Flood control plan determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a flood prevention plan determination method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing a natural language water conservancy demand input by a user based on a large model to obtain a flood control plan generation demand; wherein the large model is obtained by training based on water conservancy professional knowledge; determining a processing logic corresponding to the flood prevention plan generation demand, obtaining flood prediction associated data and a flood prediction model according to the processing logic, and performing processing to determine a flood deduction result; and generating at least one flood control plan according to the flood deduction result, and determining a target flood control plan according to the at least one flood control plan. According to the scheme, the flood prediction associated data is automatically acquired by using the large model, and the corresponding flood prediction model is automatically selected for processing to obtain the flood deduction result, so that the flood control plan is generated according to the flood deduction result, and the pertinence, effectiveness and generation efficiency of the flood control plan are improved.

Description

Technical Field

[0001] The present invention relates to the field of flood prevention and early warning technology, and in particular to a method, device, electronic equipment and storage medium for determining a flood prevention plan. Background Art

[0002] In recent years, flood disasters have occurred frequently and the intensity of disasters has been increasing, which has put forward higher requirements for flood control safety. In order to ensure that people's lives can be effectively protected and casualties can be avoided when floods come, it is important to pay attention to the rational development and utilization of water resources, optimize the spatial allocation of water resources, improve the efficiency of water resource utilization, and formulate plans, countermeasures and measures in advance for various types of rainstorms and floods that may occur.

[0003] At present, the existing flood prevention plans are mainly based on the artificial collection of historical flood data, geological and topographic information, meteorological forecast information, etc., based on historical data and current situation analysis, to predict and evaluate the possible flood intensity and disaster scope, and determine the key flood prevention areas and key facilities.

[0004] However, in the above process, there is a high degree of human participation and excessive reliance on human professional capabilities and experience, mainly drawing on historical experience and practices in other regions. This is prone to problems such as information asymmetry, lack of regional specificity, and untimely data updates, which in turn causes flood control plans to lose their guiding role in responding to emergencies. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for determining a flood prevention plan to solve the problems that the flood prevention plan relies on people's professional ability and experience when it is generated, resulting in the flood prevention plan not being regionally targeted and not being updated in a timely manner.

[0006] According to one aspect of the present invention, a method for determining a flood prevention plan is provided, the method comprising:

[0007] The user's natural language water conservancy needs are parsed based on the big model to obtain the needs for generating flood prevention plans; the big model is trained based on water conservancy professional knowledge;

[0008] Determine the processing logic corresponding to the needs of flood control plan generation, obtain and process flood prediction related data and flood prediction models according to the processing logic, and determine the flood simulation results;

[0009] At least one flood control plan is generated according to the flood simulation results, and a target flood control plan is determined according to the at least one flood control plan.

[0010] According to another aspect of the present invention, a flood prevention plan determination device is provided, the device comprising:

[0011] The demand acquisition module is used to parse the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand; the big model is trained based on water conservancy professional knowledge;

[0012] The first determination module is used to determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, and determine the flood simulation result;

[0013] The second determination module is used to generate at least one flood control plan according to the flood simulation results, and determine a target flood control plan according to the at least one flood control plan.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the flood control plan determination method of any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the flood control plan determination method of any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention is to parse the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand; wherein the big model is obtained by training based on water conservancy professional knowledge, so that the big model can recognize water conservancy professional knowledge and can recognize the flood control plan generation demand from the natural language water conservancy demand, so that the big model can generate the flood control plan in time; determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, determine the flood simulation result, and realize that the big model can automatically obtain real-time and comprehensive relevant flood prediction related data, and automatically use the corresponding flood prediction model to process the flood prediction related data to obtain the flood simulation result, thereby improving the efficiency of generating flood control plans; generate at least one flood control plan according to the flood simulation result, determine the target flood control plan according to the at least one flood control plan, realize that the big model can combine historical flood data with real-time monitoring data to generate at least one flood control plan, and select the target flood control plan from the at least one flood control plan, thereby ensuring that the flood control plan generated by the big model is targeted and effective.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flow chart of a method for determining a flood prevention plan provided in Embodiment 1 of the present invention;

[0023] Figure 2 A schematic diagram of the structure of a flood prevention plan determination device provided in Embodiment 2 of the present invention;

[0024] Figure 3 A schematic diagram of the structure of an electronic device for implementing a method for determining a flood prevention plan provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0028] Embodiment 1

[0029] Figure 1 This is a flow chart of a method for determining a flood prevention plan provided in the first embodiment of the present invention. The embodiment of the present invention can be applied to the case where a flood prevention plan is generated based on real-time monitored flood data. The method can be executed by a flood prevention plan determination device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device that implements the flood prevention plan determination method. Figure 1 As shown, the method includes:

[0030] S101. Analyze the natural language water conservancy demands input by the user based on the big model to obtain the requirements for generating flood control plans; wherein the big model is trained based on water conservancy professional knowledge.

