Water volume monitoring method and device and terminal equipment

Abstract

The invention provides a water quantity monitoring method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining initial monitoring signal representation information and wireless parameter adjustment interval 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 interval information; selecting the signal state adjustment space vector, and generating 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 a signal quality characterization quantity threshold value; and according to the target monitoring signal characterization information, measuring and transmitting water quantity information, and generating water quantity monitoring information. The wireless signal quality is adaptively optimized, the adaptability of the sensor to a complex and changeable water conservancy environment is improved, the stability of a monitoring signal is guaranteed, and the timeliness and accuracy of water quantity monitoring information are ensured.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a water volume monitoring method, device and terminal equipment. Background Art

[0002] Water conservancy projects are crucial for the rational allocation of water resources, flood prevention 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, a scientific basis can be provided for water conservancy decision-making. With the development of the economy and 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 becoming higher and higher.

[0003] In the prior art, water level and water flow velocity are usually measured using water level meters, flow meters and other equipment, or water area and water level information is obtained through satellites or drones and other technologies, so as to estimate the water volume based on the obtained water area and water level information.

[0004] However, the water volume data collection and transmission using monitoring equipment in the existing technology may be delayed or interrupted, and cannot reflect the dynamic changes of water volume in a timely manner. In addition, the monitoring equipment often has poor adaptability to the complex and changeable water conservancy environment, and has obvious defects in the accuracy, real-time performance, and environmental adaptability of water volume monitoring. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a water volume monitoring method, device and terminal equipment, aiming to solve the problems in the prior art that data collection and transmission are prone to delays or interruptions during the water volume monitoring process, and the accuracy, real-time performance and environmental adaptability of water volume monitoring in a dynamically changing water environment are low.

[0006] A first aspect of an embodiment of the present application provides a water volume monitoring method, comprising: 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 and processed to generate water volume monitoring information.

[0007] A second aspect of an embodiment of the present application provides a water volume monitoring device, comprising: 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, 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 threshold; and The water volume monitoring information module is used to measure and transmit the water volume information according to the target monitoring signal representation information to generate water volume monitoring information.

[0008] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the steps of the water volume monitoring method described in the first aspect above are implemented.

[0009] Compared with the prior art, the beneficial effect of the embodiments of the present application is as follows: compared with the prior art in which sensors use fixed wireless parameters to monitor water volume, which is difficult to adapt to the complex and changing water conservancy environment, the present application senses the status of wireless signals used to monitor water volume in real time, automatically adjusts the wireless parameters of the sensor, adaptively optimizes the quality of wireless signals, improves the anti-interference ability of wireless signals in water volume monitoring under different water conservancy scenarios and environmental changes, ensures the stability of water volume monitoring signals, reduces data transmission errors, ensures the timeliness and accuracy of water volume monitoring information, and provides effective data support for water volume analysis and research. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 This is a schematic diagram of the implementation process of the water volume monitoring method provided in Example 1 of the present application; Figure 2This is a schematic diagram of the implementation process of the water volume monitoring method provided in Example 2 of the present application; Figure 3 This is a schematic diagram of the implementation flow of the water volume monitoring method provided in Example 3 of the present application; Figure 4 This is a schematic diagram of the implementation flow of the water volume monitoring method provided in Example 4 of the present application; Figure 5 This is a schematic diagram of the implementation process of the water volume monitoring method provided in Example 5 of the present application; Figure 6 This is a schematic diagram of the implementation flow of the water volume monitoring method provided in Example 6 of the present application; Figure 7 This is a schematic diagram of the implementation process of the water volume monitoring method provided in Example 7 of the present application; Figure 8 is a schematic diagram of the structure of a water volume monitoring device provided in an embodiment of the present application; Fig. 9 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0013] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0014] Figure 1 The following is a flowchart of the water volume monitoring method according to the first embodiment of the present application, which is described in detail as follows: Step S101: Acquire initial monitoring signal characterization information of multiple sensors and wireless parameter adjustment interval information.

[0015] In this embodiment, the initial monitoring signal characterization information of the sensor may refer to parameter information used to characterize the quality of the real-time wireless signal used for water volume monitoring, which may be obtained by real-time measurement through a built-in module of the sensor. The wireless parameter adjustment interval information may refer to the adjustment interval of the wireless parameters of the sensor used for water volume monitoring, which may be obtained from the product manual of the sensor or from the communication standard.

