A method, device and terminal device for water volume monitoring

Abstract

The present application provides a water volume monitoring method, device and terminal device, which are applicable to the technical field of data processing. The method includes: obtaining the initial monitoring signal characterization information and wireless parameter adjustment range information of a sensor; generating an initial monitoring signal state space vector and a signal state adjustment space vector according to the initial monitoring signal characterization information and the wireless parameter adjustment range information; selecting the signal state adjustment space vector to generate a signal state adjustment action vector; generating target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and the signal quality characterization quantity threshold; and measuring and transmitting the water volume information according to the target monitoring signal characterization information to generate water volume monitoring information. The present application adaptively optimizes the wireless signal quality, improves the adaptability of the sensor to the complex and changeable water conservancy environment, ensures the stability of the monitoring signal, and ensures the timeliness and accuracy of the water volume monitoring information.

Description

Technical Field

[0001] This application belongs to the technical field of data processing, and particularly relates to a water volume monitoring method, device, and terminal device. Background Art

[0002] Water conservancy projects are crucial for the rational allocation of water resources, flood control and disaster reduction, agricultural irrigation, etc. Accurate water volume monitoring is the basis for ensuring the efficient operation of water conservancy projects. By mastering water volume information in real time, it can provide a scientific basis for water conservancy decision-making. With the development of the economic society and the intensification of the contradiction between water supply and demand, the requirements for the accuracy and real-time performance of water volume monitoring are getting higher and higher.

[0003] In the prior art, devices such as water level gauges and current meters are usually used to measure the water level and water flow velocity, or technologies such as satellites or drones are used to obtain the water area and water level information, so as to estimate the water volume according to the obtained water area and water level information.

[0004] However, in the prior art, the acquisition and transmission of water volume data collected by monitoring devices may be delayed or interrupted, unable to timely reflect the dynamic changes of the water volume, and the monitoring devices often have poor adaptability to complex and changeable water conservancy environments, and there are obvious defects in aspects such as the accuracy, real-time performance, and environmental adaptability of water volume monitoring. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a water volume monitoring method, device, and terminal device, aiming to solve the problems in the prior art that data acquisition and transmission are extremely prone to delay or interruption during water volume monitoring, and the accuracy, real-time performance, and environmental adaptability of water volume monitoring are all low in a dynamic and changeable water conservancy environment.

[0006] The first aspect of the embodiments of this application provides a water volume monitoring method, including:

[0007] Obtain the initial monitoring signal characterization information and wireless parameter adjustment interval information of multiple sensors;

[0008] Generate an initial monitoring signal state space vector and multiple signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information;

[0009] Select multiple signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector;

[0010] Generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector, and a preset signal quality characterization quantity threshold;

[0011] Measure and transmit the water volume information according to the target monitoring signal characterization information to generate water volume monitoring information.

[0012] The second aspect of the embodiments of the present application provides a water volume monitoring device, including:

[0013] An information acquisition module, configured to acquire the initial monitoring signal characterization information of multiple sensors and the wireless parameter adjustment interval information;

[0014] A space vector generation module, configured to generate an initial monitoring signal state space vector and multiple signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information;

[0015] A signal state adjustment action vector generation module, configured to select multiple signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector;

[0016] A monitoring signal characterization information generation module, configured to generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector, and a preset signal quality characterization quantity threshold; and

[0017] A water volume monitoring information module, configured to measure and transmit the water volume information according to the target monitoring signal characterization information to generate water volume monitoring information.

[0018] The third aspect of the embodiments of the present application provides a terminal device, which includes a memory and a processor. A computer program that can run on the processor is stored on the memory. When the processor executes the computer program, the steps of the water volume monitoring method described in the first aspect above are implemented.

[0019] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: Compared with the problem in the prior art that sensors use fixed wireless parameters for water volume monitoring and it is difficult to adapt to the complex and changing water conservancy environment at all times, the present application automatically adjusts the wireless parameters of the sensors by real-time sensing the state of the wireless signal for monitoring the water volume, adaptively optimizes the wireless signal quality, improves the anti-interference ability of the wireless signal in water volume monitoring under different water conservancy scenarios and environmental changes, ensures the stability of the water volume monitoring signal, reduces data transmission errors, ensures the timeliness and accuracy of the water volume monitoring information, and provides effective data support for water volume analysis and research work. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the first embodiment of the present application;

[0022] Figure 2 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the second embodiment of the present application;

[0023] Figure 3 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the third embodiment of the present application;

[0024] Figure 4 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the fourth embodiment of the present application;

[0025] Figure 5 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the fifth embodiment of the present application;

[0026] Figure 6 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the sixth embodiment of the present application;

[0027] Figure 7 It is a schematic flow chart of the implementation of the water volume monitoring method provided in the seventh embodiment of the present application;

[0028] Figure 8 It is a schematic structural diagram of the water volume monitoring device provided in the embodiments of the present application;

[0029] Figure 9 It is a schematic diagram of the terminal device provided in the embodiments of the present application. Specific Embodiments

[0030] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0031] To illustrate the technical solutions described in the present application, the following will be described through specific embodiments.

[0032] Figure 1The implementation flowchart of the water volume monitoring method provided in Embodiment 1 of the present application is shown and described in detail as follows:

[0033] Step S101, obtain the initial monitoring signal characterization information of multiple sensors and the wireless parameter adjustment range information.

[0034] In this embodiment, the initial monitoring signal characterization information of the sensor may refer to the parameter information used to characterize the real-time wireless signal quality for water volume monitoring, which can be obtained by real-time measurement through the built-in module of the sensor. The wireless parameter adjustment range information may refer to the adjustment range of the wireless parameters of the sensor for water volume monitoring, which can be obtained from the product manual of the sensor or from the communication standard.

[0035] In this embodiment, preferably, the initial monitoring signal characterization information includes initial signal strength information, initial signal-to-noise ratio information, and initial bit error rate information; the wireless parameter adjustment range information includes transmit power range information, communication frequency band adjustment range information, and antenna direction adjustment range information; the target monitoring signal characterization information includes target signal strength information, target signal-to-noise ratio information, and target bit error rate information.

