Extreme weather monitoring and early warning method, device and equipment and storage medium

Abstract

The application discloses a monitoring and early warning method and device for extreme weather, equipment and a storage medium, relates to the technical field of city infrastructure monitoring and early warning, and comprises the following steps: identifying an extreme weather type according to external environment information, determining a corresponding dynamic early warning threshold set, comparing running state data with thresholds in the dynamic early warning threshold set, independently judging the risk of city infrastructure according to a comparison result, obtaining an independent risk level, determining a target facility exceeding a threshold based on the independent risk level, and identifying other facilities associated with the target facility based on a cooperative relationship matrix; based on the cooperative relationship matrix, performing cross-facility linkage analysis according to the independent risk level of the target facility and the running state of the associated facilities, generating cooperative early warning information, and pushing the information to a corresponding management department terminal. The application can realize differentiated dynamic early warning and cross-facility cooperative linkage of city infrastructure under extreme weather.

Description

Technical Field

[0001] This application relates to the field of urban infrastructure monitoring and early warning technology, and in particular to monitoring and early warning methods, devices, equipment and storage media for extreme weather. Background Technology

[0002] Frequent extreme weather events pose a severe challenge to the safe operation of urban infrastructure. Urban infrastructure encompasses various types, including municipal pipelines, roads, and sanitation facilities. These facilities have complex spatial relationships and functional dependencies; for example, excessive water levels in pipe networks can cause road flooding, and overturned garbage bins can obstruct traffic. Therefore, real-time monitoring of the status of various facilities and identification of risk transmission relationships during extreme weather is an urgent problem to be solved in ensuring urban safety.

[0003] Currently, urban infrastructure monitoring and early warning systems are typically built independently according to facility type. Pipeline systems focus on leaks and water levels, road systems on water accumulation and icing, and sanitation systems on overflows and spills. There is a lack of information sharing and coordinated response between these systems. Furthermore, these systems generally use uniform static thresholds for early warning determination, failing to consider the differentiated impacts of various extreme weather types on facility risks. This results in a single facility risk event failing to trigger risk transmission assessments of related facilities, and is prone to missed or false alarms under different weather scenarios.

[0004] Therefore, how to achieve differentiated dynamic early warning and cross-infrastructure collaborative linkage of urban infrastructure under extreme weather conditions is an urgent problem to be solved.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a monitoring and early warning method, device, equipment and storage medium for extreme weather, aiming to solve the technical problem of how to realize differentiated dynamic early warning of urban infrastructure and cross-infrastructure collaborative linkage under extreme weather.

[0007] To achieve the above objectives, this application proposes a monitoring and early warning method for extreme weather, the method comprising: To obtain operational status data of urban infrastructure and information about the external environment; Based on the external environmental information, identify extreme weather types and determine a set of dynamic early warning thresholds corresponding to the extreme weather types; The operational status data is compared with the corresponding threshold in the dynamic early warning threshold set, and the independent risk assessment of the urban infrastructure is performed based on the comparison result to obtain the independent risk level. Based on the independent risk level, target urban infrastructure that exceeds the level trigger threshold is identified, and based on a preset collaborative relationship matrix, other urban infrastructure that is associated with the target urban infrastructure is identified. Based on the aforementioned collaborative relationship matrix, cross-facility linkage analysis is performed according to the independent risk level of the target city's infrastructure and the operational status of other city infrastructures to generate collaborative early warning information, which is then pushed to the corresponding management department's terminal.

[0008] In one embodiment, the step of identifying extreme weather types based on the external environmental information and determining a dynamic early warning threshold set corresponding to the extreme weather types includes: Meteorological parameters and extreme weather warnings are obtained from the external environmental information. Identify extreme weather types based on the meteorological parameters and extreme weather warning information; Retrieve a set of warning parameters that match the extreme weather type from a preset set of differentiated warning thresholds; A dynamic warning threshold is calculated based on the set of warning parameters and the meteorological parameters, and a dynamic warning threshold set is generated based on the dynamic warning threshold.

[0009] In one embodiment, the step of calculating the dynamic early warning threshold based on the early warning parameter set and the meteorological parameters includes: Load the corresponding differentiated early warning profile from the set of early warning parameters; Based on the facility type of each facility node in the urban infrastructure, the corresponding threshold calculation function is determined from the differentiated early warning profile; By substituting the meteorological parameters into the threshold calculation function, the dynamic early warning thresholds for each facility node in the urban infrastructure are calculated.

[0010] In one embodiment, the step of comparing the operational status data with the corresponding threshold in the dynamic early warning threshold set, and determining the independent risk level of the urban infrastructure based on the comparison result, includes: Iterate through each facility node in the urban infrastructure; Based on the operational status data of the currently traversed facility nodes, obtain the dynamic early warning threshold corresponding to the currently traversed facility node from the dynamic early warning threshold set; The operational status data of the currently traversed facility node is compared with the corresponding dynamic early warning threshold to determine the risk deviation value of the operational status data of the currently traversed facility node under the extreme weather type. Based on the risk deviation value, an independent risk assessment is performed on the currently traversed facility node to obtain the independent risk level of the currently traversed facility node.

[0011] In one embodiment, the step of identifying other urban infrastructures that are associated with the target urban infrastructure based on a preset collaborative relationship matrix includes: Using the facility identifier of the target city's infrastructure as an index, facilities with a preset association with the target city's infrastructure are retrieved from the collaborative relationship matrix to obtain other city infrastructures. The preset association includes at least one of risk transmission association, spatial association association, and resource synergy association.

[0012] In one embodiment, the step of performing cross-facility linkage analysis based on the collaborative relationship matrix, according to the independent risk level of the target city's infrastructure and the operational status of other city infrastructures, to generate collaborative early warning information includes: Based on the collaborative relationship matrix, obtain the linkage strategy templates corresponding to the target city infrastructure and the other city infrastructure; Based on the independent risk level of the target city's infrastructure and the operational status of other city infrastructure, the linkage strategy template is filled in to generate collaborative early warning information.

[0013] In one embodiment, the step of pushing the message to the corresponding management department terminal includes: Determine the facility type and warning level corresponding to the collaborative early warning information; The push priority is determined based on the aforementioned warning level; Match the corresponding management department based on the facility type; If the collaborative early warning information involves a single facility type, then according to the push priority, the collaborative early warning information will be pushed to the management department terminal corresponding to the single facility type; If the collaborative early warning information involves multiple facility types, the management departments corresponding to each facility type are matched according to the collaborative relationship matrix to generate a multi-department comprehensive scheduling suggestion, which is then pushed to the terminals of the matched management departments according to the push priority.

[0014] Furthermore, to achieve the above objectives, this application also proposes a monitoring and early warning device for extreme weather, which includes: The data acquisition module is used to acquire operational status data of urban infrastructure and external environmental information; The dynamic threshold determination module is used to identify extreme weather types based on the external environmental information and determine a set of dynamic early warning thresholds corresponding to the extreme weather types. An independent risk assessment module is used to compare the operational status data with the corresponding threshold in the dynamic early warning threshold set, and to make an independent risk assessment of the urban infrastructure based on the comparison result to obtain an independent risk level. The collaborative identification module is used to determine the target urban infrastructure that exceeds the level trigger threshold based on the independent risk level, and to identify other urban infrastructure that is associated with the target urban infrastructure based on a preset collaborative relationship matrix. The linkage early warning module is used to perform cross-facility linkage analysis based on the collaborative relationship matrix, according to the independent risk level of the target city's infrastructure and the operating status of other city infrastructure, to generate collaborative early warning information and push it to the corresponding management department terminal.