[0016] 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 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.

[0017] 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 into the sensor's receiver. The RSSI module refers to the signal strength indicator module. Specifically, when the receiver of the sensor used for water volume monitoring receives a wireless signal, the RSSI module will convert the received signal power into a corresponding numerical value, which is used to represent the signal strength. When the RSSI module does not receive a valid signal, it will receive the background noise power, and the signal-to-noise ratio information can be automatically calculated based on the power of the received signal and the background noise power. It can be understood that the RSSI module will be affected by various environmental factors in the actual water conservancy environment, such as multipath fading, obstacle obstruction, etc., so 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 measured by automatically sending data through the built-in receiving end and sending end of the sensor. Specifically, in actual applications, the sending end will send a known test data sequence at intervals. When the receiving end receives the test data sequence, the received data will be compared with the original sent data sequence. By counting the number of erroneous bits and dividing it by the total number of bits, the bit error rate information can be obtained, thereby measuring the bit error rate information of the wireless signal in the water volume monitoring process in real time. For the transmission power interval information, the communication frequency band adjustment interval information and the antenna direction adjustment interval information, the transmission power interval information can be obtained from the sensor product manual, the communication frequency band adjustment interval information can be determined by the communication standard and the available frequency band of the environment where the water conservancy project is located, and the antenna direction adjustment interval information can be obtained from the sensor product manual, which 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 patterns. By determining the direction and range of its main lobe and side lobe and the distribution of the monitoring points of the water conservancy project, the antenna direction adjustment interval can be converted and input into the computer, thereby completing the acquisition work. The initial signal strength information, initial signal-to-noise ratio information, and initial bit error rate information may refer to the signal strength, signal-to-noise ratio, and bit error rate before the sensor's transmit power, communication frequency band, and antenna direction are adjusted; the target signal strength information, target signal-to-noise ratio information, and target bit error rate information may refer to the signal strength information, signal-to-noise ratio information, and bit error rate information obtained after the sensor's transmit power information, communication frequency band information, and antenna direction information are adjusted.

[0018] Step S102: generating 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.

[0019] In this embodiment, the multiple initial monitoring signal characterization information may be normalized first, that is, all values ​​are mapped to a unified interval to eliminate the dimensional differences of the information data, and then the normalized values ​​are arranged in a specific order to form a multidimensional 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. The multiple wireless parameter adjustment interval information may be discretized first, starting from the minimum value in the interval, and increasing by a fixed data adjustment increment to form multiple adjustment level sets, each set represents an increment that can be formed by an optional adjustment behavior, and multiple signal state adjustment space vectors are generated through multiple sets for subsequent sensor wireless parameters to be selected and adjusted.

[0020] In an embodiment, preferably, for the signal transmission power interval information in the wireless parameter adjustment interval information, it can be started from the minimum power and increased with a fixed power increment to form a power level set, and each power level can be used to represent an optional transmission power action; for the communication frequency band adjustment interval information in the wireless parameter adjustment interval information, it is necessary to consider the regulatory standards and electromagnetic environment of the location of the water conservancy environment, and it can be firstly to clarify the available frequency band range, and then measure the anti-interference of each frequency band through tools such as spectrum analyzers, and then divide the available frequency band into multiple available sub-frequency bands, each sub-frequency band corresponds to an action option, and constitutes a frequency band set; for the antenna direction adjustment interval information in the wireless parameter adjustment interval information, it can be firstly to consult the product manual of the sensor equipment, clarify the antenna type and radiation pattern in the sensor, and combine the distribution of water conservancy monitoring points to calculate the different angles that can effectively cover the monitoring area, and discretize these angles according to a certain angle interval to obtain the antenna direction set. Then, the three-dimensional sets of signal transmission power, communication frequency band and antenna direction can be combined to obtain the signal state adjustment space vector.

[0021] Step S103 : selecting a plurality of the signal state adjustment space vectors according to a preset selection decision probability, and generating a signal state adjustment action vector.

