Water conservancy big data-based drainage basin collaborative forecast optimization system and method

Abstract

The invention discloses a drainage basin collaborative forecasting optimization system and method based on water conservancy big data, and belongs to the technical field of water conservancy forecasting. The method comprises the steps of setting drainage basin monitoring stations and deploying Internet of Things sensors, dividing monitoring areas and synchronizing timestamps, binding station identifiers to generate hydrological characteristic flows, constructing a full-drainage-basin datamation matrix, calculating state differences to update matrix elements, and finally generating a downstream station collaborative forecasting matrix to determine a flood forecasting range. The system comprises a monitoring deployment and time synchronization unit, an identifier binding and feature flow generation unit, a data capture and matrix construction unit, a difference calculation and matrix updating unit and a collaborative forecast and alarm generation unit. According to the invention, while the time-space consistency of the data is ensured, the coincidence between the collaborative forecast relevancy and the actual flood propagation path can be enhanced, the accuracy and pertinence of the boundary effect of the alarm range are improved, and the response time from data acquisition to alarm generation is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy forecasting, and in particular to a basin collaborative forecasting optimization system and method for water conservancy big data. Background Art

[0002] In the field of water conservancy forecasting, accurate and timely flood forecasting is crucial for disaster prevention and mitigation. However, traditional water conservancy forecasting technologies have significant limitations in data collection, processing, and collaborative analysis: Data collection and synchronization issues: The existing monitoring site deployment lacks systematicity, and the asynchronous sensor timestamps lead to spatiotemporal misalignment of hydrological data, making it difficult to reflect the true evolution of the basin's hydrological characteristics. For example, sensors at different sites may record data from different stages of the same flood event due to time deviations, resulting in distorted analysis results.

[0003] Insufficient coordinated analysis of river basins: Traditional methods are mostly based on independent analysis of data from a single site or local area, and lack dynamic modeling and coordinated correlation of hydrological data from the entire river basin. For example, flood propagation is predicted only through water level changes at individual sites, without considering the hydrological transmission relationship between upstream and downstream areas, resulting in inaccurate forecast ranges.

[0004] Outdated data processing methods: Existing technologies only process multi-source hydrological data (such as secondary flood rate influencing parameters and water level values) at a simple statistical level and lack in-depth exploration of the temporal and spatial dimensions of the data. For example, a basin-wide data matrix has not been constructed to capture the dual-state differences between monitoring periods and regional changes, making it difficult to quantify the dominant factors of basin hydrological changes.

[0005] Inefficient alert response: Traditional forecasting models rely on manual delineation of alert ranges and lack dynamic optimization mechanisms, resulting in blurred alert boundaries, excessive redundant information, and long response times. For example, they are unable to quickly identify the critical paths along which flood propagation occurs, delaying emergency decision-making.

[0006] In response to the above problems, the present invention proposes a basin collaborative forecast optimization system and method for water conservancy big data. Through the synchronization of IoT sensor timestamps, construction of a data matrix for the entire basin, state difference processing and collaborative forecast analysis, efficient integration and dynamic feedback of basin hydrological data are achieved, which is conducive to improving the accuracy and timeliness of flood forecasts, and recommending auxiliary solutions for water conservancy disaster early warning. Summary of the Invention

[0007] The purpose of the present invention is to provide a basin collaborative forecasting optimization system and method for water conservancy big data to solve the problems raised in the above background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: The basin collaborative forecasting optimization system for water conservancy big data includes: monitoring deployment and time synchronization unit, identification binding and feature flow generation unit, data capture and matrix construction unit, difference calculation and matrix update unit, and collaborative forecasting and alarm generation unit; The monitoring deployment and time synchronization unit is used to set up watershed monitoring sites and deploy IoT sensors, divide the monitoring area within the coverage area of ​​the watershed monitoring sites so that each IoT sensor collects hydrological data within the same monitoring area, and is also used to trigger an initialization instruction through the upstream starting watershed monitoring site to achieve synchronous calibration of the timestamps of each IoT sensor; The identifier binding and characteristic flow generation unit is used to bind a unique identifier to each watershed monitoring site and write site information. It is also used to configure the initialization synchronization timestamp. When a flood event occurs in the monitoring area, it triggers the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronization timestamp. The data capture and matrix construction unit captures the hydrological data of all IoT sensors during a flood event based on the hydrological characteristic flow and the monitoring area, and establishes a basin-wide data matrix with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; The difference calculation and matrix update unit is used to calculate the horizontal and vertical state differences with the digitized matrix elements of the entire watershed as the center, and update the matrix elements according to the maximum value of the difference; The collaborative forecast and alarm generation unit is used to remove the first row and first column of the data matrix of the entire basin to obtain the collaborative forecast matrix of the downstream site, calculate the collaborative forecast correlation between sites to determine the forecast range of the flood event, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port.

