtain the fault description matrix:

[0212]

[0213] According to [F T ]=[N T ]·[F] T , generate a fault description matrix of T structure:

[0214]

[0215] [F T ], it means that the number of rows in segment 2 and segment 6 both contain elements greater than 1 and do not contain element -1. It can be preliminarily determined that the fault occurs in segment 2 and segment 6.

[0216] Table 1 shows the parameters of the indirect failure model for extreme rainstorms, and calculates the indirect failure rate and failure probability of distribution equipment:

[0217] Table 1 Indirect failure model parameters

[0218]

[0219] According to the above model, the comprehensive failure probability of each high-voltage switch cabinet is calculated, and the time t = 60min is selected to calculate the comprehensive failure probability of the node, as shown in Table 2:

[0220] Table 2 Node comprehensive failure rate

[0221]

[0222] According to the fault section determined by the system measurement information, the user fault complaint constraint is considered and the constraint condition is added to the damage model to maximize the Bayesian posterior probability. Considering the complete information, the underreporting of RFI and users, and the false alarm of RFI, the design example is used to obtain the distribution network damage situation and the objective function P. The damage situation is shown in Figure 3:

[0223] 1) The RFI indicator and user-side feedback are normal. The fault indication vector uploaded by RFI is [F] = [1 0 0 -1 -1 00], the fault nodes are 6, 19, 26, and the objective function value is p t =-10.7232;

[0224] 2) There are omissions on the RFI indicator and user side, fault indication vector [F] = [1 0 0 0 0 0 0], no user feedback information, fault nodes are 2, 3, 6, 26, fault nodes are 2, 3, 6, 26, p t =-13.7443;

[0225] 3) RFI has false alarms, fault indication vector [F] = [1 0 011 0 0], fault nodes are 3, 7, 27, 31, p t =-14.0111.

[0226] It can be seen that when the information is complete, the distribution network disaster loss situation awareness model based on multi-source data fusion technology can determine the faulty node more accurately; however, in the case of communication loss, the false alarm and missed alarm of RFI, as well as the failure of user information, will lead to the weakening of the ability to perceive the distribution network disaster loss situation.

[0227] IEEE 33-node test system Figure 4 As shown in the figure, there are 7 RFIs in the system. The entire distribution feeder is divided into 8 sections by RFIs, and there are 4 customer service centers responsible for collecting complaint telephone information from all 33 nodes. First, based on the optimal location model, the optimal candidate communication working point of the system is calculated. Then, according to the vehicle-machine collaborative emergency communication recovery strategy, the optimal communication recovery scheduling strategy is calculated. Finally, the damage situation of the distribution network under urban rainstorm waterlogging that changes with the progress of the rainstorm is obtained.

[0228] 70×65 discrete points are evenly distributed on the plane as candidate locations for emergency communication work points. The RFI coordinates and customer service center coordinates are shown in Tables 3 and 4. These points are used as input to obtain the locations of 6 work points, and the coverage range and channel capacity are set to 10 and 10, respectively. Table 5 shows the location selection results of the communication work points. Figure 5 Each communication working point and its coverage area are drawn.

[0229] Table 3 RFI coordinates

[0230]

[0231] Table 5 Site selection results

[0232]

[0233] It can be seen that the proportion of RFI covered by work points S1 and S2 is higher, while the proportion of customer service centers covered by work point S4 is higher. The starting warehouse position is set to (30, 30). Figure 5The communication working points S1 to S6 shown in the figure determine the emergency communication vehicle working point set N and the drone working point set C; it is assumed that the driving speed of the emergency communication vehicle is the same as the flying speed of the drone, both of which are 1 unit / min; the time for the emergency communication vehicle staff to launch and recover the drone is set to 1 minute; the drone endurance time is set to 60 minutes. Optimization time range T max =120min, time interval Δt=10min; using a single emergency communication vehicle and a single drone.

[0234] Depend on Figure 4 As shown in the IEEE 33-node example diagram, node 1 is a substation node, there are 7 RFIs in the system, the entire distribution feeder consists of 8 sections of RFI, and the system contains 2 distributed generation sources, located at nodes 18 and 22, with powers of 1.65MW and 1.5MW respectively. Using the comprehensive failure probability of the nodes in Table 2, considering the RFI and user underreporting, an emergency communication recovery example is designed with a total duration of 46.64 minutes. The communication recovery solution results are shown in Tables 6 and 7.

[0235] Table 6 Vehicle-UAV path results

[0236]

[0237] Table 7 RFI / User Information Recovery Process

[0238]

[0239] The above results show that communication recovery gives priority to selecting communication working points with more RFI coverage to obtain the instant maximum distribution network disaster situation. The user side, as a supplement to the distribution network disaster situation, has a lower recovery priority. After the recovery is completed, the distribution network disaster situation p(t) is as follows: Figure 6 shown.

