A remote control system for a GNSS receiver

Abstract

The present application belongs to the technical field of hydrological monitoring and remote control, and relates to a remote control system of GNSS receiver, which comprises a data acquisition module, a data processing module, a data detection module and a system alarm module. The data acquisition module acquires water level elevation information with time stamp according to a preset sampling period and forms a water level information sequence List. The data processing module constructs a standard sequence to realize time alignment and mark blank data points. The offset interval is determined around the blank data points, and the high and low intervals are identified in the offset interval to complete the adaptive correction and completion of the blank data points. The data detection module detects the completed sequence for abnormalities according to a threshold. The system alarm module generates an alarm message when the alarm condition is met, uploads the remote platform and logs the alarm and detection results, thereby improving the fidelity of extreme value and the reliability of alarm.

Description

Technical Field

[0001] This invention belongs to the field of hydrological monitoring and remote control technology, specifically relating to a remote control system for a GNSS receiver. Background Technology

[0002] In existing technologies, water level monitoring using GNSS signals typically involves deploying GNSS receivers in locations such as rivers, reservoirs, or dams, and using differential correction and elevation conversion to continuously acquire water level elevations. These monitoring devices often operate continuously in outdoor environments for extended periods, frequently encountering complex conditions such as high humidity, salt spray, silt, acid and alkali conditions, water vapor, and temperature fluctuations. GNSS receivers, their connectors, antenna feeder interfaces, power and signal ports are prone to oxidation, corrosion, or increased contact resistance, leading to signal attenuation, increased noise, data drift, or intermittent jumps, ultimately resulting in hidden errors in water level measurement data. These anomalies caused by internal corrosion or component aging often do not manifest as continuous complete failures, but rather as a mixture of sporadic shifts, slow drifts, and sudden changes, posing challenges to data quality control and fault location. Currently, anomaly detection in water level data often employs threshold discrimination, sliding window statistics, mutation detection, or rule-based methods based on historical mean / variance, primarily targeting significant outliers or obvious outliers. When corrosion-induced errors are within the threshold range or accumulate gradually, traditional methods often struggle to promptly and accurately identify them as anomalies, and may even misjudge them as actual water level changes, leading to alarm delays or missed alarms. On the other hand, after data cleaning and time alignment, existing systems typically use linear interpolation, moving average, spline interpolation, or near-time-based completion strategies for missing data to form continuous water level sequences for display and control. However, near extreme water levels (such as flood peaks, sudden rises and falls, and switching between flood discharge conditions), water level changes are characterized by strong nonlinearity, large gradients, and frequent abrupt changes. Conventional data completion methods are prone to "peak shaving and valley filling," phase lag, or excessive smoothing, leading to inaccurate reconstruction of key peak / valley values ​​and even introducing larger errors. Since extreme water levels are crucial for dam scheduling, safety assessment, and early warning decisions, if extreme values ​​are incorrectly completed or masked by deviations caused by corrosion, the monitoring system may fail to adequately identify risky conditions, affecting the reliability of alarms and the effectiveness of decisions from the remote control platform. Therefore, it is necessary to provide an improved data quality control and anomaly detection scheme for long-term field operation scenarios of GNSS water level monitoring. This scheme should be able to more effectively identify abnormal data caused by factors such as equipment corrosion, reduce data completion errors near extreme values ​​during the missing data completion process, and ensure the authenticity of key water level processes and the accuracy of monitoring alarms. Summary of the Invention

[0003] The purpose of this invention is to provide a remote control system for a GNSS receiver to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0004] To achieve the above objectives, according to one aspect of the present invention, a remote control system for a GNSS receiver is provided, the system comprising: The data acquisition module is used to collect water level information sequences; A data processing module, connected to the data acquisition module, is used to supplement the water level information sequence to obtain a supplemented water level information sequence. A data detection module, connected to the data processing module, is used to perform anomaly detection on the supplemented water level information sequence. The system alarm module is connected to the data detection module and is used to output alarm information based on the anomaly detection results.