[0036] Among them, both the initial signal strength information and the initial signal-to-noise ratio information can be measured in real time through the RSSI module built in the receiver of the sensor. The RSSI module refers to the signal strength indication module. Specifically, when the receiver of the sensor for water volume monitoring receives a wireless signal, the RSSI module will convert the received signal power into a corresponding value, and this value is used to represent the signal strength. When the RSSI module does not receive a valid signal, the received background noise power will be obtained, and then the signal-to-noise ratio information can be automatically calculated based on the received signal power and the background noise power. It can be understood that the RSSI module will be affected by various environments in the actual water conservancy environment, such as multipath fading, obstacle occlusion, etc. Therefore, the environmental changes in the water conservancy project can be reflected through the measurement and calculation results. The initial bit error rate information can be obtained by automatically sending data measurement through the built-in receiving end and sending end of the sensor. Specifically, in actual applications, at regular intervals, the sending end will send a known test data sequence. When the receiving end receives this test data sequence, the received data will be compared with the original sent data sequence. By counting the number of incorrect bits and dividing by the total number of bits, the bit error rate information can be obtained, so as to measure the bit error rate information of the wireless signal in real time during the water volume monitoring process. For the transmit power range information, the communication frequency band adjustment range information, and the antenna direction adjustment range information, the transmit power range information can be obtained from the sensor product manual, the communication frequency band adjustment range information can be determined through the communication standard and the available frequency band of the environment where the water conservancy project is located, and the antenna direction adjustment range information can be obtained from the sensor product manual and is usually related to the radiation pattern of the antenna set by the sensor. It can be understood that different types of antennas have different radiation direction diagrams. By determining the directions and ranges of the main lobe and side lobes and the distribution of the water conservancy project monitoring points, the antenna direction adjustment range can be calculated and input into the computer to complete the acquisition work. The initial signal strength information, the initial signal-to-noise ratio information, and the initial bit error rate information can refer to the signal strength, signal-to-noise ratio, and bit error rate before the adjustment of the transmit power, communication frequency band, and antenna direction of the sensor; the target signal strength information, the target signal-to-noise ratio information, and the target bit error rate information can refer to the signal strength information, signal-to-noise ratio information, and bit error rate information obtained after the adjustment of the transmit power information, communication frequency band information, and antenna direction information of the sensor.

[0037] Step S102: Generate an initial monitoring signal state space vector and multiple signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment range information.

[0038] In this embodiment, it may be that multiple initial monitoring signal characterization information is first normalized, that is, all values are mapped to a unified interval to eliminate the dimensional differences of information data. Then, the normalized values are arranged in a specific order to form a multi-dimensional space vector, that is, the initial monitoring signal state space vector, which is used to characterize the state characteristics of the wireless signal currently used for water volume monitoring. It may be that multiple wireless parameter adjustment interval information is first discretized. Starting from the minimum value in the interval, it is incremented with a fixed data adjustment increment to form multiple adjustment level sets. Each set represents the increment that a selectable adjustment action can form. Multiple signal state adjustment space vectors are generated through multiple sets for subsequent selection and adjustment of sensor wireless parameters.

[0039] In the embodiment, preferably, for the signal transmission power interval information in the wireless parameter adjustment interval information, it may be that starting from the minimum power, it is incremented with a fixed power increment to form a power level set, and each power level can be used to represent a selectable transmission power action; for the communication frequency band adjustment interval information in the wireless parameter adjustment interval information, considering the regulatory standards and electromagnetic environment of the water conservancy environment location, it may be that the available frequency band range is first clarified, and then the anti-interference situation of each frequency band is measured through tools such as a spectrum analyzer. Then, the available frequency band is divided into multiple available sub-bands, and each sub-band corresponds to an action option to form a frequency band set; for the antenna direction adjustment interval information in the wireless parameter adjustment interval information, the antenna type and radiation pattern in the sensor can be first clarified by referring to the product manual of the sensor device, and combined with the distribution of water conservancy monitoring points, the different angles that can effectively cover the monitoring area are calculated, and these angles are discretized at a certain angle interval to obtain an antenna direction set. Then, the sets of the three dimensions of signal transmission power, communication frequency band, and antenna direction can be combined to obtain a signal state adjustment space vector.

[0040] Step S103, according to the preset selection decision probability, select multiple signal state adjustment space vectors to generate a signal state adjustment action vector.

[0041] In this embodiment, the preset selection decision probability can be set manually and can take the value of 0.1. It can be used to extract from multiple signal state adjustment space vectors according to the preset selection decision probability. If the number of signal state adjustment space vectors is large, multiple extractions are performed according to this preset selection decision probability until a signal state adjustment space vector is selected as the signal state adjustment action vector. It can also be to first randomly generate a random number within the range of (0, 1), and then compare this random number with the preset selection decision probability. When the random number is less than the preset selection decision probability, a signal state adjustment space vector is randomly selected as the signal state adjustment action vector; when the random number is greater than or equal to the preset selection decision probability, the one with the largest adjustment amplitude in the signal state adjustment space vectors can be selected as the signal state adjustment action vector, which is used to adjust the wireless parameters of the sensor in water volume monitoring.

[0042] In this embodiment, preferably, each element in the signal state adjustment space vector is a triple, which respectively represents the transmit power value range, the communication frequency band value range, and the antenna direction value range. When one of the multiple signal state adjustment space vectors is selected as the signal state adjustment action vector and an action needs to be taken to adjust the wireless parameters of the sensor, the transmit power of the sensor can be adjusted to the transmit power value range corresponding to the signal state adjustment action vector, the communication frequency band of the sensor can be adjusted to the communication frequency band value range corresponding to the signal state adjustment action vector, and the antenna direction of the sensor can be adjusted to the antenna direction value range corresponding to the signal state adjustment action vector, so as to achieve the purpose of optimizing the wireless signal quality of the sensor for water volume monitoring.