[0015] In addition, to achieve the above objectives, this application also proposes a monitoring and early warning device for extreme weather, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the monitoring and early warning method for extreme weather as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the monitoring and early warning method for extreme weather described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the monitoring and early warning method for extreme weather described above.

[0018] This application provides a monitoring and early warning method for extreme weather. Addressing the technical problems in existing technologies where static thresholds cannot adapt to weather changes and facilities operate independently, hindering cross-facility risk coordination and perception, the method first acquires operational status data of urban infrastructure and external environmental information. Based on the external environmental information, it identifies the current extreme weather type and determines a dynamic early warning threshold set corresponding to that extreme weather type. By comparing operational status data with the corresponding thresholds in this dynamic early warning threshold set, and based on the comparison results, it assesses the independent risk of each infrastructure to obtain an independent risk level. This independent risk level is then compared with a level trigger threshold to determine if any facilities require cross-facility linkage. If the detected independent risk level exceeds the level trigger threshold, the facility is identified as a target facility, indicating that its risk status has reached a level requiring coordinated action. A preset coordination relationship matrix is ​​then used to identify other facilities related to the target facility. Furthermore, cross-facility linkage analysis is performed by combining the target facility's independent risk level with the operational status of related facilities. This generates coordinated early warning information, which is then pushed to the corresponding management department's terminal, achieving automatic perception and coordinated early warning of cross-facility risk transmission.

[0019] In summary, this application solves the problem that static thresholds cannot adapt to weather changes by dynamically adjusting the early warning threshold, thus improving the accuracy of early warnings. It also solves the problem that independent operation of each facility cannot achieve cross-facility risk collaborative perception by linking and retrieving the collaborative relationship matrix, realizing a leap from single-point alarm to network collaborative response capability, and providing technical support for the chain-like prevention of secondary disasters caused by extreme weather. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an embodiment of the monitoring and early warning method for extreme weather provided in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the monitoring and early warning method for extreme weather provided in this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the monitoring and early warning method for extreme weather provided in this application; Figure 4 This is a schematic diagram of the module structure of a monitoring and early warning device for extreme weather according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the monitoring and early warning method for extreme weather in this application embodiment.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The main solution of this application embodiment is as follows: Obtain operational status data of urban infrastructure and external environmental information; identify extreme weather types based on the external environmental information, and determine a dynamic early warning threshold set corresponding to the extreme weather types; compare the operational status data with the corresponding thresholds in the dynamic early warning threshold set, and perform independent risk assessment on the urban infrastructure based on the comparison results to obtain an independent risk level; based on the independent risk level, determine the target urban infrastructure that exceeds the level trigger threshold, and based on a preset collaborative relationship matrix, identify other urban infrastructures that are associated with the target urban infrastructure; based on the collaborative relationship matrix, perform cross-facility linkage analysis according to the independent risk level of the target urban infrastructure and the operational status of the other urban infrastructures to generate collaborative early warning information, and push it to the corresponding management department terminal.

[0027] Currently, urban infrastructure monitoring and early warning systems are typically built independently according to facility type: pipeline systems focus on leaks and water levels, road systems on water accumulation and icing, and sanitation systems on overflow and spillage. There is a lack of information sharing and coordinated response between these systems. Furthermore, these systems generally use uniform static thresholds for early warning determination, failing to consider the differentiated impacts of various extreme weather types on facility risks. This results in a single facility risk event failing to trigger risk transmission assessments of related facilities, and is prone to missed or false alarms under different weather scenarios. Therefore, how to achieve differentiated dynamic early warning and cross-facility collaborative linkage for urban infrastructure under extreme weather conditions is a pressing issue that needs to be addressed.

[0028] In summary, this application solves the problem that static thresholds cannot adapt to weather changes by dynamically adjusting the early warning threshold, thus improving the accuracy of early warnings. It also solves the problem that independent operation of each facility cannot achieve cross-facility risk collaborative perception by linking and retrieving the collaborative relationship matrix, realizing a leap from single-point alarm to network collaborative response capability, and providing technical support for the chain-like prevention of secondary disasters caused by extreme weather.

[0029] It should be noted that the executing entity in this embodiment can be a monitoring and early warning system for extreme weather, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device capable of performing the above functions for a monitoring and early warning system for extreme weather. This embodiment does not specifically limit it in this way. The following uses a monitoring and early warning system for extreme weather as the executing entity as an example to describe this embodiment and the following embodiments.

[0030] Based on this, embodiments of this application provide a monitoring and early warning method for extreme weather, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the monitoring and early warning method for extreme weather proposed in this application.

[0031] In this embodiment, the monitoring and early warning method for extreme weather includes steps S10 to S50: Step S10: Obtain operational status data of urban infrastructure and external environmental information; It should be noted that urban infrastructure includes at least one of the following: municipal pipeline facilities, road traffic facilities, and sanitation facilities. Municipal pipeline facilities include, but are not limited to, stormwater pipe networks, sewage pipe networks, water supply pipe networks, and gas pipe networks. Pipeline sensors deployed at key nodes of each network collect operational status data such as water level, flow rate, pressure, and temperature. Road traffic facilities include roads and bridges. Road sensors deployed on the road surface and bridge structure collect operational status data such as water depth, icing status, road temperature, and vibration. Sanitation facilities include garbage bins and public toilets. Sensors deployed on garbage bins collect data such as garbage height, tilt angle (for detecting dumping status), and bin temperature. Sensors deployed on public toilets collect data such as temperature, humidity, ammonia concentration, and water consumption.

[0032] Additionally, it should be noted that external environmental information is obtained through a meteorological data interface, which connects to real-time meteorological data and extreme weather warnings provided by the meteorological department. External environmental information includes, but is not limited to, meteorological parameters such as typhoon level, rainfall intensity, snowfall, temperature, wind speed, and humidity. This external environmental information serves as the data foundation for subsequent identification of extreme weather types and determination of dynamic warning thresholds, enabling the warning judgment criteria to adaptively adjust with changes in weather type. Optionally, after acquiring operational status data, the collected multi-source data can be processed and preliminarily analyzed locally through edge computing nodes deployed near the facility: the edge nodes encapsulate the perceived facility status data in a normalized format, identify abnormal states exceeding preset basic thresholds, and then upload the processed structured data to the cloud computing analysis layer.

[0033] Understandably, urban infrastructure facilities are not entirely independent but have complex physical spatial relationships and functional dependencies—for example, excessive water levels in stormwater drainage networks may cause flooding on roads above, affecting traffic safety; trash cans dumped during typhoons may roll onto adjacent roads, creating traffic obstructions. Therefore, before conducting cross-facility linkage analysis, it is necessary to first obtain basic operational status data and external environmental information for each facility as a data foundation to support subsequent steps such as extreme weather type identification, dynamic early warning threshold calculation, independent risk assessment, and cross-facility linkage analysis. Step S10 provides comprehensive and accurate data input for the entire monitoring and early warning method, avoiding decreased early warning accuracy due to a single data source or inconsistent data formats, thereby improving the reliability and timeliness of subsequent early warning judgments and cross-facility collaborative linkage.