[0009] Furthermore, the identification binding and feature flow generation unit includes a regional device group module and an initialization timestamp module; The regional device group module is used to count all IoT sensors deployed in different monitoring areas to form regional device groups; The initialization timestamp module is used to configure the initialization synchronization timestamp.

[0010] Furthermore, the data capture and matrix construction unit includes a monitoring period generation module and a monitoring feature label generation module; The monitoring period generation module is used to divide the monitoring periods of different monitoring areas under the hydrological characteristic flow; The monitoring feature label generation module is used to capture the hydrological data of all IoT sensors generated during flood monitoring events to generate monitoring feature labels.

[0011] Furthermore, the difference calculation and matrix update unit includes a state replacement module and a matrix update module; The state replacement module is used to replace the horizontal state and the vertical state. The horizontal state refers to the state of the monitoring area itself when the monitoring period is replaced. The vertical state refers to the dual state between the two monitoring areas when the monitoring area is replaced downstream. The matrix update module is used to perform downstream gradient updates on the digitized matrix of the entire river basin.

[0012] The basin collaborative forecast optimization method based on water conservancy big data includes the following steps: Step S1: Set up watershed monitoring stations in the basin and deploy IoT sensors. Divide the monitoring area of ​​each watershed monitoring station. Count the IoT sensors in the monitoring area to collect hydrological data including secondary flood rate influencing parameters and water level values. At the same time, based on the upstream and downstream relationship of the basin runoff, trigger the initialization instruction through the upstream starting watershed monitoring station to achieve synchronous calibration of the timestamps of each IoT sensor. Step S2: Bind a unique identifier to each watershed monitoring site and write site information, configure an initial synchronization timestamp, and when a flood event occurs in the monitoring area, trigger the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronization timestamp; Step S3: Based on the hydrological characteristic flow and monitoring area, the hydrological data of all IoT sensors in the flood event are captured, and monitoring feature labels containing the monitoring period, influencing parameter clusters, and water level change values ​​are generated. The data matrix of the entire watershed is established with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; Step S4: With the digitized matrix element of the entire basin as the center, calculate the state differences in the horizontal direction (the state of the region itself when the monitoring period changes) and the vertical direction (the dual state between the two regions when the region changes downstream), and update the matrix element according to the maximum value of the difference; Step S5: Remove the first row and first column of the whole basin data matrix to obtain the downstream site collaborative forecast matrix, calculate the collaborative forecast correlation between sites to determine the forecast range of flood events, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port.

[0013] Furthermore, the specific implementation process of step S1 includes: Establishing watershed monitoring stations, deploying IoT sensors at each monitoring station, dividing the coverage area of ​​the monitoring stations into monitoring regions, and counting the IoT sensors deployed in each monitoring region. The IoT sensors are used to collect hydrological data within the watershed and attach synchronized timestamps. The hydrological data includes influencing parameters for secondary flood determination and water level values ​​within the monitoring region. Based on the upstream and downstream relationship of the watershed runoff, the timestamp synchronization of each IoT sensor is calibrated, and the initialization instruction of the synchronization calibration is set. The initialization instruction is triggered by the starting watershed monitoring station upstream of the watershed runoff and distributed to each watershed monitoring station downstream to instruct the synchronization calibration of the timestamps of each IoT sensor; In the above method, by setting up monitoring stations and deploying IoT sensors in the basin, the monitoring area is divided to centrally collect hydrological data (parameters affecting secondary flood rate, water level values); based on the upstream and downstream relationship of the basin runoff, the upstream starting station is used to trigger the initialization instruction, and the time consistency of the sensor data in the entire basin is ensured through the timestamp synchronization calibration mechanism; thus, the data misalignment problem caused by sensor time differences can be eliminated to ensure the spatiotemporal consistency of the hydrological data, laying the foundation for subsequent basin-wide data modeling.