[0031] In an embodiment of the present invention, a large model refers to a model that has natural language processing capabilities based on deep learning training, and can understand input natural language questions and feedback corresponding answers, such as an LLMA model. Natural language water conservancy needs refer to needs related to water conservancy expressed in natural language, such as the need to make a flood simulation for a certain area based on rainfall conditions input to the large model. Water conservancy expertise refers to knowledge related to the professional field of water conservancy, such as water conservancy professional terminology, water conservancy business rules, and knowledge related to water conservancy professional documents and water conservancy flood prevention plan reports.

[0032] Specifically, a large amount of data such as water conservancy professional knowledge and water conservancy business rules are input into the big model for learning and training, so that the big model can understand the professional terms of the water conservancy industry. Then, the big model can parse the input natural language water conservancy demand, identify the water conservancy professional terms in the natural language water conservancy demand, and form the flood control plan generation demand. The flood control plan generation demand is used to indicate the need to generate a flood control plan.

[0033] As an option, before parsing the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand, the method also includes the following steps A1-A3:

[0034] Step A1: Input water conservancy professional knowledge into the big model, and learn water conservancy professional knowledge through the big model to answer water conservancy professional questions.

[0035] Step A2: Optimize the large model according to the response of the large model.

[0036] Step A3: Test the optimized large model through the water conservancy professional question and answer database.

[0037] Specifically, before generating the flood control plan generation demand based on the big model, it is necessary to input the water conservancy professional knowledge into the big model and train the big model so that the big model can learn the water conservancy professional knowledge and thus can answer relevant questions about the water conservancy profession. Then, according to the big model's response results to the water conservancy professional questions, the big model is adjusted and optimized. For example, if the big model's response results to the water conservancy professional questions do not match the corresponding expected results, the big model needs to be trained again based on the water conservancy professional questions and the corresponding expected results. When the big model is adjusted and optimized, it is necessary to test the optimized big model based on the water conservancy professional question bank. If the big model passes the test, it can be used to generate flood control plans, otherwise continue to adjust and optimize the big model. Among them, the water conservancy professional question bank refers to a question bank containing questions related to the water conservancy profession and the corresponding answers.

[0038] For example, a water conservancy professional question paper of multiple-choice type can be created based on the water conservancy professional question and answer database, and the water conservancy professional question paper of multiple-choice type can be input into the big model for testing. Then, the score of the big model for the test paper is determined based on the big model's answer result to the water conservancy professional question paper of multiple-choice type. If the score reaches the expected score, the big model can be considered to have passed the test.

[0039] S102: Determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, and determine the flood simulation result.

[0040] In an embodiment of the present invention, processing logic refers to logic for acquiring relevant data according to the needs of generating a flood prevention plan, such as the logic of acquiring meteorological data or regional terrain data when generating a flood prevention plan. Flood prediction related data refers to relevant data required to predict the occurrence of floods, which can be obtained based on IoT data, Internet data and professional institutions. Among them, IoT data refers to real-time flood prediction data collected by sensors deployed on site; Internet data refers to relevant flood prediction data disclosed on the Internet. A flood prediction model refers to a water conservancy professional model corresponding to flood prediction related data, which can determine flood deduction results based on flood prediction related data. Among them, flood deduction results refer to relevant results when a flood is predicted to occur.

[0041] Specifically, the processing logic for obtaining flood prediction related data is determined according to the flood prevention plan generation requirements generated by the large model, and the corresponding flood prediction related data and the flood prediction model corresponding to the flood prediction related data are obtained according to the processing logic. Then, the flood prediction related data and the corresponding flood prediction model are processed to determine the flood simulation result.

[0042] As an optional method, flood prediction related data and a flood prediction model are obtained and processed according to the processing logic to determine the flood simulation result, including the following steps B1-B2:

[0043] Step B1, obtaining flood prediction related data according to processing logic; wherein the flood prediction related data includes at least one of hydrological data, meteorological data, geological data and economic data.

[0044] Step B2: Obtain a flood prediction model corresponding to the flood prediction related data, calculate the corresponding flood prediction related data based on the flood prediction model, and obtain a flood simulation result.