[0034] Step S20: Identify extreme weather types based on the external environment information, and determine a set of dynamic early warning thresholds corresponding to the extreme weather types; It should be noted that extreme weather types include at least one of typhoons, torrential rain, blizzards, extreme heat, and extreme cold. The mechanisms by which different types of extreme weather affect urban infrastructure differ: typhoons primarily cause garbage bins to tip over and public toilet structures to vibrate due to strong wind loads; torrential rains cause water levels in pipe networks to exceed limits and roads to flood due to heavy rainfall; blizzards cause roads to freeze and pipe networks to freeze due to snow accumulation and low temperatures; extreme heat causes garbage to spontaneously combust and public toilets to deteriorate in sanitation due to heat radiation; and extreme cold causes water supply pipes to burst due to freezing. Therefore, different monitoring parameters and early warning thresholds need to be configured for different types of extreme weather to achieve accurate perception of risks to various facilities.

[0035] Additionally, it should be noted that the dynamic early warning threshold set refers to a comprehensive set of early warning thresholds for various facility types, corresponding to specific extreme weather types. This set uses "extreme weather type" as the first-level index and "facility type" as the second-level index, pre-setting different monitoring parameters and early warning thresholds for each combination. Compared to traditional fixed thresholds, the dynamic early warning threshold set allows the early warning judgment criteria to adaptively switch according to changes in weather type—the same facility uses threshold parameters specific to typhoon scenarios during typhoon weather and threshold parameters specific to blizzard scenarios during blizzard weather, thus avoiding the technical defects of "one-size-fits-all" static thresholds that are prone to missed or false alarms in different weather scenarios.

[0036] Understandably, since different extreme weather types pose different risk profiles to the same facility (for example, trash cans mainly face the risk of overflowing under normal weather conditions, with the monitoring parameter being the height of the trash can; under typhoon weather, they mainly face the risk of tipping over, with the monitoring parameter being the tilt angle; under extreme high-temperature weather, they mainly face the risk of spontaneous combustion, with the monitoring parameters being the temperature of the bin and the rate of temperature rise), step S20 is required to identify the current extreme weather type based on external environmental information and determine the dynamic warning threshold set corresponding to that weather type. This allows subsequent risk assessments to be based on threshold parameters that match the current weather scenario, thereby avoiding missed or false alarms caused by using uniform static thresholds and improving the accuracy and timeliness of warnings.

[0037] In one feasible implementation, step S20 may include steps A21 to A24: Step A21: Obtain meteorological parameters and extreme weather warning information from the external environment information; It should be noted that meteorological parameters include, but are not limited to, numerical data such as real-time wind speed, rainfall intensity, snowfall, temperature, and humidity, which are used for the real-time calculation of subsequent dynamic warning thresholds. Extreme weather warning information includes, but is not limited to, typhoon level, rainstorm warning level, snowstorm warning level, high temperature warning level, and low temperature warning level issued by meteorological departments, which are used to help identify the current extreme weather type.

[0038] Additionally, it should be noted that meteorological parameters and extreme weather warnings are obtained through a meteorological data interface. This interface connects to the meteorological department's data platform, enabling it to receive and analyze meteorological data in real time, providing a data foundation for subsequent weather type identification and dynamic threshold calculation.

[0039] Step A22: Identify the type of extreme weather based on the meteorological parameters and the extreme weather warning information; It should be noted that extreme weather types can be identified using a threshold determination method: when meteorological parameters and / or extreme weather warning information meet preset extreme weather determination conditions, the corresponding weather type is identified as the current extreme weather type. For example, when the meteorological department issues a typhoon warning or the real-time wind speed continuously exceeds a preset wind force threshold, it is identified as typhoon weather; when a rainstorm warning is issued or the rainfall intensity continuously exceeds a preset rainfall threshold, it is identified as rainstorm weather.

[0040] Additionally, it should be noted that the identified extreme weather types are used to retrieve subsequent differentiated warning threshold sets. If the identification result is typhoon weather, the warning threshold set corresponding to typhoon weather is retrieved; if the identification result is heavy rain weather, the warning threshold set corresponding to heavy rain weather is retrieved, and so on. When multiple extreme weather types exist at the same time (such as a typhoon accompanied by heavy rain), the primary weather type can be determined according to preset priority rules, or the threshold sets corresponding to each weather type can be retrieved separately for comprehensive judgment.

[0041] Step A23: Retrieve a set of warning parameters that match the extreme weather type from a preset set of differentiated warning thresholds; It should be noted that the differentiated early warning threshold set is a pre-configured database. This database uses "extreme weather type" and "facility type" as a joint index to store the monitoring parameters, early warning threshold functions and additional conditions for each facility type under various extreme weather conditions.

[0042] Additionally, it should be noted that the retrieved set of warning parameters contains warning configuration information for all facility types under this extreme weather type, including the monitoring parameter types corresponding to each facility type, the warning threshold calculation function (or constant), and the additional conditions for triggering the warning (such as the rate of temperature rise). This set serves as the input data for subsequent dynamic warning threshold calculation, providing parameter basis for generating specific dynamic warning thresholds for each facility node.

[0043] Step A24: Calculate the dynamic warning threshold based on the warning parameter set and the meteorological parameters, and generate a dynamic warning threshold set based on the dynamic warning threshold; It should be noted that the dynamic warning threshold refers to the warning judgment value calculated in real time for a specific facility node under the current extreme weather type and meteorological conditions. The calculation method for the dynamic warning threshold (Thredynamic) is as follows: Thredynamic = f(env_parameters) wherein, f is a threshold calculation function corresponding to the facility type in the early warning parameter set, and env_parameters are the meteorological parameter values at the current moment (such as real-time wind speed, rainfall intensity, air temperature, etc.). For example, the dynamic early warning threshold calculation function for a trash bin under typhoon weather is θ_dynamic = 30 / V_wind, wherein V_wind is the current real-time wind speed. When V_wind=20m / s, θ_dynamic=1.5°; when V_wind=10m / s, θ_dynamic=3°. It can be seen that the dynamic early warning threshold fluctuates in real time with the change of meteorological parameters, and can reflect the real risk situation under the current environment.

[0044] In addition, it should be noted that the dynamic early warning threshold set is a data set composed of the respective dynamic early warning thresholds corresponding to each facility node. Specifically, after acquiring the early warning parameter set corresponding to the current extreme weather type, the differentiated early warning engine traverses each facility node: first, read the facility type of the facility node, and acquire the threshold calculation function f corresponding to the facility type from the early warning parameter set; then acquire the meteorological element values required by the threshold calculation function at the current moment (for example, the inclination threshold function of a trash bin only requires wind speed, and the water level threshold function of a rainwater pipe network only requires rainfall intensity); finally substitute the meteorological element values into the threshold calculation function, and calculate to obtain the dynamic early warning threshold of the facility node. After the traversal is completed, the dynamic early warning thresholds of all facility nodes together form the dynamic early warning threshold set. Each dynamic early warning threshold in the set is respectively used for comparison with the operation state data of its corresponding facility node, so as to realize independent risk judgment for each facility individually.

[0045] Step S30: comparing the operation state data with the corresponding threshold in the dynamic early warning threshold set, performing independent risk judgment on the urban infrastructure according to the comparison result, and obtaining an independent risk level; It should be noted that the independent risk level is used to characterize the risk degree faced by a single facility node under the current operation state. The independent risk level can adopt a grading system, for example, three levels of high risk, medium risk and low risk, or multi-level division such as level 1 to level 5. By comparing the operation state data with the dynamic early warning threshold, the degree of deviation of the current state of the facility from the safe operation range can be quantitatively evaluated, and then its independent risk level can be determined, providing a judgment basis for whether to trigger cross-facility linked early warning subsequently.

