Ensuring smooth functioning of system for monitoring and managing processes of cleaning bottom sediments in reservoirs

. This article discusses one way to ensure the system's functioning for monitoring and controlling the processes of cleaning bottom sediments in reservoirs. The reservoir's main elements and the system's architecture for monitoring and controlling the processes of cleaning bottom sediments as automated water-moving objects are given. The results of the analysis of the issues of ensuring the continuous operation of this system are given that to maintain a given level of system reliability, it is necessary to replace the failed measuring system with a serviceable one. The systems of differential equations are considered following that the recovery and loss processes are independent and depend on the External distribution rule with variable parameters.


Introduction
Currently, no doubt, water is one of the most crucial natural resources in the world.Its role is huge in many processes occurring in nature and ensuring human life.One-fifth of the population of the ministry, i.e., 1.6 billion.They are living in faced lack of water in the field of physics, which is the first to reduce the number of people with the latest technology, and it can be seen opportunities with underground water, and also mentioned lots of research to improve the quality of the situation.
The following factors can affect as current principles of the data of statements: the highest quality of life, the state of energy and energy consumption, the maximum quality of life, and the possibility of climatic changes and damage.
Nowadays, we are working on an innovative approach to solving the problem.The development of control systems for treating bottom sediments in reservoirs and main channels built on a modern software and hardware base.Allowing to provide remotecontrolling with high accuracy and at the same time not requiring high costs of electric power, water, and financial resources, which will serve to increase the volume of water, disinfect the bottom to reactivate the bottom flora and fauna, maintain water bodies in cleanliness, as well as improve existing control systems, is an urgent task.
Today, all over the world and especially in the Republic of Uzbekistan, more attention to the rational use of water resources of reservoirs and canals is paid.The main problem today is reducing the water volume in reservoirs and canals by filling their bottom with silt.The most important task is to create an innovative technology for the purification of bottom sediments of water guards and canals.
Many polluted main canals operate in the Republic of Uzbekistan.More than 70 large and small reservoirs are operated, where water resources with a total volume of 19.4 billion cubic meters are accumulated [1].However, the average utilization rate and efficiency of return do not exceed 75-80%.The rest of the volume is filled with silt layers.
A reservoir is an artificial reservoir formed, as a rule, in the river valley by water retaining structures for the accumulation and storage of water for its use in the national economy [2].
The need to create reservoirs is associated with such natural and anthropogenic as creating an even distribution of river flow throughout the year, preventing the harmful effects of water on the environment, i.e., combating floods, mudflows, erosion of banks, improving the climate, using water resources, as well as in the interests of several sectors of the economy [3].
Siltation of the reservoir is the loss of water volume due to the growth of absolute bottom marks, where in Figure 1, the main elements of the reservoir are shown.The reasons are the entry of suspended sediments from the catchment, the wind transport of volatile sands from the land, the precipitation of chemical compounds, the biomass of aquatic vegetation, the erosion of the banks by wave processes, the leaching of peat from under floating swamps, which are conditionally located beyond the boundary of the reservoir [4][5].
[6] considered the issue of synthesizing a system using the forecasting method at the control facility based on monitoring the system's outflow, taking into account the delay time in controlling the water level and flow in the reservoir.
[7] used ambiguous logic in measuring and controlling the pressures in the reservoir dam.In this system, the difference between the pressures in the dam was considered by the author based on low, medium, and high ratios, and a mathematical model was developed.Genetic algorithms, as well as an adaptive neuro-fuzzy conclusion system, were used in the optimization.
[8] addressed the issue of identification in automatic reservoir management.The moment method was used to solve this problem, and the parameters of the object transfer function were determined.
[9] shows a control scheme for modeling a reservoir using adaptive prediction of the hydraulic actuator, which is appropriate for nonlinear systems with unknown delays.
[10] explored the issue of system stabilization at hydraulic objects remotely controlled via GSM.In this case, unchanged and changed differential linear systems for stabilization are considered.Methods of stabilization of some systems with constant coefficients are given.[11] shows the construction of a mathematical model of the reservoir.In this model, there are ways to overcome the riot signals that cause information delays.In this study, it was proposed to compensate for the disturbances through the use of robust systems.[12] suggested the use of optimal and standard coefficient methods in reservoir management, taking into account water levels and consumption through data from sensors.This method is only suitable for cascading reservoirs.
[13] proposed a model for remote monitoring of the impact of water on a reservoir dam using piezometric sensors in the efficient use of hydraulic structures.This method has been considered effective in dams composed mainly of local soils, but it is not always effective in dams composed of special concrete.[14] solved the problem of etalon model control for a state-delayed nonlinear dynamic object in constructing a reservoir model.The proposed control algorithm is a feedback section equated with a special method, in which auxiliary contours and variable observers are used.They carry all the information about the riots and nonlinearity of the object.It uses two monitors, the first of which compensates for the filter observation error.[15] proposed algorithms for automatic identification of the structure of objects.It is based on the characteristics of the transitions and the results of the analysis of their relationship.
In [16], it was accepted to find the parameters of mathematical models of controlled objects based on data experienced as the subject of parametric identification problems in constructing a mathematical model of the reservoir.The urgency of the identification problem is that the models of dynamic objects are descriptions of the controlled system depending on the parameters.The research is based on the identification method used in the control system, which is closed with the regulator.However, this method has only been tested in monitoring systems, and most technological processes require stabilization systems.
[17] shows a system of differential equations with argument delays.They are used in modeling various events and processes.One of the current problems encountered in the analysis of such dynamic systems is the problem of stagnation.In this study, it is necessary to consider the effect of delay on the stability of solutions.It is known that stagnation can be lost even with a few delays.It is necessary to find limit values that do not break the stability of the solutions of delays and isolate systems that maintain stability.[18] shows the issues of research and design of digital control systems in the event of information delays in constructing a reservoir model.All stages of the control model, from the formation of quality indicators system experiments to the selection and implementation of the control algorithm, were observed.Control algorithms are provided to reduce the effect of information delay and adjust the adjustment parameters of the accounting methods.The architecture of a distributed computing system is also described to minimize the impact of information delays.[19] proposed a new scheme of adaptive control of the reference model to construct a mathematical reservoir model.It uses the input and output of the object to control it.The synthesis work is based on the forward-moving concept of the reference model, using an extended error method.In this case, the structure of the regulators uses only the accumulated delay blocks, in contrast to the minimal complex filters and certain configurations of the delay distribution.An example of digital modeling is given.
[20] proposed special methods for synthesizing such systems based on the analysis of the structural properties of delayed linear optimal stochastic systems in the management of hydraulic structures.These methods are more convenient than general methods in terms of computation and functional decomposition under certain conditions.
[21] is widely used in the synthesis of automatic adjustment systems, where the problem of object control in conditions where the mathematical model is uncertain.A common problem in practical application is the impossibility of directly measuring all the coordinates of the condition vector.This situation leads to many approaches for parametric indeterminate linear objects, such as adaptive algorithms with wide errors, different methods of shunting, algorithms using adaptive observers, and identified systems.