[0043] Step S104, generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector, and the preset signal quality characterization quantity threshold.

[0044] In this embodiment, the preset signal quality characterization quantity threshold may be an error rate threshold, which is used to determine whether the error rate of the wireless signal for water volume monitoring meets the standard after the wireless parameters of the sensor are adjusted, so as to ensure the real-time and accuracy of water volume monitoring. First, the wireless parameters of the sensor in water volume monitoring are adjusted according to the adjustment amount in the action vector according to the signal state. Then, the characterization information of the adjusted monitoring signal is measured, and it is judged whether the characterization information of the adjusted monitoring signal has changed compared with the value in the initial monitoring signal state space vector. When there is no change, a signal state adjustment action vector needs to be reselected for wireless parameter adjustment. When there is a change, the error rate information can be extracted and compared with the preset signal quality characterization quantity threshold. When the error rate information is less than the preset signal quality characterization quantity threshold, it indicates that the adjustment is effective and the adjustment does not need to be continued. When the error rate information is greater than or equal to the preset signal quality characterization quantity threshold, it indicates that the adjustment does not make the wireless signal for water volume monitoring meet the standard, and a signal state adjustment action vector needs to be reselected for the wireless parameter adjustment of the sensor, so as to ensure the continuity and stability of the wireless communication signal for water volume monitoring.

[0045] Step S105: Measure and transmit the water volume information according to the target monitoring signal characterization information to generate water volume monitoring information.

[0046] In this embodiment, the water volume information is measured by the wireless signal of the sensor after wireless parameter adjustment, and the measured water volume information is transmitted. The information received by the receiving terminal after transmission can be converted into a visual form to generate water volume monitoring information. The water volume monitoring information can be water level monitoring information or water flow velocity monitoring information, and can be directly measured and transmitted by various sensors.

[0047] Compared with the problem that in the prior art, the sensor uses fixed wireless parameters for water volume monitoring and it is difficult to adapt to the complex and changing water conservancy environment at all times, the water volume monitoring method provided by the embodiment of the present application can automatically adjust the wireless parameters of the sensor by real-time sensing the state of the wireless signal for monitoring water volume, adaptively optimize the wireless signal quality, improve the anti-interference ability of the wireless signal in water volume monitoring under different water conservancy scenarios and environmental changes, ensure the stability of the water volume monitoring signal, reduce data transmission errors, ensure the timeliness and accuracy of the water volume monitoring information, and provide effective data support for water volume analysis and research work.

[0048] Figure 2 The implementation flowchart of the water volume monitoring method provided by the second embodiment of the present application is shown. The difference from the first embodiment above is that the step S103 specifically includes:

[0049] Step S201: Randomly generate a decision value.

[0050] In this embodiment, a value can be randomly generated within the interval (0, 1) as the decision value, which is used to determine how to select the signal state adjustment space vector in the subsequent process, so as to avoid prematurely falling into a local optimal solution and make the selection result closer to the global optimal solution. It can be understood that by randomly selecting the signal state adjustment space vector with a certain probability, it can be used to avoid the parameter adjustment of the water volume monitoring sensor relying solely on the bit error rate result and falling into a local optimal solution, thereby improving the effectiveness of the wireless parameter adjustment of the water volume monitoring sensor.

[0051] Step S202: Determine whether the decision value is less than the preset selection decision probability; if so, proceed to step S203; if not, proceed to step S204.

[0052] In this embodiment, when the decision value is less than the preset selection decision probability, the signal state adjustment space vector can be randomly selected to obtain the signal state adjustment action vector, which can enable the calculation process to discover combinations of transmit power, communication frequency band, and antenna direction that have not been tried before, avoiding the calculation process from prematurely falling into a local optimal solution. These randomly selected combinations may result in a lower bit error rate after adjustment, thereby bringing higher communication quality. When the decision value is greater than or equal to the preset selection decision probability, it can be to first calculate to what value the bit error rate information in the monitoring signal characterization information can be adjusted for each signal state adjustment space vector, and select the signal state adjustment space vector that can minimize the bit error rate in all signal state adjustment space vectors as the signal state adjustment action vector for taking adjustment actions on the sensor parameters in water volume monitoring.

[0053] Step S203: Randomly select the signal state adjustment space vector to generate the signal state adjustment action vector.

[0054] In this embodiment, when the decision value is less than the preset selection decision probability, the signal state adjustment space vector can be randomly selected to obtain the signal state adjustment action vector, which can enable the calculation process to discover combinations of transmit power, communication frequency band, and antenna direction that have not been tried before, avoiding the calculation process from prematurely falling into a local optimal solution. These randomly selected combinations may result in a lower bit error rate after adjustment, thereby bringing higher communication quality.

[0055] Step S204: Obtain the adjusted state characterization vector according to the initial monitoring signal state space vector, the preset weight matrix, the preset offset matrix, and the preset space mapping function.

[0056] In this embodiment, the preset weight matrix, the preset offset matrix, and the preset space mapping function can all be set manually. Among them, the preset space mapping function can be designed based on the exponential function or the hyperbolic tangent function. When the decision value is greater than or equal to the preset selection decision probability, first, the possible adjustment amounts of the representation information of each monitoring signal can be calculated. It can be directly calculated that when the bit error rate information in the representation information of the monitoring signal reaches the bit error rate standard, the possible adjustment amounts of the other representation information of the monitoring signal except the bit error rate information, and the selection of the signal state adjustment space vector is made according to the adjustment amount. Specifically, because the representation information of the monitoring signal changes non-linearly during the adjustment of the wireless parameters of the sensor, the prediction calculation of the change situation can be carried out by mapping the initial monitoring signal state space vector into the non-linear space. The preset weight matrix is used to increase the difference of each numerical feature in the initial monitoring signal state space vector, and the preset offset matrix is used to further increase the difference of each numerical feature in the initial monitoring signal state space vector in the non-linear space. The initial monitoring signal state space vector after being transformed by the preset weight matrix and the preset offset matrix is mapped into the non-linear space through the preset space mapping function, so as to fully analyze and calculate the non-linear features in the initial monitoring signal representation information. The adjusted state representation vector is obtained through the prediction calculation and is used to select the signal state adjustment space vector through the adjusted state representation vector in the subsequent process.

