Real-time mobile hydrological data acquisition system

Abstract

This invention discloses a real-time mobile hydrological data acquisition system, belonging to the field of mobile hydrological data acquisition technology. It includes a data acquisition module, a multi-mode communication module, an edge control module, a breakpoint transmission module, and a dynamic early warning module. It integrates three transmission methods: satellite communication, NB-IoT cellular network, and Mesh communication. A dynamic weighting algorithm calculates a comprehensive communication value, and based on this value, it dispatches relevant personnel for emergency repairs to maintain communication survivability. It integrates water level, rainfall, flow velocity, and historical data, and adjusts the bandwidth allocation strategy of the multi-mode communication module based on dynamic prediction values ​​to achieve risk-level response. The breakpoint transmission module restores multi-mode communication from the breakpoint location based on the comprehensive communication value and performs step-by-step hash verification of data blocks, improving the efficiency of breakpoint recovery. The dynamic early warning module dynamically determines early warnings based on water level signal A, rainfall signal B, and water flow velocity signal C, sending corresponding early warning notifications to people in relevant target areas, thus improving the system's reliability.

Description

Technical Field

[0001] This invention relates to the field of mobile hydrological data acquisition technology, and in particular to a real-time mobile hydrological data acquisition system. Background Technology

[0002] Real-time hydrological data acquisition systems have achieved revolutionary breakthroughs in the field of hydrological monitoring by integrating advanced sensors, IoT communication, edge computing, and big data analytics. However, in harsh environments, such as the Yellow River Reservoir and other areas with weak communication infrastructure, when extreme weather causes public network communication to be interrupted, traditional satellite communication links have a reconnection delay of 15-30 minutes, which is difficult to meet the timeliness requirements of sudden flood warnings. Furthermore, traditional data transmission mechanisms lack the function of resuming interrupted transmissions, and historical data needs to be completely retransmitted after communication is restored, which further exacerbates the response delay, resulting in delayed flood discharge warnings for the Yellow River Reservoir and seriously threatening downstream safety. Summary of the Invention

[0003] In response to the above situation, the present invention can accurately realize risk-level response and restore multi-mode communication from the breakpoint location based on the comprehensive communication value, thereby improving the efficiency of breakpoint recovery communication.

[0004] The technical solution includes a data acquisition module, a multi-mode communication module, an edge control module, a breakpoint transmission module, and a dynamic early warning module.

[0005] The data acquisition module collects the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time.

[0006] The multi-mode communication module integrates three transmission methods: satellite communication, NB-IoT cellular network, and Mesh communication, and supports dynamic link switching.

[0007] The edge control module calculates dynamic prediction values ​​and adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction values.

[0008] The breakpoint transmission module restores multi-mode communication based on the comprehensive communication value and performs step-by-step hash verification of the data blocks;

[0009] The dynamic early warning module dynamically determines the early warning based on water level signal A, rainfall signal B, and water flow velocity signal C, and sends corresponding early warning notifications to people in the relevant target areas.

[0010] Furthermore, the specific working steps of the multi-mode communication module are as follows;

[0011] 2-1, Collect BeiDou short message signal strength D1 and transmission delay D2 for satellite communication, base station signal strength E1 and network congestion rate E2 for NB-IoT cellular network, and neighboring node connectivity hop count H1 and signal-to-noise ratio H2 for Mesh communication;

[0012] 2-2, Calculate the communication value D3 for satellite communication, D3 = D11 × 0.6 + D21 × 0.4, where D11 is 0 when D1 is less than -125dBm, and 1 otherwise; D21 is 0 when D2 is greater than 30 seconds, and 1 otherwise.

[0013] Calculate the communication value E3 of the NB-IoT cellular network, E3 = E11 × 0.7 + E21 × 0.3, where E11 is 0 when E1 is less than -110dBm, and 1 otherwise; E21 is 0 when E2 is greater than 15%, and 1 otherwise.

[0014] Calculate the communication value H3 of Mesh communication, H3 = H11 × 0.4 + H21 × 0.6, where H11 is 0 when H1 is greater than 3, and 1 otherwise; H21 is 0 when H2 is less than 20dB, and 1 otherwise.