[0046] Additionally, it should be noted that the dynamic early warning threshold set includes the dynamic early warning thresholds corresponding to each facility node. Therefore, when making independent risk assessments, it is necessary to compare the operational status data of each facility node with its own corresponding dynamic early warning threshold, rather than using a uniform threshold to assess all facilities. Different facility types have different monitoring parameters and early warning thresholds under the same extreme weather conditions—for example, during typhoon weather, the monitoring parameter for garbage bins is the tilt angle, the monitoring parameter for stormwater drainage networks is the water level, and the monitoring parameter for roads is the water depth. The threshold calculation functions and judgment criteria for each facility are different, therefore, it is necessary to make independent risk assessments for each facility individually.

[0047] Understandably, different facilities face different types of risks, and the external factors affecting their operational status also differ. Under the same extreme weather conditions, garbage bins may be at risk of tipping over due to strong winds, rainwater drainage networks may face the risk of exceeding water levels due to heavy rain, and roads may face traffic safety risks due to water accumulation and icing. If a uniform judgment standard is used to assess the risks of all facilities, it will not accurately reflect the actual risk situation faced by each facility. Therefore, step S30 is required to calculate the risk deviation of the operational status data of each facility node under the corresponding dynamic warning threshold, and independently determine the risk level of each facility node. This avoids misjudgment or omission due to the use of a uniform judgment standard, and improves the accuracy and precision of risk assessment.

[0048] In one feasible implementation, step S30 may include steps A31 to A34: Step A31: Traverse each facility node in the urban infrastructure; It should be noted that a facility node refers to a single urban infrastructure unit being monitored. For example, a storm drain, a trash can, or a section of road surface can all be considered an independent facility node. Traversal refers to visiting each facility node one by one in a preset order and performing subsequent risk assessment operations on each node.

[0049] Additionally, it should be noted that the traversal order can be organized according to facility type or facility location. For example, pipeline facility nodes can be traversed first, followed by road facility nodes, and finally sanitation facility nodes; or the facilities can be traversed sequentially from nearest to farthest in terms of geographical location. After the traversal is complete, each facility node will obtain its own independent risk level.

[0050] Step A32: Based on the operating status data of the currently traversed facility nodes, obtain the dynamic early warning threshold corresponding to the currently traversed facility node from the dynamic early warning threshold set. It should be noted that the corresponding dynamic warning threshold refers to the dynamic warning threshold in the dynamic warning threshold set that matches the facility type of the currently traversed facility node. Since different facility nodes may belong to the same facility type (e.g., multiple rainwater wells belonging to the same pipe network facility), different facility nodes under the same facility type use the same threshold calculation function and meteorological parameters under the same extreme weather type, thus obtaining the same dynamic warning threshold. However, the operational status data of different facility nodes under the same facility type are different, resulting in different judgment results.

[0051] Additionally, it should be noted that the dynamic early warning threshold set is a data set that has already been generated in step S20. Step A32 only needs to read the threshold corresponding to the current facility node from this set, without recalculating it.

[0052] Step A33: Compare the operational status data of the currently traversed facility node with the corresponding dynamic early warning threshold to determine the risk deviation value of the operational status data of the currently traversed facility node under the extreme weather type. It should be noted that the risk deviation value is used to quantitatively characterize the degree to which operational status data exceeds the corresponding dynamic warning threshold. For example, when the operational status data is greater than the dynamic warning threshold, the risk deviation value is positive, indicating that the current facility is at risk; when the operational status data is less than or equal to the dynamic warning threshold, the risk deviation value is zero or negative, indicating that the current facility is within the safe operating range. The specific calculation method for the risk deviation value can be configured according to the facility type and monitoring parameter type.

[0053] Furthermore, it should be noted that since the risk deviation value is determined under identified extreme weather types, the same operational status data may correspond to different risk deviation values ​​under different extreme weather types. For example, if a trash can's tilt angle is 10°, the dynamic warning threshold is 1.5° during typhoon weather, resulting in a positive risk deviation value and a judgment of risk; however, under normal weather conditions, the dynamic warning threshold is 45°, resulting in a negative risk deviation value and a judgment of safety. This reflects the technical essence of "differentiated dynamic warning" in this application—the same operational status data corresponds to different risk assessment results under different weather types.

[0054] Step A34: Based on the risk deviation value, perform an independent risk assessment on the currently traversed facility node to obtain the independent risk level of the currently traversed facility node; It should be noted that the independent risk level can be determined by matching the risk deviation value with a preset risk level classification range. For example, the preset risk level classification range includes a low-risk range, a medium-risk range, and a high-risk range, with different ranges corresponding to different risk levels. When the risk deviation value falls into a certain range, the risk level corresponding to that range is taken as the independent risk level of the current facility node.

[0055] Additionally, it should be noted that the independent risk level serves as the basis for determining whether to trigger cross-facility linkage in subsequent step S40. When the independent risk level exceeds the preset trigger threshold, it indicates that the risk of the facility node has reached a level requiring coordinated handling. The system identifies it as the target facility and triggers cross-facility linkage analysis, thereby achieving an automatic switch from "independent judgment of a single facility" to "cross-facility coordinated linkage".

[0056] Step S40: Based on the independent risk level, determine the target urban infrastructure that exceeds the level trigger threshold, and based on the preset collaborative relationship matrix, identify other urban infrastructure that is associated with the target urban infrastructure. It should be noted that the risk level trigger threshold is a pre-set critical value used to define the threshold at which cross-facility linkage needs to be triggered within an independent risk level. Independent risk levels are typically classified in a hierarchical manner (such as three levels: high risk, medium risk, and low risk, or multiple levels from one to five), and the risk level trigger threshold corresponds to a specific risk level boundary. When the independent risk level of a facility node reaches or exceeds this threshold, it means that the risk status of that facility is relatively serious and there is a possibility of transmission and spread to other facilities, requiring the initiation of a cross-facility linkage early warning process. For example, when the risk level is divided into five levels, the risk level trigger threshold can be set to level three, meaning that facility nodes with an independent risk level of level three or above are identified as target facilities.

[0057] Additionally, it should be noted that the collaboration relationship matrix is ​​a static data structure pre-built during system initialization, used to record the preset associations between various urban infrastructures. This matrix uses facility identifiers as row and column indices, and matrix elements store the association type and linkage strategy template between corresponding facility pairs. By using the facility identifier of the target facility as an index, the corresponding row in the matrix can be quickly located, thereby retrieving all other facilities that have an association with it.

[0058] It can be understood that due to the complex physical spatial correlation and functional dependence among various facilities of urban infrastructure, risk events of a single facility are often not isolated—for example, an overturned trash can may roll onto the road and form an obstacle, excessive water in a rainwater pipe network may cause road water accumulation, and leakage of a water supply pipe network may affect the normal use of public toilets. If risk assessment and early warning are only performed at the single-facility level without identifying whether the risk may propagate to other facilities, the opportunity for chain blocking before the occurrence of secondary disasters will be lost. Therefore, step S40 is required: target facilities requiring collaborative disposal are screened out through level trigger thresholds, and their associated facilities are automatically retrieved through a collaborative relationship matrix, so that a single-facility risk event can automatically trigger cross-facility risk propagation perception, breaking the information islands formed by independent operation of various facilities, and providing facility objects and data basis for subsequent cross-facility linkage analysis.

[0059] In a feasible implementation, step S40 comprises: using the facility identifier of the target urban infrastructure as an index, retrieving facilities having a preset association relationship with the target urban infrastructure from the collaborative relationship matrix, to obtain other urban infrastructures, wherein the preset association relationship comprises at least one of risk propagation association, spatial association and resource collaboration association.

