Power grid emergency situation dynamic deduction analysis method and related device

Abstract

The invention discloses a power grid emergency scene dynamic deduction analysis method and a related device. The method comprises the following steps: acquiring a scene state of a current power grid after a power grid emergency occurs; acquiring all emergency activities, external environments and emergency resources which can be provided at present; and deducing and analyzing the scene state of the power grid at the next moment according to the scene state of the current power grid and all the emergency activities, the external environment and the emergency resources which can be provided at present, and the method and the related device can accurately predict the scene state of the power grid at the next moment after an emergency occurs.

Description

Technical Field

[0001] This invention belongs to the field of power grid disaster prediction and analysis technology, and relates to a method and related device for dynamic simulation and analysis of power grid emergency scenarios. Background Technology

[0002] With the increasing frequency of natural disasters, extreme disasters can damage power grid equipment and facilities, causing widespread power outages and severely impacting urban transportation, communications, and other systems. However, as time progresses, the ultimate trajectory of such power grid emergencies becomes unpredictable. It is impossible to reveal the closed-loop mechanism of the disaster chain that leads to power grid system accidents induced by natural disasters, which in turn cause cascading accidents in social systems and ultimately feed back into the urban power grid system. Consequently, it is impossible to effectively predict the situation in advance after a power grid emergency, and therefore, it is impossible to allocate resources in advance to cope with the emergency and reduce the losses caused by it. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related device for dynamic simulation and analysis of power grid emergencies. This method and related device can accurately predict the situation state at the next moment after a power grid emergency occurs.

[0004] To achieve the above objectives, this invention discloses a method for dynamic simulation and analysis of power grid emergency scenarios, comprising: Obtain the current state of the power grid after a power grid emergency occurs; Obtain information on all currently available emergency activities, the external environment, and emergency resources; Based on the current power grid situation and all available emergency activities, external environment, and emergency resources, the power grid situation at the next moment is deduced and analyzed.

[0005] The further improvement of the dynamic simulation and analysis method for power grid emergencies described in this invention lies in: Furthermore, the process of deducing and analyzing the power grid's situational state at the next moment based on the current power grid situational state and all currently available emergency activities, external environment, and emergency resources is as follows: Based on the current power grid situation and all available emergency activities, external environment and emergency resources, several situation state variables are generated; Calculate the probability of occurrence of state variables for each scenario; The scenario state variable with the highest probability of occurrence is selected as the scenario state of the power grid at the next moment.

[0006] Furthermore, in the process of generating several scenario state variables based on the current power grid scenario state and all currently available emergency activities, external environment and emergency resources, one scenario state variable corresponds to one emergency activity, one external environment and one emergency resource, and each scenario state variable is generated through the corresponding emergency activity, external environment and emergency resource.

[0007] Furthermore, the process of calculating the probability of occurrence of each scenario state variable is as follows: Calculate the probability of occurrence of the emergency activities corresponding to each scenario state variable; Calculate the probability of occurrence of the external environment corresponding to each scenario state variable; The probability of occurrence of the corresponding scenario state variable is calculated based on the probability of occurrence of the emergency activity and the probability of occurrence of the external environment.

[0008] This invention discloses a dynamic simulation and analysis system for power grid emergencies, comprising: The first acquisition module is used to acquire the current state of the power grid after a power grid emergency occurs; The second acquisition module is used to acquire all currently available emergency activities, external environment, and emergency resources. The simulation and analysis module is used to simulate and analyze the state of the power grid at the next moment based on the current state of the power grid and all available emergency activities, external environment and emergency resources.

[0009] A further improvement of the dynamic simulation and analysis system for power grid emergencies described in this invention is as follows: Furthermore, the inference and analysis module includes: The generation unit is used to generate several scenario state variables based on the current scenario state of the power grid and all currently available emergency activities, external environment and emergency resources; The first calculation unit is used to calculate the probability of occurrence of each scenario state variable; The selection unit is used to select the scenario state variable with the highest probability of occurrence as the scenario state of the power grid at the next moment.