Methods and Materials
The system of control and management of processes of cleaning bottom sediments in reservoirs can be considered as a complex object of automatic control, consisting of the following constituent elements: the body as a solid body moving in water; propulsion and steering system; information (sensor, sensor) system; automatic motion control system; communication system.The architecture of the system for monitoring and managing the processes of cleaning bottom sediments as a controlled marine mobile object is given in Figure 2.
The propulsion and steering system includes the system's actuators for monitoring and controlling the cleaning processes of bottom sediments, with the help of which it performs controlled movement in the water.Usually, these are propeller electric motors with energy converters powering semiconductors and propellers.In some types, these systems, instead of electrical energy, use hydraulic energy, but the hydraulic system is used as a backup resource, so hydraulic systems are not considered further [22].Fig. 2. Architecture of the system of control and management of the processes of purification of bottom sediments in reservoirs as a controlled water mobile object Elements of the information system include sensors of internal information about the current parameters of the mechanisms of the propulsion and steering system, sensors of information on spatial parameters of movement, and sensors for measuring environmental parameters.The first group of sensors includes propeller speed sensors, motor speed sensors and current sensors of propeller motors, and temperature sensors inside sealed housings with electronics modules.The second group is formed by sensors of yaw angles, roll and trim, depth, and hydrostatic pressure.Height meters of the system for monitoring and controlling the cleaning of bottom sediments above the ground, etc. [23].

