Flood prevention emergency scene data processing method and device and computer equipment

Abstract

This disclosure proposes a method, apparatus, and computer equipment for processing scene data in flood control emergency response. The method includes: acquiring scene data of a target area; determining a target response scene based on the scene data; determining a target task template based on the target response scene; wherein the target task template is used to indicate the subject to be executed and the corresponding task to be executed; and reporting the task to be executed to the subject to be executed. By implementing the method of this disclosure, the target response scene of a target area can be determined based on scene data, thereby obtaining the target task template, enabling the rapid and accurate reporting of tasks to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response, and effectively improving the effectiveness of flood control emergency response.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to a method, apparatus, and computer equipment for processing scenario data in flood control emergency response. Background Technology

[0002] Floods are a common natural disaster, characterized by their wide impact, long duration, heavy losses, and high frequency. Therefore, flood prevention and emergency response have received widespread attention.

[0003] In related technologies, it may be impossible to take timely emergency measures during flood control emergencies, resulting in poor flood control emergency response. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the purpose of this disclosure is to propose a method, device, computer equipment, and storage medium for processing scene data in flood control emergency response. Based on the scene data, the target response scene of the target area can be determined, thereby obtaining the target task template. This allows for the rapid and accurate reporting of tasks to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response and effectively improving the effectiveness of flood control emergency response.

[0006] The method for processing scenario data in flood control emergency response according to the first aspect of this disclosure includes: acquiring scenario data of a target area; determining a target response scenario based on the scenario data; determining a target task template based on the target response scenario, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed; and reporting the task to be executed to the subject to be executed.

[0007] The method for processing scene data in flood control emergency response proposed in the first aspect of this disclosure obtains scene data of a target area, determines a target response scene based on the scene data, and determines a target task template based on the target response scene. The target task template is used to indicate the subject to be executed and the corresponding task to be executed. The task to be executed is then reported to the subject to be executed. Thus, the target response scene of the target area can be determined based on the scene data, thereby obtaining the target task template. This allows for the rapid and accurate reporting of the task to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response and effectively improving the effectiveness of flood control emergency response.

[0008] The flood control emergency scene data processing device proposed in the second aspect of this disclosure includes: a first acquisition module for acquiring scene data of a target area; a first determination module for determining a target response scene based on the scene data; a second determination module for determining a target task template based on the target response scene, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed; and a reporting module for reporting the task to be executed to the subject to be executed.

[0009] The flood control emergency scene data processing device proposed in the second aspect of this disclosure acquires scene data of a target area, determines a target response scene based on the scene data, and determines a target task template based on the target response scene. The target task template is used to indicate the subject to be executed and the corresponding task to be executed, and the task to be executed is reported to the subject to be executed. Thus, the target response scene of the target area can be determined based on the scene data, thereby obtaining the target task template, so as to quickly and accurately report the task to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response and effectively improving the flood control emergency response effect.

[0010] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for processing scene data in flood control emergency response as proposed in the first aspect of this disclosure.

[0011] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for processing scenario data in flood control emergency response as proposed in the first aspect of this disclosure.

[0012] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs a method for processing scenario data in flood control emergency response as proposed in the first aspect of this disclosure.

[0013] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0015] Figure 1 This is a flowchart illustrating a method for processing scenario data in flood control emergency response according to an embodiment of this disclosure;

[0016] Figure 2 This is a flowchart illustrating a method for processing scenario data in flood control emergency response, as proposed in another embodiment of this disclosure.

[0017] Figure 3 This is a flowchart illustrating a method for processing scenario data in flood control emergency response, as proposed in another embodiment of this disclosure.

[0018] Figure 4 This is a schematic diagram of the structure of a pre-processing module proposed in an embodiment of this disclosure;

[0019] Figure 5 This is a schematic diagram of the structure of a scene data processing system proposed in an embodiment of this disclosure;

[0020] Figure 6 This is a schematic diagram of the structure of a flood control emergency scene data processing device according to an embodiment of this disclosure;

[0021] Figure 7 This is a schematic diagram of the structure of a data processing device for flood control emergency scenarios according to another embodiment of this disclosure;

[0022] Figure 8 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0023] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0024] Figure 1 This is a flowchart illustrating a method for processing scenario data in flood control emergency response, as proposed in one embodiment of this disclosure.

[0025] It should be noted that the execution subject of the flood control emergency scene data processing method in this embodiment is the flood control emergency scene data processing device. This device can be implemented by software and / or hardware. The device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a PDA, etc.

[0026] like Figure 1 As shown, the data processing method for this flood control emergency scenario includes:

[0027] S101: Obtain scene data for the target area.

[0028] The target area refers to the area where flood prevention and emergency response measures are to be implemented.

[0029] Among them, scenario data refers to data in the target area that can be used for flood control and emergency response, such as monitoring and alarm data, meteorological early warning data, risk early warning data, and sudden events, and there are no restrictions on this.