[0005] Furthermore, the data acquisition module includes a GNSS receiver, a GNSS antenna, and a deployment component for mounting the GNSS receiver and the GNSS antenna. The deployment component is set on the shore or on the water surface and is used to deploy the GNSS antenna at a preset height position to collect water level information sequences to form a List.

[0006] Furthermore, the data acquisition module is used to output the water level information sequence, wherein the water level information sequence is the water level elevation information generated by the GNSS receiver based on the received satellite navigation signal and the acquisition time corresponding to the elevation information, denoted as Ti, where i represents the sequence number of the acquisition time, Ti represents the i-th acquisition time, and the water level elevation information corresponding to time Ti is denoted as Hi.

[0007] Furthermore, the supplementary processing method specifically includes: traversing the collection time of all data points in the sequence List, calculating the time interval between adjacent collection times, taking the minimum value of the time interval as the minimum period t0, constructing a standard sequence List1 of length 24h with the minimum period t0 as the step size, aligning the standard sequence List1 with the sequence List obtained in the most recent 24 hours in time, filling the blank data points in the aligned sequence by calculating the average of the water level elevation information of the previous moment and the water level elevation information of the next moment for the blank data points, and updating the filled sequence as List.

[0008] In current GNSS-based online water level monitoring systems, field GNSS receivers typically upload observation results or calculated water level values ​​to a data platform in real time via communication links such as 5G / Ethernet / serial ports. Especially during long-term field operation, communication interfaces, power supply terminals, or wiring terminals are susceptible to corrosion from moisture, salt spray, etc., leading to increased contact resistance, power supply voltage fluctuations, or abnormal link impedance, which in turn causes communication jitter, intermittent disconnections, and reconnections. To ensure no data loss, terminals or communication modules often enable caching and retransmission mechanisms: data is temporarily stored during link anomalies and retransmitted in batches after recovery. If the platform uses "arrival time" as the recording timestamp, or if the retransmission merging does not strictly adhere to the GNSS data within the message... If time is used as the standard, the water level sequence will lag behind or exhibit segmented time misalignment on the timeline. This manifests as a regular delay in the event response (the start time of rise and fall, the time of peak occurrence) relative to the actual occurrence time. The above methods, after constructing a standard sequence to align the acquired water level information sequence with time, show the time points where spatiotemporal offsets occur in the traversed blank data. The standard sequence is a sequence constructed based on the minimum time interval. However, the above methods use the average value of adjacent time points to fill in missing values. Since the time delay at this point is caused by segmented misalignment after the retransmission and merging, and since the system uses batch retransmission, the data will show amplitude offset for a period of time after the retransmission is completed. Specifically, it manifests as a slight superposition of amplitudes. Especially if there is an extreme point of water level elevation corresponding to the retransmission time point, this average method of filling will greatly interfere with the acquired water level information sequence, especially causing the extreme point of water level elevation to deviate greatly from the true value. To avoid the above problems, this invention proposes the following method.

[0009] Furthermore, the method of supplementary processing is replaced with: Align the standard sequence List1 with the sequence List obtained in the last 24 hours in terms of time, mark the blank data points in the aligned sequence, and denote the blank data points as pj. Retrieve the offset time period from blank data points simultaneously, both forward and backward, from the sequence list; Mark the low-level and high-level intervals within the offset time period; The standard for calculating the decrease in the lower range is Qj, and the standard for calculating the increase in the higher range is Jj. The blank data were filled in according to the decrease and increase standards.