Results
To ensure the normal functioning of the monitoring and control system, i.e., to ensure the smooth operation of a certain number of monitoring and control systems.It becomes necessary to carry out several measures aimed at maintaining a given level of reliability of the system, for example, replacing the failed monitoring and control system with a serviceable one, which allows you to maintain the required number of monitoring and control systems in the process of cleaning bottom sediments in reservoirs, ensuring its normal functioning.At the same time, the failed system goes to restoration, and after restoration, it enters the reserve fund or into operation.The monitoring and management of the system are maintained according to the flowchart shown in Figure 3: Fig. 3. Form of controlling and management system ‫ܭ‬ is the control and control of the complex, consisting of r microprocessor systems; P is an organization that restores failed systems coming from the control and control complex (this transition is shown by an arrow going from K to P).After the restoration, the systems are used to replenish the reserve fund of complex N (as indicated by the arrow running from P to N), from where, in the event of a system failure in the complex, the system from the reserve fund goes to its place.It may happen that the system in the restoration process was not repairable.It can be considered "lost" for operation (indicated by the arrow coming to the right of P).Usually, their number is some fraction of the restored systems.This circumstance cannot be ignored when calculating the reserve fund, the uninterrupted functioning of the complex.Therefore, adding an additional number of systems to the existing reserve fund is necessary based on considering irrecoverable losses in the recovery process.The processes of failure, recovery, and losses are random, so the most convenient for their assessment is the mathematical apparatus of probability theory.To take into account irrecoverable losses, we introduce the concept of system loss intensity, similar to the concept of failure rate, recovery intensity, which is statistically defined as the ratio of the number of lost systems ∆ni for the time interval ∆ti to the number of systems under restoration (repair) to the moment (0, ∆ti-1) -Ni.If y is the intensity of the loss of the complex.m E is the intensity of the recovery of the complex.m O is the failure rate of the complex.t R is the probability that at the time of t comes into the repair of t systems and E3S Web of Conferences 365, 03027 (2023) https://doi.org/10.1051/e3sconf/202336503027CONMECHYDRO -2022 lost n systems, then it seems possible to consider the states in which the complex of monitoring and control systems can be located.

Discussions
By the time t is in repair, there are t systems, and systems are lost, and during the time (t, t+∆t), there has not been a single failure, not a single loss, not a single recovery.The probability that the complex remained in the state (t, p) is determined by the expression By time t, m+1 monitoring and control systems are under repair, and n systems are lost.There was one recovery in time (t, t+∆t).The probability that the complex will go into a state (t, p) will be By the time of t, t-1 of the monitoring and control systems are under repair when the systems are lost.During the time (t, t+∆t), the complex of systems goes into the state (m, n) due to system failure; the probability of the state (t, p) is characterized by the ratio By time t were the m+1 system under repair, and the p-1 systems were lost.For the time (t, t+∆t) of the monitoring and control systems has one loss, then the probability of the state (t, p) will be Let's find the probability that during the period of time (0, t + ∆t), the complex of systems will be in the state (m, n); that is, the systems will be under repair, and the n will be lost.Considering two-time intervals (0, t) and (t, t+∆t), according to the formula of complete probability, taking into account the previously obtained possible states, we have

0≤m≤r; 0≤n≤r
Dividing the resulting expression by ∆t and moving to the limit at ∆t→0, we get a system of differential equations ௗ , ௗ௧ = λ -ଵ ܲ -ଵ, + β ାଵ ܲ ାଵ, + ߛ ାଵ ܲ ାଵ,ିଵ − (ߣ + ߚ + ߛ )ܲ , (6) 0≤m≤r; 0≤n≤r Based on the formulation of the problem, the complex can be characterized by the following parameters: where λ, β, γ are the parameters inherent in this type of monitoring and control systems; r is the number of control and control systems operated in the complex.
To solve the resulting system of differential equations, we use the method of producing functions by putting Multiply the system of equations ( 6) by htupa and, summing by t, p (we count for the commonality P -1 (t) ≡ 0), we get Therefore, provided (7) after elementary transformations, we will have To solve the resulting equation, it is necessary to consider the system from which it follows that here Opening (11) into rows separately on x and y and multiplying the series, we get The function F(t, x, y) is also defined by the expression (7).Comparing the expressions ( 7) and ( 12), we have i.e., the recovery process and the loss process are independent processes and obey Poisson's law of distribution with a variable parameter (for B, C depend on t): To ensure the proper functioning of the monitoring and management complex, the average number of systems lost and under rehabilitation should be equal to the number of systems in the reserve fund.Given the property of Poisson's law, we obtain where N is the number of control and management systems in the reserve fund.Expanded here, Tr is the average repair time of monitoring and control systems (Tr = 1/β).T is the average operating time of the system (Tr = 1/ λ).α is the number characterizing the proportion of decommissioned (lost) systems from the total number of restored control and control systems (γ = αβ).t is the estimated operating time of the systems in the complex.Consider, for example, determining the number of control and management systems in the reserve fund for a complex having TR = 5 hours, T = 250 hours, α = 0.01, t = 150 hours at r = 243.Therefore, it is necessary to take N = 7.If you do not consider losses during operation, then N = 5.

Conclusions
Thus, the obtained quantitative value of the reserve fund (15) of the complex of control and management systems allows operating organizations to plan the costs necessary to ensure the smooth functioning of the operating complex and can serve as the basis for determining the norms of the reserve fund of reusable systems.

Fig. 1 .
Fig. 1.The main elements of the reservoir, where the FPU is a forced retaining level; NPU is a normal retaining level; UMO is the level of dead volume