[0010] Furthermore, in the process of generating several scenario state variables based on the current power grid scenario state and all currently available emergency activities, external environment and emergency resources, one scenario state variable corresponds to one emergency activity, one external environment and one emergency resource, and each scenario state variable is generated through the corresponding emergency activity, external environment and emergency resource.

[0011] Furthermore, the first computing unit includes: The second calculation unit is used to calculate the probability of occurrence of emergency activities corresponding to each scenario state variable; The third calculation unit is used to calculate the probability of occurrence of the external environment corresponding to each scenario state variable; The fourth calculation unit is used to calculate the probability of occurrence of the corresponding scenario state variable based on the probability of occurrence of the emergency activity and the probability of occurrence of the external environment.

[0012] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the dynamic simulation and analysis method for power grid emergency scenarios.

[0013] This invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the dynamic simulation and analysis method for power grid emergency scenarios.

[0014] The present invention has the following beneficial effects: The dynamic scenario simulation and analysis method and related device for power grid emergencies described in this invention, in specific operation, represent the actions and measures taken to deal with the emergency object by changing the scenario state through emergency activities, represent the attitude of the outside world in response to the current power grid emergency through the external environment, and represent the external constraints of emergency activities through emergency resources. Based on emergency activities, external environment, and emergency resources, the scenario state of the power grid at the next moment is simulated and analyzed, thereby realizing the dynamic scenario simulation and analysis of power grid emergencies and assisting in the response and handling of power grid emergencies. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the method of the present invention; Figure 2 A schematic diagram of the basic units for the evolution of emergency scenarios; Figure 3 A schematic diagram illustrating the evolution of emergency scenarios; Figure 4 This is a path diagram illustrating the evolution of emergency scenarios. Figure 5 A schematic diagram of a dynamic Bayesian network model for emergency situation analysis; Figure 6 A schematic diagram of a scenario simulation model for an emergency event; Figure 7 This is a system structure diagram of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0019] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0020] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. 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.

[0021] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0024] refer to Figure 1 The dynamic simulation and analysis method for power grid emergencies described in this invention includes: 1) Obtain the current state of the power grid and all available emergency activities, external environment, and emergency resources; From a dynamic perspective, the disaster-causing body and the disaster-bearing body can be combined to form the elements of the scenario state. This reduces the complexity of the model by reducing the number of elements. From this perspective, the scenario evolution process of a sudden event can be divided into the following four key elements: (1) Scenario state, represented by the letter S, mainly refers to the state of the emergency object, including the scenario state of the disaster-causing body and the scenario state of the disaster-bearing body; (2) Emergency activities, represented by the letter B, refer to the actions and measures taken by the emergency subject to change the scenario state of the emergency object; (3) External environment, represented by the letter H; (4) Emergency resources, represented by the letter M, belong to the external constraints of emergency activities. The interaction between these four key elements constitutes a basic unit, such as Figure 2 As shown.

[0025] Since emergency resources do not directly affect the situation, therefore Figure 3 In this context, emergency resources are treated as a constraint variable. Figure 3In this context, S represents the current situational state. Under the influence of the external environment (H) and the intervention of the emergency response agency (B), and under the constraints of emergency resources (M), the situational state changes and enters the next situational state (S1). The transition from situational state S to S1 completes a full situational evolution process, which is called a basic unit of emergency situational evolution.

[0026] Suppose a sudden event undergoes n scenario transitions from occurrence to disappearance, with scenario states denoted as S0, S1, S2, ..., Sn-1, Sn, where S0 is the initial scenario state, Sn is the disappearing scenario state, and the time of each scenario state is t0, t1, t2, ..., tn-1, tn, respectively. Bi, Hi, and Mi represent the emergency activities, external environment, and emergency resource constraints at time ti, respectively, where i ∈ (1, 2, ..., n). Then, the evolution law of the sudden event scenario can be expressed as follows: Figure 2 As shown.