[0010] Since the time delay at this moment is due to segment misalignment caused by the retransmission and merging process, and because the system uses batch retransmission, there will be amplitude shifts in the data for a period of time after the retransmission is completed. Specifically, this manifests as a slight superposition of amplitudes. The offset interval is used to locate the data segments where amplitude shifts occur. However, because the timing of the system's batch retransmission is uncertain, the change in data amplitude within the offset interval cannot be interpreted as the sum of peak values ​​after retransmission, nor can the minimum value be interpreted as the sum of minimum values ​​after retransmission. However, since the amplitude shift is only due to the superposition of small batches of data, the basic change in the data will not change. Therefore, the above method iterates through the data points from the maximum and minimum values ​​towards both ends to identify the points where the change patterns are inconsistent, which are the intervals where the true maximum and minimum values ​​are located. Since the dam monitoring system requires high accuracy and response speed for the maximum and minimum water levels, it is only necessary to ensure that the maximum and minimum water levels received by the receiver are not corrupted during the retransmission process.

[0011] Furthermore, the calculation method for the reduction standard is as follows: starting from the first data point in the low-level interval, the water level elevation of the current data point is successively subtracted from the water level elevation of the data point at the next moment. The absolute value of the difference is taken as the reduction qr of the current data point, where r represents the data point number in the low-level interval. The reduction standard for the low-level interval is calculated according to the formula. , where N is the number of data points in the lower-order interval.

[0012] Furthermore, the calculation method for the rise standard is as follows: starting from the first data point in the high-level interval, the water level elevation of the current data point is successively subtracted from the water level elevation of the data point at the next moment. The absolute value of the difference is taken as the rise value jr of the current data point, and s represents the data point index in the low-level interval. The rise standard of the low-level interval is calculated according to the formula. , where M is the number of data points in the high-order interval.

[0013] Furthermore, the method for filling in blank data according to the decrease and increase standards is as follows: within the range of i, traverse all blank data points and the offset intervals where the blank data points are located in the sequence list. Let the decrease standard of the offset interval be Qj and the increase standard be Jj. Let the first data point retrieved from the sequence list in reverse order be H0 and the first data point retrieved from the sequence list in forward order be H1. Then, the water level elevation value of the filled blank data point is: The sequence list is updated after filling in all blank data.

[0014] The amplitude increase standard represents the degree of water level superposition of the water level elevation value in the interval where the maximum value is located. The larger the amplitude increase standard, the more superposition there is, which means the more serious the deviation of the maximum value. Similarly, the amplitude decrease standard represents the degree of water level superposition of the water level elevation value in the interval where the minimum value is located. The larger the amplitude decrease standard, the more superposition there is, which means the more serious the deviation of the minimum value. In the water level monitoring system of the dam, the extreme values of the water level often determine the regulation decision of the project. Therefore, when the blank data point happens to be an extreme value, if only the average method is used for filling, it is easy to cause a large deviation between the filled data and the true value. The above method first marks the data interval with deviation, then marks the deviation interval where the extreme value is located according to the change rule of adjacent data in the data interval, and then calculates the standard value in the deviation interval where the extreme value is located respectively. The calculated standard value is used to evaluate the deviation degree of the interval. The more deviation there is, that is, the larger the superposition, the larger the result after taking the square root of the product of adjacent data differences. At the minimum value, it will be smaller than the true value. Therefore, this part is added during the filling process. Similarly, for the maximum value, this part needs to be subtracted to make the filled data point closer to the true value. If the filled data point is not an extreme value, will approach 1, and at this time, the filling method can be regarded as the average filling method.

[0015] Furthermore, the method for performing anomaly detection on the filled water level information sequence specifically includes: obtaining the filled water level information sequence List, where each water level information includes at least a timestamp and the corresponding water level elevation value; Presetting a water level elevation anomaly detection threshold, including a water level elevation upper threshold H_max and a water level elevation lower threshold H_min; Traversing the water level information sequence List in chronological order, and extracting the water level elevation value H_i for any water level information; Comparing the water level elevation value H_i with the water level elevation upper threshold H_max and the water level elevation lower threshold H_min: When H_i > H_max, it is determined as a high water level anomaly and a high water level alarm message is generated; When H_i < H_min, it is determined as a low water level anomaly and a low water level alarm message is generated; When H_min ≤ H_i ≤ H_max, it is determined as normal water level information.