[0015] 2-3. Calculate the comprehensive communication value W, W=D3×0.5+E3×0.3+H3×0.2. If W is greater than 0.8, it indicates that the multi-mode communication is normal; otherwise, it indicates that the multi-mode communication is interrupted.

[0016] Furthermore, the edge control module includes a data segmentation module, a dynamic prediction module, and a dynamic adjustment module. The data segmentation module divides the data signal sent by the data acquisition module into independent data blocks every 5 minutes. Each block is appended with a timestamp T, a hash identifier S, and a priority identifier Y. Where the water level change rate is greater than or equal to 5 cm / min, Y indicates the first priority; the rainfall is greater than or equal to 50 mm / h, Y indicates the second priority; and the rest are regarded as routine monitoring data, Y indicates the third priority.

[0017] The dynamic prediction module records the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time, and extracts the corresponding flood limit water level A1 and the historical average water flow velocity C1 from the database to calculate the dynamic prediction value M.

[0018] The dynamic adjustment module adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction value M.

[0019] Furthermore, the dynamic prediction module calculates the dynamic prediction value M, M=Y1×0.6+A2×0.3+C2×0.1, where Y1 is 1 when Y is the first priority, Y1 is 0.7 when Y is the second priority, Y1 is 0.3 when Y is the third priority, A2 is 0.2 when the water level signal A is less than or equal to 70% of the flood limit water level A1, A2 is 0.6 when the water level signal A is greater than 70% and less than or equal to 80% of the flood limit water level A1, A2 is 1 when the water level signal A is greater than 80% of the flood limit water level A1, and C2 is 1 when C2 is greater than the historical average water flow velocity C1 for the same period, and C2 is 0 when C2 is less than or equal to the historical average water flow velocity C1 for the same period.

[0020] Furthermore, the dynamic adjustment module adjusts the bandwidth allocation strategy of the multi-mode communication module based on the dynamic prediction value M. If M is greater than or equal to 0.8, it indicates high risk. The bandwidth ratio of satellite communication is increased to 80%, the first priority data transmission is prioritized, the third priority data transmission is forcibly interrupted, and the non-first priority data transmission of NB-IoT cellular network and Mesh communication network is suspended.

[0021] If M is greater than or equal to 0.6 and less than 0.8, it indicates medium risk. Adjust the proportion of satellite communication bandwidth to 50%, NB-IoT cellular network to 30%, and Mesh communication network to 20%.

[0022] When M is less than 0.6, it indicates low risk. The default is to use NB-IoT cellular network for transmission, with a bandwidth ratio of 70%. Mesh communication network asynchronously retransmits historical data, and satellite communication enters deep sleep mode.

[0023] Furthermore, the specific working steps of the breakpoint transmission module are as follows;

[0024] 6-1. When W is greater than 0.8 and multimode communication is normal, delete the corresponding data block in the buffer of multimode communication and multimode communication will transmit data normally. Conversely, when multimode communication is interrupted, record the breakpoint position and temporarily store the data block in the corresponding buffer.

[0025] 6-2, When W is less than or equal to 0.8 and greater than 0.6, it indicates that the multi-mode communication is abnormal but controllable. Notify relevant personnel to investigate the cause of the fault. When W is less than or equal to 0.6, it indicates that the multi-mode communication is paralyzed. Notify relevant personnel to issue a communication warning and immediately dispatch relevant professional personnel for emergency repair. After communication is restored, continue to the next step.

[0026] 6-3. Resume multi-mode communication from the breakpoint position based on the timestamp T, and verify the data blocks level by level. Verify the data blocks by using the hash identifier S. If the hash verification fails, trigger the retransmission at the breakpoint position until successful. After the transmission is completed, clear the local cache, and clear the data blocks from the third priority to the first priority in sequence.

[0027] Furthermore, the dynamic early warning module operates as follows;

[0028] When the water flow velocity signal C is greater than the historical average value C1 for the same period, a level 3 warning is activated, and Mesh communication is controlled to periodically broadcast to the population in the relevant target area once every 5 minutes.

[0029] When the rainfall signal B is greater than 50 mm / h for 30 minutes, a level 2 warning is activated, and the NB-IoT cellular network is controlled to send emergency SMS notifications to people in the relevant target areas.