[0014] Furthermore, the specific implementation process of step S2 includes: Bind a unique identifier to each watershed monitoring site and write site information, which includes: Any m-th watershed monitoring station is denoted as , record the flood event with flood number n as , any e-th monitoring area is recorded as , any r-th IoT sensor is recorded as , then the statistical monitoring area All IoT sensors deployed within the area constitute the regional device group , R represents the total number of IoT sensors; The synchronization timestamp of configuration initialization is , Indicates the gth synchronization time node in the initialized synchronization timestamp, G indicates the total number of synchronization time nodes in the initialized synchronization timestamp; when a flood event occurs in the monitoring area When the regional device group is triggered Each IoT sensor collects hydrological data and generates a hydrological feature stream with synchronized time stamps ,and , q represents the number of triggers; In the above method, a unique identifier is bound to each monitoring site, and a site information model including flood number, monitoring area, and sensor is constructed; when a flood event is triggered, a hydrological characteristic stream with a time tag is generated by initializing a synchronized timestamp to achieve accurate triggering and structured storage of data collection; thus, it can ensure a one-to-one correspondence between flood events and monitoring data, facilitate subsequent data tracing and feature analysis, and improve data management efficiency.

[0015] Furthermore, the specific implementation process of step S3 includes: Based on hydrological characteristics and monitoring areas, capture the flood events in monitoring The hydrological data of all IoT sensors generated at the time of generation are used to generate monitoring feature labels, which are recorded as ,in, Represents hydrological characteristic flow Lower monitoring area monitoring period, Indicates monitoring period Lower monitoring area The cluster of influencing parameters used in the secondary flood calibration is Monitoring period Lower monitoring area The water level change value within the monitoring period is the difference between the water level value in the monitoring area at the start synchronization time node within the monitoring period and the water level value in the monitoring area at the end synchronization time node within the monitoring period. If the water level change value is a positive value, it means that the water level in the monitoring area has dropped, and if the water level change value is a negative value, it means that the water level in the monitoring area has increased; Establish a basin-wide data matrix with hydrological characteristic flow as the horizontal dimension and monitoring area as the vertical dimension, and divide the basin monitoring stations into Monitoring flood events The whole basin data matrix generated when , then the whole basin data matrix The matrix element corresponding to the qth row and the eth column is recorded as ; In the above method, based on the hydrological characteristic flow and monitoring area, the sensor data of the entire river basin is captured to generate monitoring characteristic labels including the monitoring period, influencing parameter clusters, and water level change values; a data matrix of the entire river basin is constructed with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension to realize the spatial gridding and time serialization of multi-source data; furthermore, the discrete hydrological data can be converted into a structured matrix, which facilitates the exploration of the spatiotemporal laws of hydrological changes in the river basin through matrix operations, and provides a data basis for collaborative analysis.

[0016] Furthermore, the specific implementation process of step S4 includes: Based on the data matrix of the entire basin, select the matrix elements is the center of the matrix, calculate the matrix elements The horizontal state difference and the vertical state difference of the monitoring period Monitoring area during replacement The longitudinal state difference refers to the monitoring area When the monitoring area is replaced downstream, the monitoring area The dual status difference between the downstream monitoring area and the monitoring period refers to the two dimensions of monitoring area: ; Where, Indicates the horizontal state difference, represents the longitudinal state difference, Indicates monitoring period Change to monitoring period Time monitoring area The difference of its own state, and the difference value of its own state is the influencing parameter cluster of the secondary flood rate and The number of identical influencing parameters contained in the intersection set; Select the maximum value between the horizontal state difference and the vertical state difference ,like , then for the matrix elements Update and let the matrix elements Is 0, if , then for the matrix elements Update and let the matrix elements is 1; In the above method, horizontal state differences (changes in the state of the region itself when the monitoring period changes) and vertical state differences (double state changes when the monitoring area changes downstream) are defined, and the matrix values ​​(0 or 1) are updated by calculating the differences in matrix elements to characterize the dominant factors of the basin state changes (period changes or regional evolution); furthermore, through dynamic matrix updates, the key nodes and propagation directions of the basin's hydrological changes are highlighted, secondary information is filtered out, and the data processing efficiency and the sensitivity of the forecast model are improved.