[0022] In this embodiment, the preset selection decision probability can be set manually and can be set to 0.1. It can be that multiple signal state adjustment space vectors are extracted according to the preset selection decision probability. If the number of signal state adjustment space vectors is large, multiple extractions are performed according to the preset selection decision probability until a signal state adjustment space vector is selected as the signal state adjustment action vector. It can also be that a random number in the range of (0, 1) is randomly generated first, and then the random number is compared 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 vector can be selected as the signal state adjustment action vector for adjusting the wireless parameters of the sensor in water volume monitoring.

[0023] In this embodiment, preferably, each element in the signal state adjustment space vector is a triplet, respectively representing a transmission power numerical range, a communication frequency band numerical range and an antenna direction numerical range. One of the multiple signal state adjustment space vectors is selected as a signal state adjustment action vector. When action needs to be taken to adjust the wireless parameters of the sensor, the transmission power of the sensor can be adjusted to the transmission power numerical range corresponding to the signal state adjustment action vector, the communication frequency band of the sensor can be adjusted to the communication frequency band numerical range corresponding to the signal state adjustment action vector, and the antenna direction of the sensor can be adjusted to the antenna direction numerical range corresponding to the signal state adjustment action vector, thereby achieving the purpose of optimizing the wireless signal quality of the sensor used for water volume monitoring.

[0024] Step S104: generating 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.

[0025] In this embodiment, the preset signal quality characterization threshold may be a bit error rate threshold, which is used to determine whether the bit error rate of the wireless signal used 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. The wireless parameters of the sensor in water volume monitoring may be adjusted according to the adjustment amount in the signal state adjustment action vector, and then the adjusted monitoring signal characterization information is measured, and it is determined whether the adjusted monitoring signal characterization information has changed compared with the value in the initial monitoring signal state space vector. When there is no change, the signal state adjustment action vector needs to be reselected to adjust the wireless parameters. When there is a change, the bit error rate information may be extracted and compared with the preset signal quality characterization threshold. When the bit error rate information is less than the preset signal quality characterization threshold, it means that the adjustment is effective and the adjustment may not be continued. When the bit error rate information is greater than or equal to the preset signal quality characterization threshold, it means that the adjustment does not make the water volume monitoring wireless signal meet the standard, and the signal state adjustment action vector needs to be reselected to adjust the sensor wireless parameters, so as to ensure the continuity and stability of the wireless communication signal used for water volume monitoring.

[0026] Step S105, measuring and transmitting the water volume information according to the target monitoring signal representation information to generate water volume monitoring information.

[0027] In this embodiment, the water volume information is measured and transmitted through the wireless signal of the sensor after the wireless parameters are adjusted. The information received by the user terminal after the 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, which can be directly measured and transmitted by various sensors.

[0028] The water volume monitoring method provided in the embodiment of the present application is different from the prior art in which sensors use fixed wireless parameters to monitor water volume, which is difficult to adapt to the complex and changing water conservancy environment. The present application senses the status of wireless signals used to monitor water volume in real time, automatically adjusts the wireless parameters of the sensor, adaptively optimizes the quality of wireless signals, improves the anti-interference ability of wireless signals in water volume monitoring under different water conservancy scenarios and environmental changes, ensures the stability of water volume monitoring signals, reduces data transmission errors, ensures the timeliness and accuracy of water volume monitoring information, and provides effective data support for water volume analysis and research.

[0029] Figure 2 The flowchart of the water volume monitoring method provided in the second embodiment of the present application is shown. The difference between the second embodiment and the first embodiment is that the step S103 specifically includes: Step S201, randomly generate a decision value.

[0030] In this embodiment, a value can be randomly generated in the interval (0, 1) as a decision value, which is used to determine how to select the signal state adjustment space vector in the subsequent process, thereby avoiding falling into a local optimal solution too early and making the selection result closer to the global optimal solution. It can be understood that by setting a certain probability to randomly select the signal state adjustment space vector, it can be used to avoid falling into a local optimal solution for the parameter adjustment of the water volume monitoring sensor by relying solely on the bit error rate result, thereby improving the effectiveness of the wireless parameter adjustment of the water volume monitoring sensor.

[0031] Step S202, determining whether the decision value is less than a preset selection decision probability; if so, proceeding to step S203; if not, proceeding to step S204.