[0030] If the water level signal A is greater than 90% of the flood limit water level A1, a Level I early warning will be activated, and satellite communication will be used to send short messages via BeiDou to the relevant target area.

[0031] Furthermore, the adjacent timestamps TL and AL corresponding to the water level signal A are extracted, and then the difference AL1 between A and AL, the difference TL1 between T and TL are calculated. The ratio of AL1 to TL1 is used to obtain the water level change rate.

[0032] Due to the adoption of the above technical solutions, the present invention has the following advantages compared with the prior art;

[0033] 1. Integrating three transmission methods—satellite communication, NB-IoT cellular network, and Mesh communication—it calculates the comprehensive communication value through a dynamic weighting algorithm, dispatches relevant professional personnel for emergency repairs based on the comprehensive communication value, maintains the communication survival rate, integrates water level, rainfall, flow velocity, and historical data, and adjusts the bandwidth allocation strategy of the multi-mode communication module based on dynamic prediction values ​​to achieve risk-level response;

[0034] 2. The breakpoint transmission module restores multi-mode communication from the breakpoint location based on the comprehensive communication value and performs step-by-step hash verification of data blocks, which improves the efficiency of breakpoint recovery communication. The dynamic early warning module dynamically judges the early warning based on water level signal A, rainfall signal B, and water flow velocity signal C, and sends corresponding early warning notifications to the population in the relevant target areas, which improves the reliability of the system. Attached Figure Description

[0035] Figure 1 This is a flowchart of the real-time mobile hydrological data acquisition system of the present invention.

[0036] Figure 2 This is a flowchart of the edge control module in the real-time mobile hydrological data acquisition system of the present invention. Detailed Implementation

[0037] The foregoing and other technical contents, features and effects of the present invention are described in conjunction with the appendix below. Figures 1 to 2 The detailed description of the embodiments will make this clear. All structural details mentioned in the following embodiments are based on the accompanying drawings.

[0038] Example 1, based on existing technology, addresses the issue of data loss or delay caused by reliance on a single communication link during public network communication outages due to extreme weather, requiring full retransmission upon communication restoration, resulting in low efficiency. Specifically, it includes a data acquisition module, a multi-mode communication module, an edge control module, a breakpoint transmission module, and a dynamic early warning module.

[0039] The data acquisition module collects the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time.

[0040] The multi-mode communication module integrates three transmission methods: satellite communication, NB-IoT cellular network, and Mesh communication, and supports dynamic link switching.

[0041] The edge control module calculates dynamic prediction values ​​and adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction values.

[0042] The breakpoint transmission module restores multi-mode communication based on the comprehensive communication value and performs step-by-step hash verification of the data blocks;

[0043] The dynamic early warning module dynamically determines the early warning based on water level signal A, rainfall signal B, and water flow velocity signal C, and sends corresponding early warning notifications to people in the relevant target areas.

[0044] Example 2: Based on the existing technology, the working steps of the multi-mode communication module are as follows;

[0045] 2-1, Collect BeiDou short message signal strength D1 and transmission delay D2 for satellite communication, base station signal strength E1 and network congestion rate E2 for NB-IoT cellular network, and neighboring node connectivity hop count H1 and signal-to-noise ratio H2 for Mesh communication;

[0046] 2-2, Calculate the communication value D3 for satellite communication, D3 = D11 × 0.6 + D21 × 0.4, where D11 is 0 when D1 is less than -125dBm, and 1 otherwise; D21 is 0 when D2 is greater than 30 seconds, and 1 otherwise.

[0047] Calculate the communication value E3 of the NB-IoT cellular network, E3 = E11 × 0.7 + E21 × 0.3, where E11 is 0 when E1 is less than -110dBm, and 1 otherwise; E21 is 0 when E2 is greater than 15%, and 1 otherwise.

[0048] Calculate the communication value H3 of Mesh communication, H3 = H11 × 0.4 + H21 × 0.6, where H11 is 0 when H1 is greater than 3, and 1 otherwise; H21 is 0 when H2 is less than 20dB, and 1 otherwise.