[0016] Furthermore, the system alarm module is used to generate an alarm message and send it to the remote control platform when the preset alarm condition is met, and save the alarm message and / or the anomaly detection result in the log The beneficial effects of the present invention: Alignment and correction: By constructing a standard sequence with the minimum period and aligning it with the water level sequence of the past 24 hours, the segmented misalignment caused by the retransmission and merging is explicitly converted into blank points, thereby realizing the consistent processing of water level data on the time axis and reducing the impact of event time lag and segmented misalignment on the monitoring results.

[0017] Extreme value fidelity: Locate the offset interval around the blank point, and further identify the high and low intervals where the extreme value is located. Use the rise standard J and fall standard Q to characterize the degree of amplitude superposition offset, and adaptively correct and fill the blank point to avoid "peak shaving and valley filling" and ensure that key operating points such as the maximum / minimum water level are closer to the true value. Attached Figure Description

[0018] Figure 1 The diagram shown is a structural diagram of a remote control system for a GNSS receiver. Figure 2 The diagram shown is a flowchart of the replacement and supplementation process. Figure 3 The diagram shows a flowchart of a method for anomaly detection of the supplemented water level information sequence. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1 As shown, the present invention provides a remote control system for a GNSS receiver, the system comprising: The data acquisition module is used to collect water level information sequences; A data processing module, connected to the data acquisition module, is used to supplement the water level information sequence to obtain a supplemented water level information sequence. A data detection module, connected to the data processing module, is used to perform anomaly detection on the supplemented water level information sequence. The system alarm module is connected to the data detection module and is used to output alarm information based on the anomaly detection results.

[0021] Furthermore, the data acquisition module of this embodiment includes at least: a GNSS receiver, a GNSS antenna, and a deployment assembly. The GNSS antenna and the GNSS receiver are electrically connected (e.g., via an RF feeder). The GNSS receiver receives satellite navigation signals acquired by the GNSS antenna and outputs a water level information sequence, which includes water level elevation information and its corresponding acquisition time.

[0022] In one possible embodiment, the deployment components include a shore-based fixed support. The shore-based fixed support can be installed on the top of the dam, the dam slope, or a stable foundation on the bank slope (e.g., a concrete foundation, a metal pole foundation, etc.) to fix the GNSS antenna in a predetermined position. Installation method: The GNSS antenna is installed on top of the fixed support via bolts, clamps, or a base connection; the fixed support ensures the stability and repeatability of the antenna installation position; deployment principle: preferably in an open location to minimize obstruction; and as far away as possible from local structures that may cause obstruction or strong reflections to improve data acquisition stability. Connection method: The GNSS receiver can be installed in a protective enclosure, which is fixed to the support or a nearby fixed structure; the GNSS antenna and the GNSS receiver are connected via a feed cable, and the feed cable interface can be waterproofed and sealed.

[0023] In another possible embodiment, the deployment components include a floating body mounting structure, where the floating body can be a buoy, a floating platform, or a shipborne floating body. A GNSS antenna is mounted on top of a support structure above the floating body, positioned at a predetermined height above the water surface to collect water level information sequences. Installation method: The GNSS antenna can be fixed to the top of the floating body's column or mast via an antenna base; the GNSS receiver can be installed inside a sealed compartment or protective case of the floating body. Stabilization design: The floating body mounting structure can be equipped with counterweights or anti-roll structures to reduce the impact of the floating body's swaying on the water level information sequence; alternatively, a mooring structure can be used to fix the floating body at a predetermined water area position to maintain the stability of the observation point. Data output: The GNSS receiver generates water level elevation information based on the received satellite navigation signals and, combined with the acquisition time, forms a water level information sequence which is output to the data processing module.