[0017] Furthermore, the specific implementation process of step S5 includes: Basin-wide data matrix After the matrix elements are updated, the whole basin data matrix is ​​removed. The matrix elements in the first row and first column of , the downstream station collaborative forecast matrix is ​​obtained, which is recorded as ; Based on the collaborative forecast matrix of downstream stations, calculate the correlation between collaborative forecasts of monitoring stations and optimize flood events Forecast range: ; Where, In the event of flood Lower monitoring station With monitoring sites The correlation between the collaborative forecasts, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logic operation between them, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logical OR operation between them; Preset collaborative forecast threshold, if collaborative forecast correlation If the threshold is greater than or equal to the collaborative forecast threshold, the monitoring station With monitoring sites Record flood events Forecast range In the forecast range The monitoring sites in the ,are deduplicated, and the monitoring sites with the largest continuous range are selected to form the ,alarm range and send it to the staff port; In the above method, the generation of the collaborative forecast matrix of downstream stations is based only on the downstream objects in the monitoring area. When studying the differences of downstream objects, the time dimension is also considered, and the first row and first column of the whole basin matrix are removed to obtain the collaborative forecast matrix of downstream stations. By calculating the collaborative forecast correlation between stations (based on Boolean logic and or operations), the forecast range is determined in combination with the preset threshold, and the maximum continuous range segment is selected after deduplication to generate an alarm. Furthermore, the hydrological correlation between stations can be quantified, the dynamic optimization of the forecast range can be achieved, redundant alarms can be avoided, and the accuracy and pertinence of the boundary effect of the alarm can be improved.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the basin collaborative forecast optimization system and method for water conservancy big data provided by the present invention, by setting up basin monitoring sites and deploying Internet of Things sensors, dividing the monitoring area and synchronizing timestamps, binding site identifiers to generate hydrological characteristic flows, constructing a data matrix for the entire basin, calculating state differences and updating matrix elements, and finally generating a downstream site collaborative forecast matrix to determine the flood forecast range. The system includes a monitoring deployment and time synchronization unit, an identifier binding and characteristic flow generation unit, a data capture and matrix construction unit, a difference calculation and matrix update unit, and a collaborative forecast and alarm generation unit. While ensuring the spatiotemporal consistency of data, the present invention can enhance the consistency of the collaborative forecast correlation with the actual flood propagation path, improve the accuracy and pertinence of the boundary effect of the alarm range, and shorten the response time from data collection to alarm generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0020] Figure 1 Schematic diagram of the steps of the basin collaborative forecast optimization method for water conservancy big data of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] In the first embodiment, a watershed collaborative forecasting and optimization system for water conservancy big data is provided, which includes: a monitoring deployment and time synchronization unit, an identification binding and feature flow generation unit, a data capture and matrix construction unit, a difference calculation and matrix update unit, and a collaborative forecasting and alarm generation unit; The monitoring deployment and time synchronization unit is used to set up watershed monitoring sites and deploy IoT sensors. The monitoring area is divided within the coverage area of ​​the watershed monitoring sites so that each IoT sensor can collect hydrological data within the same monitoring area. The unit is also used to trigger initialization instructions through the upstream starting watershed monitoring site to achieve synchronous calibration of the timestamps of each IoT sensor. The identification binding and characteristic flow generation unit is used to bind a unique identification to each watershed monitoring site and write site information. It is also used to configure the initial synchronization timestamp. When a flood event occurs in the monitoring area, it triggers the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronized timestamp. Among them, the identification binding and feature flow generation unit includes a regional device group module and an initialization timestamp module; The regional device group module is used to count all IoT sensors deployed in different monitoring areas to form regional device groups; Initialization timestamp module, used to configure the initialization synchronization timestamp; The data capture and matrix construction unit captures the hydrological data of all IoT sensors during flood events based on the hydrological characteristic flow and monitoring area, and establishes a basin-wide data matrix with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; Among them, the data capture and matrix construction unit includes a monitoring period generation module and a monitoring feature label generation module; Monitoring period generation module, used to divide the monitoring periods of different monitoring areas under hydrological characteristic flow; A monitoring feature label generation module is used to capture the hydrological data of all IoT sensors generated during flood monitoring events to generate monitoring feature labels; The difference calculation and matrix update unit is used to calculate the horizontal and vertical state differences with the digitized matrix elements of the entire watershed as the center, and update the matrix elements according to the maximum value of the difference; Among them, the difference calculation and matrix update unit includes a state replacement module and a matrix update module; The state replacement module is used to replace the horizontal state and the vertical state. The horizontal state refers to the state of the monitoring area itself when the monitoring period changes. The vertical state refers to the dual state between the two monitoring areas when the monitoring area changes downstream. The matrix update module is used to perform downstream gradient updates on the data matrix of the entire basin; The collaborative forecast and alarm generation unit is used to remove the first row and first column of the data matrix of the entire basin to obtain the collaborative forecast matrix of the downstream sites, calculate the collaborative forecast correlation between sites to determine the forecast range of flood events, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port.