[0032] 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 find the previously untried combination of transmission power, communication frequency band and antenna direction, and avoid the calculation process from falling into the local optimal solution too early. These randomly selected combinations may make the adjusted bit error rate lower, thereby bringing higher communication quality. When the decision value is greater than or equal to the preset selection decision probability, it can be calculated first that the adjustment value corresponding to each signal state adjustment space vector can adjust the bit error rate information in the monitoring signal characterization information to what value, and select the signal state adjustment space vector signal state adjustment action vector that can make the bit error rate in the monitoring signal characterization information the lowest among all signal state adjustment space vectors, which is used to take adjustment actions on the sensor parameters in water volume monitoring.

[0033] Step S203: randomly select the signal state adjustment space vector to generate a signal state adjustment action vector.

[0034] 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. This allows the calculation process to discover combinations of transmission power, communication frequency band, and antenna direction that have not been tried before, thereby avoiding the calculation process from falling into a local optimal solution too early. These randomly selected combinations may make the adjusted bit error rate lower, thereby bringing higher communication quality.

[0035] Step S204: 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.

[0036] In this embodiment, the preset weight matrix, the preset offset matrix and the preset space mapping function can all be manually set, wherein the preset space mapping function can be designed based on an exponential function or a hyperbolic tangent function. When the decision value is greater than or equal to the preset selection decision probability, the possible adjustment amount of each monitoring signal characterization information can be calculated first, and the possible adjustment amount of other monitoring signal characterization information other than the bit error rate information can be directly calculated when the bit error rate information in the monitoring signal characterization information reaches the bit error rate standard, and the signal state adjustment space vector is selected according to the adjustment amount. Specifically, because the monitoring signal characterization information undergoes nonlinear changes during the sensor wireless parameter adjustment process, the initial monitoring signal state space vector can be mapped to the nonlinear space for predictive calculation of the changes, and the preset weight matrix is ​​used to increase the difference between the various numerical features in the initial monitoring signal state space vector. The preset offset matrix is ​​used to further increase the difference between the various numerical features in the initial monitoring signal state space vector in the nonlinear space, and the initial monitoring signal state space vector transformed by the preset weight matrix and the preset offset matrix is ​​mapped to the nonlinear space through a preset spatial mapping function, thereby achieving full analytical calculation of the nonlinear characteristics in the initial monitoring signal characterization information, and obtaining the adjusted state characterization vector through predictive calculation, which is used for subsequent selection of the signal state adjustment space vector through the adjusted state characterization vector.

[0037] 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.

[0038] In this embodiment, the monitoring signal parameter adjustment amount information may be obtained by calculating the difference between each value in the initial monitoring signal state space vector and the adjusted state representation vector.

[0039] Step S206: Match the monitoring signal parameter adjustment amount information with the signal state adjustment space vector to generate a signal state adjustment action vector.

[0040] In this embodiment, the monitoring signal parameter adjustment amount information and the signal state adjustment space vector are matched and processed by consulting the numerical values ​​of each signal state adjustment information in the historical adjustment record, and consulting the signal parameter adjustment amount sizes that can be brought about by each sensor wireless parameter adjustment amount combination, so as to select the signal parameter adjustment amount size corresponding to the monitoring signal parameter adjustment amount, and match the numerical value in the corresponding sensor wireless parameter combination adjustment amount combination with the numerical value in the signal state adjustment space vector, so as to select the signal state adjustment space vector as the signal state adjustment action vector.

[0041] The water volume monitoring method provided in the embodiment of the present application randomly selects the signal state adjustment space vector by setting a certain probability, thereby avoiding the parameter adjustment of the water volume monitoring sensor relying solely on the bit error rate result and falling into the local optimal solution, thereby enabling the wireless parameter adjustment range of the sensor to be fully developed. Furthermore, there is no need to add an additional algorithm to optimize the adjustment amount, so as to explore the room for improving the quality of wireless communication signals in water volume monitoring and reduce software development costs. At the same time, by improving the flexibility of the adjustment space of the sensor wireless parameters, the adaptability of the sensor to different water conservancy environments is improved, the real-time and accuracy of water volume monitoring is improved, the effectiveness and reliability of water volume monitoring information are ensured, and effective data support is provided for the regulation and decision-making operations in water conservancy projects.