[0024] Furthermore, the data acquisition module is deployed at the water level monitoring point and can be set on a fixed support on the shore or a floating installation structure on the water surface. The data acquisition module includes at least a GNSS receiver and a GNSS antenna electrically connected to it. The GNSS antenna is used to receive satellite navigation signals from multiple navigation satellites and input the satellite navigation signals to the GNSS receiver. After receiving the satellite navigation signals, the GNSS receiver performs positioning calculations based on the observation results of the satellite navigation signals to obtain the elevation information corresponding to the monitoring point. At the same time, the GNSS receiver obtains the acquisition time corresponding to the elevation information. The acquisition time can be the UTC time obtained by satellite timing or the timestamp after the internal clock of the GNSS receiver is synchronized with the satellite timing. The data acquisition module collects elevation information at a preset sampling period and associates the elevation information obtained each time with the corresponding acquisition time to generate a water level information sequence containing multiple data points. The water level information sequence is represented using a data structure arranged in chronological order, where each data point includes: the acquisition time and the corresponding water level elevation information. The acquisition time is denoted as Ti, where i represents the sequence number of the acquisition time, and Ti represents the i-th acquisition time. The water level elevation information corresponding to time Ti is denoted as Hi, and the water level information sequence is List.

[0025] Furthermore, the method for supplementing the water level information sequence to obtain the supplemented water level information sequence specifically includes: traversing the collection time of all data points in the sequence List within the range of values ​​of i, calculating the time interval between adjacent collection times, taking the minimum value of the time interval as the minimum period t0, constructing a standard sequence List1 of length 24h with the minimum period t0 as the step size, aligning the standard sequence List1 with the sequence List obtained in the most recent 24 hours in time, filling the blank data points in the aligned sequence by calculating the average of the water level elevation information of the previous moment and the water level elevation information of the next moment for the blank data points, and updating the supplemented sequence to List.

[0026] In current GNSS-based online water level monitoring systems, field GNSS receivers typically upload observation results or calculated water level values ​​to a data platform in real time via communication links such as 5G / Ethernet / serial ports. Especially during long-term field operation, communication interfaces, power supply terminals, or wiring terminals are susceptible to corrosion from moisture, salt spray, etc., leading to increased contact resistance, power supply voltage fluctuations, or abnormal link impedance, which in turn causes communication jitter, intermittent disconnections, and reconnections. To ensure no data loss, terminals or communication modules often enable caching and retransmission mechanisms: data is temporarily stored during link anomalies and retransmitted in batches after recovery. If the platform uses "arrival time" as the recording timestamp, or if the retransmission merging does not strictly adhere to the GNSS data within the message... If time is used as the standard, the water level sequence will lag behind or exhibit segmented time misalignment on the timeline. This manifests as a regular delay in the event response (the start time of rise and fall, the time of peak occurrence) relative to the actual occurrence time. The above methods, after constructing a standard sequence to align the acquired water level information sequence with time, show the time points where spatiotemporal offsets occur in the traversed blank data. The standard sequence is a sequence constructed based on the minimum time interval. However, the above methods use the average value of adjacent time points to fill in missing values. Since the time delay at this point is caused by segmented misalignment after the retransmission and merging, and since the system uses batch retransmission, the data will show amplitude offset for a period of time after the retransmission is completed. Specifically, it manifests as a slight superposition of amplitudes. Especially if there is an extreme point of water level elevation corresponding to the retransmission time point, this average method of filling will greatly interfere with the acquired water level information sequence, especially causing the extreme point of water level elevation to deviate greatly from the true value. To avoid the above problems, this invention proposes the following method.