[0057] Step S205: Calculate the monitoring signal parameter adjustment amount information according to the initial monitoring signal state space vector and the adjusted state representation vector.

[0058] In this embodiment, the monitoring signal parameter adjustment amount information can be obtained by finding the difference between each value in the initial monitoring signal state space vector and the adjusted state representation vector.

[0059] Step S206: Perform a matching process on the monitoring signal parameter adjustment amount information and the signal state adjustment space vector to generate a signal state adjustment action vector.

[0060] In this embodiment, when performing the matching process on the monitoring signal parameter adjustment amount information and the signal state adjustment space vector, the values of each signal state adjustment information in the historical adjustment record can be consulted, and the magnitudes of the signal parameter adjustment amounts that can be brought by consulting each combination of the wireless parameter adjustment amounts of the sensors can be found, so as to select the magnitude of the signal parameter adjustment amount corresponding to the monitoring signal parameter adjustment amount, and match the values in the corresponding combination of the wireless parameter adjustment amounts of the sensors with the values in the signal state adjustment space vector, so as to select the signal state adjustment space vector as the signal state adjustment action vector.

[0061] The water volume monitoring method provided by the embodiments of the present application randomly selects the signal state adjustment space vector with a certain probability, avoiding the parameter adjustment of the water volume monitoring sensor relying solely on the bit error rate result and falling into a local optimal solution, thereby promoting the full development of the wireless parameter adjustment range of the sensor. Moreover, without additionally adding an algorithm to optimize the adjustment amount, it can explore the improvement space of the wireless communication signal quality in water volume monitoring, reduce the software development cost, and at the same time improve the flexibility of the adjustment space of the wireless parameters of the sensor, enhance the adaptability of the sensor to different water conservancy environments, improve the real-time performance and accuracy of water volume monitoring, ensure the effectiveness and reliability of water volume monitoring information, and provide effective data support for the regulation and decision-making operations in water conservancy projects.

[0062] Figure 3 The implementation flowchart of the water volume monitoring method provided by Embodiment 3 of the present application is shown, and its difference from Embodiment 2 above is that: Step S204 specifically includes:

[0063] Step S301, calculate the initial monitoring signal state transformation matrix according to the initial monitoring signal state space vector, the preset weight matrix, and the preset offset matrix.

[0064] In this embodiment, it may be to first multiply the initial monitoring signal state space vector by the preset weight matrix, and then add the multiplication result to the preset offset matrix, and the obtained addition result is output as the initial monitoring signal state transformation matrix for subsequent deep analysis by mapping it into a high-dimensional non-linear space through a preset space mapping function.

[0065] Step S302, calculate the initial monitoring signal state mapping matrix according to the initial monitoring signal state transformation matrix and the preset space mapping function.

[0066] In this embodiment, it may be to use the initial monitoring signal state transformation matrix as the independent variable of the space mapping function, and the obtained function value is used as the initial monitoring signal state mapping matrix.

[0067] Step S303, extract the bit error rate information in the initial monitoring signal state mapping matrix.

[0068] In this embodiment, it may be to first extract each value in the initial monitoring signal state mapping matrix, then screen the values representing the bit error rate, and convert them into the dimension range where the bit error rate information is located, so as to obtain the bit error rate information.

[0069] Step S304, determine whether the bit error rate information is less than or equal to the preset bit error rate threshold; if so, go to Step S305; if not, go to Step S306.

[0070] In this embodiment, the preset error rate threshold can be set manually, and can be set with reference to the existing implementation standards in the field of wireless communication, and is used to determine whether the error rate of the wireless communication signal currently used for water volume monitoring in the water conservancy project meets the standard. When the error rate information is less than or equal to the preset error rate threshold, it indicates that the wireless communication quality of the currently calculated monitoring signal is good, and minor adjustments or no adjustments can be made based on the currently calculated monitoring signal. This can be achieved by adding a small adjustment amount or adjustment amount to each value based on the currently calculated monitoring signal, so as to obtain the adjusted state representation vector. When the error rate information is greater than the preset error rate threshold, it indicates that the wireless communication quality of the currently calculated monitoring signal is poor, and predictive calculations need to be continued based on the monitoring signal obtained from the current calculation results until a monitoring signal state with a qualified error rate is predicted. Only then can the signal state adjustment space vector be selected according to the prediction results.

[0071] Step S305: Obtain the adjusted state representation vector according to the initial monitoring signal state mapping matrix.

[0072] In this embodiment, when the error rate information is less than or equal to the preset error rate threshold, it indicates that the wireless communication quality of the currently calculated monitoring signal is good, and minor adjustments or no adjustments can be made based on the currently calculated monitoring signal. This can be achieved by adding a small adjustment amount or adjustment amount to each value based on the currently calculated monitoring signal, so as to obtain the adjusted state representation vector.

[0073] Step S306: Use the initial monitoring signal state mapping matrix as the initial monitoring signal state space vector, and return to step S301.

[0074] In this embodiment, when the error rate information is greater than the preset error rate threshold, it indicates that the wireless communication quality of the currently calculated monitoring signal is poor, and predictive calculations need to be continued based on the monitoring signal obtained from the current calculation results until a monitoring signal state with a qualified error rate is predicted. Only then can the signal state adjustment space vector be selected according to the prediction results. And the continued predictive calculation is performed by using the initial monitoring signal state mapping matrix as the initial monitoring signal state space vector, and returning to the step of calculating with the weight matrix and the offset matrix to perform continuous non-linear space mapping calculations, continuously capturing and analyzing non-linear numerical features, so as to obtain the prediction results.