[0030] In this embodiment of the disclosure, when acquiring scene data of the target area, the meteorological data of the target area can be acquired by the meteorological data sensing system as the aforementioned scene data. Alternatively, a communication link between the execution subject of this embodiment of the disclosure and the big data server can be established in advance, and then the scene data can be acquired from the big data server. There are no restrictions on this.

[0031] S102: Determine the target response scenario based on the scenario data.

[0032] Here, the response scenario refers to the scenario in which an action is to be taken. The target response scenario refers to the response scenario corresponding to the scenario data mentioned above.

[0033] In this embodiment of the disclosure, when determining the target response scene based on scene data, the scene data may be input into a pre-trained scene recognition model to determine the target response scene, or it may be based on a pre-configured relation table, which can indicate the mapping relationship between scene data and target response scene, without limitation.

[0034] It is understood that different response scenarios may require different execution entities to perform different tasks. In this embodiment of the disclosure, when the target response scenario is determined based on the scenario data, reliable reference information can be provided for the subsequent determination of the execution entity and the execution task corresponding to the scenario data.

[0035] S103: Based on the target response scenario, determine the target task template, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed.

[0036] Among them, the task template refers to the template pre-configured for each response scenario, which can be used to indicate the task and task execution entity corresponding to the response scenario. The target task template, on the other hand, refers to the task template corresponding to the target response scenario.

[0037] The subject to be executed refers to the entity capable of performing the aforementioned tasks. This subject can be, for example, a department, or a specific individual; there are no restrictions on this.

[0038] Among them, the tasks to be executed refer to the tasks pre-configured for the above-mentioned target response scenarios to achieve the purpose of flood control and emergency response.

[0039] In this embodiment of the disclosure, when the target task template is determined according to the target response scenario, the subject to be executed and the corresponding task to be executed can be determined quickly and accurately, thereby effectively improving the timeliness of flood control emergency response.

[0040] S104: Report the tasks to be executed to the entity to be executed.

[0041] It is understandable that in flood control emergency scenarios, valuable time may be wasted because the entity to be executed is unaware of the tasks it should perform, thus affecting the effectiveness of flood control emergency response. When the tasks to be executed are reported to the entity to be executed, the tasks can be reported quickly and accurately, thereby ensuring the orderliness of the flood control emergency process.

[0042] In this embodiment, by acquiring scene data of the target area, a target response scenario is determined based on the scene data, and a target task template is determined based on the target response scenario. The target task template is used to indicate the subject to be executed and the corresponding task to be executed. The task to be executed is then reported to the subject to be executed. Thus, the target response scenario of the target area can be determined based on the scene data, thereby obtaining the target task template. This allows for the rapid and accurate reporting of the task to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response and effectively improving the flood control emergency response effect.

[0043] Figure 2 This is a flowchart illustrating a method for processing scenario data in flood control emergency response, as proposed in another embodiment of this disclosure.

[0044] like Figure 2 As shown, the data processing method for this flood control emergency scenario includes:

[0045] S201: Obtain multiple sample plans.

[0046] Among them, a contingency plan refers to the response measures configured for response scenarios during flood control emergencies. A sample contingency plan refers to a contingency plan used as a sample for analysis and processing.

[0047] S202: Based on the sample plan, determine the candidate response scenarios, as well as the candidate identification information and candidate tasks corresponding to the candidate response scenarios. The candidate identification information is used to indicate the executing entity of the candidate task.

[0048] Among them, candidate response scenarios refer to the response scenarios targeted in the sample contingency plan.

[0049] Among them, candidate identification information refers to the identification information used to indicate the executing entity of the candidate task.

[0050] Among them, candidate tasks refer to the tasks in the candidate response scenario that correspond to the candidate identifier information.

[0051] In other words, in this embodiment of the present disclosure, after obtaining multiple sample plans, the sample plans can be decomposed to determine candidate response scenarios, as well as candidate identification information and candidate tasks corresponding to the candidate response scenarios, thereby providing reliable data support for the subsequent generation of candidate task templates.

[0052] S203: Generate a candidate task template corresponding to the candidate response scenario based on the candidate identifier information and candidate task.

[0053] Among them, the candidate task template refers to the task template generated based on the candidate identifier information and the candidate task.

[0054] In other words, in this embodiment of the present disclosure, multiple sample plans can be obtained, and candidate response scenarios, candidate identification information and candidate tasks corresponding to the candidate response scenarios can be determined based on the sample plans. The candidate identification information is used to indicate the execution subject of the candidate task. Based on the candidate identification information and candidate tasks, a candidate task template corresponding to the candidate response scenario is generated. Thus, the candidate task template corresponding to the candidate response scenario can be obtained in advance based on multiple sample plans, thereby providing reliable candidate objects for the subsequent determination of the target task template.

[0055] S204: Obtain scene data for the target area.

[0056] S205: Determine the target response scenario based on the scenario data.