[0049] 2-3. Calculate the comprehensive communication value W, W=D3×0.5+E3×0.3+H3×0.2. If W is greater than 0.8, it indicates that the multi-mode communication is normal; otherwise, it indicates that the multi-mode communication is interrupted.

[0050] In Example 3, based on the existing technology, the edge control module includes a data segmentation module, a dynamic prediction module, and a dynamic adjustment module. The data segmentation module divides the data signal sent by the data acquisition module into independent data blocks every 5 minutes. Each block is attached with a timestamp T, a hash identifier S, and a priority identifier Y. Where the water level change rate is greater than or equal to 5 cm / min, Y represents the first priority; the rainfall is greater than or equal to 50 mm / h, Y represents the second priority; and the rest are regarded as routine monitoring data, Y represents the third priority.

[0051] The dynamic prediction module records the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time, and extracts the corresponding flood limit water level A1 and the historical average water flow velocity C1 from the database to calculate the dynamic prediction value M.

[0052] The dynamic adjustment module adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction value M.

[0053] Furthermore, the dynamic prediction module calculates the dynamic prediction value M, M=Y1×0.6+A2×0.3+C2×0.1, where Y1 is 1 when Y is the first priority, Y1 is 0.7 when Y is the second priority, Y1 is 0.3 when Y is the third priority, A2 is 0.2 when the water level signal A is less than or equal to 70% of the flood limit water level A1, A2 is 0.6 when the water level signal A is greater than 70% and less than or equal to 80% of the flood limit water level A1, A2 is 1 when the water level signal A is greater than 80% of the flood limit water level A1, and C2 is 1 when C2 is greater than the historical average water flow velocity C1 for the same period, and C2 is 0 when C2 is less than or equal to the historical average water flow velocity C1 for the same period.

[0054] Example 4: Based on the existing technology, the dynamic adjustment module adjusts the bandwidth allocation strategy of the multi-mode communication module based on the dynamic prediction value M. If M is greater than or equal to 0.8, it indicates high risk. The bandwidth ratio of satellite communication is increased to 80%, the first priority data transmission is prioritized, the third priority data transmission is forcibly interrupted, and the non-first priority data transmission of NB-IoT cellular network and Mesh communication network is suspended.

[0055] If M is greater than or equal to 0.6 and less than 0.8, it indicates medium risk. Adjust the proportion of satellite communication bandwidth to 50%, NB-IoT cellular network to 30%, and Mesh communication network to 20%.

[0056] When M is less than 0.6, it indicates low risk. The default is to use NB-IoT cellular network for transmission, with a bandwidth ratio of 70%. Mesh communication network asynchronously retransmits historical data, and satellite communication enters deep sleep mode.

[0057] Example 5: Based on the existing technology, the specific working steps of the breakpoint transmission module are as follows;

[0058] 6-1. When W is greater than 0.8 and multimode communication is normal, delete the corresponding data block in the buffer of multimode communication and multimode communication will transmit data normally. Conversely, when multimode communication is interrupted, record the breakpoint position and temporarily store the data block in the corresponding buffer.

[0059] 6-2, When W is less than or equal to 0.8 and greater than 0.6, it indicates that the multi-mode communication is abnormal but controllable. Notify relevant personnel to investigate the cause of the fault. When W is less than or equal to 0.6, it indicates that the multi-mode communication is paralyzed. Notify relevant personnel to issue a communication warning and immediately dispatch relevant professional personnel for emergency repair. After communication is restored, continue to the next step.

[0060] 6-3. Resume multi-mode communication from the breakpoint position based on the timestamp T, and verify the data blocks level by level. Verify the data blocks by using the hash identifier S. If the hash verification fails, trigger the retransmission at the breakpoint position until successful. After the transmission is completed, clear the local cache, and clear the data blocks from the third priority to the first priority in sequence.

[0061] Furthermore, the dynamic early warning module operates as follows;

[0062] When the water flow velocity signal C is greater than the historical average value C1 for the same period, a level 3 warning is activated, and Mesh communication is controlled to periodically broadcast to the population in the relevant target area once every 5 minutes.

[0063] When the rainfall signal B is greater than 50 mm / h for 30 minutes, a level 2 warning is activated, and the NB-IoT cellular network is controlled to send emergency SMS notifications to people in the relevant target areas.