[0023] See also Figure 1 In the second embodiment, a basin collaborative forecast optimization method based on water conservancy big data is provided, which is applicable to the first embodiment. The method includes the following steps: Parameter settings: Basin area: 1000 , set up 5 monitoring stations ( ), each site is divided into 2 monitoring areas ( ), the total number of IoT sensors deployed ; Initialize the synchronization timestamp node (time interval 1 hour), the collaborative forecast threshold is set to 0.7; The influencing parameters of secondary flood rating include: rainfall, runoff coefficient, and soil moisture.

[0024] Implementation steps: Step S1: Set up watershed monitoring stations in the basin and deploy IoT sensors. Divide the monitoring area of ​​each watershed monitoring station. Count the IoT sensors in the monitoring area to collect hydrological data including secondary flood rate influencing parameters and water level values. At the same time, based on the upstream and downstream relationship of the basin runoff, trigger the initialization instruction through the upstream starting watershed monitoring station to achieve synchronous calibration of the timestamps of each IoT sensor. For example, a watershed monitoring station is set up, and IoT sensors are deployed at each watershed monitoring station. The coverage area of ​​the watershed monitoring station is divided into monitoring areas, and the number of IoT sensors deployed in each monitoring area is counted. The IoT sensors are used to collect hydrological data in the watershed and attach synchronized timestamps. The hydrological data includes influencing parameters of secondary flood rate determination and water level values ​​in the monitoring area. Based on the upstream and downstream relationship of the watershed runoff, the timestamp synchronization of each IoT sensor is calibrated, and the initialization instruction of the synchronization calibration is set. The initialization instruction is triggered by the starting watershed monitoring station upstream of the watershed runoff and distributed to each watershed monitoring station downstream to instruct the synchronization calibration of the timestamps of each IoT sensor; For example: at the upstream site of the basin Trigger timestamp synchronization instructions, downstream sites Receive the command to complete the sensor time calibration.

[0025] Step S2: Bind a unique identifier to each watershed monitoring site and write site information, configure an initial synchronization timestamp, and when a flood event occurs in the monitoring area, trigger the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronization timestamp; For example, a unique identifier is bound to each watershed monitoring site and site information is written. The site information includes: Any m-th watershed monitoring station is denoted as , record the flood event with flood number n as , any e-th monitoring area is recorded as , any r-th IoT sensor is recorded as , then the statistical monitoring area All IoT sensors deployed within the area constitute the regional device group , R represents the total number of IoT sensors; The synchronization timestamp of configuration initialization is , Indicates the gth synchronization time node in the initialized synchronization timestamp, G indicates the total number of synchronization time nodes in the initialized synchronization timestamp; when a flood event occurs in the monitoring area When the regional device group is triggered Each IoT sensor collects hydrological data and generates a hydrological feature stream with synchronized time stamps ,and , q represents the number of triggers; For example: When monitoring area ( Flood signal at site When an event occurs, the regional device group is triggered 20 sensors collect data to generate hydrological characteristic flow (Contains 5 time nodes).