[0027] Please refer to Figure 2 As shown, the present invention also provides the following methods for supplementing blank data. Furthermore, the standard sequence List1 is time-aligned with the sequence List obtained in the last 24 hours, and blank data points in the aligned List are marked as pj, where j represents the index of the blank data point and pj represents the j-th blank data point; Let i=1. Within the range of values ​​of i, traverse all blank data points in the sequence list. Search all data points in reverse order from the blank data points. Record the first non-blank data point found as the upper limit data point. Search all data points in forward order from the blank data points. Record the first non-blank data point found as the lower limit data point. Record the data interval formed by the upper limit data point and the lower limit data point as the offset interval. The maximum water level elevation within the offset interval is denoted as Hmax, and the minimum water level elevation within the offset interval is denoted as Hmin. Within the offset interval, data points from the previous time step are sequentially retrieved from the data point corresponding to Hmax until the next retrieved data point is greater than the current data point. The water level elevation value of the next data point is then denoted as E0. Similarly, data points from the next time step are sequentially retrieved from the data point corresponding to Hmax within the offset interval until the next retrieved data point is greater than the current data point. The water level elevation value of the next data point is then denoted as E1. Within the offset interval, data points from the previous time step are sequentially retrieved from the data point corresponding to Hmin until the next data point is found to be less than the current data point. The water level elevation of the next data point is then recorded as S0. Within the offset interval, data points from the next time step are sequentially retrieved from the data point corresponding to Hmin until the next data point is found to be less than the current data point. The water level elevation of the next data point is then recorded as S1. Since the time delay at this moment is due to segment misalignment caused by the retransmission and merging process, and because the system uses batch retransmission, there will be amplitude shifts in the data for a period of time after the retransmission is completed. Specifically, this manifests as a slight superposition of amplitudes. The offset interval is used to locate the data segments where amplitude shifts occur. However, because the timing of the system's batch retransmission is uncertain, the change in data amplitude within the offset interval cannot be interpreted as the sum of peak values ​​after retransmission, nor can the minimum value be interpreted as the sum of minimum values ​​after retransmission. However, since the amplitude shift is only due to the superposition of small batches of data, the basic change in the data will not change. Therefore, the above method iterates through the data points from the maximum and minimum values ​​towards both ends to identify the points where the change patterns are inconsistent, which are the intervals where the true maximum and minimum values ​​are located. Since the dam monitoring system requires high accuracy and response speed for the maximum and minimum water levels, it is only necessary to ensure that the maximum and minimum water levels received by the receiver are not corrupted during the retransmission process.

[0028] The data interval formed by the data points corresponding to S0 and S1 is designated as the low-level interval, and the data interval formed by the data points corresponding to E0 and E1 is designated as the high-level interval. In the low-level interval, starting from the first data point, the water level elevation of the current data point is successively subtracted from the water level of the data point at the next time step. The absolute value of the difference is taken as the decrease qr of the current data point, where r represents the data point index in the low-level interval. The standard decrease in the low-level interval is calculated using the formula... , where N is the number of data points in the lower-order interval; In the high-level interval, starting from the first data point, the water level elevation of the current data point is successively subtracted from the water level of the next data point. The absolute value of the difference is taken as the rise value jr of the current data point, and s represents the data point index in the low-level interval. The standard rise value in the low-level interval is calculated according to the formula. , where M is the number of data points in the high-order interval; Within the range of values ​​for i, iterate through all blank data points and their offset intervals in the sequence list. Let Qj be the standard for the decrease in the offset interval and Jj be the standard for the increase. Let H0 be the first data point retrieved from the sequence list in reverse order and H1 be the first data point retrieved from the sequence list in forward order. Then, the water level elevation of the filled blank data points is: .

[0029] The beneficial effects of the above steps are as follows: the standard for increase represents the degree of water level overlap between the maximum and minimum water level intervals. A larger standard for increase indicates more overlap, which means a more severe deviation from the maximum value. Similarly, the standard for decrease represents the degree of water level overlap between the minimum and minimum water level intervals. A larger standard for decrease indicates more overlap, which means a more severe deviation from the minimum value. In dam water level monitoring systems, extreme water levels often determine engineering regulation decisions. Therefore, when blank data points happen to be extreme values, simply using the averaging method to fill in the gaps can easily lead to a large deviation between the filled data and the true value. The above method first marks the data intervals where offsets occur. Based on the changing patterns of adjacent data within these intervals, it then marks the offset intervals containing extreme values. Next, it calculates a standard value for each extreme value's offset interval. The calculated standard value is used to evaluate the degree of offset within the intervals. The greater the offset, the larger the cumulative effect, and the larger the square root of the product of the differences between adjacent data points in the formula. This means the minimum value will be smaller than the true value. Therefore, this portion is added during the supplementation process. Similarly, for the maximum value, this supplementation needs to be removed to make the supplemented data points closer to the true value. If the supplemented data point is not an extreme value, the formula... The value will approach 1, at which point the supplementation method can be regarded as the average supplementation method.

