Vehicle-to-home energy system control method and device, electronic equipment and storage medium

Abstract

The invention relates to a vehicle-to-home energy system control method and device, electronic equipment and a storage medium, and is applied to the technical field of vehicles, and the method comprises the steps: obtaining meteorological data and energy state data of a vehicle-to-home energy system; based on the meteorological data and the energy state data, determining a target danger level when the vehicle-to-home energy system performs energy interaction; determining a target energy regulation and control strategy corresponding to the target danger level; and performing energy regulation and control on the vehicle-to-home energy system according to the target energy regulation and control strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and in particular to a control method and device for a vehicle-to-home energy system, an electronic device, and a storage medium. BACKGROUND

[0002] With the transformation of global energy structure and the frequent occurrence of extreme weather events, vehicle-to-home (V2H) energy systems, as a distributed energy storage solution, have gradually become a key technology for improving the resilience of power grids and the autonomy of household energy. V2H systems can store energy during low-price periods and supply power during peak periods through the bidirectional energy interaction between electric vehicle batteries and household power grids, significantly reducing household electricity costs and alleviating pressure on power grids. However, existing technologies focus more on energy management in regular scenarios, lacking dynamic adaptability to extreme weather conditions such as typhoons, snowstorms, and cold waves. Extreme weather not only causes fluctuations in power grids and overloads on equipment, but also may cause delays in V2H system protection due to communication interruptions or sensor failures, threatening household electricity safety.

[0003] Current protection strategies for V2H systems mainly rely on local threshold triggering or fixed rules, such as static charging and discharging limits based on historical data, which are difficult to cope with rapid changes in weather conditions and compound disaster risks. SUMMARY

[0004] The present application provides a control method and device for a vehicle-to-home energy system, an electronic device, and a storage medium, to solve the problem that the control method of fixed rules in the prior art has limitations and cannot quickly respond to rapid changes in weather.

[0005] According to a first aspect of an embodiment of the present application, a control method for a vehicle-to-home energy system is provided, comprising:

[0006] obtaining meteorological data and energy state data of the vehicle-to-home energy system;

[0007] determining a target risk level of the vehicle-to-home energy system during energy interaction based on the meteorological data and the energy state data;

[0008] determining a target energy regulation strategy corresponding to the target risk level;

[0009] regulating energy of the vehicle-to-home energy system according to the target energy regulation strategy.

[0010] Optionally, determining a target risk level of the vehicle-to-home energy system during energy interaction based on the meteorological data and the energy state data comprises:

[0011] determining risk weights of the meteorological data and the energy state data respectively;

[0012] normalize the meteorological data and the energy state data respectively to obtain respective risk factors of the meteorological data and the energy state data;

[0013] determine the target risk level based on the risk weight and the risk factor.

[0014] Optionally, determining the respective risk weight of the meteorological data and the energy state data comprises:

[0015] obtaining an initial risk weight of the meteorological data and the energy state data respectively;

[0016] in a case where the meteorological data meets preset disaster meteorological data, increasing the initial risk weight of the meteorological data and decreasing the initial risk weight of the energy state data.

[0017] Optionally, determining the target energy regulation strategy corresponding to the target risk level comprises:

[0018] in a case where the target risk level is a warning level, the target energy regulation strategy comprises at least one of the following: configuring a first preset proportion of rated power value of the charging and discharging power of the car-to-home energy system, and releasing a second preset proportion of reserve power of the car-to-home energy system;

[0019] in a case where the target risk level is an emergency level, the target energy regulation strategy comprises at least one of the following: powering off a designated power consumption device in the car-to-home energy system,

[0020] in a case where the target risk level is a disaster level, the target energy regulation strategy comprises at least one of the following: the car-to-home energy system is powered by a vehicle power supply, the connection with a fault power grid is disconnected, and a power grid collapse warning information is output.

[0021] Optionally, after obtaining the meteorological data, the method further comprises:

[0022] predicting a meteorological change trend according to the meteorological data to obtain predicted meteorological data;

[0023] determining a probability of occurrence of a natural disaster based on the predicted meteorological data;

[0024] in a case where the probability of occurrence is greater than a preset probability, generating a pre-interruption strategy for the natural disaster;

[0025] regulating energy of the car-to-home energy system based on the pre-interruption strategy.

[0026] Optionally, after obtaining the meteorological data, the method further comprises:

[0027] determining a target disaster scenario to which the meteorological data belongs among a plurality of preset disaster scenarios;

[0028] determine a target communication link corresponding to the target disaster scene;

[0029] control the car-to-home energy system to communicate according to the target communication link.