[0042] Figure 3 The flowchart of the water volume monitoring method provided in the third embodiment of the present application is shown. The difference between the third embodiment and the second embodiment is that the step S204 specifically includes: Step S301, 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.

[0043] In this embodiment, the initial monitoring signal state space vector is first multiplied by a preset weight matrix, and then the multiplication result is added to a preset offset matrix. The calculated addition result is output as the initial monitoring signal state transformation matrix, which is then mapped to a high-dimensional nonlinear space through a preset spatial mapping function for deep analysis.

[0044] Step S302: Calculate an initial monitoring signal state mapping matrix according to the initial monitoring signal state transformation matrix and a preset space mapping function.

[0045] In this embodiment, the initial monitoring signal state transformation matrix may be used as an independent variable of the space mapping function, and the calculated function value may be used as the initial monitoring signal state mapping matrix.

[0046] Step S303: extracting bit error rate information from the initial monitoring signal state mapping matrix.

[0047] In this embodiment, each value in the initial monitoring signal state mapping matrix may be extracted first, and then the value representing the bit error rate may be screened and converted into a dimensional range where the bit error rate information is located, thereby obtaining the bit error rate information.

[0048] Step S304, determining whether the bit error rate information is less than or equal to a preset bit error rate threshold; if so, proceeding to step S305; if not, proceeding to step S306.

[0049] In this embodiment, the preset bit error rate threshold can be set manually, and can be set with reference to the existing standards in the field of wireless communication, and is used to determine whether the bit error rate of the wireless communication signal currently used for water volume monitoring in water conservancy projects meets the standard. When the bit error rate information is less than or equal to the preset bit error rate threshold, it means that the wireless communication quality of the currently calculated monitoring signal is good, and a small adjustment or no adjustment can be made on the basis of the currently calculated monitoring signal, which can be achieved by adding a small adjustment amount or an adjustment amount to each numerical value on the basis of the currently calculated monitoring signal, thereby obtaining an adjusted state characterization vector. When the bit error rate information is greater than the preset bit error rate threshold, it means that the wireless communication quality of the currently calculated monitoring signal is poor, and it is necessary to continue to perform prediction calculations on the basis of the monitoring signal obtained by the current calculation result, until the monitoring signal state with a bit error rate that meets the standard is predicted, and then the signal state adjustment space vector can be selected according to the prediction result.

[0050] Step S305: obtaining an adjusted state representation vector according to the initial monitoring signal state mapping matrix.

[0051] In this embodiment, when the bit error rate information is less than or equal to the preset bit error rate threshold, it means that the wireless communication quality of the currently calculated monitoring signal is good, and a smaller adjustment or no adjustment can be made based on the currently calculated monitoring signal. This can be achieved by adding a smaller adjustment amount or an adjustment amount to each numerical value based on the currently calculated monitoring signal, thereby obtaining an adjusted state characterization vector.

[0052] Step S306: Use the initial monitoring signal state mapping matrix as the initial monitoring signal state space vector, and return to step S301.

[0053] In this embodiment, when the bit error rate information is greater than the preset bit error rate threshold, it means that the wireless communication quality of the currently calculated monitoring signal is poor, and it is necessary to continue the prediction calculation based on the monitoring signal obtained by the current calculation result, until the monitoring signal state with the bit error rate reaching the standard is predicted, and then the signal state adjustment space vector can be selected according to the prediction result. The prediction calculation is continued by taking 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, so as to perform continuous nonlinear space mapping calculation, continuously capture and analyze nonlinear numerical features, and thus obtain the prediction result.

[0054] The water volume monitoring method provided in the embodiment of the present application automatically extracts the nonlinear characteristics 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 an offset matrix, and transforms and calculates the nonlinear characteristics through a preset spatial mapping function, thereby calculating the possible numerical changes of signal strength, signal-to-noise ratio, and bit error rate in the current water conservancy environment, and realizing in-depth analysis of the complex mapping relationship between state information such as signal strength, signal-to-noise ratio, bit error rate, and action information such as transmission power, communication frequency band, and antenna direction, thereby automatically selecting the wireless device parameter adjustment amount combination information that can quickly reduce the bit error rate in the current water conservancy environment, so as 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, so that the sensor can quickly adapt to the new water conservancy environment, reducing the amount of manual labor while effectively ensuring the stability and accuracy of the wireless signal for water volume monitoring in the current water conservancy environment.

