Low-energy-consumption charging pipeline machine and control method

Abstract

The invention relates to the field of intelligent water drinking equipment, and discloses a low-energy-consumption charging pipeline machine and a control method.The pipeline machine comprises a main controller, a communication module, a heating module, a refrigeration module, an electromagnetic valve and a high-precision flowmeter, the main controller obtains historical water use data of a user and interacts with a cloud service platform through the communication module, and the heating module is connected with the electromagnetic valve; the method comprises the following steps: acquiring a water consumption behavior prediction result generated based on historical water consumption data, and according to the prediction result: on one hand, selectively closing a heating module or a refrigeration module in a predicted water consumption window period so as to reduce energy consumption of equipment in a standby state, and on the other hand, selectively closing the heating module or the refrigeration module in the predicted water consumption window period so as to reduce energy consumption of the equipment in a standby state; and on the other hand, after the upcoming long-time unused time period is recognized, the electromagnetic valve is controlled to execute automatic water changing operation, so that old water is discharged, and the water quality is guaranteed. According to the invention, through prospective management of the operation state of the equipment, both energy saving and water quality guarantee in the operation process are taken into account.

Description

Technical Field

[0001] This invention relates to the field of intelligent drinking water equipment technology, and in particular to a low-energy billing pipeline machine and control method. Background Technology

[0002] Water dispensers and various drinking water systems are essential devices for providing convenient drinking water. These devices typically have built-in water storage containers and integrate heating or cooling functions to meet users' different water temperature needs. However, while fulfilling their basic functions, existing water dispensers generally face the dual technical challenges of energy consumption and ensuring drinking water quality. How to reduce the operating energy consumption of the equipment and ensure the water quality in the storage container without affecting normal user operation has become an urgent technical problem to be solved in this field.

[0003] In existing technologies, water temperature control in water dispensers is relatively simple. Their heating or cooling modules typically employ start-stop logic based on temperature thresholds. When the water temperature in the storage container deviates from the preset value, the control system activates the corresponding module. The direct consequence of this control method is that regardless of whether there are users in front of the equipment, or whether it is during long periods of inactivity such as late at night or holidays, the equipment will periodically start to maintain the water temperature, generating a large amount of wasted energy. This control strategy, which disregards actual needs, is a root cause of excessive energy consumption during equipment operation.

[0004] Furthermore, existing technologies have shortcomings in their mechanisms for ensuring water quality. If water in a storage container remains stagnant for extended periods, bacteria will proliferate, forming what is known as "stagnant water." Some devices address this issue with fixed-cycle automatic water replacement. However, this fixed-time water replacement logic is rigid. It cannot adapt to dynamically changing usage frequencies. For frequently used devices, this water replacement may be unnecessary; while for devices that are idle for extended periods, the fixed water replacement cycle may be too long, failing to update the stored water in a timely manner. Simultaneously, models relying on a single network connection for billing and data management face the risk of service interruption or loss of critical consumption data when the network signal is weak or interrupted. Summary of the Invention

[0005] The purpose of this invention is to provide a low-energy-consumption billing water dispenser and control method, which solves the technical problems of traditional water dispensers that cannot predict users' dynamic water demand, resulting in unnecessary standby energy consumption during operation and the inability to change water in a timely manner to ensure water quality.

[0006] To address the aforementioned technical problems, this invention provides a low-energy billing pipeline machine and a corresponding control method.

[0007] The first aspect of this invention provides a low-energy billing pipeline machine, comprising: body; The main controller, communication module, heating module, cooling module, solenoid valve for controlling the water circuit, and high-precision flow meter are all installed inside the machine body.

[0008] The main controller is configured to perform the following operations: Retrieve historical water usage data for users with multiple recorded historical water usage events; The communication module interacts with the cloud service platform to obtain water use behavior prediction results generated based on the user's historical water use data. Based on the predicted water usage behavior, the system coordinates the operation of the heating module or the cooling module, and coordinates the control of the solenoid valve to perform water exchange operations or respond to the user's water supply request.