[0057] For a detailed description of S204 and S205, please refer to the above embodiments, which will not be repeated here.

[0058] S206: Determine the comparison results between the target response scenario and multiple candidate response scenarios.

[0059] The comparison result refers to the result obtained by comparing the target response scenario with multiple candidate response scenarios. For example, it can be used to indicate whether the target response scenario and the candidate response scenarios are the same response scenario.

[0060] S207: Based on the comparison results, determine the target task template from multiple candidate task templates, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed.

[0061] In other words, after determining the target response scenario from multiple candidate response scenarios based on the scenario matching results, the embodiments of this disclosure can determine the comparison results between the target response scenario and multiple candidate response scenarios. Based on the comparison results, the target task template can be determined from multiple candidate task templates. Thus, the target task template can be determined quickly and accurately from multiple candidate task templates based on the comparison results, thereby effectively improving the reliability of the target task template acquisition process.

[0062] S208: Report the tasks to be executed to the entity to be executed.

[0063] For a detailed description of S208, please refer to the above embodiments, which will not be repeated here.

[0064] In this embodiment, multiple sample plans are acquired, and candidate response scenarios, candidate identification information, and candidate tasks corresponding to these scenarios are determined based on these plans. The candidate identification information indicates the executing entity of the candidate task. Based on the candidate identification information and candidate tasks, a candidate task template corresponding to the candidate response scenario is generated. This allows for the pre-acquisition of candidate task templates corresponding to candidate response scenarios based on multiple sample plans, providing reliable candidate objects for subsequent determination of the target task template. By determining the comparison results between the target response scenario and multiple candidate response scenarios, the target task template is determined from the multiple candidate task templates based on the comparison results. This allows for the rapid and accurate determination of the target task template from multiple candidate task templates based on the comparison results, effectively improving the reliability of the target task template acquisition process.

[0065] Figure 3 This is a flowchart illustrating a method for processing scenario data in flood control emergency response, as proposed in another embodiment of this disclosure.

[0066] like Figure 3 As shown, the data processing method for this flood control emergency scenario includes:

[0067] S301: Obtain the implementation plan corresponding to the target area and candidate response scenarios.

[0068] Among them, the implementation plan refers to the plan that is configured for the candidate response scenarios of the target area and is within the implementation validity period.

[0069] It is understandable that flood control emergency response processes may differ in different regions. Obtaining the implementation plans corresponding to the target area and candidate response scenarios can provide reliable reference information for subsequently determining the trigger attribute information of the target area.

[0070] S302: Determine the trigger attribute information according to the implementation plan.

[0071] Among them, trigger attribute information refers to the attribute information of the corresponding candidate response scenario in the implementation plan. For example, it can be the data type, feature value, number of data types, etc. that trigger the candidate response scenario.

[0072] S303: Determine the trigger type of the candidate response scenario.

[0073] The trigger type refers to the type of scenario that triggers the candidate response mentioned above. For example, it can be triggered by a single object or by multiple objects, and there are no restrictions on this.

[0074] S304: Generate scene triggering conditions corresponding to the candidate response scene based on the triggering attribute information and triggering type.

[0075] In other words, in this implementation, it is possible to obtain the implementation plan corresponding to the target area and the candidate response scenario, determine the trigger attribute information and the trigger type of the candidate response scenario based on the implementation plan, and generate the scenario trigger conditions corresponding to the candidate response scenario based on the trigger attribute information and the trigger type. Thus, the applicability of the obtained scenario trigger conditions to the target area can be effectively improved based on the implementation plan corresponding to the target area and the candidate response scenario, and a reliable trigger basis can be provided for determining the target response scenario.

[0076] S305: Acquire scene data for the target area.

[0077] For a detailed description of S305, please refer to the above embodiments; it will not be repeated here.

[0078] S306: Determine the scene matching result between scene data and scene triggering conditions.

[0079] The scene matching result refers to the result obtained by matching scene data with scene triggering conditions. For example, it can be used to indicate whether scene data matches the relevant characteristics of a candidate response scene, thereby determining whether the candidate response scene can be used as the target response scene.

[0080] Optionally, in some embodiments, the scene data includes at least one of the following: target scene type; object identification information, wherein the object identification information is used to indicate the data acquisition object, and the data acquisition object belongs to the target scene type; object feature value; number of identification information, wherein the number of identification information is used to indicate the number of object identification information in the scene data.

[0081] The target scene type refers to the scene type to which the scene data belongs.

[0082] For example, in this embodiment of the disclosure, the entire flood control emergency scenario can be divided into an S1 prevention action scenario library, an S2 early warning response scenario library, and an S3 emergency event scenario library based on different periods of flood control emergency response.

[0083] The S1 prevention action scenario library can be further divided into the S11 precipitation forecast scenario library, and the scenario type corresponding to the S11 precipitation forecast scenario library can include C1 precipitation forecast.