[0060] It should be noted that the facility identifier is an identity mark for uniquely identifying each facility node, which may be a facility number, a facility name or a facility ID (Identity). Retrieval with the facility identifier as an index means that the system uses the facility identifier of the target facility as a query key, locates the row corresponding to the target facility in the collaborative relationship matrix, then traverses all columns in the row, extracts facilities corresponding to columns with non-empty matrix elements as candidate associated facilities. The retrieved other urban infrastructures may include one or more facilities, and these facilities will be used as objects for cross-facility linkage analysis in subsequent step S50.

[0061] In addition, it should be noted that the preset association relationship comprises at least one of risk propagation association, spatial association and resource collaboration association. Wherein: Risk propagation association means that the risk state of a source facility directly aggravates the risk of a target facility through a physical medium or a spatial path. For example, after the water level of a rainwater pipe network exceeds the limit, water body propagates through the pipe network system, leading to an increase in the water accumulation risk in the upper or downstream road areas.

[0062] Spatial association means that there is a risk of object movement or impact between a source facility and a target facility due to geographical proximity. For example, an overturned trash can in typhoon weather may roll onto an adjacent road, posing a barrier risk to road traffic.

[0063] Resource-coordinated linkage refers to situations where risk management of the source facility requires the mobilization of emergency resources associated with the target facility or collaborative operations between the target facility's management department. For example, a leak in a water supply network requires joint investigation and handling by the water supply repair department and the sanitation department.

[0064] In the specific retrieval process, the collaborative early warning engine uses the facility identifier of the target facility as an index. After locating the row corresponding to the target facility in the collaborative relationship matrix, it traverses each column in that row. For each column, if the matrix element is not empty, it extracts the relationship type recorded in that element; then, based on the currently identified extreme weather type, it filters out the relationships that match the current weather scenario from all relationships—for example, in typhoon weather, it prioritizes spatial and risk transmission relationships, and in rainstorm weather, it prioritizes risk transmission relationships; finally, the facilities corresponding to the filtered relationships are considered as other urban infrastructure that are related to the target facility.

[0065] When the search results are empty, it means that there are no associated facilities matching the current extreme weather type for the target facility. In this case, there is no need to perform subsequent cross-facility linkage analysis; it is only necessary to push the early warning information of the target facility to its corresponding management department terminal. When the search results are multiple facilities, it is necessary to perform linkage analysis on each associated facility separately to generate collaborative early warning information for different facility types and different management departments.

[0066] Step S50: Based on the collaborative relationship matrix, according to the independent risk level of the target city's infrastructure and the operating status of other city infrastructures, perform cross-facility linkage analysis to generate collaborative early warning information and push it to the corresponding management department terminal. It should be noted that cross-facility linkage analysis refers to the process of taking the target facility as the risk source, other retrieved facilities as the affected objects, combining the independent risk level of the target facility (reflecting the severity of the risk source) and the operational status of related facilities (reflecting the current capacity of the affected objects) to comprehensively judge the possibility and degree of risk transmission, and generate collaborative early warning information accordingly.

[0067] Additionally, it should be noted that collaborative early warning information is a comprehensive set of information including risk source information, affected facility information, risk transmission path, and recommended handling measures. This information is generated through preset linkage strategy templates in the collaborative relationship matrix, with different early warning content and handling suggestions corresponding to different relationship types. Pushing to the corresponding management department terminal means sending the collaborative early warning information to the terminal equipment of the management department corresponding to that facility, based on the facility type. For example, traffic-related early warnings are pushed to the traffic management department, sanitation-related early warnings to the sanitation department, and pipeline-related early warnings to the drainage or water supply maintenance department.

[0068] Understandably, since risk events at a single facility can propagate to other facilities through physical spatial connections and functional dependencies between facilities, if cross-facility linkage analysis and collaborative early warning push are not performed after identifying related facilities, these related facilities will continue to operate in information silos, unaware of the existence of risk propagation, thus losing the time window for coordinated response before secondary disasters occur. Therefore, step S50 is necessary to generate collaborative early warning information through cross-facility linkage analysis and push it to the corresponding management department terminals, achieving a leap from single-point alarm to network-based collaborative response capabilities, providing technical support for the chain-like prevention of secondary disasters under extreme weather conditions.

[0069] This embodiment provides a monitoring and early warning method for extreme weather. Addressing the technical problems in existing technologies where static thresholds cannot adapt to weather changes and facilities operate independently, hindering cross-facility risk coordination and perception, the method first acquires operational status data of urban infrastructure and external environmental information. Based on the external environmental information, it identifies the current extreme weather type and determines a dynamic early warning threshold set corresponding to that extreme weather type. By comparing operational status data with the corresponding thresholds in this dynamic early warning threshold set, and based on the comparison results, it assesses the independent risk of each infrastructure to obtain an independent risk level. This independent risk level is then compared with a level trigger threshold to determine if any facilities require cross-facility linkage. If the detected independent risk level exceeds the level trigger threshold, the facility is identified as a target facility, indicating that its risk status has reached a level requiring coordinated action. A preset coordination relationship matrix is ​​then used to identify other facilities associated with the target facility. Furthermore, cross-facility linkage analysis is performed by combining the target facility's independent risk level with the operational status of associated facilities. This generates coordinated early warning information, which is then pushed to the corresponding management department's terminal, achieving automatic perception and coordinated early warning of cross-facility risk transmission.

[0070] In summary, this embodiment solves the problem that static thresholds cannot adapt to weather changes by dynamically adjusting the warning threshold, thus improving the accuracy of warnings. It also solves the problem that independent operation of each facility cannot achieve cross-facility risk collaborative perception by using a collaborative relationship matrix linkage retrieval, realizing a leap from single-point alarm to network collaborative response capability, and providing technical support for the chain-like prevention of secondary disasters caused by extreme weather.

[0071] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S50 may include steps S51 to S57: Step S51: Based on the collaborative relationship matrix, obtain the linkage strategy templates corresponding to the target city infrastructure and the other city infrastructures; It should be noted that the linkage strategy template is a pre-defined structured data unit in the collaboration relationship matrix, corresponding to each pair of associated facilities. It is used to define the specific collaborative response plan that should be executed for the target facility when the source facility triggers an early warning. The linkage strategy template contains populated parameter placeholders, including but not limited to risk description placeholders, time window placeholders, and handling suggestion placeholders.

[0072] Additionally, it should be noted that the specific content of the linkage strategy template varies depending on the type of linkage relationship—the template for risk transmission linkage focuses on suggestions for improving monitoring accuracy and risk level prediction; the template for spatial linkage linkage focuses on obstacle early warning and protection suggestions; and the template for resource coordination linkage focuses on scheduling instructions for joint actions by multiple departments.

[0073] Step S52: Based on the independent risk level of the target city's infrastructure and the operational status of the other city's infrastructure, fill in the linkage strategy template to generate collaborative early warning information; It should be noted that filling the linkage strategy template means taking the independent risk level of the target facility (reflecting the severity of the risk source) and the operational status of related facilities (reflecting the current resilience of the affected object) as input parameters and writing them into the corresponding parameter placeholders in the linkage strategy template to generate complete early warning information containing specific values, risk descriptions and handling suggestions.

[0074] Additionally, it should be noted that the completed collaborative early warning information includes risk source information, affected facility information, a description of the risk transmission path, and specific handling recommendations for the associated facility. The format and content of the early warning information differ depending on the type of association.