[0026] Step S3: Based on the hydrological characteristic flow and monitoring area, the hydrological data of all IoT sensors in the flood event are captured, and monitoring feature labels containing the monitoring period, influencing parameter clusters, and water level change values ​​are generated. The data matrix of the entire watershed is established with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; For example, based on the hydrological characteristics of the flow and monitoring area, the monitoring of flood events is captured The hydrological data of all IoT sensors generated at the time of generation are used to generate monitoring feature labels, which are recorded as ,in, Represents hydrological characteristic flow Lower monitoring area monitoring period, Indicates monitoring period Lower monitoring area The cluster of influencing parameters used in the secondary flood calibration is Monitoring period Lower monitoring area The water level change value within the monitoring period is the difference between the water level value in the monitoring area at the start synchronization time node within the monitoring period and the water level value in the monitoring area at the end synchronization time node within the monitoring period. If the water level change value is a positive value, it means that the water level in the monitoring area has dropped. If the water level change value is a negative value, it means that the water level in the monitoring area has increased. Establish a basin-wide data matrix with hydrological characteristic flow as the horizontal dimension and monitoring area as the vertical dimension, and divide the basin monitoring stations into Monitoring flood events The whole basin data matrix generated when , then the whole basin data matrix The matrix element corresponding to the qth row and the eth column is recorded as ; For example: capture The whole basin data in the event is used to build a 5×2 whole basin matrix , the matrix elements include the monitoring period T, parameter cluster U and water level change value d.

[0027] Step S4: With the digitized matrix element of the entire basin as the center, calculate the state differences in the horizontal direction (the state of the region itself when the monitoring period changes) and the vertical direction (the dual state between the two regions when the region changes downstream), and update the matrix element according to the maximum value of the difference; For example, based on the whole basin data matrix, select the matrix elements is the center of the matrix, calculate the matrix elements The horizontal state difference and vertical state difference of the monitoring period Monitoring area during replacement The state difference of the monitoring area When the monitoring area is replaced downstream, the monitoring area The dual status difference between the downstream monitoring area and the monitoring period refers to the two dimensions of monitoring area: ; Where, Indicates the horizontal state difference, represents the longitudinal state difference, Indicates monitoring period Change to monitoring period Time monitoring area The difference of its own state, and the difference value of its own state is the influencing parameter cluster of the secondary flood rate and The number of identical influencing parameters contained in the intersection set; Select the maximum value between the horizontal state difference and the vertical state difference ,like , then for the matrix elements Update and let the matrix elements Is 0, if , then for the matrix elements Update and let the matrix elements is 1; For example: with matrix elements As the center, calculate the horizontal difference (Time period Cluster changes in parameters) and longitudinal differences (area parameter cluster and water level change), update the matrix elements is 1 (if ).

[0028] Step S5: Remove the first row and first column of the whole basin data matrix to obtain the downstream site collaborative forecast matrix, calculate the inter-site collaborative forecast correlation to determine the forecast range of the flood event, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port; Exemplary, full-basin data matrix After the matrix elements are updated, the whole basin data matrix is ​​removed. The matrix elements in the first row and first column of , the downstream station collaborative forecast matrix is ​​obtained, which is recorded as ; Based on the collaborative forecast matrix of downstream stations, calculate the correlation between collaborative forecasts of monitoring stations and optimize flood events Forecast range: ; Where, In the event of flood Lower monitoring station With monitoring sites The correlation between the collaborative forecasts, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logic operation between them, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logical OR operation between them; Preset collaborative forecast threshold, if collaborative forecast correlation If the threshold is greater than or equal to the collaborative forecast threshold, the monitoring station With monitoring sites Record flood events Forecast range In the forecast range The monitoring sites in the ,are deduplicated, and the monitoring sites with the largest continuous range are selected to form the ,alarm range and send it to the staff port; For example, removing the first row and first column to get the downstream matrix , computing site and The collaborative forecast correlation (>Threshold ),Will Recorded in forecast range After deduplication, the continuous alarm range is determined to be , sent to the worker port.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A basin collaborative forecasting optimization method based on water conservancy big data, characterized by: The method comprises the following steps: Step S1: Set up watershed monitoring stations in the basin and deploy IoT sensors. Divide the monitoring area of ​​each watershed monitoring station. Count the IoT sensors in the monitoring area to collect hydrological data including secondary flood rate influencing parameters and water level values. At the same time, based on the upstream and downstream relationship of the basin runoff, trigger the initialization instruction through the upstream starting watershed monitoring station to achieve synchronous calibration of the timestamps of each IoT sensor. Step S2: Bind a unique identifier to each watershed monitoring site and write site information, configure an initial synchronization timestamp, and when a flood event occurs in the monitoring area, trigger the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronization timestamp; Step S3: Based on the hydrological characteristic flow and monitoring area, the hydrological data of all IoT sensors in the flood event are captured, and monitoring feature labels containing the monitoring period, influencing parameter clusters, and water level change values ​​are generated. The data matrix of the entire watershed is established with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; Step S4: With the digitized matrix element of the entire basin as the center, calculate the state differences in the horizontal direction (the state of the region itself when the monitoring period changes) and the vertical direction (the dual state between the two regions when the region changes downstream), and update the matrix element according to the maximum value of the difference; Step S5: Remove the first row and first column of the whole basin data matrix to obtain the downstream site collaborative forecast matrix, calculate the collaborative forecast correlation between sites to determine the forecast range of flood events, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port.