[0064] If the water level signal A is greater than 90% of the flood limit water level A1, a Level I early warning will be activated, and satellite communication will be used to send short messages via BeiDou to the relevant target area.

[0065] Furthermore, the adjacent timestamps TL and AL corresponding to the water level signal A are extracted, and then the difference AL1 between A and AL, the difference TL1 between T and TL are calculated. The ratio of AL1 to TL1 is used to obtain the water level change rate.

[0066] In specific use of this invention, based on existing technology, the working steps of the multi-mode communication module are as follows:

[0067] 2-1, Collect BeiDou short message signal strength D1 and transmission delay D2 for satellite communication, base station signal strength E1 and network congestion rate E2 for NB-IoT cellular network, and neighboring node connectivity hop count H1 and signal-to-noise ratio H2 for Mesh communication;

[0068] 2-2, Calculate the communication value D3 for satellite communication, D3 = D11 × 0.6 + D21 × 0.4, where D11 is 0 when D1 is less than -125dBm, and 1 otherwise; D21 is 0 when D2 is greater than 30 seconds, and 1 otherwise.

[0069] Calculate the communication value E3 of the NB-IoT cellular network, E3 = E11 × 0.7 + E21 × 0.3, where E11 is 0 when E1 is less than -110dBm, and 1 otherwise; E21 is 0 when E2 is greater than 15%, and 1 otherwise.

[0070] Calculate the communication value H3 of Mesh communication, H3 = H11 × 0.4 + H21 × 0.6, where H11 is 0 when H1 is greater than 3, and 1 otherwise; H21 is 0 when H2 is less than 20dB, and 1 otherwise.

[0071] 2-3. Calculate the comprehensive communication value W, W=D3×0.5+E3×0.3+H3×0.2. If W is greater than 0.8, it indicates that the multi-mode communication is normal; otherwise, it indicates that the multi-mode communication is interrupted.

[0072] The edge control module includes a data segmentation module, a dynamic prediction module, and a dynamic adjustment module. The data segmentation module divides the data signal sent by the data acquisition module into independent data blocks every 5 minutes. Each block is attached with a timestamp T, a hash identifier S, and a priority identifier Y. Where the water level change rate is greater than or equal to 5 cm / min, Y represents the first priority; the rainfall is greater than or equal to 50 mm / h, Y represents the second priority; and the rest are regarded as routine monitoring data, Y represents the third priority.

[0073] The dynamic prediction module records the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time, and extracts the corresponding flood limit water level A1 and the historical average water flow velocity C1 from the database to calculate the dynamic prediction value M.

[0074] The dynamic adjustment module adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction value M.

[0075] The dynamic adjustment module adjusts the bandwidth allocation strategy of the multi-mode communication module based on the dynamic prediction value M. If M is greater than or equal to 0.8, it indicates high risk. The bandwidth ratio of satellite communication is increased to 80%, the first priority data transmission is prioritized, the third priority data transmission is forcibly interrupted, and the non-first priority data transmission of NB-IoT cellular network and Mesh communication network is suspended.

[0076] If M is greater than or equal to 0.6 and less than 0.8, it indicates medium risk. Adjust the proportion of satellite communication bandwidth to 50%, NB-IoT cellular network to 30%, and Mesh communication network to 20%.

[0077] When M is less than 0.6, it indicates low risk. The default is to use NB-IoT cellular network for transmission, with a bandwidth ratio of 70%. Mesh communication network asynchronously retransmits historical data, and satellite communication enters deep sleep mode.

[0078] The specific working steps of the breakpoint transmission module are as follows:

[0079] 6-1. When W is greater than 0.8 and multimode communication is normal, delete the corresponding data block in the buffer of multimode communication and multimode communication will transmit data normally. Conversely, when multimode communication is interrupted, record the breakpoint position and temporarily store the data block in the corresponding buffer.

[0080] 6-2, When W is less than or equal to 0.8 and greater than 0.6, it indicates that the multi-mode communication is abnormal but controllable. Notify relevant personnel to investigate the cause of the fault. When W is less than or equal to 0.6, it indicates that the multi-mode communication is paralyzed. Notify relevant personnel to issue a communication warning and immediately dispatch relevant professional personnel for emergency repair. After communication is restored, continue to the next step.