[0240] It can be seen that at 33.48 minutes, the underreported RFI4 and RFI5 resumed uploading, which greatly improved the ability to perceive the disaster situation of the distribution network; at 37.61 minutes, the user information was restored, which supplemented the distribution network situation.

[0241] To sum up, the present invention provides a method and system for distribution network disaster damage situation awareness based on emergency communication during rain and flood, which integrates multi-source data such as system measurement information, meteorological data, and user-side feedback information, and utilizes emergency communication vehicles and drone mobile base stations to collaboratively send remote fault indication equipment and user feedback information to the dispatching control center. It determines the most likely distribution network failure scenario based on maximizing the Bayesian posterior probability, thereby better improving the distribution network disaster damage situation awareness capability and achieving rapid recovery of the distribution network after a disaster.

[0242] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0243] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0244] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0245] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0246] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0247] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0248] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0249] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0250] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0251] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0252] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for sensing the damage situation of a distribution network based on emergency communication during rain and flood, characterized in that: The following steps are involved: The system measurement data, user-side feedback information and meteorological data are used as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders. According to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model and taking the maximization of Bayesian posterior probability as the criterion, a distribution network disaster loss situation awareness model under urban rainstorm waterlogging is established. Build a communication work point site selection model to determine the possible stop points for mobile drone stations and emergency communication vehicles; based on the possible stop points for mobile drone stations and emergency communication vehicles, consider path constraints and time constraints, determine the scheduling plan and transfer path for mobile drone stations and emergency communication vehicles, and build a vehicle-machine collaborative communication recovery model; Based on the constructed distribution network disaster damage situation awareness model under urban rainstorm and waterlogging and the constructed vehicle-machine cooperative communication recovery model, a distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is constructed, and the distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is solved to obtain the situation awareness improvement strategy.

2. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 1 is characterized in that: The specific model for establishing the damage situation awareness model of the distribution network under urban rainstorm waterlogging is as follows: Considering the information integrity, a mapping relationship model between RFI fault alarm information and distribution network damage section is established, and the post-disaster distribution network damage situation is determined based on system measurement data; A direct failure model of ring network high-voltage switchgear based on meteorological data is established. Based on the benchmark failure rate of ring network high-voltage switchgear, the ambient humidity and temperature inside the high-voltage switchgear are taken as covariates of the indirect failure model to improve the indirect failure model of high-voltage switchgear. By improving the indirect failure model, the indirect failure probability of the high-voltage switchgear is determined at time t. According to the independent event probability formula, the comprehensive failure probability P of the node under heavy rain and waterlogging is obtained. i (t); Locate distribution network faults based on user-side feedback information; The system measurement data, meteorological data and user-side feedback information are integrated to comprehensively analyze the post-disaster fault scenario of the distribution network and obtain a distribution network disaster damage situation awareness model under heavy rain and waterlogging.

3. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 2 is characterized in that: The damage situation of the distribution network after the disaster is determined based on the system measurement data as follows: According to the distribution network topology, a network description matrix [N] is established, and the element N kl Determined according to the following rules: If feeder LS1 is the starting point of RFIk, N kl =1; If feeder LS1 is the end point of RFIk, N kl = -1; If the feeder LS1 is not the starting point or the end point of RFIk, N kl =0; Fault discrimination vector [FS] = [N] [F] T , T-type structure fault discrimination vector [F T ]=[N T ]·[F] T ; For the non-T-type section LSk, according to the value of the kth element in the fault discrimination vector [FS], if FS k ≥1, it is determined that there is a fault in segment k, otherwise it is determined that there is no fault in segment k; For a T-shaped structure segment, if the corresponding element FS k ≥1, and the corresponding [F T ] element, it is determined that there is a fault in the T segment, otherwise there is no fault.

4. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 2 is characterized in that: The comprehensive failure probability P of nodes under heavy rain and waterlogging i (t) Specifically: P i (t)=1-(1-P i F (t))(1-P i J (t)) Among them, P i F (t) is the direct failure probability of the high-voltage switchgear, P i J (t) is the indirect failure probability of the high-voltage switchgear.

5. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 2 is characterized in that: The specific positioning of distribution network faults based on user-side feedback information is as follows: in, is the node path from the power supply end to the node k; is the binary variable of the state of node i at time t, The node i works normally at time t; d k is the binary variable of the feedback result of user k, d k =1 means user k has made a fault complaint; Ω CF A collection of user feedback status;.

6. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 2 is characterized in that: The comprehensive failure probability P of distribution network nodes under heavy rain and flooding i (t), the maximum Bayesian posterior probability is established as the objective function to determine the most likely fault scenario under the rainstorm disaster at time t, and the logarithmic function is used to linearize the objective function. The distribution network disaster loss situation awareness model under urban rainstorm waterlogging is described as: stx=g1(c) x=g2(d) in, For each node in the system, X i is the binary variable of the node i state, is the comprehensive failure probability of node i.