[0075] The water volume monitoring method provided by the embodiment of the present application automatically extracts the non-linear features of signal strength, signal-to-noise ratio, and bit error rate from a complex and changeable water conservancy environment through a preset weight matrix and offset matrix, and performs transformation calculations on the non-linear features through a preset space mapping function, so as to calculate the possible numerical changes of the signal strength, signal-to-noise ratio, and bit error rate in the current water conservancy environment, and realize the in-depth analysis of the complex mapping relationship between the state information such as signal strength, signal-to-noise ratio, and bit error rate and the action information such as transmission power, communication frequency band, and antenna direction. Thus, the combination information of the wireless device parameter adjustment amount that can quickly reduce the bit error rate in the current water conservancy environment is automatically selected to timely adjust the wireless parameters of the sensor, without the need for manual re-debugging and control of the wireless parameters of the sensor, enabling the sensor to quickly adapt to the new water conservancy environment, reducing the manual labor amount, and effectively ensuring the stability and accuracy of the wireless signal for water volume monitoring in the current water conservancy environment.

[0076] Figure 4 The implementation flowchart of the water volume monitoring method provided by the fourth embodiment of the present application is shown, and its difference from the first embodiment is as follows: Step S104 specifically includes:

[0077] Step S401: Generate an intermediate monitoring signal state vector according to the initial monitoring signal state vector and the signal state adjustment action vector.

[0078] In this embodiment, it may be that based on the initial monitoring signal characterization information represented by the initial monitoring signal state vector, after taking the adjustment amount in the signal state adjustment action vector, the monitoring signal characterization information changes, and the value of the changed monitoring signal characterization information is used to generate the intermediate monitoring signal state vector.

[0079] Step S402: Calculate an intermediate monitoring signal quality characterization variable according to the intermediate monitoring signal state vector.

[0080] In this embodiment, it may be to extract the bit error rate information in the intermediate monitoring signal state vector, take the reciprocal of the bit error rate information, and the processed result is used as the intermediate monitoring signal quality characterization variable.

[0081] Step S403: Determine whether the intermediate monitoring signal quality characterization variable is greater than or equal to a preset signal quality characterization threshold; if so, go to Step S404; if not, go to Step S405.

[0082] In this embodiment, the preset signal quality characterization quantity threshold can be set manually. When the intermediate monitored signal quality characterization variable is greater than or equal to the preset signal quality characterization quantity threshold, it indicates that after the sensor adjusts the value in the action vector according to the signal state for wireless parameter adjustment, the wireless communication signal quality for water volume monitoring work is good, can adapt to the current water conservancy environment, and can effectively measure and transmit water volume monitoring information in the current water conservancy environment. Then, the monitoring signal characterization information of the current sensor is used as the target monitoring signal characterization information for measuring and transmitting water volume information.

[0083] When the intermediate monitored signal quality characterization variable is less than the preset signal quality characterization quantity threshold, it indicates that after the current sensor adjusts the value in the action vector according to the signal state for wireless parameter adjustment, the wireless communication signal quality for water volume monitoring work is still poor, does not adapt to the current water conservancy environment, and there are still situations of easy delay or interruption for the water volume monitoring signal in the current water conservancy environment. Then, it is necessary to select the signal state adjustment space vector again to adjust the wireless parameters of the sensor again.

[0084] Step S404: Generate a plurality of target monitoring signal characterization information according to the intermediate monitored signal state vector.

[0085] In this embodiment, when the intermediate monitored signal quality characterization variable is greater than or equal to the preset signal quality characterization quantity threshold, it indicates that after the sensor adjusts the value in the action vector according to the signal state for wireless parameter adjustment, the wireless communication signal quality for water volume monitoring work is good, can adapt to the current water conservancy environment, and can effectively measure and transmit water volume monitoring information in the current water conservancy environment. Then, the monitoring signal characterization information of the current sensor is used as the target monitoring signal characterization information for measuring and transmitting water volume information.

[0086] Step S405: Take the intermediate monitored signal state vector as the initial monitored signal state vector and return to the step S103.

[0087] In this embodiment, when the intermediate monitored signal quality characterization variable is less than the preset signal quality characterization quantity threshold, it indicates that after the current sensor adjusts the value in the action vector according to the signal state for wireless parameter adjustment, the wireless communication signal quality for water volume monitoring work is still poor, does not adapt to the current water conservancy environment, and there are still situations of easy delay or interruption for the water volume monitoring signal in the current water conservancy environment. Then, it is necessary to select the signal state adjustment space vector again to adjust the wireless parameters of the sensor again.

[0088] The water volume monitoring method provided by the embodiments of the present application adjusts the wireless parameters of the sensors for water volume monitoring in a water conservancy project by selecting signal state adjustment spatial vectors multiple times, improves the anti-interference ability of the wireless communication signal for water volume monitoring, enables the sensors for water volume monitoring to adapt to the changing water conservancy environment such as weather and surrounding interference, continuously optimizes the signal quality, ensures the stable and reliable transmission of water volume monitoring information, and improves the adaptability and robustness of water volume monitoring in the complex and changeable water conservancy environment.

[0089] Figure 5 The implementation flowchart of the water volume monitoring method provided in the fifth embodiment of the present application is shown, and its difference from the first embodiment above is that:

[0090] The water volume monitoring information includes water level height monitoring information and water flow velocity monitoring information;

[0091] After the step S105, it further includes:

[0092] Step S501, generating a water volume monitoring vector according to the water level height monitoring information and the water flow velocity monitoring information.

[0093] In this embodiment, the water level height monitoring information can be measured and transmitted through a water level height measurement sensor. The water level height measurement sensor can be a pressure type water level sensor, an ultrasonic water level sensor, or a radar water level sensor. The water flow velocity monitoring information can be measured and transmitted through an electromagnetic current meter or through a Doppler current meter. It can be to first obtain the cross-sectional area of the water passage according to the hydraulics principle, and multiply the cross-sectional area of the water passage by the water flow velocity monitoring information to obtain the water flow rate information. It can also be to first map the water level height monitoring information, the water flow velocity monitoring information, and the water flow rate information to the [0,1] interval for normalization processing, and then combine the normalized information to generate a water volume monitoring vector.

[0094] Step S502, obtaining water volume monitoring characteristic variables according to the water volume monitoring vector and a preset characteristic weight matrix.