[0009] In one possible implementation, the water usage behavior prediction result includes a quantified prediction of the probability of water usage within one or more future time periods. This quantified prediction is generated by the main controller or the cloud service platform through time-series analysis of the user's historical water usage data to establish a water usage behavior model. This water usage behavior model can be formally described as: P usage (t future )=f(H seq ); In the formula, P usage (t future ) represents the future time point t f Uture's predicted water usage probability; H seq The input time series is composed of the user's historical water usage data; f() is a preset time series analysis algorithm function.

[0010] In one possible implementation, the main controller coordinates the operation of the heating module or the cooling module, specifically by: the main controller controlling the water usage probability prediction value P in the water usage behavior prediction result. usage Compared with the preset energy-saving threshold θ p Compare, when condition P is satisfied usage <θ p When necessary, the power supply to the heating module or the cooling module will be automatically turned off.

[0011] In one possible implementation, the main controller coordinates the control of the solenoid valve. Specifically, the main controller identifies a long-term water-use gap period where the predicted water use probability value is continuously lower than a specific threshold based on the water use behavior prediction results. When the duration of the gap period reaches a preset duration condition, the main controller controls the solenoid valve to perform an automatic water replacement operation to discharge the stagnant water in the water storage container.

[0012] In one possible implementation, the pipeline machine further includes a first power management circuit for supplying power to the main controller, and a second power management circuit for independently supplying power to the communication module via a buck-boost circuit. The second power management circuit provides a stable power supply to the communication module to ensure the reliability of data interaction between the main controller and the cloud service platform.

[0013] In one possible implementation, the pipeline machine further includes a local memory. The main controller is configured to temporarily store the generated user consumption data in the local memory when communication between the communication module and the cloud service platform is interrupted, and to report the temporarily stored data after communication is restored. This mechanism ensures the integrity of the historical user water consumption data used to generate the water consumption behavior prediction results.

[0014] In one possible implementation, the main controller is further configured to receive a billing mode instruction issued by the cloud service platform, and, according to the instruction, selectively perform billing based on the metering data output by the high-precision flow meter or the timing data of its internal timer.

[0015] In one possible implementation, when the main controller interacts with the cloud service platform via the communication module, it uses a preset data encryption protocol and a cyclic redundancy check algorithm to process the transmitted data, so as to ensure the transmission security of the water use behavior prediction results and the confidentiality and integrity of the user's historical water use data.

[0016] In one possible implementation, the communication module includes a cellular communication unit and a Bluetooth communication unit. The main controller is configured to automatically switch to the Bluetooth communication unit to communicate with the user's mobile terminal to complete consumption authorization when an abnormal communication status of the cellular communication unit is detected, thereby ensuring the continuity of user interaction and the continuous acquisition of the user's historical water usage data.

[0017] A second aspect of the present invention provides a control method for a low-energy billing pipeline machine, the method being applied to the aforementioned low-energy billing pipeline machine, comprising the following steps: Obtain the user's historical water usage data for the pipeline machine; Based on the user's historical water usage data, a water usage behavior prediction result is generated; Based on the water usage behavior prediction results, the operating status of the heating module or the cooling module of the pipeline machine is controlled in a coordinated manner, as is the opening and closing of the solenoid valves used for the water circuit of the pipeline machine.

[0018] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention acquires historical user water usage data and generates water usage behavior prediction results. The main controller can identify the water usage gaps in the future. During the predicted water usage gaps, the main controller controls the heating module or the cooling module to stop operating. This avoids the ineffective energy consumption caused by the continuous or timed operation of these modules during periods when the equipment is not in use, thereby reducing the overall energy consumption of the machine.