[0084] The S2 early warning response scenario library can be further divided into the S21 monitoring station alarm scenario library and the S22 early warning scenario library. The scenario types of the S21 monitoring station alarm scenario library include C2 rain gauge stations, C3 waterlogging point monitoring stations, C4 river stations, and C5 reservoir monitoring stations. The scenario types of the S22 early warning scenario library include C6 rainstorm warning, C7 flood warning, C8 flash flood warning, and C9 urban waterlogging warning.

[0085] The S3 emergency scenario library includes the following scenario types: C10 Water conservancy projects and rivers, C11 Road flooding and collapse, C12 Mountain torrents and geological disasters, C13 Underground pipeline accidents, C14 Urban housing and construction projects, and C15 Tourists trapped.

[0086] Among them, object identification information refers to the identification information used to indicate the object to be acquired. The data acquisition object refers to the object in the target scene type from which relevant data is to be extracted. For example, when the target scene type is a river station, the data acquisition object can be one or more rivers in the target area that meet preset conditions.

[0087] Among them, object feature values ​​refer to the feature values ​​corresponding to the data acquisition object. When the data acquisition object is a river, the object feature value could be, for example, whether the river's water level has risen above the flood season level.

[0088] Optionally, in some embodiments, the scene triggering conditions include: a first scene type, multiple first identification information, and a first value range corresponding to the first identification information; when determining the scene matching result between scene data and scene triggering conditions, it may be to determine the first matching result between the target scene type and the first scene type, determine the second matching result between the object identification information and the first identification information, determine the third matching result between the object feature value and the first value range, and generate a scene matching result based on the first matching result, the second matching result, and the third matching result.

[0089] The first scenario type refers to a scenario type applicable to determining the scenario triggering conditions based on the range of values ​​of object feature values.

[0090] The first identification information can be used to indicate the data acquisition object corresponding to the first scene type.

[0091] The first value range can be used to indicate the range of values ​​for the corresponding object's feature value when the response scenario is triggered.

[0092] Among them, the first matching result, the second matching result, and the third matching result refer to the results obtained by matching the target scene type with the first scene type, the object identification information with the first identification information, and the object feature value with the first value range, respectively.

[0093] In this embodiment of the disclosure, when generating a scene matching result based on the first matching result, the second matching result, and the third matching result, the matching result can be used as the scene matching result if all three results are a match; or if any one of the three results is a non-match, the non-match can be used as the scene matching result.

[0094] For example, the initial configuration rules are as follows: Select data type T0 and associate it with the corresponding database table. Select one or more feature value types from the database table, and the values ​​L0, L1...Ln for each feature value. Input the thresholds wn1 and wn2 corresponding to each pair of feature values. The data type of wn1 and wn2 is: DECIMAL. wn1 <wn2。

[0095] Triggering condition rules: If the received data meets the following conditions: {data type = T0 and the value of each feature type = L0, L1...Ln, and the monitored data is between wn1 and wn2}, then an alert is triggered; otherwise, no alert is triggered.

[0096] Optionally, in some embodiments, the scene triggering conditions include: a second scene type, second identification information, a reference quantity, and a reference feature value corresponding to the second identification information; when determining the scene matching result between the scene data and the scene triggering conditions, it may be to determine the fourth matching result between the target scene type and the second scene type, the fifth matching result between the object identification information and the second identification information, the sixth matching result between the object feature value and the reference feature value, and the seventh matching result between the identification information quantity and the reference quantity, and generate the scene matching result based on the fourth matching result, the fifth matching result, the sixth matching result, and the seventh matching result.

[0097] Here, the second scenario type refers to the scenario type applicable to determining scenario triggering conditions based on reference feature values. The second identification information refers to the identification information used in the second scenario type to indicate the data acquisition object. The reference quantity refers to the number of data acquisition objects indicated by the scenario triggering conditions. The reference feature value refers to the feature value used as a reference for the object feature value.

[0098] Among them, the fourth matching result, the fifth matching result, the sixth matching result, and the seventh matching result refer to the matching results between the target scene type and the second scene type, the object identification information and the second identification information, the object feature value and the reference feature value, and the number of identification information and the reference number, respectively.

[0099] In this embodiment of the disclosure, when generating a scene matching result based on the fourth matching result, the fifth matching result, the sixth matching result, and the seventh matching result, the matching result can be used as the scene matching result if all four matching results are a match; or the non-matching result can be used as the scene matching result if any one of the four matching results is a non-match.

[0100] For example, the initial configuration rules are as follows: Select data type T0 and associate it with the corresponding database table. Select one or more feature value types from the database table, and the feature value types L0, L1, ..., Ln. Input quantity N0. N0 is an integer.

[0101] For example: if the data type is selected as "reservoir", then the corresponding reservoir database table is associated. From the database table, the feature value type is selected as "reservoir level", and the feature value L0 = large (1); another feature value type is "whether it exceeds the flood limit", and the feature value L1 = yes. The number of inputs N0 = 2.