[0045] In the embodiments of the present invention, hydrological data refers to the main physical quantities that describe the hydrological situation, including various hydrological variables and hydrological phenomena, such as water flow, water temperature, water consumption, and water consumption. Meteorological data refers to data related to weather forecasts and warnings, climate predictions and estimates, various meteorological services, and scientific research, such as air humidity, wind direction, wind speed, and rainfall. Geological data refers to data that represents geological characteristics and reflects geological entities, such as topography and strata. Economic data refers to data that reflects the economic operation status, such as economic benefits, economic losses, and other data.

[0046] Specifically, at least one flood prediction related data among hydrological data, meteorological data, geological data and economic data is obtained according to the processing logic corresponding to the flood control plan generation demand. Then, the flood prediction related data is input into the flood prediction model corresponding to the flood prediction related data for calculation to obtain the corresponding flood deduction result.

[0047] As an optional method, a flood prediction model corresponding to the flood prediction associated data is obtained, and the corresponding flood prediction associated data is calculated based on the flood prediction model to obtain a flood deduction result, including the following steps C1-C2:

[0048] Step C1: Calculate the corresponding flood prediction related data based on the flood prediction model to obtain results of different flood indicators under the influence of different flood prediction related data.

[0049] Step C2: different flood indicators under the influence of different flood prediction related data are integrated to obtain flood simulation results.

[0050] Specifically, the corresponding flood prediction model is selected for calculation according to the different associated data contained in the flood prediction associated data, and the calculation results of different flood indicators corresponding to the different flood prediction associated data are determined. Among them, different flood indicators refer to the flood-related parameters contained in the flood simulation results, such as water level, inundation range, and victims. Then, the calculation results of different flood indicators corresponding to different flood prediction associated data are merged to obtain the flood simulation results.

[0051] For example, the water flow can be calculated by selecting a flood prediction model corresponding to the hydrological data according to the hydrological data included in the flood prediction associated data; the water level change can be calculated by selecting a flood prediction model corresponding to the meteorological data according to the meteorological data included in the flood prediction associated data; the flood range can be calculated by selecting a flood prediction model corresponding to the geological data according to the geological data included in the flood prediction associated data; the economic loss and the number of victims can be calculated by selecting a flood prediction model corresponding to the economic data according to the economic data included in the flood prediction associated data. Furthermore, the water flow, water level change, flood range, economic loss and victims can be integrated to obtain the flood simulation results.

[0052] S103: Generate at least one flood control plan according to the flood simulation results, and determine a target flood control plan according to the at least one flood control plan.

[0053] In the embodiment of the present invention, the flood prevention plan refers to the defense plan, countermeasures and measures prepared in advance for the occurrence of flood disasters. For example, the flood prevention plan can be the number of sluice gates to be opened, the types of materials to be provided, the number of people to be evacuated and the number of rescue teams to be formed when a flood disaster is expected to occur.

[0054] Specifically, the flood simulation results determined by the flood prediction model are input into the large model, and the large model can select at least one flood control plan similar to the flood simulation results from the historical flood control plans based on the flood simulation results. Then, the various flood control plans are simulated and analyzed to determine the target flood control plan. The target flood control plan can achieve the least economic loss, the fastest rescue speed and the least impact of flood disasters when a flood occurs compared to other flood control plans.

[0055] As an option, before generating at least one flood prevention plan according to the flood simulation results, the method further includes:

[0056] The historical flood simulation results, historical flood prevention plans and empirical flood prevention decision-making data are input into the big model, and the big model is trained so that it can learn the ability to generate flood prevention plans.

[0057] Specifically, before the big model generates at least one flood control plan based on the flood simulation results, it is necessary to input the historical flood simulation results, historical flood control plans and empirical flood control decision data corresponding to floods in historical periods into the big model for learning and training, so that the big model has the ability to generate flood control plans based on flood simulation results. Among them, the empirical flood control decision data can be the relevant flood control decision data corresponding to the measures that flood experts believe need to be taken based on their own experience when floods occur in historical periods.

[0058] As an optional method, generating at least one flood control plan according to the flood simulation results, and determining a target flood control plan according to the at least one flood control plan include the following steps D1-D2:

[0059] Step D1: acquiring dynamically updated flood prediction related data in real time, and updating at least one flood prevention plan based on the dynamically updated flood prediction related data and a flood prediction model.

[0060] Step D2: determining a target flood control plan from at least one flood control plan.

[0061] Specifically, since the flood prediction related data will be dynamically updated in real time, it is necessary to obtain the dynamically updated flood prediction related data and input it into the corresponding flood prediction model to obtain the corresponding dynamically updated flood simulation results. Furthermore, the dynamically updated flood simulation results are input into the large model to obtain at least one dynamically updated flood control plan, and the target flood control plan is determined from the at least one dynamically updated flood control plan, so that the large model can dynamically adjust and optimize the flood control plan according to the actual situation.