[0027] 2) Generate several scenario states based on the current power grid situation and all available emergency activities, external environment, and emergency resources; In the current scenario, without the intervention of an emergency response agency and influenced only by the external natural environment, the scenario evolution generally follows its own inherent laws and trajectory, exhibiting randomness and a unique life cycle. However, once a real-world emergency occurs, an emergency response agency quickly intervenes, introducing a human factor into the external environment. Therefore, scenario evolution is simultaneously influenced by human intervention, the external environment, and resource constraints, disrupting its inherent evolutionary laws and trajectory and leading to new developmental patterns and paths.

[0028] Because scenario evolution is a continuous process, emergency response is also a continuous process. Emergency decision-makers make emergency decisions and take measures based on the current scenario state, thereby changing the current scenario state and entering the next scenario state. Before making an emergency decision for the next scenario state, the effectiveness of the previous emergency response is generally assessed, with results generally falling into two categories: achieving expectations or failing to achieve expectations. Achieving expectations means the emergency decision has had the desired effect, the situation has been controlled and is developing in a positive direction; failing to achieve expectations means the emergency decision was ineffective or insignificant, the situation is still serious or may even worsen further. The decision-making body must constantly adjust the pre-determined next emergency decision based on the assessment results. Because each assessment result is either achieving expectations or failing to achieve expectations, the scenario evolution path of an emergency can also be divided into optimistic and pessimistic directions. Drawing inspiration from the binary tree principle in computer data structures, an emergency scenario evolution path can be formed as follows: Figure 4 As shown.

[0029] exist Figure 4 There are 23 scenario states in total. S0 is the initial scenario state after the occurrence of the emergency, and S1 to S22 are the development and evolution scenarios. Each scenario state Si (i starts from 0) undergoes state changes and scenario evolution under the influence of emergency activities Bi+1 and the external environment Hi+1, and under the constraint of emergency resources Mi. Each scenario evolution has two paths: an optimistic path (achieving expectations) and a pessimistic path (not achieving expectations). The horizontal arrow to the right (→) indicates that the expectations have been met, and the scenario evolution develops in an optimistic direction; the vertical arrow downward (↓) indicates that the expectations have not been met, and the scenario evolution develops in a pessimistic direction. Therefore, Figure 3 This results in two possible evolutionary paths: a most optimistic path: S0→S2→S6→S14→S22, and a most pessimistic path: S0→S1→S3→S7→S15. From the above analysis, we can see that if a sudden event occurs and there are n scenario evolutions (meaning there are n scenarios besides the initial one), then theoretically there are 2^n possible paths. Each emergency decision determines the direction of the event, forming different evolutionary trajectories and paths. There is only one most optimistic and one most pessimistic path. The most optimistic path represents the best-case scenario, where every decision achieves the expected outcome; the most pessimistic path is the opposite, representing the worst-case scenario, where every decision fails to achieve the expected outcome. Therefore, due to the dynamic nature of emergency events, emergency decision-makers must be extremely cautious in making each decision, striving to ensure the event evolves along the most optimistic path possible.

[0030] 3) Calculate the probability of occurrence of each scenario state, and select the scenario state with the highest probability of occurrence as the scenario state to be predicted for the next moment.

[0031] Specifically, the probability of each scenario state is calculated based on prior probability and conditional probability.

[0032] Since the evolution of a sudden event scenario is a continuous and dynamic process, let's divide the entire scenario evolution into T time points, t0, t1, t2, ..., tn, where T0 is the initial time of the sudden event. Then, the dynamic Bayesian network for the entire scenario evolution can be used... Figure 5 express.

[0033] exist Figure 5In the context of the dynamic Bayesian network, for the initial scenario state S0, the scenario state S1 at the next time step is reached under the influence of input node variables B1 and H1. Here, S0, B2, and H2 become the input node variables of S1, and so on. It can be seen that, from the perspective of input variables, the entire dynamic Bayesian network can be divided into two parts: the initial scenario network, with scenario state S0 and only two input variables, nodes B and H; and the development scenario network, starting from scenario state S1, with three input node variables: the emergency activity B, the external environment H, and the scenario state S from the previous time step.