[0019] 2. By analyzing the predicted water usage behavior, the main controller of this invention can determine whether the water in the storage container has been stagnant for an extended period. When this state is identified, the main controller controls the solenoid valve to perform an automatic water exchange operation. This water exchange mechanism, triggered by actual usage prediction, replaces the traditional fixed-cycle water exchange method, and can update the stored water in a timely manner according to the dynamically changing water usage frequency, thereby more effectively ensuring water quality.

[0020] 3. By configuring a local storage device within the pipeline machine, this invention ensures that user consumption data can still be completely recorded even in the event of an interruption in communication between the communication module and the cloud service platform. This guarantees the continuity and integrity of users' historical water consumption data and provides a data foundation for accurate prediction of water consumption behavior. Simultaneously, by configuring a communication module including cellular and Bluetooth communication units, a backup communication solution is provided when the main communication link fails, ensuring the continuity of user interaction and data collection and improving the overall operational reliability of the system. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the hardware system and power supply scheme of the present invention; Figure 3 This is a schematic diagram of the data interaction and reliability assurance process of the present invention; Figure 4 This is a schematic diagram illustrating the construction and application process of the water use behavior prediction model of the present invention; Figure 5 This is a schematic diagram of the collaborative control strategy execution process of the present invention; Figure 6 This is a flowchart of the method of the present invention; Among them, 100 is the main controller; 110 is the communication module; 120 is the heating module; 130 is the cooling module; 140 is the solenoid valve; 150 is the high-precision flow meter; 160 is the local memory; 170 is the power management circuit; and 180 is the human-machine interface. Detailed Implementation

[0022] The following is in conjunction with the appendix Figure 1 - Appendix Figure 6The present invention will be further described in detail below.

[0023] This invention provides a low-energy billing pipeline machine and its control method. See attached document Figure 1 , Figure 1 This is a schematic diagram of the system architecture of a low-energy billing pipeline machine according to an embodiment of the present invention. The present invention provides a low-energy billing pipeline machine, which includes a main controller 100, a communication module 110, a heating module 120, a cooling module 130, a solenoid valve 140 for controlling the water circuit, a high-precision flow meter 150, a local memory 160, a power management circuit 170, and a human-machine interface 180, all disposed inside the machine body.

[0024] The main controller 100, as the system's control unit, is responsible for executing preset control logic, processing data, and coordinating the operation of other components. The main controller 100 is electrically connected to the control relays of the heating module 120 and the cooling module 130, as well as the solenoid valve 140, via its control interface to control their start / stop or open / close states. The main controller 100 is also connected to a high-precision flow meter 150 via a data interface to receive pulse signals used to measure the water output, and is connected to a local memory 160 to perform data read / write operations.

[0025] The main controller 100 is connected to the communication module 110 through a serial communication interface. The communication module 110 is responsible for establishing a wireless data link between the pipeline machine and the external cloud service platform.

[0026] In this embodiment, the overall workflow of the system is as follows: When a user initiates a water usage request through the human-machine interface 180 or a user mobile terminal interacting with the communication module 110, the main controller 100 records the timestamp of this water usage event and collects the volume data of the water discharged this time through the high-precision flow meter 150. These data together constitute a part of the user's historical water usage data.

[0027] The main controller 100 periodically, or after each water usage event, uploads the collected historical water usage data of users to the cloud service platform via the communication module 110. The cloud service platform (or, in another embodiment, locally on the main controller 100) builds a water usage behavior model based on the time series of the received historical water usage data of users and generates water usage behavior prediction results.

[0028] This water use behavior prediction result is a quantitative prediction of the probability of water use at a future point in time. The prediction process can be formally described as follows.

[0029] See attached document Figure 2 , Figure 2 This is a schematic block diagram of a hardware system and power supply scheme according to an embodiment of the present invention.

[0030] The hardware system of the low-energy billing pipeline machine described in this embodiment is centered on the main controller 100. Multiple control and data pins of the main controller 100 are electrically connected to other hardware units of the system. The main controller 100 executes internally programmed code to receive data from each sensor unit, process the data, generate control commands, and drive each execution unit.