[0030] Optionally, after the energy regulation on the car-to-home energy system according to the target energy regulation strategy, the method further includes:

[0031] obtain an energy execution result of the car-to-home energy system;

[0032] evaluate effectiveness of the target energy regulation strategy according to the energy execution result;

[0033] in a case where the energy execution result indicates that the target energy regulation strategy is invalid, re-determine a target energy regulation strategy.

[0034] According to a second aspect of an embodiment of the present application, a control device of a car-to-home energy system is provided, including:

[0035] an obtaining unit, configured to obtain meteorological data and energy state data of the car-to-home energy system;

[0036] a first determining unit, configured to determine a target danger level when the car-to-home energy system performs energy interaction, based on the meteorological data and the energy state data;

[0037] a second determining unit, configured to determine a target energy regulation strategy corresponding to the target danger level;

[0038] a control unit, configured to regulate energy of the car-to-home energy system according to the target energy regulation strategy.

[0039] According to a third aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor;

[0040] the memory is connected with the processor, and is configured to store a program;

[0041] the processor is configured to realize the control method of the car-to-home energy system according to the first aspect by running the program in the memory.

[0042] According to a fourth aspect of an embodiment of the present application, a storage medium is provided, and the storage medium stores a computer program. When the computer program is run by a processor, the control method of the car-to-home energy system according to the first aspect is realized.

[0043] According to a fifth aspect of an embodiment of the present application, a computer program product is provided, including computer program instructions. When the computer program instructions are run by a processor, the processor executes the control method of the car-to-home energy system according to the first aspect.

[0044] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art: the method provided by the embodiment of the present application acquires meteorological data and energy state data of a car-to-home energy system; determines a target danger level when the car-to-home energy system performs energy interaction based on the meteorological data and the energy state data; determines a target energy regulation strategy corresponding to the target danger level; and regulates energy of the car-to-home energy system according to the target energy regulation strategy. In this way, the danger level when the car-to-home energy system performs energy interaction is evaluated based on the meteorological data and the energy state data, and then the car-to-home energy system is regulated by the target energy regulation strategy determined based on the target danger level, so that the car-to-home energy system is regulated according to the meteorological data of the weather change, which can cope with the change of the weather and reduce the failure risk of the car-to-home energy system. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by the provided drawings without creative labor for those skilled in the art.

[0046] FIG. 1 The flowchart of the control method of the car-to-home energy system provided by an embodiment of the present application is shown in FIG. 1.

[0047] FIG. 2 The structural diagram of the electronic device provided by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] Example implementation environment

[0050] The control method of the car-to-home energy system according to the embodiments of the present application can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like. The server can be a physical server, a server cluster composed of multiple physical servers, or a cloud server capable of cloud computing. The method can be implemented by a processor invoking computer readable program instructions stored in a memory. The present application takes the control method of the car-to-home energy system executed by the server as an example for explanation and description, but is not limited thereto.

[0051] Example method

[0052] Referring to FIG. 1 In an exemplary embodiment, a control method of a car-to-home energy system is provided, comprising:

[0053] In step 101, meteorological data and energy state data of the car-to-home energy system are acquired.

[0054] In some embodiments, the meteorological data can include real-time wind speed field, pressure gradient, precipitation intensity data detected by meteorological satellites, and real-time monitoring values of local wind speed, temperature and humidity, and precipitation monitored by ground monitoring stations, to supplement the insufficient spatial resolution of satellite data. Historical meteorological data can also be included, by retrieving associated parameters of historical extreme weather events (such as typhoon path, duration of cold wave), a disaster feature library is constructed.

[0055] The energy state data can include real-time collected voltage, frequency, load power fluctuation rate of the home power grid in the car-to-home energy system, to detect abnormal harmonic components; and vehicle battery SOC (state of charge), SOH (state of health), battery temperature and charge-discharge power limit value, and can also include evaluation of power grid vulnerability of the car-to-home energy system.

[0056] The power grid vulnerability can be determined by line load rate and equipment aging degree, which divides the risk area of the power grid vulnerability into three risk areas, including low, medium and high.

[0057] The line load rate can be collected by the smart meter and the PMU (synchronous phasor measurement device) through the distribution network terminal equipment (such as the opening and closing terminal equipment (DTU), the feeder terminal equipment (FTU)).