[0034] Let eS1, eS2, ..., eSn represent the current scenario state variables, iS0, iS1, iS2, ..., iSk represent the input scenario variables under the current scenario state, and oS0, oS1, oS2, ..., oSi represent the output scenario variables under the current scenario state. Then, the sudden event scenario inference model based on dynamic Bayesian networks is as follows: Figure 6 As shown.

[0035] exist Figure 6 In this context, for the situational state variable eS1, its input situational variables include iS0, iS1, iS2, ..., iSk. As long as the prior probabilities P(iS0), P(iS1), P(iS2), ..., P(iSk) of all input situational variables are given, as well as the conditional probabilities P(eS1|iS0, iS1, iS2, ..., iSk), the occurrence probability P(eS1) of the situational state variable can be calculated. Similarly, P(eS1), P(eS2), ..., P(eSn) can be calculated respectively. Using iS0, iS1, eS1, and eS2 as input situational variables, and combining them with the estimated conditional probability P(oS0|iS0, iS1, eS1, eS2), we can deduce the probability P(oS0) of the occurrence of situational variable oS0 at the next moment. Similarly, we can calculate the probabilities of P(oS0), P(oS1), P(oS2), ..., P(oSi), thus completing one situational deduction.

[0036] After the dynamic Bayesian network for a sudden event scenario is constructed, in order to successfully deduce the scenario state, it is necessary to first determine the prior probabilities or expert-estimated probabilities of some network node variables. Then, the state probabilities of the scenario state are calculated using the prior probabilities or expert-estimated probabilities, thereby deducing the probability of the next scenario state occurring and completing the scenario deduction process. Specifically, this can be divided into two steps: determining the prior probabilities or expert-estimated probabilities and calculating the scenario state probabilities.

[0037] a) Determine the prior probabilities of some network node variables; Determining the prior probability or expert-estimated probability of network node variables involves two aspects: first, determining the prior probability for node variables without parent nodes; and second, determining the conditional probability for node variables with parent nodes.

[0038] a1) Determining the prior probability; Prior probability refers to the probability derived from historical experience or analysis; it is an unconditional probability. Figure 4 In the algorithm, the node variables Bi and Hi (i=1,2,…,n) have no parent nodes, so the prior probabilities need to be determined in advance, that is, the probability distribution of P(Bi) and P(Hi) (i=1,2,…,n) needs to be determined.

[0039] a2) Determination of expert-estimated probabilities; For node variables that have a parent node, Figure 5 The conditional probabilities of the node variables Si (i=0,2,…,n) need to be determined based on historical experience data or expert estimation methods. Due to the special nature of sudden events, there may be limited historical experience data available for reference; therefore, expert estimation methods are more commonly used. To overcome the limitations of expert knowledge and the influence of personal preferences, the arithmetic mean of the estimation results from multiple experts is generally used as the final estimate.

[0040] b) Calculation method for scenario state probability; To illustrate the method for calculating the probability of a scenario state, we will use... Figure 5 Taking the network node variables at two times, t0 and t1, as an example, we will explain the calculation method of the scenario state variables S0 and S1.

[0041] b1) Calculation of the initial scenario state probability P(S0) (at time t0); Step 1: Determine the type of network node variables and their set of values, as shown in Table 1.

[0042] Table 1

[0043] Step 2: Confirm as well as ; Assumption: , ; , ; ; ; ; ; Step 3: Calculation ; According to the law of total probability, we have:

[0044] Because the premise of Bayesian networks is conditional independence, i.e.

[0045] Therefore, substituting equation (2) into equation (1) yields:

[0046] Further expansion yields:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Based on this, it can be calculated that: .

[0054] b2) Probability of situation at time Calculation; Step 1: Determine the type of network node variables and their set of possible values; Step 2: Confirm as well as ; Based on the above calculation results, we have:

[0055] Assumption: , ; , ; ; ; ; ; ; ; ; ; Step 3: Calculation ; According to the law of total probability, we have:

[0056] Similarly, since the premise of Bayesian networks is conditional independence, i.e.