[0031] The hardware system includes an execution unit and a sensing unit. The execution unit includes a heating module 120 and a cooling module 130 connected to the main controller 100 via relays, and a solenoid valve 140 directly connected to the drive pin of the main controller 100. The main controller 100 controls the start and stop of the heating module 120 or the cooling module 130, and the opening and closing of the solenoid valve 140, respectively, by outputting high and low level control signals to the relays or drive pins.

[0032] The sensing unit includes a high-precision flow meter 150. The high-precision flow meter 150 is installed in the water path of the water dispenser. When water flows through it, it generates a pulse signal proportional to the volume of water flowing through. The signal output terminal of the high-precision flow meter 150 is connected to a counter pin of the main controller 100. The main controller 100 calculates the actual water volume output by the user by counting the received pulse signals.

[0033] The hardware system in this embodiment also includes a power management circuit 170. This power management circuit 170 receives externally input AC power and converts it into DC power required by the system. The power management circuit 170 includes a first power management circuit and a second power management circuit.

[0034] The first power management circuit is responsible for powering the main board circuit of the system. Its output stable DC power provides working power for the main controller 100, local memory 160 and human-machine interface 180 in this embodiment.

[0035] The second power management circuit includes an independent buck-boost circuit. The output of the second power management circuit is dedicated to independently powering the communication module 110 in this embodiment. This design isolates the power supply path of the communication module 110 from the power supply path of the main controller 100. Since the communication module 110 generates a large instantaneous current demand when transmitting wireless signals, this independent power supply scheme prevents the resulting voltage fluctuations from being conducted to the power supply pins of the main controller 100, thereby ensuring the stability of the main controller 100's operation and the reliability of the entire system's control command execution and data interaction processes.

[0036] See attached document Figure 3 , Figure 3 This is a schematic diagram of the data interaction and reliability assurance process according to an embodiment of the present invention.

[0037] In a specific application scenario, a user interacts with the pipeline machine described in this embodiment via their mobile terminal. The user's mobile terminal establishes a communication connection with the communication module 110 and initiates a water usage request. After receiving the request, the main controller 100 executes the corresponding billing process according to the billing mode instruction issued by the cloud service platform. If the instruction is metered billing, the main controller 100 continuously collects data from the high-precision flow meter 150 during the user's water usage; if the instruction is time-based billing, the main controller 100 starts its internal timer. After the water usage ends, the main controller 100 packages the user identifier, timestamp, and billing data (metered data or timed data) of this water usage event to generate user consumption data.

[0038] To ensure the security of data transmission between the main controller 100 and the cloud service platform, the main controller 100 encrypts data packets using a preset data encryption protocol before sending any data through the communication module 110. Simultaneously, the main controller 100 performs a Cyclic Redundancy Check (CRC) algorithm on the encrypted data packets to generate a checksum. This process can be formally described as follows: R(x)=(M(x)·x k )mod G(x); In the formula: M(x) is the polynomial representation of the original data packet to be sent; k is the order of the generating polynomial G(x) (i.e., the exponent of the highest-order term of G(x)); x k The operation of shifting the original data packet M(x) left by k bits at the binary level aims to reserve k bits at the end of the data for storing the checksum. G(x) is a specific generator polynomial pre-defined by both communicating parties; mod represents the modulo division operation, i.e., calculating M(x)·x. k The remainder after dividing by G(x); R(x) is the remainder obtained from the modulo division operation, which is the polynomial corresponding to the checksum. The main controller 100 appends this checksum to the end of the data packet and sends it together. After receiving the data packet, the cloud service platform uses the same generator polynomial G(x) to verify the received data to confirm the integrity of the data during transmission.