[0062] As an option, determining a target flood prevention plan from at least one flood prevention plan includes the following steps E1-E2:

[0063] Step E1: Obtain the user's rating of at least one flood prevention plan.

[0064] Step E2: The flood control plan with the highest score is used as the target flood control plan.

[0065] Specifically, at least one flood control plan is simulated and analyzed, the flood control effects of each flood control plan are evaluated, and each flood control plan is scored. Then, the flood control plan with the highest score can be used as the target flood control plan.

[0066] The technical solution of the embodiment of the present invention is to parse the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand; wherein the big model is obtained by training based on water conservancy professional knowledge, so that the big model can recognize water conservancy professional knowledge and can recognize the flood control plan generation demand from the natural language water conservancy demand, so that the big model can generate the flood control plan in time; determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, determine the flood simulation result, and realize that the big model can automatically obtain real-time and comprehensive relevant flood prediction related data, and automatically use the corresponding flood prediction model to process the flood prediction related data to obtain the flood simulation result, thereby improving the efficiency of generating flood control plans; generate at least one flood control plan according to the flood simulation result, determine the target flood control plan according to the at least one flood control plan, realize that the big model can combine historical flood data with real-time monitoring data to generate at least one flood control plan, and select the target flood control plan from the at least one flood control plan, thereby ensuring that the flood control plan generated by the big model is targeted and effective.

[0067] Embodiment 2

[0068] Figure 2 This is a schematic diagram of the structure of a flood prevention plan determination device provided in the second embodiment of the present invention. The embodiment of the present invention can be applied to the situation where a flood prevention plan is generated based on real-time monitored flood data. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device that implements a flood prevention plan determination method. Figure 2 As shown, the device comprises:

[0069] The demand acquisition module 201 is used to parse the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand; wherein the big model is obtained by training based on water conservancy professional knowledge;

[0070] The first determination module 202 is used to determine the processing logic corresponding to the flood control plan generation demand, obtain the flood prediction related data and the flood prediction model according to the processing logic and process them to determine the flood simulation result;

[0071] The second determination module 203 is used to generate at least one flood control plan according to the flood simulation result, and determine a target flood control plan according to the at least one flood control plan.

[0072] As an option, before parsing the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand, the flood control plan determination device is also used to:

[0073] Input water conservancy expertise into the big model, and use the big model to learn water conservancy expertise to answer water conservancy professional questions;

[0074] Optimize the big model based on its responses;

[0075] The optimized large model is tested through the water conservancy professional question and answer database.

[0076] As an option, flood prediction related data and flood prediction models are obtained and processed according to the processing logic to determine the flood simulation results, including:

[0077] Acquire flood prediction related data according to the processing logic; wherein the flood prediction related data includes at least one of hydrological data, meteorological data, geological data and economic data;

[0078] A flood prediction model corresponding to the flood prediction related data is obtained, and the corresponding flood prediction related data is calculated based on the flood prediction model to obtain the flood simulation result.

[0079] As an optional method, a flood prediction model corresponding to the flood prediction related data is obtained, and the corresponding flood prediction related data is calculated based on the flood prediction model to obtain flood deduction results, including:

[0080] Based on the flood prediction model, the corresponding flood prediction related data are calculated to obtain the results of different flood indicators under the influence of different flood prediction related data;

[0081] Different flood indicators under the influence of different flood prediction related data are integrated to obtain the flood simulation results.

[0082] As an option, before generating at least one flood control plan according to the flood simulation results, the flood control plan determination device is further used to:

[0083] The historical flood simulation results, historical flood prevention plans and empirical flood prevention decision-making data are input into the big model, and the big model is trained so that it can learn the ability to generate flood prevention plans.

[0084] As an option, generating at least one flood control plan according to the flood simulation results, and determining a target flood control plan according to the at least one flood control plan include:

[0085] Acquire dynamically updated flood prediction related data in real time, and update at least one flood prevention plan based on the dynamically updated flood prediction related data and a flood prediction model;

[0086] A target flood prevention plan is determined from at least one flood prevention plan.

[0087] As an option, a target flood prevention plan is determined from at least one flood prevention plan, including:

[0088] Obtaining a user's rating of at least one flood prevention plan;

[0089] The flood control plan with the highest score will be used as the target flood control plan.