[0058] In this embodiment, by integrating multi-dimensional data such as meteorological satellites, ground monitoring stations, and historical disaster databases, the time and space alignment algorithm can be used to eliminate data delay and noise, and then a dynamic meteorological feature library can be constructed. By real-time fusion of multi-source meteorological data, the traditional static threshold limit is broken through, the influence of extreme weather on the home power grid and vehicle battery is dynamically predicted, and the protection strategy is triggered in advance. For example, when heavy rain causes power grid overload and communication interruption, the system can predict the power grid load peak and communication link failure risk based on the precipitation intensity and wind speed prediction, and realize advanced intervention.

[0059] In an optional embodiment, to improve the reliability of data and reduce the interference of abnormal data, after obtaining the meteorological data and energy state data, the above data can be preprocessed. The preprocessing process includes: meteorological data cleaning and power grid data anomaly detection in energy state data.

[0060] The time series of meteorological data is aligned, the minute-level meteorological data is interpolated into second-level time series, and the grid monitoring data is synchronized. In addition, the outliers are removed, and the sliding window mean filter is used to eliminate transient noise (such as abnormal peak value of wind speed sensor caused by flying birds).

[0061] The differential threshold method is used to calculate the absolute value of the voltage difference of adjacent time, and if it exceeds the physical limit (such as ΔV> 20% rated voltage), it is determined as sensor failure. For harmonic component analysis in power grid data, abnormal harmonics are identified by fast Fourier transform (FFT), and harmonic interference caused by device aging is isolated.

[0062] In an optional embodiment, after obtaining the meteorological data, it further includes:

[0063] According to the meteorological data, the trend of meteorological change is predicted to obtain predicted meteorological data;

[0064] Based on the predicted meteorological data, the occurrence probability of natural disasters is determined;

[0065] In the case where the occurrence probability is greater than the preset probability, a pre-blocking strategy for natural disasters is generated;

[0066] Based on the pre-blocking strategy, the energy of the car-to-home energy system is regulated.

[0067] In some embodiments, by predicting the meteorological data, the potential harm caused by the current meteorological data can be known in advance, and thus prevention can be made in advance. A multi-modal feature extraction model (such as a spatio-temporal convolution network and a long short-term memory network) can be used to identify early signs of extreme weather (such as sudden pressure drop, sudden temperature and humidity change), and predict the influence range and intensity evolution trend. Through a spatio-temporal alignment algorithm, data delay is eliminated, and early warning of extreme weather (such as sudden pressure drop prediction) is achieved.

[0068] Furthermore, the occurrence probability of natural disasters can be determined according to the predicted meteorological data. For example, by using a disaster database, secondary disasters that can be caused by the current meteorological data (such as rainstorm causing road waterlogging, and thus causing substation waterlogging) can be identified. Early composite features of extreme weather (such as cold wave superimposed on road icing leading to traffic paralysis, and indirectly causing power grid load imbalance) can be identified.

[0069] In this embodiment, the occurrence probability of secondary disasters can be calculated based on a Markov chain, and a pre-disruption strategy can be generated. Taking rainstorm weather as an example, when the rainfall reaches a preset rainfall threshold, the specific steps can include:

[0070] Building a disaster state transition model: defining state nodes (rainstorm causing road waterlogging, substation waterlogging, power grid short circuit) based on historical disaster cases (such as rainstorm cases) and training a probability transition matrix, so as to generate the occurrence probability of natural disasters using the probability transition matrix; a mapping relationship between meteorological parameters and power grid load (such as a probability model of rainstorm causing sudden drop of photovoltaic power generation) can also be established to obtain the occurrence probability.

[0071] Real-time dynamic correction of occurrence probability: integrating drainage-related sensor data (such as 30% sudden drop of waterway drainage rate), updating the road waterlogging probability (for example, increasing to 1.0), superimposing equipment corrosion index, and improving the V2H failure probability;

[0072] Key disruption point identification: performing a depth-first search on the maximum risk path (such as the “rainstorm, waterlogging, short circuit” chain with a cumulative probability of 0.663), and locating the minimum cost disruption node (such as cutting off the power grid before substation waterlogging);

[0073] Generating and executing a pre-disruption strategy: outputting hierarchical instructions (intelligent circuit breaker tripping, and activating the waterproof mode of charging piles), and verifying the effectiveness of the strategy through power grid simulation, finally suppressing the secondary disaster loss by 70% and compressing the response time to seconds, forming a disaster chain disruption closed loop of “probability prediction, path cutting, and active isolation”.