[0102] Triggering condition rules: If the received data meets the following conditions: {data type = T0 and the value of each feature type = L0, L1...Ln, and the number of data entries of that type = N0}, then an alert is triggered; otherwise, no alert is triggered.

[0103] Optionally, in some embodiments, the scene triggering conditions include: a third scene type, target identification information, and a target value range corresponding to the target identification information. When determining the scene matching result between the scene data and the scene triggering conditions, it may also be to determine the eighth matching result between the target scene type and the third scene type, the ninth matching result between the object identification information and the target identification information, and the tenth matching result between the object feature value and the target value range. The scene matching result is generated based on the eighth matching result, the ninth matching result, and the tenth matching result.

[0104] The third scenario type refers to the scenario type applicable to determining scenario matching results based on target identification information. Target identification information refers to the identification information used in the third scenario type to indicate the object from which data is acquired. The target value range refers to the range of values ​​for the object's feature values ​​corresponding to the target identification information.

[0105] Among them, the eighth matching result, the ninth matching result, and the tenth matching result refer to the matching results of the target scene type with the third scene type, the object identification information with the target identification information, and the object feature value with the target value range, respectively.

[0106] In this embodiment of the disclosure, when generating a scene matching result based on the eighth matching result, the ninth matching result, and the tenth matching result, the matching result can be used as the scene matching result if all three results are a match; or if any one of the eight, nine, and ten matching results is a non-match, the non-matching result can be used as the scene matching result.

[0107] For example, the initial configuration rules are as follows: Select the data type and associate it with the corresponding database table. Select one or more feature value types from the database table, and the values ​​of the feature value types = L0, L1, ..., Ln. Select a specific object object0 in the database for association.

[0108] Triggering condition rules: If the received data satisfies {data type = T0 and the value of each feature type = L0, L1, ..., Ln, and the judgment object = object0}, then an alert is triggered; otherwise, no alert is triggered.

[0109] In other words, in this embodiment of the present disclosure, the scene triggering conditions can be flexibly configured based on personalized application scenarios, thereby effectively improving the practicality of the scene triggering conditions.

[0110] S307: Based on the scene matching results, determine the target response scene from multiple candidate response scenes.

[0111] In other words, after obtaining scene data of the target area, the embodiments of this disclosure can determine the scene matching result between the scene data and the scene triggering conditions, and determine the target response scene from multiple candidate response scenes based on the scene matching result. Thus, the accuracy and reliability of the obtained target response scene can be effectively improved.

[0112] S308: Based on the target response scenario, determine the target task template, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed.

[0113] S309: Report the tasks to be executed to the entity to be executed.

[0114] For a detailed description of S308 and S309, please refer to the above embodiments, which will not be repeated here.

[0115] In this embodiment, by acquiring the implementation plans corresponding to the target area and candidate response scenarios, trigger attribute information is determined based on the implementation plans, and the trigger type of the candidate response scenario is determined. Based on the trigger attribute information and trigger type, scene triggering conditions corresponding to the candidate response scenario are generated. Therefore, the applicability of the obtained scene triggering conditions to the target area can be effectively improved based on the implementation plans corresponding to the target area and candidate response scenarios, providing a reliable triggering basis for determining the target response scenario. By determining the scene matching results between scene data and scene triggering conditions, the target response scenario is determined from multiple candidate response scenarios based on the scene matching results. This effectively improves the accuracy and reliability of the obtained target response scenario.

[0116] For example, the execution entity in this embodiment can be divided into an application end and a receiving end. The application end consists of a perception database, a contingency plan processing module, an automatic response reminder module, a task library, and a task distribution module. It receives data from external systems, including monitoring alarms, weather warnings, risk warnings, and emergency data. It determines whether the data meets the custom scene triggering conditions defined in the contingency plan configuration module. If the triggering conditions are met, it automatically extracts relevant tasks and task subjects from the task library and sends notifications to the receiving end via built-in communication capabilities.

[0117] like Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of a contingency plan processing module proposed in this embodiment. The contingency plan decomposition module decomposes and stores the relevant elements of the flood control emergency plan, namely "responsibility conditions," "responsible entities," and "tasks," resulting in responsible entity A1, corresponding conditions A2, and tasks A3. Responsible entity A1 is then associated with the address book. Various response scenarios during flood control emergencies are analyzed to construct a flood control response scenario library, and configuration rules are defined for different scenario libraries.

[0118] The contingency plan configuration module performs initial configuration based on the actual contingency plans in various regions: that is, based on the configuration rules defined for different scenarios in the contingency plan processing module. This may involve one or more trigger conditions. These trigger conditions are related by "AND" or "OR".

[0119] For example, a user can set a response action scenario "Activate Level 3 Response" RAS1.