[0090] The technical solution of the embodiment of the present invention is to parse the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand; wherein the big model is obtained by training based on water conservancy professional knowledge, so that the big model can recognize water conservancy professional knowledge and can recognize the flood control plan generation demand from the natural language water conservancy demand, so that the big model can generate the flood control plan in time; determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, determine the flood simulation result, and realize that the big model can automatically obtain real-time and comprehensive relevant flood prediction related data, and automatically use the corresponding flood prediction model to process the flood prediction related data to obtain the flood simulation result, thereby improving the efficiency of generating flood control plans; generate at least one flood control plan according to the flood simulation result, determine the target flood control plan according to the at least one flood control plan, realize that the big model can combine historical flood data with real-time monitoring data to generate at least one flood control plan, and select the target flood control plan from the at least one flood control plan, thereby ensuring that the flood control plan generated by the big model is targeted and effective. The flood control plan determination device provided in the embodiment of the present invention can execute the flood control plan determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0091] Embodiment 3

[0092] Figure 3 A schematic diagram of the structure of an electronic device for implementing a method for determining a flood prevention plan provided in Embodiment 3 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0093] like Figure 3As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0094] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0095] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a flood prevention plan determination method.

[0096] In some embodiments, the flood prevention plan determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the flood prevention plan determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the flood prevention plan determination method in any other appropriate manner (e.g., by means of firmware).

[0097] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0099] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0100] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0101] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0102] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0103] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0104] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a flood prevention plan, characterized in that: The method comprises: Based on the big model, the natural language water conservancy demand input by the user is parsed to obtain the flood control plan generation demand; wherein the big model is trained based on water conservancy professional knowledge; Determine the processing logic corresponding to the flood control plan generation requirements, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, and determine the flood simulation results; At least one flood control plan is generated according to the flood simulation results, and a target flood control plan is determined according to the at least one flood control plan.

2. The method according to claim 1, characterized in that Before parsing the natural language water conservancy demand input by the user based on the big model to obtain the flood control plan generation demand, the method further includes: Inputting water conservancy expertise into the large model, and learning water conservancy expertise through the large model to answer water conservancy professional questions; optimizing the large model according to the response of the large model; The optimized large model is tested through the water conservancy professional question and answer database.

3. The method according to claim 1, characterized in that According to the processing logic, flood prediction related data and flood prediction model are obtained and processed to determine flood simulation results, including: Acquire flood prediction related data according to the processing logic; wherein the flood prediction related data includes at least one of hydrological data, meteorological data, geological data and economic data; A flood prediction model corresponding to the flood prediction associated data is obtained, and the corresponding flood prediction associated data is calculated based on the flood prediction model to obtain a flood deduction result.

4. The method according to claim 3, characterized in that Acquiring a flood prediction model corresponding to the flood prediction associated data, and calculating the corresponding flood prediction associated data based on the flood prediction model to obtain a flood deduction result, including: Based on the flood prediction model, the corresponding flood prediction related data are calculated to obtain the results of different flood indicators under the influence of different flood prediction related data; Different flood indicators under the influence of different flood prediction related data are integrated to obtain the flood simulation results.

5. The method according to claim 1, characterized in that: Before generating at least one flood prevention plan according to the flood simulation results, the method further includes: The historical flood simulation results, historical flood prevention plans and empirical flood prevention decision data are input into the large model, and the large model is trained so that the large model can learn the generation capability of flood prevention plans.

6. The method according to claim 1, characterized in that Generating at least one flood control plan according to the flood simulation results, and determining a target flood control plan according to the at least one flood control plan, including: Acquire dynamically updated flood prediction related data in real time, and update at least one flood prevention plan based on the dynamically updated flood prediction related data and a flood prediction model; A target flood prevention plan is determined from at least one flood prevention plan.

7. The method according to claim 6, characterized in that Determine a target flood prevention plan from at least one flood prevention plan, including: Obtaining a user's rating of at least one flood prevention plan; The flood control plan with the highest score will be used as the target flood control plan.

8. A flood prevention plan determination device, characterized in that: The device comprises: A demand acquisition module is used to parse the natural language water conservancy demand input by the user based on a large model to obtain the flood control plan generation demand; wherein the large model is obtained by training based on water conservancy professional knowledge; The first determination module is used to determine the processing logic corresponding to the flood control plan generation demand, obtain and process the flood prediction related data and the flood prediction model according to the processing logic, and determine the flood simulation result; The second determination module is used to generate at least one flood control plan according to the flood simulation results, and determine a target flood control plan according to the at least one flood control plan.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the flood control plan determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the flood control plan determination method according to any one of claims 1 to 7 when executed.

Images