[0030] Please see Figure 3 As shown, the following methods are also run in the data detection module: Furthermore, GNSS receivers are deployed near rivers / reservoirs / dams to collect antenna elevation data and convert it to water level elevation using a reference surface; remote control terminals communicate with the GNSS receivers via cellular networks / Ethernet; after the server completes the supplementation of missing water level information, it generates a list of supplemented water level information sequences and performs anomaly detection on it. In this embodiment, the supplemented water level information sequence List can be stored in the form of an array / linked list, where each element is a structure or dictionary object. Each piece of water level information contains at least a timestamp t_i and a water level elevation H_i. Each record in the List contains a timestamp t_i and a water level elevation H_i. The server pre-reads the upper limit threshold H_max and the lower limit threshold H_min of the water level elevation from the site parameter table or sends them to the remote control terminal. The upper limit threshold and the lower limit threshold can be obtained through any of the following methods: Manual configuration: The administrator enters the configuration information in the remote control terminal and sends it to the server / receiver; Site configuration file: The server reads the site parameter table (including site ID, alarm threshold, etc.). Dynamic threshold: The threshold is updated according to the season or scheduling conditions (this embodiment uses manual configuration). After traversing the List in chronological order, extract the water level elevation value H_i for each record and compare it with the threshold: when H_i > H_max, immediately generate a high water level alarm message and mark the record as "high water level abnormal". At the same time, write the alarm information (including at least the station number, abnormality type, occurrence time t_i, abnormal water level value H_i, and threshold H_max / H_min) into the alarm log and push it to the remote control terminal; when H_i < H_min, generate a low water level alarm message and perform the same recording and pushing; when H_min ≤ H_i ≤ H_max, mark it as normal water level information and do not trigger an alarm.

[0031] Furthermore, the alarm module is deployed on the server or GNSS receiver and pre-configured with preset alarm conditions (e.g., water level exceeding the upper threshold or falling below the lower threshold, abnormal status persisting for a preset number of times / duration, etc.). When the anomaly detection result output by the anomaly detection module meets the preset alarm conditions, the alarm module generates an alarm message in a unified message format. The alarm message includes at least the device / site identifier, alarm type, alarm level, trigger time, anomaly value, threshold parameter, and data source identifier. It is then sent to the remote control platform via cellular network / Ethernet using any of the HTTP / MQTT / TCP communication methods to achieve alarm display and linkage. Simultaneously, the alarm module writes the alarm message and / or the anomaly detection result into a local or server database / file log for storage. The log content includes at least the generation time, sending result (success / failure), number of retransmissions, message summary, and corresponding original water level record index, to facilitate subsequent tracing, statistical analysis, and fault diagnosis.

Claims

1. A remote control system for a GNSS receiver, characterized in that, The system includes: The data acquisition module is used to collect water level information sequences; A data processing module, connected to the data acquisition module, is used to supplement the water level information sequence to obtain a supplemented water level information sequence. A data detection module, connected to the data processing module, is used to perform anomaly detection on the supplemented water level information sequence. The system alarm module is connected to the data detection module and is used to output alarm information based on the anomaly detection results.

2. The remote control system for a GNSS receiver according to claim 1, characterized in that, The data acquisition module includes a GNSS receiver, a GNSS antenna, and a deployment component for mounting the GNSS receiver and the GNSS antenna. The deployment component is set on the shore or on the water surface and is used to deploy the GNSS antenna at a preset height position to collect water level information sequences to form a List.