[0075] Step S53: Determine the facility type and warning level corresponding to the collaborative early warning information; It should be noted that facility type refers to the category of the facilities involved in the collaborative early warning information, including pipeline facilities, road facilities, and sanitation facilities. Early warning level refers to the risk level of the collaborative early warning information, which is inherited from the independent risk level of the target facility or comprehensively assessed based on the results of cross-facility linkage analysis.

[0076] Additionally, it should be noted that the purpose of determining the facility type and warning level is to match different push strategies for warning information of different facility types and warning levels in the future.

[0077] Step S54: Determine the push priority based on the warning level; It should be noted that push priority refers to the ranking of the urgency of collaborative early warning information when it is pushed out. The higher the warning level, the higher the corresponding push priority, ensuring that high-risk early warning information can be processed and responded to first.

[0078] Additionally, it should be noted that push notification priorities can be divided into multiple levels. For example, high-priority alerts are pushed immediately, medium-priority alerts are pushed in batches at scheduled times, and low-priority alerts are archived only for reference.

[0079] Step S55: Match the corresponding management department according to the facility type; It should be noted that the management department refers to the functional department responsible for the operation, management, and emergency response of a specific type of facility. Different types of facilities correspond to different management departments—for example, road facilities correspond to traffic management departments, pipeline facilities correspond to drainage or water supply maintenance departments, and sanitation facilities correspond to sanitation departments.

[0080] Additionally, it should be noted that the mapping relationship between facility types and management departments is pre-stored in the system configuration. When collaborative early warning information involves multiple facility types, the corresponding management departments for each facility type are matched to ensure that the early warning information is accurately pushed to the departments responsible for handling the situation.

[0081] Step S56: If the collaborative early warning information involves a single facility type, then according to the push priority, the collaborative early warning information is pushed to the management department terminal corresponding to the single facility type. It should be noted that a single facility type means that the risk source and the affected facilities in the collaborative early warning information belong to the same facility type. For example, if the water level in the rainwater pipe network exceeds the limit, it only involves facilities within the jurisdiction of the drainage department.

[0082] Additionally, it should be noted that for early warning information for a single facility type, the target of the push notification is a single management department, and the content includes the risk assessment result for that facility and corresponding handling recommendations. The push method is determined according to the push priority; high priority uses immediate push notifications, while low priority uses scheduled push notifications.

[0083] Step S57: If the collaborative early warning information involves multiple facility types, then the management departments corresponding to each facility type are matched according to the collaborative relationship matrix to generate a multi-department comprehensive scheduling suggestion, and the suggestion is pushed to the terminals of each matched management department according to the push priority. It should be noted that "multiple facility types" refers to situations where the risk source and affected facilities in the collaborative early warning information involve two or more different facility types. For example, a water supply network leak may simultaneously affect road traffic (requiring notification to the traffic management department) and public toilet water outages (requiring notification to the sanitation department). Multi-department integrated dispatch recommendations refer to comprehensive recommendations generated for scenarios involving multiple facilities and incorporating collaborative handling processes across multiple departments.

[0084] Additionally, it should be noted that the multi-department integrated dispatch recommendations are generated based on the types of relationships recorded in the collaboration matrix. For risk transmission-type relationships, the integrated dispatch recommendations focus on the connection and coordination between upstream and downstream departments; for spatially related relationships, the recommendations focus on physical isolation and traffic control; and for resource-coordinated relationships, the recommendations focus on the division of labor and timing of joint actions by multiple departments. Through these multi-department integrated dispatch recommendations, it can be ensured that multiple management departments perform their respective duties and respond collaboratively in the same risk event. When the alert is pushed out, the warning information received by each management department's terminal includes the specific handling tasks that the department needs to perform and the coordination requirements with other departments.

[0085] This embodiment provides a monitoring and early warning method for extreme weather. Based on the first embodiment described above, it addresses the technical problem in existing technologies where facilities operate independently and cannot achieve cross-facility risk collaborative perception. First, it uses the facility identifier of the target facility as an index to retrieve other facilities with preset associations with the target facility from the collaborative relationship matrix to automatically identify related objects that may transmit risks. Then, it obtains linkage strategy templates corresponding to the target facility and other facilities based on the collaborative relationship matrix, and fills these templates according to the independent risk level of the target facility and the operating status of related facilities to generate collaborative early warning information containing specific risk descriptions, time windows, and handling suggestions, thereby completing cross-facility linkage analysis. Finally, it determines the facility type and early warning level corresponding to the collaborative early warning information, determines the push priority based on the early warning level, and matches the corresponding management department according to the facility type. When the collaborative early warning information involves a single facility type, it is pushed to the terminal of a single management department according to the push priority. When the collaborative early warning information involves multiple facility types, it matches the management departments corresponding to each facility type and generates multi-department comprehensive dispatch suggestions, which are then pushed to the terminals of each matched management department according to the push priority, thereby realizing cross-facility collaborative early warning and hierarchical linkage handling from single-point alarm to network collaborative response.

[0086] In summary, this embodiment solves the problem of cross-facility risk collaborative perception that cannot be achieved when facilities operate independently by linking and retrieving the collaborative relationship matrix, breaking down the information silos of independent facility operation; it achieves automated generation of collaborative early warning information in cross-facility linkage analysis by filling linkage strategy templates, without manual intervention; and it achieves accurate delivery of early warning information to corresponding management departments by hierarchical push driven by facility type and early warning level. In particular, in multi-facility associated scenarios, it achieves multi-department collaborative response by generating multi-department comprehensive scheduling suggestions, thereby realizing a leap from single-point alarm to network collaborative response capability, and providing technical support for the chain-like prevention of secondary disasters after extreme weather.

[0087] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Following step S50, the monitoring and early warning method for extreme weather further includes steps S61-S64: Step S61: Display the risk distribution heat map of each city's infrastructure on the GIS visualization interface. The risk distribution heat map is marked with different colors according to the independent risk level of each facility. It should be noted that the GIS (Geographic Information System) visualization interface refers to the large-screen display interface of the city's operation command center, used to visualize the spatial location and risk status of various facilities on the same map interface. The risk distribution heat map is a spatial distribution map that uses color depth or hue changes to represent the risk level of each facility. Different colors correspond to different risk levels, making it easier for city managers to quickly identify the distribution of high-risk areas.

[0088] Additionally, it should be noted that the different color codes correspond to risk levels as follows: red for high risk, yellow for medium risk, and blue for low risk. Through this color coding, city managers can intuitively understand the risk distribution of various infrastructure types across the entire city on a single GIS map and quickly locate areas with a high concentration of high-risk facilities.

[0089] Step S62: On the GIS visualization interface, based on the inter-facility relationships recorded in the collaborative relationship matrix, draw and display the early warning relationship lines between the target city infrastructure and the other city infrastructures; It should be noted that the early warning correlation line refers to a visual connection on a GIS map that links two facilities with a correlation, used to represent the transmission path of risk between facilities. This correlation line is displayed differently based on the type of correlation recorded in the collaboration matrix—for example, risk transmission correlations can be represented by solid arrows indicating the direction of risk transmission, spatial correlations can be represented by dashed lines indicating geographical proximity, and resource collaboration correlations can be represented by dotted lines indicating collaborative operations.

[0090] Additionally, it should be noted that the display of the early warning correlation lines allows city managers to clearly see which related facilities a single facility risk event may transmit to, intuitively understand the path and scope of risk transmission, and provide visual decision support for formulating cross-facility collaborative response strategies.