[0057] Therefore, substituting equation (5) into equation (4), we get:

[0058] Expanding further, we get:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Based on this, it can be calculated that: .

[0074] From state S0 to state S1, under the conditions of a positive external environment (H) and appropriate emergency activities (B), the probability of developing towards the optimistic path is 0.669, and the probability of developing towards the pessimistic path is 0.331.

[0075] Example 2 In a large-scale power outage, emergency activities and the external environment act as causes affecting the scenario state, meaning the scenario state is the final result. Emergency resources serve as constraints on this interaction. It can be assumed that, ideally, emergency resources are sufficient, so the constraints are always satisfied. The model primarily considers the causal relationships between the scenario state, emergency activities, and the external environment.

[0076] This paper analyzes and simulates a power grid emergency situation by considering the process of natural disasters damaging the power grid and causing widespread power outages. First, from the acquisition of natural disaster information to the occurrence of the disaster, the current situation can be considered the emergency preparedness phase of power grid emergency response, denoted as S0. At this stage, there is no damage to power facilities, and no power outage occurs. When a natural disaster occurs, such as a typhoon or torrential rain, the external environment changes, and H1 becomes negative. Due to considerations of personnel safety, emergency activities B1 are also negative during the disaster. Scenario S0 progresses towards S1 in a pessimistic direction, resulting in damage to power grid equipment. When the natural disaster worsens, such as a typhoon making landfall or torrential rain causing flooding, scenario S1 progresses towards S2. Because the external environment H2 becomes even more negative, and due to the increased severity of the disaster and considerations of emergency rescue personnel safety, no active emergency activities are undertaken. If emergency activity B2 is also negative, then the possible outcome of scenario S2 is more damage to power equipment, potentially leading to a large-scale power outage. As scenario S2 progresses to S3, the external environment H3 gradually improves (e.g., typhoon winds and rainfall decrease), making H3 positive. At this point, emergency response decision-makers deploy resources and implement positive emergency activity B3. Previously, it was assumed that emergency resources were fully sufficient, so resource constraints are not considered. Scenario S3 then develops positively, preventing a large-scale power outage. Finally, as emergency activity B4 continues to be implemented and strengthened, the external environment H4 also gradually improves (typhoon stops, rainfall stops, etc.). Scenario S3 progresses positively towards S4, ultimately preventing a large-scale power outage. Throughout the model simulation, although there was damage to power equipment and some power outages, the changing external environment and the continuous strengthening of emergency activities transformed the entire scenario from critical to safe, until all power equipment was repaired and all power supply was restored.

[0077] Example 3 refer to Figure 7The power grid emergency scenario dynamic simulation and analysis system of the present invention includes: The first acquisition module is used to acquire the current state of the power grid after a power grid emergency occurs; The second acquisition module is used to acquire all currently available emergency activities, external environment, and emergency resources. The simulation and analysis module is used to simulate and analyze the state of the power grid at the next moment based on the current state of the power grid and all available emergency activities, external environment and emergency resources.

[0078] In this embodiment, the deduction and analysis module includes: The generation unit is used to generate several scenario state variables based on the current scenario state of the power grid and all currently available emergency activities, external environment and emergency resources; The first calculation unit is used to calculate the probability of occurrence of each scenario state variable; The selection unit is used to select the scenario state variable with the highest probability of occurrence as the scenario state of the power grid at the next moment.

[0079] In this embodiment, during the process of generating several scenario state variables based on the current power grid scenario state and all currently available emergency activities, external environment and emergency resources, one scenario state variable corresponds to one emergency activity, one external environment and one emergency resource, and each scenario state variable is generated through the corresponding emergency activity, external environment and emergency resource.

[0080] In this embodiment, the first computing unit includes: The second calculation unit is used to calculate the probability of occurrence of emergency activities corresponding to each scenario state variable; The third calculation unit is used to calculate the probability of occurrence of the external environment corresponding to each scenario state variable; The fourth calculation unit is used to calculate the probability of occurrence of the corresponding scenario state variable based on the probability of occurrence of the emergency activity and the probability of occurrence of the external environment.