[0055] Figure 4 The flowchart of the water volume monitoring method provided in the fourth embodiment of the present application is shown. The difference between the fourth embodiment and the first embodiment is that the step S104 specifically includes: Step S401: Generate an intermediate monitoring signal state vector according to the initial monitoring signal state vector and the signal state adjustment action vector.

[0056] In this embodiment, the monitoring signal characterization information may be changed after taking the adjustment amount in the signal state adjustment action vector based on the initial monitoring signal characterization information represented by the initial monitoring signal state vector, and the value of the changed monitoring signal characterization information is used to generate an intermediate monitoring signal state vector.

[0057] Step S402: Calculate an intermediate monitoring signal quality characterization variable according to the intermediate monitoring signal state vector.

[0058] In this embodiment, the bit error rate information in the intermediate monitoring signal state vector may be extracted, the bit error rate information is processed by taking the inverse, and the processed result is used as the intermediate monitoring signal quality characterization variable.

[0059] Step S403, determine whether the intermediate monitoring signal quality characterization variable is greater than or equal to a preset signal quality characterization value threshold; if so, proceed to step S404; if not, proceed to step S405.

[0060] In this embodiment, the preset signal quality characterization threshold can be set manually. When the intermediate monitoring signal quality characterization variable is greater than or equal to the preset signal quality characterization threshold, it means that after the sensor adjusts the wireless parameters according to the value in the action vector according to the signal state, the quality of the wireless communication signal used for water quantity monitoring is good, and it can adapt to the current water conservancy environment, and can achieve effective measurement and transmission of water quantity monitoring information under 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 the water quantity information.

[0061] When the intermediate monitoring signal quality characterization variable is less than the preset signal quality characterization threshold, it means that after the current sensor adjusts the wireless parameters according to the value in the signal state adjustment action vector, the quality of the wireless communication signal used for water volume monitoring is still poor and has not adapted to the current water conservancy environment. The water volume monitoring signal under the current water conservancy environment is still prone to delay or interruption. It is necessary to select the signal state adjustment space vector again to adjust the wireless parameters of the sensor again.

[0062] Step S404: Generate multiple target monitoring signal representation information according to the intermediate monitoring signal state vector.

[0063] In this embodiment, when the intermediate monitoring signal quality characterization variable is greater than or equal to the preset signal quality characterization threshold, it means that after the sensor adjusts the wireless parameters according to the value in the action vector according to the signal state, the wireless communication signal quality used for water volume monitoring is good and can adapt to the current water conservancy environment. It can realize the effective measurement and transmission of water volume monitoring information under the current water conservancy environment. The monitoring signal characterization information of the current sensor is used as the target monitoring signal characterization information for measuring and transmitting the water volume information.

[0064] Step S405: Use the intermediate monitoring signal state vector as the initial monitoring signal state vector, and return to step S103.

[0065] In this embodiment, when the intermediate monitoring signal quality characterization variable is less than the preset signal quality characterization value threshold, it means that after the current sensor adjusts the wireless parameters according to the value in the signal state adjustment action vector, the quality of the wireless communication signal used for water volume monitoring is still poor and has not adapted to the current water conservancy environment. The water volume monitoring signal under the current water conservancy environment is still prone to delay or interruption, and it is necessary to select the signal state adjustment space vector again to adjust the wireless parameters of the sensor again.

[0066] The water volume monitoring method provided in the embodiment of the present application adjusts the wireless parameters of the sensor used for water volume monitoring in the water conservancy project by selecting the signal state adjustment space vector multiple times, thereby improving the anti-interference ability of the wireless communication signal used for water volume monitoring, so that the sensor used for water volume monitoring can adapt to the water conservancy environment with constantly changing factors such as weather and surrounding interference, continuously optimize the signal quality, ensure the stable and reliable transmission of water volume monitoring information, and improve the adaptability and robustness of water volume monitoring in complex and changeable water conservancy environments.

[0067] Figure 5 The flowchart of the water volume monitoring method provided in the fifth embodiment of the present application is shown, which differs from the first embodiment described above in that: The water volume monitoring information includes water level monitoring information and water flow speed monitoring information; After step S105, the method further includes: Step S501, generating a water volume monitoring vector according to the water level monitoring information and the water flow velocity monitoring information.