[0091] Step S63: Based on the warning level of the collaborative early warning information, dynamically adjust the display priority of the risk distribution heatmap and the early warning correlation line; It should be noted that display priority refers to the display hierarchy and prominence of each facility and its associated lines on the GIS interface when multiple facilities simultaneously have warning information. The higher the warning level, the higher the corresponding risk distribution heatmap layer and warning association line layer will appear in the display hierarchy, and the more prominent they will be.

[0092] Additionally, it should be noted that the dynamic adjustment of display priority can be achieved by displaying facilities with high-level warnings and their associated lines at the top layer of the GIS interface, facilities with medium-level warnings at the middle layer, and facilities with low-level warnings at the bottom layer. This avoids a large amount of low-level warning information obscuring high-level warning information and ensures that city managers prioritize monitoring the situational changes in high-risk areas.

[0093] Step S64: Based on the real-time updated operating status data and collaborative early warning information, dynamically update the risk distribution heat map and early warning correlation lines on the GIS visualization interface to demonstrate the dynamic evolution of the risk situation. It should be noted that the dynamic evolution of the risk situation refers to the process by which the risk levels of each facility and the risk transmission relationships between facilities change over time. As the system continuously acquires the latest operational status data and meteorological data, independent risk levels and collaborative early warning information are constantly updated, and the GIS visualization interface is refreshed synchronously, presenting the complete evolution trajectory of risk from its generation, transmission to its dissipation.

[0094] Additionally, it should be noted that the demonstration function of the dynamic evolution process enables city managers to grasp the spatiotemporal changes of risks in real time, predict the possible development paths of secondary disasters, and provide a visual basis for dynamically adjusting emergency response strategies.

[0095] In summary, this embodiment addresses the problem of urban managers' inability to intuitively perceive the spatial distribution of facility risks through a risk distribution heatmap, achieving a spatial and visual presentation of the risk levels of each facility; it solves the problem of invisible risk transmission paths through early warning correlation lines, enabling a graphical display of cross-facility risk transmission relationships; it addresses the problem of key information being overwhelmed when multiple early warnings are superimposed through dynamic adjustment of display priorities, achieving prominent display of high-level early warning information; and it solves the problem of difficulty in predicting risk development trends through dynamic demonstration of risk situation evolution, achieving visual tracking of the entire process from risk generation to dissipation. Therefore, this embodiment provides urban managers with an intuitive, real-time, and dynamic ability to perceive urban infrastructure risk situations, providing visual support for cross-facility collaborative decision-making.

[0096] This application also provides a monitoring and early warning device for extreme weather, please refer to... Figure 4 The monitoring and early warning device for extreme weather includes: The data acquisition module 10 is used to acquire operational status data of urban infrastructure and external environmental information; The dynamic threshold determination module 20 is used to identify extreme weather types based on the external environment information and determine a set of dynamic early warning thresholds corresponding to the extreme weather types. The independent risk assessment module 30 is used to compare the operating status data with the corresponding threshold in the dynamic early warning threshold set, and to make an independent risk assessment of the urban infrastructure based on the comparison result to obtain an independent risk level. The collaborative identification module 40 is used to determine the target urban infrastructure that exceeds the level trigger threshold based on the independent risk level, and to identify other urban infrastructure that is associated with the target urban infrastructure based on a preset collaborative relationship matrix. The linkage early warning module 50 is used to perform cross-facility linkage analysis based on the collaborative relationship matrix, according to the independent risk level of the target city infrastructure and the operating status of other city infrastructure, to generate collaborative early warning information and push it to the corresponding management department terminal.

[0097] In one embodiment, the dynamic threshold determination module 20 is further configured to obtain meteorological parameters and extreme weather warning information from the external environment information; Identify extreme weather types based on the meteorological parameters and extreme weather warning information; Retrieve a set of warning parameters that match the extreme weather type from a preset set of differentiated warning thresholds; A dynamic warning threshold is calculated based on the set of warning parameters and the meteorological parameters, and a dynamic warning threshold set is generated based on the dynamic warning threshold.

[0098] In one embodiment, the dynamic threshold determination module 20 is further configured to load a corresponding differentiated early warning profile from the set of early warning parameters; Based on the facility type of each facility node in the urban infrastructure, the corresponding threshold calculation function is determined from the differentiated early warning profile; By substituting the meteorological parameters into the threshold calculation function, the dynamic early warning thresholds for each facility node in the urban infrastructure are calculated.

[0099] In one embodiment, the independent risk assessment module 30 is also used to traverse each facility node in the urban infrastructure; Based on the operational status data of the currently traversed facility nodes, obtain the dynamic early warning threshold corresponding to the currently traversed facility node from the dynamic early warning threshold set; The operational status data of the currently traversed facility node is compared with the corresponding dynamic early warning threshold to determine the risk deviation value of the operational status data of the currently traversed facility node under the extreme weather type. Based on the risk deviation value, an independent risk assessment is performed on the currently traversed facility node to obtain the independent risk level of the currently traversed facility node.

[0100] In one embodiment, the collaborative identification module 40 is further configured to retrieve facilities with a preset association relationship with the target city infrastructure from the collaborative relationship matrix using the facility identifier of the target city infrastructure as an index, thereby obtaining other city infrastructures, wherein the preset association relationship includes at least one of risk transmission association, spatial association association and resource collaboration association.

[0101] In one embodiment, the linkage early warning module 50 is further configured to obtain linkage strategy templates corresponding to the target city infrastructure and the other city infrastructure based on the collaborative relationship matrix; Based on the independent risk level of the target city's infrastructure and the operational status of other city infrastructure, the linkage strategy template is filled in to generate collaborative early warning information.

[0102] In one embodiment, the linkage early warning module 50 is further configured to determine the facility type and early warning level corresponding to the collaborative early warning information; The push priority is determined based on the aforementioned warning level; Match the corresponding management department based on the facility type; If the collaborative early warning information involves a single facility type, then according to the push priority, the collaborative early warning information will be pushed to the management department terminal corresponding to the single facility type; If the collaborative early warning information involves multiple facility types, the management departments corresponding to each facility type are matched according to the collaborative relationship matrix to generate a multi-department comprehensive scheduling suggestion, which is then pushed to the terminals of the matched management departments according to the push priority.

[0103] The monitoring and early warning device for extreme weather provided in this application, employing the monitoring and early warning method for extreme weather described in the above embodiments, can solve the technical problem of how to achieve differentiated dynamic early warning and cross-facility collaborative linkage of urban infrastructure under extreme weather conditions. Compared with the prior art, the beneficial effects of the monitoring and early warning device for extreme weather provided in this application are the same as those of the monitoring and early warning method for extreme weather provided in the above embodiments, and other technical features in the monitoring and early warning device for extreme weather are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0104] This application provides a monitoring and early warning device for extreme weather. The monitoring and early warning device for extreme weather includes: at least one processor; and 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the monitoring and early warning method for extreme weather in the above embodiment 1.

[0105] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a monitoring and early warning device for extreme weather suitable for implementing embodiments of this application. The monitoring and early warning device for extreme weather in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), tablets, PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The monitoring and early warning equipment for extreme weather shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0106] like Figure 5As shown, the monitoring and early warning equipment for extreme weather may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the monitoring and early warning equipment for extreme weather. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the extreme weather monitoring and early warning equipment to exchange data wirelessly or via wired communication with other devices. Although the figure shows extreme weather monitoring and early warning equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0107] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0108] The monitoring and early warning equipment for extreme weather provided in this application, employing the monitoring and early warning method for extreme weather described in the above embodiments, can solve the technical problem of how to achieve differentiated dynamic early warning and cross-facility collaborative linkage of urban infrastructure under extreme weather conditions. Compared with the prior art, the beneficial effects of the monitoring and early warning equipment for extreme weather provided in this application are the same as those of the monitoring and early warning method for extreme weather provided in the above embodiments, and other technical features of this monitoring and early warning equipment for extreme weather are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0109] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0111] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the monitoring and early warning method for extreme weather described in the above embodiments.