[0081] 6-3. Resume multi-mode communication from the breakpoint position based on the timestamp T, and verify the data blocks level by level. Verify the data blocks by using the hash identifier S. If the hash verification fails, trigger the retransmission at the breakpoint position until successful. After the transmission is completed, clear the local cache, and clear the data blocks from the third priority to the first priority in sequence.

[0082] The dynamic early warning module works as follows:

[0083] When the water flow velocity signal C is greater than the historical average value C1 for the same period, a level 3 warning is activated, and Mesh communication is controlled to periodically broadcast to the population in the relevant target area once every 5 minutes.

[0084] When the rainfall signal B is greater than 50 mm / h for 30 minutes, a level 2 warning is activated, and the NB-IoT cellular network is controlled to send emergency SMS notifications to people in the relevant target areas.

[0085] If water level signal A exceeds 90% of the flood control limit level A1, a Level 1 early warning is activated, and satellite communication is controlled to transmit BeiDou short messages to the population in the relevant target area. By adopting the above method, risk-level response can be accurately achieved, and multi-mode communication can be restored from the point of failure based on comprehensive communication values, thus improving the efficiency of communication recovery from failure.

[0086] The above description is a further detailed explanation of the present invention in conjunction with specific embodiments, and it should not be considered that the specific implementation of the present invention is limited to this. For those skilled in the art to which the present invention pertains and related fields, any extensions, operation methods, and data substitutions made based on the technical solution concept of the present invention should fall within the protection scope of the present invention.

Claims

1. A real-time mobile hydrological data acquisition system, characterized in that, It includes a data acquisition module, a multi-mode communication module, an edge control module, a breakpoint transmission module, and a dynamic early warning module. The data acquisition module collects the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time. The multi-mode communication module integrates three transmission methods: satellite communication, NB-IoT cellular network, and Mesh communication, and supports dynamic link switching. The edge control module calculates dynamic prediction values ​​and adjusts the bandwidth allocation strategy of the multi-mode communication module based on the dynamic prediction values. The breakpoint transmission module restores multi-mode communication based on the comprehensive communication value and performs step-by-step hash verification of the data blocks; The dynamic early warning module dynamically determines the early warning based on water level signal A, rainfall signal B, and water flow velocity signal C, and sends corresponding early warning notifications to people in the relevant target areas. The specific working steps of the multimode communication module are as follows; 2-1, Collect BeiDou short message signal strength D1 and transmission delay D2 for satellite communication, base station signal strength E1 and network congestion rate E2 for NB-IoT cellular network, and neighboring node connectivity hop count H1 and signal-to-noise ratio H2 for Mesh communication; 2-2, Calculate the communication value D3 for satellite communication, D3 = D11 × 0.6 + D21 × 0.4, where D11 is 0 when D1 is less than -125dBm, and 1 otherwise; D21 is 0 when D2 is greater than 30 seconds, and 1 otherwise. Calculate the communication value E3 of the NB-IoT cellular network, E3 = E11 × 0.7 + E21 × 0.3, where E11 is 0 when E1 is less than -110dBm, and 1 otherwise; E21 is 0 when E2 is greater than 15%, and 1 otherwise. Calculate the communication value H3 of Mesh communication, H3 = H11 × 0.4 + H21 × 0.6, where H11 is 0 when H1 is greater than 3, and 1 otherwise; H21 is 0 when H2 is less than 20dB, and 1 otherwise. 2-3. Calculate the comprehensive communication value W, W=D3×0.5+E3×0.3+H3×0.

2. If W is greater than 0.8, it indicates that the multi-mode communication is normal; otherwise, it indicates that the multi-mode communication is interrupted. The specific working steps of the breakpoint transmission module are as follows: 6-1. When W is greater than 0.8 and multimode communication is normal, delete the corresponding data block in the buffer of multimode communication and multimode communication will transmit data normally. Conversely, when multimode communication is interrupted, record the breakpoint position and temporarily store the data block in the corresponding buffer. 6-2, When W is less than or equal to 0.8 and greater than 0.6, it indicates that the multi-mode communication is abnormal but controllable. Notify relevant personnel to investigate the cause of the fault. When W is less than or equal to 0.6, it indicates that the multi-mode communication is paralyzed. Notify relevant personnel to issue a communication warning and immediately dispatch relevant professional personnel for emergency repair. After communication is restored, continue to the next step. 6-3. Resume multi-mode communication from the breakpoint position based on the timestamp T, and verify the data blocks level by level. Verify the data blocks by using the hash identifier S. If the hash verification fails, trigger the retransmission at the breakpoint position until successful. After the transmission is completed, clear the local cache, and clear the data blocks from the third priority to the first priority in sequence.