3. The remote control system for a GNSS receiver according to claim 1, characterized in that, The data acquisition module is used to output the water level information sequence, wherein the water level information sequence is the water level elevation information generated by the GNSS receiver based on the received satellite navigation signal and the acquisition time corresponding to the elevation information. The acquisition time is denoted as Ti, where i represents the sequence number of the acquisition time, Ti represents the i-th acquisition time, and the water level elevation information corresponding to time Ti is denoted as Hi.

4. The remote control system for a GNSS receiver according to claim 1, characterized in that, The supplementary processing method specifically includes: traversing the collection time of all data points in the sequence List, calculating the time interval between adjacent collection times, taking the minimum value of the time interval as the minimum period t0, constructing a standard sequence List1 with a length of 24 hours using the minimum period t0 as the step size, aligning the standard sequence List1 with the sequence List obtained in the most recent 24 hours in time, filling the blank data points in the aligned sequence by calculating the average of the water level elevation information of the previous moment and the water level elevation information of the next moment for the blank data points, and updating the filled sequence as List.

5. The remote control system for a GNSS receiver according to claim 1, characterized in that, The supplementary processing method is replaced with: Align the standard sequence List1 with the sequence List obtained in the last 24 hours in terms of time, mark the blank data points in the aligned sequence, and denote the blank data points as pj. Retrieve the offset time period from blank data points simultaneously, both forward and backward, from the sequence list; Mark the low-level and high-level intervals within the offset time period; The standard for calculating the decrease in the lower range is Qj, and the standard for calculating the increase in the higher range is Jj. The blank data were filled in according to the decrease and increase standards.

6. A remote control system for a GNSS receiver according to claim 5, characterized in that, The calculation method for the reduction standard is as follows: Starting from the first data point in the low-level interval, the water level elevation of the current data point is successively subtracted from the water level elevation of the data point at the next time moment. The absolute value of the difference is taken as the reduction qr of the current data point, where r represents the data point number in the low-level interval. The reduction standard for the low-level interval is calculated according to the formula. , where N is the number of data points in the lower-order interval.

7. A remote control system for a GNSS receiver according to claim 5, characterized in that, The calculation method for the elevation standard is as follows: Starting from the first data point in the high-level interval, the water level elevation of the current data point is successively subtracted from the water level elevation of the data point at the next moment. The absolute value of the difference is taken as the elevation value jr of the current data point, and s represents the data point index in the low-level interval. The elevation standard for the low-level interval is calculated according to the formula. , where M is the number of data points in the high-order interval.

8. A remote control system for a GNSS receiver according to claim 5, characterized in that, The method for filling blank data according to the decrease and increase standards is as follows: Within the range of i, traverse all blank data points and the offset intervals where the blank data points are located in the sequence list. Let the decrease standard of the offset interval be Qj and the increase standard be Jj. Let the first data point retrieved from the sequence list in reverse order be H0 and the first data point retrieved from the sequence list in forward order be H1. Then the water level elevation value of the filled blank data point is: The sequence list is updated after filling in all blank data.

9. A remote control system for a GNSS receiver according to claim 1, characterized in that, The method for anomaly detection of the supplemented water level information sequence specifically includes: obtaining the supplemented water level information sequence List, wherein each water level information includes at least a timestamp and a corresponding water level elevation value; Pre-set water level elevation anomaly detection thresholds, including the upper limit threshold H_max and the lower limit threshold H_min; Traverse the water level information sequence List in chronological order and extract the water level elevation value H_i for any water level information; The water level elevation value H_i is compared with the upper limit threshold H_max and the lower limit threshold H_min: When H_i > H_max, it is determined to be a high water level anomaly and a high water level alarm message is generated; When H_i < H_min, it is determined to be a low water level anomaly and a low water level alarm message is generated; When H_min ≤ H_i ≤ H_max, it is determined to be normal water level information.

10. A remote control system for a GNSS receiver according to claim 1, characterized in that, The system alarm module is used to generate an alarm message and send it to the remote control platform when the preset alarm conditions are met, and to log the alarm message and / or the anomaly detection results.

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