7. The method for sensing the damage situation of distribution network based on emergency communication under rain and flood according to claim 1 is characterized in that: In step S2, the objective function of the vehicle-machine cooperative communication recovery model is as follows: minF sum =b 1 F obj1 +b 2 F obj2 Among them, F obj1 is the normalized communication recovery time; F obj2 is the normalized probability of damage to the distribution network; β 1 , β 2 are the weights of the two sub-objective functions, p k is a binary decision variable that characterizes whether the discrete point k is selected as the emergency communication working point, and n is the number of discrete points.

8. The method for sensing the damage situation of distribution network based on emergency communication during rain and flood according to claim 7 is characterized in that: The constraints of the vehicle-machine cooperative communication recovery model are as follows: Path constraints: Among them, C N represents the set of all work points; N represents all vehicle work points including the warehouse; N0 represents the set of vehicle starting points, N + represents the set of vehicle arrival points; P is composed of<i,j,k> It consists of three points, i represents the starting point of the drone, j represents the working point where the drone reaches and takes communication recovery work; x (i，j) is a set of 0-1 variables; y (i，j，k) is a set of 0-1 variables; u i is a continuous variable; Time constraints: Among them, τ ij represents the time taken by the vehicle to travel from work point (or warehouse) i to work point j (or warehouse); τ ij represents the time taken by the drone to travel from work point (or warehouse) i to work point j (or warehouse); s L Indicates the operation time required for vehicle staff to launch the drone; s R It represents the time required for the vehicle staff to recover and replace the battery of the drone until the drone can complete the launch again; e represents the endurance of the drone; t j is a continuous variable, indicating the time when the vehicle arrives at work point (or warehouse) j; t′ j The time when the UAV arrives at j is; t j =t′ j =0, indicating the initial time when the vehicle and the drone leave the warehouse; Access order constraints: Among them, p (i，j) is a set of 0-1 variables. When the i workstation is visited before j on the vehicle path, i∈Cj∈{C:j≠i}, p (i，j) =1; Additional constraints: t0＝0 t′0＝0 The distribution network fault location constraints are as follows: Continuous time corresponds to discrete timestamps: Among them, γ i，t is a binary variable indicating whether the working point i is repaired at time t, γ i，t =1 means that the communication of working point i is successfully repaired at time t; Δt is the time interval of the entire model; t is the discrete time parameter; ε is the error value; Distribution network fault section location constraints: [Fs t ]=[N][F t ] T =[z 1，t， z 2，t ,z 3，t ,...,z n，t ] T in, Recovering an indication vector for segment communication; Client communication recovery indication vector; C is the set of communication working points to be restored; Ω station(i) is the set of RFI points covered by the communication working point i; Ω call(i) is the set of customer service center user points covered by the communication working point i; [N] is the network description matrix of the distribution network; [F] is the true value of the fault indication vector that the master station should receive; [C F ] is the true value of the user-side feedback information vector; [Fs t ] is the fault indication vector generated at time t; F i，t T is the actual fault vector received by the master station at time t; C i，t T is the actual user feedback vector received by the master station at time t; is the indicator vector of segment k at time t, There is a fault inside the section; C LS is a collection of sections; is the fault state of node i at time t; is the comprehensive failure probability of node i.

9. The method for sensing the damage situation of distribution network based on emergency communication during rain and flood according to claim 7, characterized in that: The objective function of the communication working point location selection model is: Constraints: Among them, S UAV is a set of discrete point coordinates; S CN is the coordinate set of communication demand points; N c is the transmission capacity; p k is a binary decision variable that characterizes whether the discrete point k is selected as the emergency communication working point; x c,k is a binary variable indicating whether the communication demand point c is covered by the discrete point k; (x c ,y c ),(x k ,y k ) are the location coordinates of the communication demand point c and the discrete point k; d k,c represents the distance between communication node c and discrete point k and the coverage capability of the base station.

10. A distribution network disaster damage situation awareness system based on emergency communication during rain and flood, characterized in that: include: The construction module takes system measurement data, user-side feedback information and meteorological data as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream feeders. According to the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, based on the component vulnerability model and taking the maximization of Bayesian posterior probability as the criterion, a distribution network disaster loss situation awareness model under urban rainstorm waterlogging is established; The collaborative module builds a communication work point site selection model to determine the possible stop points for the mobile drone station and the emergency communication vehicle. Based on the possible stop points for the mobile drone station and the emergency communication vehicle, the scheduling scheme and transfer path of the mobile drone station and the emergency communication vehicle are determined, and a vehicle-machine collaborative communication recovery model is built, taking into account the path constraints and time constraints. The output module constructs a distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging based on the constructed distribution network disaster damage situation awareness model under urban rainstorm and waterlogging and the constructed vehicle-machine cooperative communication recovery model. The distribution network disaster damage situation awareness improvement model under urban rainstorm and waterlogging is solved to obtain the situation awareness improvement strategy.

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