[0068] In this embodiment, the water level height monitoring information can be measured and transmitted by a water level height measuring sensor, which can be a pressure 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 by an electromagnetic flow meter, or by a Doppler flow meter. The cross-sectional area of ​​the water flow can be obtained according to the hydraulic principle, and the water flow information can be obtained by multiplying the cross-sectional area of ​​the water flow with the water flow velocity monitoring information. The water level height monitoring information, the water flow velocity monitoring information, and the water flow information can be mapped to the [0,1] interval for normalization, and then the normalized information is combined to generate a water volume monitoring vector.

[0069] Step S502: obtaining a water volume monitoring characteristic variable according to the water volume monitoring vector and a preset characteristic weight matrix.

[0070] In this embodiment, the preset characteristic weight matrix may be artificially set, and the water volume monitoring vector may be multiplied by the preset characteristic weight matrix to obtain the water volume monitoring characteristic variable.

[0071] 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.

[0072] 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. The water volume monitoring characteristic variables can be first multiplied by the preset prediction weight matrix, and then the multiplication result is added to the preset prediction bias matrix, and then the addition result is multiplied by the preset prediction space transformation matrix, and the calculation result is output as the water volume prediction information.

[0073] The water volume monitoring method provided in the embodiment of the present application effectively extracts data features in the water volume monitoring information through a preset feature weight matrix, and performs multi-dimensional transformation on the data features in the extracted water volume monitoring information through a preset prediction bias matrix and a preset prediction space transformation matrix. It can capture the complex nonlinear relationship between monitoring information such as water level and flow rate, thereby improving the accuracy of prediction calculations. Through water volume prediction information, the trend of water volume changes can be estimated in advance, and early warnings can be given for sudden disasters such as floods or droughts. At the same time, strong data support is provided for the construction or scheduling of water conservancy facilities.

[0074] Figure 6 The flowchart of the water volume monitoring method provided in the sixth embodiment of the present application is shown, which differs from the fifth embodiment described above 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 S502 specifically includes: Step S601, obtaining water quantity information query feature variables, water quantity information key feature variables and water quantity information value feature variables 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.

[0075] In this embodiment, the preset query feature weight matrix, the preset key feature weight matrix, and the preset value feature weight matrix can be manually set. The water volume monitoring vector can be multiplied with the preset query feature weight matrix, the preset key feature weight matrix, and the preset value feature weight matrix, respectively, to obtain the multiplication results as the water volume information query feature variable, the water volume information key feature variable, and the water volume information value feature variable.

[0076] Step S602: performing a dot product operation on the water volume information query characteristic variable and the water volume information key characteristic variable to obtain a water volume information query key intermediate variable.

[0077] In this embodiment, the dot product operation can be performed on the water quantity information query characteristic variable and the water quantity information key characteristic variable, and the calculation result can be used as the water quantity information query key intermediate variable and as the feature extraction intermediate variable of the water quantity monitoring information, which is used to output as the characteristic information of the water quantity monitoring information after subsequent nonlinear transformation and spatial transformation.

[0078] Step S603, performing a hyperbolic tangent transformation on the water volume information query key intermediate variable to obtain water volume information query key space transformation information.

[0079] In this embodiment, the water quantity information query key intermediate variable can be used as the independent variable of the hyperbolic tangent function, and the nonlinear transformation of the feature extraction intermediate variable of the water quantity monitoring information can be realized through the hyperbolic tangent function. The function value of the hyperbolic tangent function is calculated as the water quantity information query key space transformation information.

[0080] Step S604: performing weighted summation on the water volume information value characteristic variables according to the water volume information query key space transformation information to obtain water volume monitoring characteristic variables.

[0081] In this embodiment, the water quantity information query key space transformation information can be used as the weight information for weighted summation, and the water quantity information value characteristic variables can be weighted summed, thereby realizing the transformation of the feature extraction intermediate variables of the captured water quantity monitoring information in the nonlinear space, and the calculation result after the weighted summation is output as the water quantity monitoring characteristic variable.