[0074] For example, the pre-disruption strategy can include cutting off the connection between the intelligent circuit breaker and the outdoor substation to prevent fault propagation; defining a network (SDN) to reconfigure the routing table and isolate the fault node.

[0075] The moving path and influence radius of extreme weather (e.g., typhoon) can also be calculated based on the pressure gradient field in the satellite data. By accurately predicting the moving track of the typhoon and its spatial influence range (e.g., storm circle radius, precipitation area), the location of the V2H device that will be affected by the disaster can be accurately positioned, and then a hierarchical protection strategy (e.g., automatically cutting off the charging circuit in high-risk areas, reserving 80% of the vehicle battery capacity to ensure emergency power supply for the home in disaster) can be generated in combination with the grid vulnerability assessment results. At the same time, meteorological prediction rules are preloaded into the edge gateway to ensure that local dynamic protection actions (e.g., automatically relaxing the charging and discharging restrictions after the typhoon path deviates) can still be performed based on real-time updated disaster confidence intervals when communication is interrupted, thereby breaking through the technical bottleneck of the traditional V2H system that the response to complex meteorological disasters is lagging due to static rules.

[0076] In an optional embodiment, after obtaining the meteorological data, the method further comprises:

[0077] determining a target disaster scenario to which the meteorological data belongs among a plurality of preset disaster scenarios;

[0078] determining a target communication link corresponding to the target disaster scenario;

[0079] controlling the vehicle-to-home energy system to communicate according to the target communication link.

[0080] In some embodiments, the communication links can be hierarchically pre-configured, for example, the priority from high to low is satellite communication, low-power wide-area network (LPWAN), 4G, and Wi-Fi. The link priority can be automatically adjusted according to the disaster scenario (e.g., the priority of the satellite link is increased during a typhoon).

[0081] For different disaster scenarios, the communication link with the best communication effect can be configured to improve the communication effect and reduce the damage caused by the disaster.

[0082] To avoid frequent switching of communication links, the switching timing of the communication link can be configured, for example, the number of packet losses is set. For example, when the packet loss rate of the primary link is greater than 10% for three consecutive times, the secondary link switching is triggered.

[0083] Further, a backup redundant link can be set to start a local cache instruction execution mode to maintain basic protection actions in the case of failure of all links.

[0084] Step 102, based on the meteorological data and energy state data, determining a target danger level when the vehicle-to-home energy system interacts with energy.

[0085] In some embodiments, potential dangers (e.g., battery overheating causing fire, power grid overload causing short circuit, etc.) can be identified in advance through meteorological data (e.g., extreme weather, lightning, high temperature, etc.) and energy state data (e.g., battery temperature, voltage, power grid load, etc.). By determining the target danger level, clear decision criteria are provided for user and system automatic control. Users can determine whether to manually intervene in energy interaction (e.g., manually pause at high danger level) according to the danger level, and the system can also automatically execute preset strategies based on the level to reduce human judgment errors and delays.

[0086] In some embodiments, the danger level can be divided into warning level, emergency level and disaster level.

[0087] In an optional embodiment, based on the meteorological data and the energy state data, the target danger level of the vehicle-to-home energy system when performing energy interaction is determined, comprising:

[0088] Determining the risk weight of the meteorological data and the energy state data respectively;

[0089] Normalizing the meteorological data and the energy state data respectively to obtain their respective risk factors;

[0090] Determining the target risk level based on the risk weight and the risk factor.

[0091] In some embodiments, the risk weight can be a pre-set fixed value, or a weight value adjusted according to actual conditions. In the case of multiple data in the meteorological data and the energy state data, a risk weight can be configured for each data. By normalizing the meteorological data and the energy state data, the risk factors between 0 and 1 are obtained, and the target risk level is determined.

[0092] After obtaining the risk factor and the risk weight, they are multiplied and summed to obtain the risk coefficient. By comparing the risk coefficient with the preset risk level corresponding to the coefficient interval, the corresponding target risk level is determined.

[0093] After normalizing the above-mentioned multi-source data (meteorological data, power grid load rate, equipment aging index) into risk factors between 0 and 1, the weighted synthesis of the basic risk value is obtained, and then the nonlinear piecewise function is corrected (e.g., the index amplifies the risk when the typhoon is ≥8), and finally the dynamic risk coefficient (R_final) is output. The value is calibrated by the grading threshold of the historical disaster loss data (e.g., R≤0.4 is the warning level, R>0.7 is the disaster level, and other conditions are the emergency level), which drives the V2H protection action (e.g., cutting off the charging or switching to emergency power supply), realizes the adaptive quantization and precise protection response of the core risk elements in the composite disaster scenario.