[0095] In this embodiment, the preset characteristic weight matrix can be set manually. It can be to first multiply the water volume monitoring vector by the preset characteristic weight matrix to obtain the water volume monitoring characteristic variables.

[0096] Step S503, calculating water volume prediction information according to the water volume monitoring characteristic variables, a preset prediction weight matrix, a preset prediction bias matrix, and a preset prediction space transformation matrix.

[0097] In this embodiment, the preset prediction bias matrix can be set manually, and the preset prediction space transformation matrix can be set manually, both of which are used to perform analytical transformation operations on the water volume monitoring characteristic variables. It can be to first multiply the water volume monitoring characteristic variables by the preset prediction weight matrix, then add the multiplication result to the preset prediction bias matrix, and then multiply the addition result by the preset prediction space transformation matrix, and the calculation result is output as the water volume prediction information.

[0098] The water volume monitoring method provided by the embodiment of the present application effectively extracts data features in the water volume monitoring information through the preset feature weight matrix, and performs multi-dimensional transformation on the data features in the extracted water volume monitoring information through the preset prediction bias matrix and the preset prediction space transformation matrix, which can capture complex non-linear relationships between monitoring information such as water level and flow velocity, thereby improving the accuracy of prediction calculation. Through the water volume prediction information, the change trend of the water volume can be estimated in advance to provide early warning for sudden disasters such as floods or droughts, and at the same time provide strong data support for the construction or scheduling of water conservancy facilities.

[0099] Figure 6 The flowchart showing the implementation of the water volume monitoring method provided in the sixth embodiment of the present application is different from the fifth embodiment above in that:

[0100] The preset feature weight matrix includes a preset query feature weight matrix, a preset key feature weight matrix, and a preset value feature weight matrix;

[0101] The step S502 specifically includes:

[0102] Step S601, according to the water volume monitoring vector, the preset query feature weight matrix, the preset key feature weight matrix, and the preset value feature weight matrix, obtain the water volume information query feature variable, the water volume information key feature variable, and the water volume information value feature variable.

[0103] In this embodiment, the preset query feature weight matrix, the preset key feature weight matrix, and the preset value feature weight matrix can be set manually. It can be to perform multiplication calculations on the water volume monitoring vector with the preset query feature weight matrix, the preset key feature weight matrix, and the preset value feature weight matrix respectively, and the multiplication results are used as the water volume information query feature variable, the water volume information key feature variable, and the water volume information value feature variable.

[0104] Step S602, perform a dot product operation on the water volume information query feature variable and the water volume information key feature variable to obtain a water volume information query key intermediate variable.

[0105] In this embodiment, it may be to perform a dot product operation on the water volume information query feature variable and the water volume information key feature variable, and use the calculation result as the intermediate variable of the water volume information query key, which serves as the intermediate variable for feature extraction of the water volume monitoring information and is used to output the feature information of the water volume monitoring information after subsequent non-linear transformation and spatial transformation.

[0106] Step S603: Perform a hyperbolic tangent transformation on the intermediate variable of the water volume information query key to obtain the spatial transformation information of the water volume information query key.

[0107] In this embodiment, it may be to use the intermediate variable of the water volume information query key as the independent variable of the hyperbolic tangent function, and through the hyperbolic tangent function, perform a non-linear transformation on the intermediate variable for feature extraction of the water volume monitoring information, and calculate the function value of the hyperbolic tangent function as the spatial transformation information of the water volume information query key.

[0108] Step S604: Perform a weighted sum on the water volume information value feature variable according to the spatial transformation information of the water volume information query key to obtain the water volume monitoring feature variable.

[0109] In this embodiment, it may be to use the spatial transformation information of the water volume information query key as the weight information for weighted summation, perform a weighted sum on the water volume information value feature variable, so as to realize the transformation of the intermediate variable for feature extraction of the captured water volume monitoring information in the non-linear space, and the calculation result after weighted summation is output as the water volume monitoring feature variable.

[0110] The water volume monitoring method provided by the embodiment of the present application captures the numerical features of self-correlation and long correlation in the water volume monitoring information through a plurality of preset feature weight matrices. Different feature weight matrices can respectively strengthen the features of water level, flow velocity or water flow, so that the important features in the water volume monitoring information are not missed. By performing a hyperbolic tangent transformation on the intermediate variable of the data feature, the multi-dimensional space scaling process of the data feature is realized, avoiding the data feature of the water volume monitoring information exceeding the effective dimension in the calculation process, thereby improving the accuracy and effectiveness of predicting the water volume monitoring information and providing effective data support for the scheduling of water conservancy facilities.

[0111] Figure 7 The flowchart showing the implementation of the water volume monitoring method provided by the seventh embodiment of the present application is different from that of the fifth embodiment above in that: step S503 specifically includes:

[0112] Step S701: Multiply the water volume monitoring feature variable and a preset prediction weight matrix to obtain a water volume feature enhancement variable.

[0113] In this embodiment, the water quantity monitoring characteristic variable and the preset prediction weight matrix can be multiplied, and the product result can be used as the water quantity characteristic enhancement variable to increase the logical distance between each numerical feature in the water quantity monitoring information, thereby enhancing the extracted water quantity monitoring characteristic variable.

[0114] Step S702: sum the water volume characteristic enhancement variable and the preset prediction bias matrix to obtain the water volume characteristic displacement variable.

[0115] In this embodiment, the water quantity characteristic enhancement variable can be summed with the preset prediction bias matrix, so as to further increase the logical distance between each numerical feature in the water quantity monitoring information on the basis of the water quantity characteristic enhancement variable, and the summation result is output as the water quantity characteristic displacement variable for further prediction calculation through the prediction space transformation matrix in the future.

[0116] Step S703, calculating water volume prediction information according to the water volume characteristic displacement variable and a preset prediction space transformation matrix.