[0082] The water volume monitoring method provided in the embodiment of the present application captures the autocorrelation and long-range correlation numerical characteristics in the water volume monitoring information through a plurality of preset feature weight matrices. Different feature weight matrices can respectively enhance the processing of water level or flow velocity or water flow characteristics, so that important features in the water volume monitoring information are not omitted. By performing a hyperbolic tangent transformation on the intermediate variables of the data features, multi-dimensional spatial scaling processing of the data features is achieved, and the data features of the water volume monitoring information are prevented from exceeding the effective dimension during the calculation process, thereby improving the accuracy and effectiveness of the prediction of the water volume monitoring information and providing effective data support for the scheduling of water conservancy facilities.

[0083] Figure 7 The flowchart of the water volume monitoring method provided in the seventh embodiment of the present application is shown. The difference between the seventh embodiment and the fifth embodiment is that the step S503 specifically includes: Step S701, multiplying the water volume monitoring characteristic variable and the preset prediction weight matrix to obtain the water volume characteristic enhancement variable.

[0084] 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.

[0085] Step S702: sum the water volume characteristic enhancement variable and the preset prediction bias matrix to obtain the water volume characteristic displacement variable.

[0086] 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.

[0087] Step S703, calculating water volume prediction information according to the water volume characteristic displacement variable and a preset prediction space transformation matrix.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] Reference Figure 8 , the water volume monitoring device comprises: The information acquisition module 810 is used to acquire initial monitoring signal representation information of multiple sensors and wireless parameter adjustment interval information; A space vector generating module 820, configured 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 generating module 830, configured to select a plurality of signal state adjustment space vectors according to a preset selection decision probability, and generate a signal state adjustment action vector; 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 threshold; and The water volume monitoring information module 850 is used to measure and transmit the water volume information according to the target monitoring signal representation information to generate water volume monitoring information.

[0092] The process of each module in the water volume monitoring device provided in the embodiment of the present application realizing its own function can be specifically referred to the aforementioned Figure 1 The description of the first embodiment is not repeated here.

[0093] The method implemented by the water volume monitoring device provided in the embodiment of the present application addresses the problem that the sensors in the prior art use fixed wireless parameters for water volume monitoring and are difficult to adapt to the complex and changing water conservancy environment. By real-time sensing of the status of the wireless signal used to monitor the water volume, the wireless parameters of the sensor are automatically adjusted, and the quality of the wireless signal is adaptively optimized. The anti-interference ability of the wireless signal in water volume monitoring under different water conservancy scenarios and environmental changes is improved, the stability of the water volume monitoring signal is guaranteed, the data transmission error is reduced, the timeliness and accuracy of the water volume monitoring information are ensured, and effective data support is provided for water volume analysis and research.

[0094] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0095] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of 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 combinations thereof.

[0096] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0097] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0098] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions, and cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as 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.

[0099] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0100] The water volume monitoring method provided in the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0101] 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 function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted 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 TV set top box (STB), a customer premises equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0102] As an example but not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0103] Fig. 9Schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Fig. 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Fig. 9 Only one is shown in the figure), a memory 91, wherein the memory 91 stores a computer program 92 that can be run on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-mentioned water volume monitoring method embodiments are implemented, such as Figure 1 Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 8 Functions of modules 810 to 850 are shown.

[0104] The terminal device 9 may be a computing device such as a desktop computer, a notebook, a PDA, or 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 will appreciate that Fig. 9 It 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 a combination of certain components, or different components. For example, the terminal device may also include an input sending device, a network access device, a bus, etc.

[0105] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0106] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a 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, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 9. Further, the memory 91 may also include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store an operating system, an application program, a boot loader (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 is to be sent.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] An embodiment of the present application also 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, wherein when the processor executes the computer program, the terminal device implements the steps in any of the above-mentioned method embodiments.

[0109] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0110] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0111] If 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, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0112] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] 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 may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present 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 and processed to generate water volume monitoring information.

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 step of generating target monitoring signal characterization information according to the initial monitoring signal state 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 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 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.

6. The water volume monitoring method according to claim 1, characterized in that: 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.

7. The water volume monitoring method according to claim 6, 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.

8. The water volume monitoring method according to claim 6, 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.

9. 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 The water volume monitoring information module is used to measure and transmit the water volume information according to the target monitoring signal representation information to generate water volume monitoring information.

10. 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 the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

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