[0094] In an optional embodiment, the risk weight of the meteorological data and the energy status data is determined respectively, including:

[0095] The initial risk weight of the meteorological data and the energy status data is obtained respectively;

[0096] In the case that the meteorological data meets the preset disaster meteorological data, the initial risk weight of the meteorological data is increased, and the initial risk weight of the energy status data is decreased.

[0097] In some embodiments, in the case that the meteorological data reaches the meteorological data of a certain preset disaster, it indicates that there is a risk of occurrence of the preset disaster, so the initial risk weight of the meteorological data can be increased to increase the proportion of the meteorological data, so that the determined target risk level is more accurate.

[0098] The dynamic adjustment of the risk weight can be triggered by the disaster type identification (such as heavy rain, typhoon) to trigger the preset rule library for risk weight distribution (such as the risk weight of power grid overload in heavy rain scenario is increased to 60%),

[0099] Step 103, determining a target energy regulation strategy corresponding to the target risk level.

[0100] In some embodiments, there is a corresponding relationship between the risk level and the energy regulation strategy, and the corresponding relationship between the risk level and the energy regulation strategy can be pre-configured, so that after the target risk level is determined, the target energy regulation strategy can be determined from the corresponding relationship.

[0101] In an optional embodiment, the target energy regulation strategy corresponding to the target risk level is determined, including:

[0102] In the case that the target risk level is the warning level, the target energy regulation strategy includes at least one of the following: configuring the charging and discharging power of the car-to-home energy system as a first preset proportion of the rated power value, and releasing a second preset proportion of the reserve power of the car-to-home energy system.

[0103] In the case that the target risk level is the emergency level, the target energy regulation strategy includes at least one of the following: powering off the designated power consumption equipment in the car-to-home energy system,

[0104] In the case that the target risk level is the disaster level, the target energy regulation strategy includes at least one of the following: the car-to-home energy system is powered by the vehicle power supply, the connection with the fault power grid is disconnected, and the power grid collapse warning information is output.

[0105] In some embodiments, when the target risk level is the warning level, power adjustment is performed to limit the V2H charging and discharging power to 60% of the rated value to avoid exacerbating grid fluctuations, and energy storage buffering is started to release 5% of the reserve power of the home energy storage system to balance local load fluctuations.

[0106] When the target risk level is the emergency level, non-essential loads are cut off, devices with a priority less than three (such as air conditioners and water heaters) are disconnected, medical devices are kept powered, and the energy storage system is switched to directly power critical loads.

[0107] When the target risk level is the disaster level, island mode is activated, the connection with the faulty grid is disconnected, the vehicle battery is used to independently power the core loads, and emergency communication is started to send a grid collapse warning to the emergency center through a satellite link to request external assistance.

[0108] Step 104: Energy regulation is performed on the vehicle-to-home energy system according to the target energy regulation strategy.

[0109] In some embodiments, energy regulation according to the target energy regulation strategy can avoid risks in the operation of the vehicle-to-home energy system, cope with risks caused by different weather conditions, improve energy utilization efficiency and supply-demand matching, and reduce energy loss.

[0110] In an optional embodiment, after energy regulation is performed on the vehicle-to-home energy system according to the target energy regulation strategy, the following steps are further included:

[0111] An energy execution result of the vehicle-to-home energy system is obtained.

[0112] The effectiveness of the target energy regulation strategy is evaluated according to the energy execution result.

[0113] In the case where the energy execution result indicates that the target energy regulation strategy is ineffective, a new target energy regulation strategy is determined.

[0114] In some embodiments, the energy execution result can include the grid voltage fluctuation rate and the battery SOC change rate every interval of a preset time length (e.g., 500 ms) to evaluate the effectiveness of the target energy regulation strategy. An abnormal rollback mechanism can be used, and if the voltage fluctuation rate is greater than 5%, the system automatically reverts to the previous strategy.

[0115] The above process can be configured in a control model, and reinforcement learning training can be performed on the control model to adjust the risk weight based on the energy execution result (e.g., increase the low temperature weight of the cold wave scenario). The optimized strategy can also be synchronized to other home nodes through federated learning to form a collaborative defense network.

[0116] The federal learning strategy synchronization mechanism can realize cross-node collaboration through a three-stage process.