[0117] In this embodiment, the preset prediction space transformation matrix is ​​used to scale the water volume characteristic displacement variable to within the effective dimension range. The water volume characteristic displacement variable and the prediction space transformation matrix can be multiplied multiple times to fully perform the space transformation operation, ensure that each numerical feature in the water volume monitoring information can be fully analyzed, and the final output calculation result is output as the water volume prediction information.

[0118] The water volume monitoring method provided in the embodiment of the present application accurately increases the logical distance between each numerical feature in the water volume monitoring information through a prediction weight matrix, and further increases the logical distance between each numerical feature through a prediction bias matrix, thereby ensuring that each data feature in the water volume monitoring information is fully captured, and then multiple transformation processes are performed on each data feature in the water volume monitoring information through a prediction space transformation matrix, so as to accurately map the complex relationship between factors such as water level information and flow rate information and water volume information, so as to predict the change of water volume in a dynamically changing water conservancy environment, gain precious time for decisions such as water conservancy scheduling, flood control and drought relief, and improve the efficiency, effectiveness and reliability of water conservancy management.

[0119] Corresponding to the method of the above embodiment, Figure 8 A structural block diagram of a water volume monitoring device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 8 The exemplary water volume monitoring device may be an execution body of the water volume monitoring method provided in the aforementioned first embodiment.

[0120] Reference Figure 8 , the water volume monitoring device comprises:

[0121] An information acquisition module 810, configured to acquire initial monitoring signal characterization information of multiple sensors and wireless parameter adjustment interval information;

[0122] A space vector generation module 820, configured to generate an initial monitoring signal state space vector and multiple signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information;

[0123] A signal state adjustment action vector generation module 830, configured to select multiple signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector;

[0124] A monitoring signal characterization information generation module 840, configured to generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector, and a preset signal quality characterization quantity threshold; and

[0125] A water volume monitoring information module 850, configured to measure and transmit process water volume information according to the target monitoring signal characterization information to generate water volume monitoring information.

[0126] For the process of each module in the water volume monitoring device provided in the embodiments of the present application to implement its respective functions, reference may be specifically made to the description of the foregoing Figure 1 Example 1 shown, which will not be elaborated here.

[0127] The method implemented by the water volume monitoring device provided in the embodiments of the present application aims at the problem that in the prior art, sensors use fixed wireless parameters for water volume monitoring and it is difficult to adapt to the complex and changing water conservancy environment at all times. By real-time sensing the state of the wireless signal for water volume monitoring, automatically adjusting the wireless parameters of the sensors, adaptively optimizing the wireless signal quality, enhancing the anti-interference ability of the wireless signal in water volume monitoring under different water conservancy scenarios and environmental changes, ensuring the stability of the water volume monitoring signal, reducing data transmission errors, and ensuring the timeliness and accuracy of the water volume monitoring information, providing effective data support for the water volume analysis and research work.

[0128] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0129] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0130] It should also be understood that the term "and / or" as used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0131] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0132] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text in some embodiments of this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first table can be named the second table, and similarly, the second table can be named the first table, without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0133] Reference to "an embodiment" or "some embodiments" or the like described in the specification of this application means that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0134] The water volume monitoring method provided by the embodiments of this application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The embodiments of this application do not impose any restrictions on the specific types of terminal devices.

[0135] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing devices connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a television set-top box (STB), a customer premise equipment (CPE), and / or other devices used for communication on a wireless system, as well as next-generation communication systems. For example, a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0136] By way of example and not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for devices that apply wearable technology to the intelligent design of daily wear and develop wearable devices, such as glasses, gloves, watches, clothing, and shoes, etc. A wearable device is a portable device that is either directly worn on the body or integrated into the user's clothes or accessories. A wearable device is not just a hardware device, but more importantly, it realizes powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can realize complete or partial functions without relying on a smartphone, such as smart watches or smart glasses, etc., and those that only focus on a certain type of application function and need to cooperate with other devices such as smartphones, such as various smart bracelets and smart jewelry for physical sign monitoring.

[0137] Figure 9It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As Figure 9 shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 only one is shown in the figure), a memory 91, and a computer program 92 that can run on the processor 90 is stored in the memory 91. When the processor 90 executes the computer program 92, the steps in the above-mentioned embodiments of each water volume monitoring method are implemented, for example Figure 1 the steps S101 to S105 shown. Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 8 the functions of the modules 810 to 850 shown.

[0138] The terminal device 9 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art can understand that Figure 9 this is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the terminal device may further include an input sending device, a network access device, a bus, etc.

[0139] The so-called processor 90 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0140] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as the hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk equipped on the terminal device 9, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 91 may also include both the internal storage unit and the external storage device of the terminal device 9. The memory 91 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory 91 may also be used to temporarily store data that has been sent or will be sent.

[0141] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist separately physically for each unit, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0142] An embodiment of the present application further provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, the terminal device implements the steps in any of the above method embodiments.

[0143] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in any of the above method embodiments can be implemented.

[0144] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is caused to execute the steps in any of the above method embodiments.

[0145] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of this application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0146] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] The above-mentioned embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A water volume monitoring method, characterized in that: include: Obtaining initial monitoring signal characterization information of multiple sensors and wireless parameter adjustment interval information; Generate an initial monitoring signal state space vector and a plurality of signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information; According to a preset selection decision probability, a plurality of the signal state adjustment space vectors are selected to generate a signal state adjustment action vector; Generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and a preset signal quality characterization threshold; According to the target monitoring signal representation information, the water volume information is measured and transmitted to generate water volume monitoring information; The step of generating target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and the preset signal quality characterization threshold specifically includes: Generating an intermediate monitoring signal state vector according to the initial monitoring signal state space vector and the signal state adjustment action vector; Calculating an intermediate monitoring signal quality characterization variable according to the intermediate monitoring signal state vector; Determine whether the intermediate monitoring signal quality characterization variable is greater than or equal to a preset signal quality characterization value threshold; If so, generating a plurality of target monitoring signal representation information according to the intermediate monitoring signal state vector; If not, the intermediate monitoring signal state vector is used as the initial monitoring signal state space vector, and the step of selecting a plurality of signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector is returned; The water volume monitoring information includes water level monitoring information and water flow speed monitoring information; After the step of measuring and transmitting the water volume information according to the target monitoring signal representation information to generate the water volume monitoring information, the method further includes: Generate a water volume monitoring vector according to the water level monitoring information and the water flow velocity monitoring information; Obtaining a water volume monitoring characteristic variable according to the water volume monitoring vector and a preset characteristic weight matrix; The water volume prediction information is calculated according to the water volume monitoring characteristic variables, a preset prediction weight matrix, a preset prediction bias matrix and a preset prediction space transformation matrix.