2. The real-time mobile hydrological data acquisition system according to claim 1, characterized in that, The edge control module includes a data segmentation module, a dynamic prediction module, and a dynamic adjustment module. The data segmentation module divides the data signal sent by the data acquisition module into independent data blocks every 5 minutes. Each block is attached with a timestamp T, a hash identifier S, and a priority identifier Y. Where the water level change rate is greater than or equal to 5 cm / min, Y represents the first priority; the rainfall is greater than or equal to 50 mm / h, Y represents the second priority; and the rest are regarded as routine monitoring data, Y represents the third priority. The dynamic prediction module records the reservoir's water level signal A, rainfall signal B, and water flow velocity signal C in real time, and extracts the corresponding flood limit water level A1 and the historical average water flow velocity C1 from the database to calculate the dynamic prediction value M. The dynamic adjustment module adjusts the bandwidth allocation strategy of the multimode communication module based on the dynamic prediction value M.

3. The real-time mobile hydrological data acquisition system according to claim 2, characterized in that, The dynamic prediction module calculates the dynamic prediction value M, M=Y1×0.6+A2×0.3+C2×0.1, where Y1 is 1 when Y is the first priority, Y1 is 0.7 when Y is the second priority, Y1 is 0.3 when Y is the third priority, A2 is 0.2 when the water level signal A is less than or equal to 70% of the flood limit water level A1, A2 is 0.6 when the water level signal A is greater than 70% and less than or equal to 80% of the flood limit water level A1, A2 is 1 when the water level signal A is greater than 80% of the flood limit water level A1, and C2 is 1 when C2 is greater than the historical average water flow velocity C1 for the same period, and C2 is 0 when C2 is less than or equal to the historical average water flow velocity C1 for the same period.

4. The real-time mobile hydrological data acquisition system according to claim 3, characterized in that, The dynamic adjustment module adjusts the bandwidth allocation strategy of the multi-mode communication module based on the dynamic prediction value M. If M is greater than or equal to 0.8, it indicates high risk. The bandwidth ratio of satellite communication is increased to 80%, the first priority data transmission is prioritized, the third priority data transmission is forcibly interrupted, and the non-first priority data transmission of NB-IoT cellular network and Mesh communication network is suspended. If M is greater than or equal to 0.6 and less than 0.8, it indicates medium risk. Adjust the proportion of satellite communication bandwidth to 50%, NB-IoT cellular network to 30%, and Mesh communication network to 20%. When M is less than 0.6, it indicates low risk. The default is to use NB-IoT cellular network for transmission, with a bandwidth ratio of 70%. Mesh communication network asynchronously retransmits historical data, and satellite communication enters deep sleep mode.

5. The real-time mobile hydrological data acquisition system according to claim 1, characterized in that, The dynamic early warning module works as follows: When the water flow velocity signal C is greater than the historical average value C1 for the same period, a level 3 warning is activated, and Mesh communication is controlled to periodically broadcast to the population in the relevant target area once every 5 minutes. When the rainfall signal B is greater than 50 mm / h for 30 minutes, a level 2 warning is activated, and the NB-IoT cellular network is controlled to send emergency SMS notifications to people in the relevant target areas. If water level signal A is greater than 90% of the flood limit water level A1, a level-one early warning will be activated, and satellite communication will be used to send short messages via BeiDou to the relevant target area.

6. The real-time mobile hydrological data acquisition system according to claim 2, characterized in that, Extract the adjacent timestamps TL and AL corresponding to the water level signal A, then calculate the difference AL1 between A and AL, the difference TL1 between T and TL, and the ratio of AL1 to TL1 to obtain the water level change rate.

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