[0117] Edge node local encryption training: based on disaster response history (such as low temperature failure events in cold waves), each household optimizes the risk model parameters locally, and generates a differential privacy protected gradient increment;

[0118] Regional aggregation and global update: community-level edge gateway, fusion of encrypted increments of multiple nodes, through federal average to generate new strategy weight (such as cold wave scenario low temperature weight increase by 15%);

[0119] Lightweight strategy real-time distribution: the cloud control center broadcasts the compressed global model (sparse to <10KB) to the unaffected nodes for dynamic loading and effect, realizing minute-level collaborative migration of disaster prevention experience (such as synchronizing the anti-overload strategy of the typhoon area to the cold wave risk area for pre-deployment).

[0120] The control method of the car-to-home energy system of the present application can also set data caching strategy to store the last 1 hour meteorological data and household state, support continuous decision-making when offline; and instruction retransmission mechanism, after communication recovery, to resend control instructions during interruption, ensuring instruction integrity.

[0121] The control method of the car-to-home energy system of the present application integrates meteorological satellites, ground monitoring stations and historical disaster databases, eliminates data delay through spatio-temporal alignment algorithm, realizes early warning of extreme weather (such as sudden pressure drop prediction). Establishes a correlation model between meteorological parameters and household power grid load to dynamically assess risk levels (such as warning level, emergency level) instead of relying on a single threshold trigger. Design a three-level response mechanism: reduce charging and discharging power in advance at the warning level, start household energy storage buffer (such as dynamic power adjustment not realized by the Nigijima system); cut off unnecessary loads at the emergency level, and prioritize power supply to critical equipment (such as medical equipment); enable vehicle battery island power supply at the disaster level to isolate the fault power grid area (to avoid cascading failures). Feedback optimization module can also be used to dynamically correct the strategy (such as adjusting the response level according to the voltage fluctuation of the household microgrid).

[0122] The design of the redundant communication link design integrates LPWAN (low power wide area network) and satellite communication module, automatically switches when the base station is paralyzed due to disasters, and ensures the continuity of command transmission. The lightweight model (such as dynamic risk assessment) is executed locally in the home gateway, reducing the dependence on the cloud, and the response time is shortened to milliseconds (compared with the existing technology with a delay of seconds). Localized decision-making reduces data upload delay, for example, in a communication interruption scenario, the edge node can still execute the preset protection strategy based on the locally cached weather data and home state. Through multi-source data fusion, the composite disaster risk (such as precipitation intensity and wind speed prediction) is identified, the power grid load peak and communication link failure probability are predicted, and the vehicle battery charging and discharging strategy (such as limiting the charging and discharging power to avoid exacerbating the power grid fluctuation) is dynamically adjusted.

[0123] Example apparatus

[0124] Correspondingly, the embodiment of the application also provides a control device of a vehicle-to-home energy system, comprising:

[0125] an acquisition unit configured to acquire weather data and energy state data of the vehicle-to-home energy system;

[0126] a first determination unit configured to determine a target danger level of the vehicle-to-home energy system during energy interaction based on the weather data and the energy state data;

[0127] a second determination unit configured to determine a target energy regulation strategy corresponding to the target danger level;

[0128] a control unit configured to regulate the energy of the vehicle-to-home energy system according to the target energy regulation strategy.

[0129] The control device of the vehicle-to-home energy system provided by the embodiment belongs to the same application concept as the control method of the vehicle-to-home energy system provided by the above-mentioned embodiments of the application, can execute the method provided by any of the above-mentioned embodiments of the application, and has the corresponding functional modules and beneficial effects of the execution method.

[0130] The technical details described in detail in the embodiment can refer to the specific processing content of the control method of the vehicle-to-home energy system provided by the above-mentioned embodiments of the application, which will not be repeated here.

[0131] The functions realized by each unit in the above control device of the vehicle-to-home energy system can be realized by the same or different processors, and the embodiments of the application are not limited.

[0132] It should be understood that each unit in the above apparatus can be implemented in the form of processor calling software. For example, the apparatus includes a processor connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the apparatus, wherein the processor can be a general processor such as CPU or microprocessor, and the memory can be an internal memory or an external memory of the apparatus. Alternatively, the units in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units can be realized by the design of the hardware circuit, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is ASIC, and the functions of part or all of the units are realized by the design of the logical relationship of elements in the circuit. For another example, in another implementation, the hardware circuit can be realized by PLD, and FPGA is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the units. All the units of the above apparatus can be realized in the form of processor calling software, or realized in the form of hardware circuit, or part of them are realized in the form of processor calling software, and the remaining part is realized in the form of hardware circuit.