2. The water volume monitoring method according to claim 1, characterized in that: The initial monitoring signal characterization information includes initial signal strength information, initial signal-to-noise ratio information and initial bit error rate information; the wireless parameter adjustment interval information includes transmission power interval information, communication frequency band adjustment interval information and antenna direction adjustment interval information; the target monitoring signal characterization information includes target signal strength information, target signal-to-noise ratio information and target bit error rate information.

3. The water volume monitoring method according to claim 2, characterized in that: The step of selecting a plurality of signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector specifically includes: Randomly generate decision values; Determine whether the decision value is less than a preset selection decision probability; If yes, randomly select the signal state adjustment space vector to generate a signal state adjustment action vector; If not, obtaining an adjusted state representation vector according to the initial monitoring signal state space vector, a preset weight matrix, a preset offset matrix and a preset space mapping function; Calculating the monitoring signal parameter adjustment amount information according to the initial monitoring signal state space vector and the adjusted state representation vector; The monitoring signal parameter adjustment amount information is matched with the signal state adjustment space vector to generate a signal state adjustment action vector.

4. The water volume monitoring method according to claim 3, characterized in that: The step of obtaining the adjusted state representation vector according to the initial monitoring signal state space vector, a preset weight matrix, a preset offset matrix and a preset space mapping function specifically includes: Calculating an initial monitoring signal state transformation matrix according to the initial monitoring signal state space vector, a preset weight matrix and a preset offset matrix; Calculating an initial monitoring signal state mapping matrix according to the initial monitoring signal state transformation matrix and a preset spatial mapping function; Extracting bit error rate information from the initial monitoring signal state mapping matrix; Determining whether the bit error rate information is less than or equal to a preset bit error rate threshold; If yes, then the adjusted state representation vector is obtained according to the initial monitoring signal state mapping matrix; If not, the initial monitoring signal state mapping matrix is ​​used as the initial monitoring signal state space vector, and the step of calculating the initial monitoring signal state transformation matrix based on the initial monitoring signal state space vector, the preset weight matrix and the preset offset matrix is ​​returned.

5. The water volume monitoring method according to claim 1, characterized in that: The preset feature weight matrix includes a preset query feature weight matrix, a preset key feature weight matrix, and a preset value feature weight matrix; The step of obtaining the water volume monitoring characteristic variable according to the water volume monitoring vector and the preset characteristic weight matrix specifically includes: According to the water quantity monitoring vector, a preset query feature weight matrix, a preset key feature weight matrix and a preset value feature weight matrix, a water quantity information query feature variable, a water quantity information key feature variable and a water quantity information value feature variable are obtained; Performing a dot product operation on the water quantity information query characteristic variable and the water quantity information key characteristic variable to obtain a water quantity information query key intermediate variable; Performing a hyperbolic tangent transformation on the water quantity information query key intermediate variable to obtain water quantity information query key space transformation information; According to the water volume information query key space transformation information, weighted summation is performed on the water volume information value characteristic variables to obtain water volume monitoring characteristic variables.

6. The water volume monitoring method according to claim 1, characterized in that: The step of calculating the water volume prediction information according to the water volume monitoring characteristic variables, a preset prediction weight matrix, a preset prediction bias matrix, and a preset prediction space transformation matrix specifically includes: The water quantity monitoring characteristic variable and the preset prediction weight matrix are multiplied to obtain the water quantity characteristic enhancement variable; The water quantity characteristic enhancement variable and the preset prediction bias matrix are summed to obtain the water quantity characteristic displacement variable; The water volume prediction information is calculated according to the water volume characteristic displacement variable and a preset prediction space transformation matrix.

7. A water volume monitoring device, characterized in that: include: An information acquisition module, used to obtain initial monitoring signal representation information of multiple sensors and wireless parameter adjustment interval information; A space vector generation module, used to generate an initial monitoring signal state space vector and a plurality of signal state adjustment space vectors according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information; A signal state adjustment action vector generation module, used to select a plurality of signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector; A monitoring signal characterization information generation module, used to generate target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and a preset signal quality characterization threshold; as well as A water volume monitoring information module is used to measure and transmit water volume information according to the target monitoring signal representation information to generate water volume monitoring information; The step of generating target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and the preset signal quality characterization threshold specifically includes: Generating an intermediate monitoring signal state vector according to the initial monitoring signal state space vector and the signal state adjustment action vector; Calculating an intermediate monitoring signal quality characterization variable according to the intermediate monitoring signal state vector; Determine whether the intermediate monitoring signal quality characterization variable is greater than or equal to a preset signal quality characterization value threshold; If so, generating a plurality of target monitoring signal representation information according to the intermediate monitoring signal state vector; If not, the intermediate monitoring signal state vector is used as the initial monitoring signal state space vector, and the step of selecting a plurality of signal state adjustment space vectors according to a preset selection decision probability to generate a signal state adjustment action vector is returned; The water volume monitoring information includes water level monitoring information and water flow speed monitoring information; After the step of measuring and transmitting the water volume information according to the target monitoring signal representation information to generate the water volume monitoring information, the method further includes: Generate a water volume monitoring vector according to the water level monitoring information and the water flow velocity monitoring information; Obtaining a water volume monitoring characteristic variable according to the water volume monitoring vector and a preset characteristic weight matrix; The water volume prediction information is calculated according to the water volume monitoring characteristic variables, a preset prediction weight matrix, a preset prediction bias matrix and a preset prediction space transformation matrix.

8. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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