[0120] During initial configuration, input the response action scenario RAS1 = "Initiate Level 3 Response of the Prevention and Control Command", and the corresponding custom conditions are any of the following:

[0121] Select scene library S=C5, select reservoir=K reservoir, select feature value type=water level exceeds flood limit;

[0122] Select scenario library S=C3, select river station type=key river, select feature value type=water level exceeds warning level, quantity is 4.

[0123] For example, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of a scene data processing system proposed in an embodiment of this disclosure.

[0124] The scene data can be matched with multiple scene triggering conditions (condition 1, condition 2, ..., condition N) to determine whether multiple tasks need to be executed simultaneously.

[0125] Figure 6 This is a schematic diagram of the structure of a flood control emergency scene data processing device proposed in one embodiment of this disclosure.

[0126] like Figure 6 As shown, the data processing device 60 for flood control emergency scenarios includes:

[0127] The first acquisition module 601 is used to acquire scene data of the target area;

[0128] 0. The first determining module 602 is used to determine the target response scenario based on the scenario data;

[0129] The second determining module 603 is used to determine the target task template based on the target response scenario, wherein...

[0130] The target task template is used to indicate the subject to be executed, and the corresponding task to be executed;

[0131] The reporting module 604 is used to report the tasks to be executed to the subject to be executed.

[0132] In some embodiments of this disclosure, such as Figure 7 As shown, Figure 7 This is a schematic diagram of a data processing device for flood control emergency scenarios according to another embodiment of this disclosure. The device further includes:

[0133] The second acquisition module 605 is used to acquire multiple sample plans;

[0134] The third determining module 606 is used to determine candidate response scenarios, candidate identification information and candidate tasks corresponding to the candidate response scenarios based on the sample plan. The candidate identification information is used to indicate the executing entity of the candidate task.

[0135] 0. The first generation module 607 is used to generate candidate responses based on candidate identifier information and candidate tasks.

[0136] Candidate task templates corresponding to the scenario.

[0137] In some embodiments of this disclosure, the second determining module 603 is specifically used for:

[0138] Determine the comparison results between the target response scenario and multiple candidate response scenarios;

[0139] Based on the comparison results, the target task template is determined from multiple candidate task templates.

[0140] 5. In some embodiments of this disclosure, it further includes:

[0141] The third acquisition module 608 is used to acquire the implementation plan corresponding to the target area and the candidate response scenario;

[0142] The fourth determination module 609 is used to determine the trigger attribute information based on the implementation plan;

[0143] The fifth determining module 610 is used to determine the trigger type of the candidate response scenario;

[0144] The second generation module 611 is used to generate scene triggering conditions corresponding to the candidate response scene based on the triggering attribute information and the triggering type.

[0145] In some embodiments of this disclosure, the first determining module 602 is specifically used for:

[0146] Determine the scene matching result between scene data and scene triggering conditions;

[0147] Based on the scene matching results, the target response scene is determined from multiple candidate response scenes.

[0148] In some embodiments of this disclosure, the scene data includes at least one of the following:

[0149] Target scenario type;

[0150] Object identification information, which indicates the data acquisition object and the data acquisition object belongs to the target scene type;

[0151] Object characteristic values;

[0152] Number of identification information, where the number of identification information is used to indicate the number of object identification information in the scene data.

[0153] In some embodiments of this disclosure, the scene triggering conditions include: a first scene type, multiple first identification information, and a first value range corresponding to the first identification information;

[0154] The first determining module 602 is further configured to:

[0155] Determine the first matching result between the target scene type and the first scene type;

[0156] Determine the second matching result between the object identification information and the first identification information;

[0157] Determine the third matching result between the object's feature value and the first value range;

[0158] Based on the first matching result, the second matching result, and the third matching result, generate the scene matching result.

[0159] In some embodiments of this disclosure, the scene triggering conditions include: a second scene type, second identification information, a number of references, and a reference feature value corresponding to the second identification information;

[0160] The first determining module 602 is further configured to:

[0161] Determine the fourth matching result between the target scene type and the second scene type;

[0162] Determine the fifth matching result between the object identification information and the second identification information;

[0163] Determine the sixth matching result between the object's feature values ​​and the reference feature values;

[0164] The seventh matching result is determined by comparing the number of identifiers with the reference number;

[0165] Based on the fourth, fifth, sixth, and seventh matching results, generate scene matching results.

[0166] In some embodiments of this disclosure, the scene triggering conditions include: a third scene type, target identification information, and a target value range corresponding to the target identification information;

[0167] The first determining module 602 is further configured to:

[0168] Determine the eighth matching result between the target scene type and the third scene type;

[0169] Determine the ninth matching result between the object identification information and the target identification information;

[0170] Determine the tenth matching result between the object's feature values ​​and the target's value range;

[0171] Based on the eighth, ninth, and tenth matching results, generate scene matching results.

[0172] It should be noted that the aforementioned explanation of the method for processing scene data in flood control emergency also applies to the flood control emergency scene data processing device in this embodiment, and will not be repeated here.