[0133] In the embodiments of the present application, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as CPU, microprocessor, GPU, or DSP, etc. In another implementation, the processor can realize certain functions through the logical relationship of hardware circuit, which is fixed or can be reconfigured, such as ASIC or PLD implemented hardware circuit, such as FPGA, etc. In the reconfigurable hardware circuit, the process of the processor loading configuration document to realize hardware circuit configuration can be understood as the process of the processor loading instructions to realize the functions of part or all of the units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a kind of ASIC, such as NPU, TPU, DPU, etc.

[0134] It can be seen that each unit in the above apparatus can be one or more processors (or processing circuits) configured to implement the above methods, such as CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0135] In addition, all or part of each unit in the above device can be integrated together or can be independently implemented. In one implementation, these units are integrated together to be implemented in the form of a SOC. The SOC can include at least one processor for implementing any of the above methods or functions of the units of the device, and the at least one processor can be different, such as including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0136] Example electronic device

[0137] Another embodiment of the present application also provides an electronic device, as shown in FIG. 2 The device includes:

[0138] a memory 200 and a processor 210;

[0139] The memory 200 is connected with the processor 210, and is configured to store a program.

[0140] The processor 210 is configured to realize the control method of the car-to-home energy system disclosed in any of the above embodiments by running the program stored in the memory 200.

[0141] Specifically, the control device of the car-to-home energy system can further include a bus, a communication interface 220, an input device 230 and an output device 240.

[0142] The processor 210, the memory 200, the communication interface 220, the input device 230 and the output device 240 are connected with each other through the bus. Among them:

[0143] The bus can include a path for transmitting information between various components of a computer system.

[0144] The processor 210 can be a general processor, such as a general central processing unit (CPU), a microprocessor, etc., or can be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs of the present application. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-use programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0145] The processor 210 can include a main processor, and can also include a baseband chip, a modem, etc.

[0146] The memory 200 stores programs for implementing the technical solutions of the present application, and can also store an operating system and other key services. Specifically, the programs can include program codes, which include computer operation instructions. More specifically, the memory 200 can include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash, and the like.

[0147] The input device 230 can include devices that receive data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, and the like.

[0148] The output device 240 can include devices that allow information to be output to a user, such as a display screen, a printer, a speaker, and the like.

[0149] The communication interface 220 can include devices using any transceiver to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), and the like.

[0150] The processor 210 executes the programs stored in the memory 200 and calls other devices, which can be used to implement each step of the control method of any one of the vehicle-to-home energy systems provided by the above-mentioned embodiments.

[0151] Example computer program product and storage medium

[0152] In addition to the above-mentioned methods and devices, the embodiments of the present application can also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps of the control method of the vehicle-to-home energy system according to various embodiments of the present application described in any of the above-mentioned embodiments.

[0153] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of the present application, including an object-oriented programming language, such as Java, C++, and the like, and a conventional procedural programming language, such as the "C" language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0154] In addition, the embodiments of the present application can also be storage media having computer programs stored thereon, and the computer programs are executed by a processor to implement the steps of the control method of the car-to-home energy system according to various embodiments of the present application described in any embodiment of the present specification. Specifically, the following steps can be implemented:

[0155] Obtaining meteorological data and energy state data of the car-to-home energy system;

[0156] Based on the meteorological data and the energy state data, determining a target danger level when the car-to-home energy system performs energy interaction;

[0157] Determining a target energy regulation strategy corresponding to the target danger level;

[0158] According to the target energy regulation strategy, performing energy regulation on the car-to-home energy system.

[0159] For each method embodiment described above, in order to simply describe, it is expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0160] It should be noted that each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, it is described relatively simply, and the relevant parts can be referred to the part of the method embodiment.

[0161] The steps in the method of each embodiment of the present application can be adjusted, combined and reduced in sequence according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0162] The modules and sub-modules in the device and terminal in each embodiment of the present application can be combined, divided and reduced according to actual needs.

[0163] It should be understood that the disclosed terminal, device and method can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or sub-modules is merely a logical function division. In actual implementation, another division manner can be used. For example, a plurality of sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0164] The modules or sub-modules described as separate components can or can not be physically separate, and the components of the modules or sub-modules can or can not be physical modules or sub-modules, i.e. can be located in one place or distributed on a plurality of network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0165] In addition, the functional modules or sub-modules in each embodiment of the present application can be integrated into a processing module, or each module or sub-module can exist physically, or two or more modules or sub-modules can be integrated into one module. The integrated module or sub-module can be realized in the form of hardware or software functional module or sub-module.