[0112] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0113] The aforementioned computer-readable storage medium may be included in monitoring and early warning equipment for extreme weather; or it may exist independently and not be assembled into monitoring and early warning equipment for extreme weather.

[0114] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a monitoring and early warning device for extreme weather, the monitoring and early warning device for extreme weather performs the following actions: acquires operational status data of urban infrastructure and external environmental information; identifies extreme weather types based on the external environmental information and determines a dynamic early warning threshold set corresponding to the extreme weather type; compares the operational status data with the corresponding thresholds in the dynamic early warning threshold set, and performs an independent risk assessment of the urban infrastructure based on the comparison result to obtain an independent risk level; based on the independent risk level, identifies target urban infrastructure that exceeds the level trigger threshold, and identifies other urban infrastructures that are associated with the target urban infrastructure based on a preset collaborative relationship matrix; based on the collaborative relationship matrix, performs cross-facility linkage analysis according to the independent risk level of the target urban infrastructure and the operational status of the other urban infrastructures to generate collaborative early warning information and pushes it to the corresponding management department terminal.

[0115] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0118] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the aforementioned monitoring and early warning method for extreme weather. This addresses the technical problem of how to achieve differentiated dynamic early warning and cross-facility collaborative linkage of urban infrastructure under extreme weather conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the monitoring and early warning method for extreme weather provided in the above embodiments, and will not be elaborated upon here.

[0119] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the monitoring and early warning method for extreme weather described above.

[0120] The computer program product provided in this application can solve the technical problem of how to achieve differentiated dynamic early warning and cross-infrastructure collaborative linkage of urban infrastructure under extreme weather conditions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the monitoring and early warning method for extreme weather provided in the above embodiments, and will not be repeated here.

[0121] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A monitoring and early warning method for extreme weather, characterized in that, The method includes: To obtain operational status data of urban infrastructure and information about the external environment; Based on the external environmental information, identify extreme weather types and determine a set of dynamic early warning thresholds corresponding to the extreme weather types; The operational status data is compared with the corresponding threshold in the dynamic early warning threshold set, and the independent risk assessment of the urban infrastructure is performed based on the comparison result to obtain the independent risk level. Based on the independent risk level, target urban infrastructure that exceeds the level trigger threshold is identified, and based on a preset collaborative relationship matrix, other urban infrastructure that is associated with the target urban infrastructure is identified. Based on the aforementioned collaborative relationship matrix, cross-facility linkage analysis is performed according to the independent risk level of the target city's infrastructure and the operational status of other city infrastructures to generate collaborative early warning information, which is then pushed to the corresponding management department's terminal.

2. The method as described in claim 1, characterized in that, The step of identifying extreme weather types based on the external environmental information and determining the dynamic early warning threshold set corresponding to the extreme weather types includes: Meteorological parameters and extreme weather warnings are obtained from the external environmental information. Identify extreme weather types based on the meteorological parameters and extreme weather warning information; Retrieve a set of warning parameters that match the extreme weather type from a preset set of differentiated warning thresholds; A dynamic warning threshold is calculated based on the set of warning parameters and the meteorological parameters, and a dynamic warning threshold set is generated based on the dynamic warning threshold.

3. The method as described in claim 2, characterized in that, The step of calculating the dynamic early warning threshold based on the set of early warning parameters and the meteorological parameters includes: Load the corresponding differentiated early warning profile from the set of early warning parameters; Based on the facility type of each facility node in the urban infrastructure, the corresponding threshold calculation function is determined from the differentiated early warning profile; By substituting the meteorological parameters into the threshold calculation function, the dynamic early warning thresholds for each facility node in the urban infrastructure are calculated.

4. The method as described in claim 1, characterized in that, The step of comparing the operational status data with the corresponding threshold in the dynamic early warning threshold set, and determining the independent risk level of the urban infrastructure based on the comparison result includes: Iterate through each facility node in the urban infrastructure; Based on the operational status data of the currently traversed facility nodes, obtain the dynamic early warning threshold corresponding to the currently traversed facility node from the dynamic early warning threshold set; The operational status data of the currently traversed facility node is compared with the corresponding dynamic early warning threshold to determine the risk deviation value of the operational status data of the currently traversed facility node under the extreme weather type. Based on the risk deviation value, an independent risk assessment is performed on the currently traversed facility node to obtain the independent risk level of the currently traversed facility node.

5. The method as described in claim 1, characterized in that, The step of identifying other urban infrastructures that are associated with the target urban infrastructure based on a preset collaborative relationship matrix includes: Using the facility identifier of the target city's infrastructure as an index, facilities with a preset association with the target city's infrastructure are retrieved from the collaborative relationship matrix to obtain other city infrastructures. The preset association includes at least one of risk transmission association, spatial association association, and resource synergy association.

6. The method as described in claim 1, characterized in that, The step of generating collaborative early warning information by performing cross-facility linkage analysis based on the collaborative relationship matrix, according to the independent risk level of the target city's infrastructure and the operational status of other city infrastructures, includes: Based on the collaborative relationship matrix, obtain the linkage strategy templates corresponding to the target city infrastructure and the other city infrastructure; Based on the independent risk level of the target city's infrastructure and the operational status of other city infrastructure, the linkage strategy template is filled in to generate collaborative early warning information.

7. The method as described in claim 1, characterized in that, The step of pushing the message to the corresponding management department terminal includes: Determine the facility type and warning level corresponding to the collaborative early warning information; The push priority is determined based on the aforementioned warning level; Match the corresponding management department based on the facility type; If the collaborative early warning information involves a single facility type, then according to the push priority, the collaborative early warning information will be pushed to the management department terminal corresponding to the single facility type; If the collaborative early warning information involves multiple facility types, the management departments corresponding to each facility type are matched according to the collaborative relationship matrix to generate a multi-department comprehensive scheduling suggestion, which is then pushed to the terminals of the matched management departments according to the push priority.

8. A monitoring and early warning device for extreme weather, characterized in that, The device includes: The data acquisition module is used to acquire operational status data of urban infrastructure and external environmental information; The dynamic threshold determination module is used to identify extreme weather types based on the external environment information and determine a set of dynamic early warning thresholds corresponding to the extreme weather types. An independent risk assessment module is used to compare the operational status data with the corresponding threshold in the dynamic early warning threshold set, and to make an independent risk assessment of the urban infrastructure based on the comparison result to obtain an independent risk level. The collaborative identification module is used to determine the target urban infrastructure that exceeds the level trigger threshold based on the independent risk level, and to identify other urban infrastructure that is associated with the target urban infrastructure based on a preset collaborative relationship matrix. The linkage early warning module is used to perform cross-facility linkage analysis based on the collaborative relationship matrix, according to the independent risk level of the target city's infrastructure and the operating status of other city infrastructures, to generate collaborative early warning information and push it to the corresponding management department terminal.

9. A monitoring and early warning device for extreme weather, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the monitoring and early warning method for extreme weather as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the monitoring and early warning method for extreme weather as described in any one of claims 1 to 7.

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