[0173] In this embodiment, by acquiring scene data of the target area, a target response scenario is determined based on the scene data, and a target task template is determined based on the target response scenario. The target task template is used to indicate the subject to be executed and the corresponding task to be executed. The task to be executed is then reported to the subject to be executed. Thus, the target response scenario of the target area can be determined based on the scene data, thereby obtaining the target task template. This allows for the rapid and accurate reporting of the task to be executed to the subject to be executed, ensuring the timeliness of flood control emergency response and effectively improving the flood control emergency response effect.

[0174] Figure 8 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 8 The computer device 12 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0175] like Figure 8 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0176] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0177] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0178] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 8 Not shown; usually referred to as a "hard drive".

[0179] although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0180] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0181] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0182] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the flood control emergency scene data processing method mentioned in the foregoing embodiments.

[0183] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for processing scenario data in flood control emergency response as proposed in the foregoing embodiments of this disclosure.

[0184] To implement the above embodiments, this disclosure also proposes a computer program product, which, when executed by an instruction processor, performs a method for processing scenario data in flood control emergency response as proposed in the foregoing embodiments of this disclosure.

[0185] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0186] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended five claims.

[0187] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0188] Any process or method description in the flowchart or otherwise herein can be understood as representing code containing one or more executable instructions for implementing a particular logical function or process.

[0189] The scope of the preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order of the functions involved, as will be understood by those skilled in the art to which the embodiments of this disclosure pertain.

[0190] 5. It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0191] In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: having

[0192] Discrete logic circuits are logic gate circuits used to implement logical functions on data signals. Application-specific integrated circuits (ASICs) with suitable combinational 0 logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0193] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer.

[0194] The program, when executed, includes one or a combination of steps from the method embodiments in a computer-readable storage medium. Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a single processing module.

[0195] The units can exist as separate physical entities, or two or more units can be integrated into a single module. This integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0196] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0197] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0198] Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for processing scenario data in flood control emergency response, characterized in that, include: Acquire scene data of the target area, wherein the target area is the area to be subject to flood control emergency response, and the scene data is the data in the target area used for flood control emergency response; Based on the scenario data, determine the target response scenario; Based on the target response scenario, a target task template is determined, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed, and the task to be executed is a task pre-configured by the target response scenario to achieve the purpose of flood control emergency response; The task to be executed is reported to the entity to be executed; Prior to acquiring the scene data of the target area, the process also includes: Multiple sample contingency plans were obtained, among which the relevant elements of the sample contingency plans include response conditions, responsible parties, and tasks; Based on the sample plan, candidate response scenarios are determined, along with candidate identification information and candidate tasks corresponding to the candidate response scenarios. The candidate identification information is used to indicate the executing entity of the candidate task. Based on the candidate identifier information and the candidate task, a candidate task template corresponding to the candidate response scenario is generated. The method further includes: Obtain the implementation plan corresponding to the target area and the candidate response scenario, wherein the implementation plan is a plan configured for the candidate response scenario of the target area that is within the implementation validity period; According to the implementation plan, the triggering attribute information is determined, wherein the triggering attribute information refers to the attribute information of the corresponding candidate response scenario in the implementation plan, and the triggering attribute information includes the data type, feature value, and number of data types that trigger the candidate response scenario; Determine the trigger type of the candidate response scenario; Based on the trigger attribute information and the trigger type, generate scene trigger conditions corresponding to the candidate response scenario; The step of determining the target response scenario based on the scenario data includes: Determine the scene matching result between the scene data and the scene triggering conditions; Based on the scenario matching results, a target response scenario is determined from multiple candidate response scenarios; The scene data includes at least one of the following: Target scene type, wherein the target scene type is the scene type to which the scene data belongs; Object identification information, wherein the object identification information is used to indicate the data acquisition object, the data acquisition object belongs to the target scene type, and the data acquisition object is the object in the target scene type from which relevant data is to be extracted; Object feature value, wherein the object feature value is the feature value corresponding to the data acquisition object; The number of identification information, wherein the number of identification information is used to indicate the number of object identification information in the scene data.

2. The method as described in claim 1, characterized in that, The step of determining the target task template based on the target response scenario includes: Determine the comparison results between the target response scenario and multiple candidate response scenarios; Based on the comparison results, the target task template is determined from the multiple candidate task templates.

3. The method as described in claim 1, characterized in that, The scene triggering conditions include: a first scene type, multiple first identifier information, and a first value range corresponding to the first identifier information; The step of determining the scene matching result between the scene data and the scene triggering condition includes: Determine the first matching result between the target scene type and the first scene type; Determine the second matching result between the object identification information and the first identification information; Determine the third matching result between the object feature value and the first value range; The scene matching result is generated based on the first matching result, the second matching result, and the third matching result.