2. The basin collaborative forecast optimization method based on water conservancy big data according to claim 1 is characterized in that: The specific implementation process of step S1 includes: Establishing watershed monitoring stations, deploying IoT sensors at each monitoring station, dividing the coverage area of ​​the monitoring stations into monitoring regions, and counting the IoT sensors deployed in each monitoring region. The IoT sensors are used to collect hydrological data within the watershed and attach synchronized timestamps. The hydrological data includes influencing parameters for secondary flood determination and water level values ​​within the monitoring region. Based on the upstream and downstream relationship of the basin runoff, the timestamp synchronization calibration of each IoT sensor is performed, and the initialization instruction of the synchronization calibration is set. The initialization instruction is triggered by the starting basin monitoring station upstream of the basin runoff and distributed to each basin monitoring station downstream to instruct the timestamp synchronization calibration of each IoT sensor.

3. The basin collaborative forecasting optimization method based on water conservancy big data according to claim 2 is characterized in that: The specific implementation process of step S2 includes: Bind a unique identifier to each watershed monitoring site and write site information, which includes: Any m-th watershed monitoring station is denoted as , record the flood event with flood number n as , any e-th monitoring area is recorded as , any r-th IoT sensor is recorded as , then the statistical monitoring area All IoT sensors deployed within the area constitute the regional device group , R represents the total number of IoT sensors; The synchronization timestamp of configuration initialization is , Indicates the gth synchronization time node in the initialized synchronization timestamp, G indicates the total number of synchronization time nodes in the initialized synchronization timestamp; when a flood event occurs in the monitoring area When the regional device group is triggered Each IoT sensor collects hydrological data and generates a hydrological feature stream with synchronized time stamps ,and , q represents the number of triggers.

4. The basin collaborative forecasting optimization method based on water conservancy big data according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Based on hydrological characteristics and monitoring areas, capture the flood events in monitoring The hydrological data of all IoT sensors generated at the time of generation are used to generate monitoring feature labels, which are recorded as ,in, Represents hydrological characteristic flow Lower monitoring area monitoring period, Indicates monitoring period Lower monitoring area The cluster of influencing parameters used in the secondary flood calibration is Monitoring period Lower monitoring area The water level change value within the monitoring period is the difference between the water level value in the monitoring area at the start synchronization time node within the monitoring period and the water level value in the monitoring area at the end synchronization time node within the monitoring period. If the water level change value is a positive value, it means that the water level in the monitoring area has dropped, and if the water level change value is a negative value, it means that the water level in the monitoring area has increased; Establish a basin-wide data matrix with hydrological characteristic flow as the horizontal dimension and monitoring area as the vertical dimension, and divide the basin monitoring stations into Monitoring flood events The whole basin data matrix generated when , then the whole basin data matrix The matrix element corresponding to the qth row and the eth column is recorded as .

5. The basin collaborative forecasting optimization method based on water conservancy big data according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Based on the data matrix of the entire basin, select the matrix elements is the center of the matrix, calculate the matrix elements The horizontal state difference and the vertical state difference of the monitoring period Monitoring area during replacement The longitudinal state difference refers to the monitoring area When the monitoring area is replaced downstream, the monitoring area The dual status difference between the downstream monitoring area and the monitoring period refers to the two dimensions of monitoring area: ； Where, Indicates the horizontal state difference, represents the longitudinal state difference, Indicates monitoring period Change to monitoring period Time monitoring area The difference of its own state, and the difference value of its own state is the influencing parameter cluster of the secondary flood rate and The number of identical influencing parameters contained in the intersection set; Select the maximum value between the horizontal state difference and the vertical state difference ,like , then for the matrix elements Update and let the matrix elements Is 0, if , then for the matrix elements Update and let the matrix elements is 1.