[0166] The skilled person can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0167] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly implemented by hardware, software units executed by a processor, or a combination of both. The software units can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0168] Finally, it should be noted that, in this document, the term "only" is used simply to set off from one entity or action to another in order to avoid the use of the term "and / or" or the like for the sake of clarity. In no way should the term "only" be interpreted as implying that there is an implied exclusion of any referenced entity or action. Moreover, the terms "comprising", "including", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0169] The above description of disclosed embodiments provides enabling teaching for making or using the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method of a vehicle-to-home energy system, characterized by, The method comprises the following steps: obtaining meteorological data and energy state data of a vehicle-to-home energy system; determining a target risk level of the vehicle-to-home energy system during energy interaction based on the meteorological data and the energy state data; determining a target energy regulation strategy corresponding to the target risk level; regulating the energy of the vehicle-to-home energy system according to the target energy regulation strategy.

2. The method of claim 1, wherein, Based on the meteorological data and the energy state data, the target risk level of the vehicle-to-home energy system during energy interaction is determined, which comprises the following steps: determining the risk weight of the meteorological data and the energy state data respectively; normalizing the meteorological data and the energy state data respectively to obtain their respective risk factors; determining the target risk level based on the risk weight and the risk factor.

3. The method of claim 2, wherein, Determining the risk weight of the meteorological data and the energy state data respectively comprises the following steps: obtaining the initial risk weight of the meteorological data and the energy state data respectively; in the case that the meteorological data meets the preset disaster meteorological data, increasing the initial risk weight of the meteorological data and reducing the initial risk weight of the energy state data.

4. The method of claim 1, wherein, Determining the target risk level corresponding to the target energy regulation strategy comprises the following steps: in the case that the target risk level is a warning level, the target energy regulation strategy comprises at least one of the following: configuring the charging and discharging power of the vehicle-to-home energy system as a first preset proportion of the rated power value, releasing a second preset proportion of the reserve capacity of the vehicle-to-home energy system; in the case that the target risk level is an emergency level, the target energy regulation strategy comprises at least one of the following: disconnecting the specified electrical equipment in the vehicle-to-home energy system, in the case that the target risk level is a disaster level, the target energy regulation strategy comprises at least one of the following: the vehicle-to-home energy system is powered by the vehicle power supply, the connection with the fault power grid is disconnected, and the power grid collapse warning information is output.

5. The method of claim 1, wherein, After obtaining the meteorological data, the method further comprises the following steps: predicting the meteorological change trend according to the meteorological data to obtain predicted meteorological data; determining the occurrence probability of natural disasters based on the predicted meteorological data; generating a pre-blocking strategy for natural disasters in the case that the occurrence probability is greater than a preset probability; regulating the energy of the vehicle-to-home energy system based on the pre-blocking strategy.

6. The method of claim 1, wherein, After obtaining the meteorological data, the method further comprises the following steps: determining the target disaster scene to which the meteorological data belongs in a plurality of preset disaster scenes; determining the target communication link corresponding to the target disaster scene; controlling the vehicle-to-home energy system to communicate according to the target communication link.

7. The method of claim 1, wherein, After regulating the energy of the vehicle-to-home energy system according to the target energy regulation strategy, the method further comprises the following steps: obtaining the energy execution result of the vehicle-to-home energy system; evaluating the effectiveness of the target energy regulation strategy according to the energy execution result; in the case that the energy execution result indicates that the target energy regulation strategy is invalid, re-determining the target energy regulation strategy.

8. A control device of a car-to-home energy system, characterized by, The method comprises the following steps: an obtaining unit is configured to obtain meteorological data and energy state data of a vehicle-to-home energy system; a first determining unit is configured to determine a target risk level of the vehicle-to-home energy system during energy interaction based on the meteorological data and the energy state data; A second determining unit is configured to determine a target energy regulation strategy corresponding to the target danger level. A control unit is configured to regulate the energy of the car-to-home energy system according to the target energy regulation strategy.

9. An electronic device, comprising: comprising a memory and a processor; The memory is connected with the processor and is configured to store programs; The processor is configured to realize the control method of the car-to-home energy system according to any one of claims 1 to 7 by running the programs in the memory.

10. A storage medium, characterized by The storage medium has a computer program stored thereon, and the computer program is run by the processor to realize the control method of the car-to-home energy system according to any one of claims 1 to 7.

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