[0081] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0082] Example 4 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a dynamic simulation and analysis method for power grid emergencies. For example, the steps include: acquiring the current state of the power grid after an emergency occurs; acquiring all currently available emergency activities, external environment, and emergency resources; and, based on the current state of the power grid and all currently available emergency activities, external environment, and emergency resources, simulating and analyzing the state of the power grid at the next moment. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus may be classified as an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0083] Example 5 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a dynamic scenario simulation and analysis method for power grid emergencies. For example, the method includes: acquiring the current state of the power grid after an emergency occurs; acquiring all currently available emergency activities, external environment, and emergency resources; and, based on the current state of the power grid and the available emergency activities, external environment, and emergency resources, simulating and analyzing the state of the power grid at the next moment. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0089] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0090] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power grid emergency scenario dynamic reasoning analysis method, characterized in that, The method comprises the following steps: acquiring the current grid situation state after the occurrence of the power grid emergency event; acquiring all emergency activities, external environments and emergency resources that can be provided at present; deducing and analyzing the grid situation state at the next moment according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present.

2. The grid contingency scenario dynamic propagation analysis method of claim 1, wherein, The process of deducing and analyzing the grid situation state at the next moment according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present comprises the following steps: generating a plurality of situation state variables according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present; calculating the occurrence probability of each situation state variable; selecting the situation state variable with the maximum occurrence probability as the grid situation state at the next moment.

3. The grid contingency scenario dynamic propagation analysis method of claim 2, wherein, In the process of generating a plurality of situation state variables according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present, one situation state variable corresponds to one emergency activity, one external environment and one emergency resource, and each situation state variable is generated through the corresponding emergency activity, external environment and emergency resource.

4. The grid contingency scenario dynamic propagation analysis method of claim 2, wherein, The process of calculating the occurrence probability of each situation state variable comprises the following steps: calculating the occurrence probability of the emergency activity corresponding to each situation state variable; calculating the occurrence probability of the external environment corresponding to each situation state variable; calculating the occurrence probability of the corresponding situation state variable according to the occurrence probability of the emergency activity and the occurrence probability of the external environment.

5. A power grid contingency scenario dynamic reasoning analysis system, characterized in that, The method comprises the following steps: a first acquisition module for acquiring the current grid situation state after the occurrence of the power grid emergency event; a second acquisition module for acquiring all emergency activities, external environments and emergency resources that can be provided at present; a deduction and analysis module for deducing and analyzing the grid situation state at the next moment according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present.

6. The power grid contingency scenario dynamic propagation analysis system of claim 5, wherein, The deduction and analysis module comprises: a generation unit for generating a plurality of situation state variables according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present; a first calculation unit for calculating the occurrence probability of each situation state variable; a selection unit for selecting the situation state variable with the maximum occurrence probability as the grid situation state at the next moment.

7. The power grid contingency scenario dynamic push- ahead analysis system of claim 6, wherein, In the process of generating a plurality of situation state variables according to the current grid situation state and all emergency activities, external environments and emergency resources that can be provided at present, one situation state variable corresponds to one emergency activity, one external environment and one emergency resource, and each situation state variable is generated through the corresponding emergency activity, external environment and emergency resource.

8. The grid contingency scenario dynamic propagation analysis system of claim 6, wherein, The first calculation unit comprises: a second calculation unit for calculating the occurrence probability of the emergency activity corresponding to each situation state variable; a third calculation unit for calculating the occurrence probability of the external environment corresponding to each situation state variable; A fourth computing unit is configured to calculate a probability of occurrence of a corresponding scenario state variable according to the probability of occurrence of the emergency event and the probability of occurrence of the external environment.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the power grid emergency scenario dynamic deduction analysis method according to any one of claims 1-4 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the power grid emergency scenario dynamic deduction analysis method according to any one of claims 1-4.

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