4. The method as described in claim 1, characterized in that, The scene triggering conditions include: a second scene type, second identification information, a reference quantity, and a reference feature value corresponding to the second identification information; The step of determining the scene matching result between the scene data and the scene triggering condition includes: Determine the fourth matching result between the target scene type and the second scene type; Determine the fifth matching result between the object identification information and the second identification information; Determine the sixth matching result between the object feature value and the reference feature value; Determine the seventh matching result between the number of identification information and the reference number; The scene matching result is generated based on the fourth matching result, the fifth matching result, the sixth matching result, and the seventh matching result.

5. The method as described in claim 1, characterized in that, The scene triggering conditions include: a third scene type, target identification information, and the target value range corresponding to the target identification information; The step of determining the scene matching result between the scene data and the scene triggering condition includes: Determine the eighth matching result between the target scene type and the third scene type; Determine the ninth matching result between the object identification information and the target identification information; Determine the tenth matching result between the object feature value and the target value range; The scene matching result is generated based on the eighth matching result, the ninth matching result, and the tenth matching result.

6. A device for processing scenario data in flood control emergency response, characterized in that, include: The first acquisition module is used to acquire scene data of the target area, wherein the target area is the area to be subject to flood control emergency response, and the scene data is the data in the target area used for flood control emergency response. The first determining module is used to determine the target response scenario based on the scenario data; The second determining module is used to determine a target task template based on the target response scenario, wherein the target task template is used to indicate the subject to be executed and the task to be executed corresponding to the subject to be executed, and the task to be executed is a task pre-configured by the target response scenario to achieve the purpose of flood control emergency response; The reporting module is used to report the task to be executed to the subject to be executed; The device further includes: The second acquisition module is used to acquire multiple sample plans, wherein the relevant elements of the sample plans include response conditions, responsible entities, and tasks; The third determining module is used to determine candidate response scenarios, candidate identification information and candidate tasks corresponding to the candidate response scenarios based on the sample plan, wherein the candidate identification information is used to indicate the executing entity of the candidate task; The first generation module is used to generate a candidate task template corresponding to the candidate response scenario based on the candidate identifier information and the candidate task. The third acquisition module is used to acquire the implementation plan corresponding to the target area and the candidate response scenario; The fourth determining module is used to determine the triggering attribute information according to the implementation plan. The triggering attribute information refers to the attribute information of the corresponding candidate response scenario in the implementation plan. The triggering attribute information includes the data type, feature value, and number of data types that trigger the candidate response scenario. The fifth determining module is used to determine the trigger type of the candidate response scenario; The second generation module is used to generate scene triggering conditions corresponding to the candidate response scene based on the triggering attribute information and the triggering type. The first determining module is specifically used for: Determine the scene matching result between the scene data and the scene triggering conditions; Based on the scenario matching results, a target response scenario is determined from multiple candidate response scenarios; The scene data includes at least one of the following: Target scene type, wherein the target scene type is the scene type to which the scene data belongs; Object identification information, wherein the object identification information is used to indicate the data acquisition object, the data acquisition object belongs to the target scene type, and the data acquisition object is the object in the target scene type from which relevant data is to be extracted; Object feature value, wherein the object feature value is the feature value corresponding to the data acquisition object; The number of identification information, wherein the number of identification information is used to indicate the number of object identification information in the scene data.

7. The apparatus as claimed in claim 6, characterized in that, The second determining module is specifically used for: Determine the comparison results between the target response scenario and multiple candidate response scenarios; Based on the comparison results, the target task template is determined from the multiple candidate task templates.

8. The apparatus as claimed in claim 6, characterized in that, The scene triggering conditions include: a first scene type, multiple first identifier information, and a first value range corresponding to the first identifier information; The first determining module is further configured to: Determine the first matching result between the target scene type and the first scene type; Determine the second matching result between the object identification information and the first identification information; Determine the third matching result between the object feature value and the first value range; The scene matching result is generated based on the first matching result, the second matching result, and the third matching result.

9. The apparatus as claimed in claim 6, characterized in that, The scene triggering conditions include: a second scene type, second identification information, a reference quantity, and a reference feature value corresponding to the second identification information; The first determining module is further configured to: Determine the fourth matching result between the target scene type and the second scene type; Determine the fifth matching result between the object identification information and the second identification information; Determine the sixth matching result between the object feature value and the reference feature value; Determine the seventh matching result between the number of identification information and the reference number; The scene matching result is generated based on the fourth matching result, the fifth matching result, the sixth matching result, and the seventh matching result.

10. The apparatus as claimed in claim 6, characterized in that, The scene triggering conditions include: a third scene type, target identification information, and the target value range corresponding to the target identification information; The first determining module is further configured to: Determine the eighth matching result between the target scene type and the third scene type; Determine the ninth matching result between the object identification information and the target identification information; Determine the tenth matching result between the object feature value and the target value range; The scene matching result is generated based on the eighth matching result, the ninth matching result, and the tenth matching result.

11. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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