6. The basin collaborative forecasting optimization method based on water conservancy big data according to claim 5 is characterized in that: The specific implementation process of step S5 includes: Basin-wide data matrix After the matrix elements are updated, the whole basin data matrix is ​​removed. The matrix elements in the first row and first column of , the downstream station collaborative forecast matrix is ​​obtained, which is recorded as ; Based on the collaborative forecast matrix of downstream stations, calculate the correlation between collaborative forecasts of monitoring stations and optimize flood events Forecast range: ； Where, In the event of flood Lower monitoring station With monitoring sites The correlation between collaborative forecasts, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logic operation between them, represents the collaborative forecast matrix of downstream stations Collaborative forecast matrix with downstream stations The number of 1s contained in the Boolean logical OR operation between them; Preset collaborative forecast threshold, if collaborative forecast correlation If the threshold is greater than or equal to the collaborative forecast threshold, the monitoring station With monitoring sites Record flood events Forecast range In the forecast range The monitoring sites in the data are deduplicated, and after deduplication, the monitoring sites with the largest continuous range are selected to form the alarm range and sent to the staff port.

7. A watershed collaborative forecasting and optimization system for water conservancy big data, which executes the watershed collaborative forecasting and optimization method according to any one of claims 1 to 6, characterized in that: The system includes: a monitoring deployment and time synchronization unit, an identification binding and feature flow generation unit, a data capture and matrix construction unit, a difference calculation and matrix update unit, and a collaborative forecast and alarm generation unit; The monitoring deployment and time synchronization unit is used to set up watershed monitoring sites and deploy IoT sensors, divide the monitoring area within the coverage area of ​​the watershed monitoring sites so that each IoT sensor collects hydrological data within the same monitoring area, and is also used to trigger an initialization instruction through the upstream starting watershed monitoring site to achieve synchronous calibration of the timestamps of each IoT sensor; The identifier binding and characteristic flow generation unit is used to bind a unique identifier to each watershed monitoring site and write site information. It is also used to configure the initialization synchronization timestamp. When a flood event occurs in the monitoring area, it triggers the IoT sensors in the monitoring area to collect data and generate a hydrological characteristic flow with a synchronization timestamp. The data capture and matrix construction unit captures the hydrological data of all IoT sensors during a flood event based on the hydrological characteristic flow and the monitoring area, and establishes a basin-wide data matrix with the hydrological characteristic flow as the horizontal dimension and the monitoring area as the vertical dimension; The difference calculation and matrix update unit is used to calculate the horizontal and vertical state differences with the digitized matrix elements of the entire watershed as the center, and update the matrix elements according to the maximum value of the difference; The collaborative forecast and alarm generation unit is used to remove the first row and first column of the data matrix of the entire basin to obtain the collaborative forecast matrix of the downstream site, calculate the collaborative forecast correlation between sites to determine the forecast range of the flood event, remove duplicate monitoring sites within the forecast range, select the largest continuous range segment to form the alarm range and send it to the staff port.

8. The basin collaborative forecasting and optimization system for water conservancy big data according to claim 7 is characterized in that: The identification binding and feature flow generation unit includes a regional device group module and an initialization timestamp module; The regional device group module is used to count all IoT sensors deployed in different monitoring areas to form regional device groups; The initialization timestamp module is used to configure the initialization synchronization timestamp.

9. The basin collaborative forecasting and optimization system for water conservancy big data according to claim 7 is characterized in that: The data capture and matrix construction unit includes a monitoring period generation module and a monitoring feature label generation module; The monitoring period generation module is used to divide the monitoring periods of different monitoring areas under the hydrological characteristic flow; The monitoring feature label generation module is used to capture the hydrological data of all IoT sensors generated during flood monitoring events to generate monitoring feature labels.

10. The basin collaborative forecasting and optimization system for water conservancy big data according to claim 7 is characterized in that: The difference calculation and matrix update unit includes a state replacement module and a matrix update module; The state replacement module is used to replace the horizontal state and the vertical state. The horizontal state refers to the state of the monitoring area itself when the monitoring period is replaced. The vertical state refers to the dual state between the two monitoring areas when the monitoring area is replaced downstream. The matrix update module is used to perform downstream gradient updates on the digitized matrix of the entire river basin.

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