[0039] The communication module 110 in this embodiment includes a cellular communication unit and a Bluetooth communication unit. Under normal operating conditions, the main controller 100 uses the cellular communication unit by default to interact with the cloud service platform. The main controller 100 is configured to periodically detect the communication status of the cellular communication unit. When an abnormal communication status is detected, such as a cellular signal strength below a preset threshold or a failure to connect to the cloud service platform, the main controller 100 automatically switches the user-authorized communication method to the Bluetooth communication unit. In this state, the terminal can establish direct communication with the user's mobile terminal through the Bluetooth communication unit to complete the consumption authorization process, thereby ensuring the continuity of user interaction in areas with poor cellular network coverage.

[0040] To ensure the integrity of users' historical water consumption data, this embodiment also includes a local storage device 160. In the event of an interruption in communication with the cloud service platform, the main controller 100 will temporarily store the generated user consumption data in the local storage device 160. The main controller 100 will periodically attempt to re-establish the communication connection with the cloud service platform. Once communication is restored, the main controller 100 will read the temporarily stored data from the local storage device 160 and send it to the cloud service platform sequentially until all temporarily stored data is synchronized. This mechanism ensures that users' historical water consumption data is not lost even during prolonged network offline periods.

[0041] See attached document Figure 4 , Figure 4 This is a schematic diagram illustrating the construction and application process of a water use behavior prediction model according to an embodiment of the present invention. In this embodiment, the data input for the construction and application of the water use behavior prediction model is the user's historical water use data. This historical water use data is collected by the main controller 100 from each of the user's water use events and uploaded to the cloud service platform via the communication module 110, forming a time series H. seq This time series can be represented as H. sep ={(t1,v1),(t2,v2),...,(t n ,v n )}, where t i Let v be the timestamp of the i-th water usage event. i The volume of water discharged during this event is measured by a high-precision flow meter 150, and n is the total number of historical water usage events. The generation of the water usage behavior model is executed on the cloud service platform. The cloud service platform receives and stores the user's historical water usage data time series H. seq The platform uses a pre-defined time-series analysis algorithm to process the time series data to identify periodic patterns in user water usage behavior, high-frequency water usage periods, and long periods of inactivity. The results of this analysis are used to build a quantitative model of water usage behavior.

[0042] The function of this water use behavior model is to predict the probability of water use at any future point in time based on existing historical water use data. The application process of this model can be described by the following formula: P usage (t future )=f(H seq ); In the formula: H seq The time series of historical water usage data of users that has occurred and which has been used as input to the model; f() is a water usage behavior model function established through time series analysis on a cloud service platform; t future For future time points that need to be predicted; P usage (t future ) is the model output at a future time point t f Quantitative probability prediction of water use events occurring in uture.

[0043] In one embodiment, model generation and updates are performed periodically on a cloud service platform. The platform integrates the latest uploaded historical water usage data of users and retrains the existing model f to adapt the model's predictions to potential changes in users' water usage habits. During model deployment and application, the cloud service platform uses the generated model f to calculate a sequence of predicted water usage probabilities for one or more future time periods. Subsequently, the platform sends this prediction sequence to the main controller 100 of the pipeline machine via communication module 110. In another embodiment, the cloud service platform can also directly send the parameters of model f to the main controller 100, which then performs the calculations using the aforementioned formula locally to obtain the predicted values. Based on the received prediction results, the main controller 100 executes subsequent equipment control operations.

[0044] See attached document Figure 5 , Figure 5 This is a schematic diagram of the execution flow of a prediction-based collaborative control strategy according to an embodiment of the present invention. The main controller 100 receives the water usage behavior prediction result, i.e., the quantified water usage probability prediction value P, issued by a cloud service platform or generated locally. usage (t future Following this prediction, a forward-looking energy management strategy and a dynamic water quality assurance strategy will be implemented.

[0045] The specific execution process of the forward-looking energy management strategy is as follows: The main controller 100 will generate the water usage probability prediction value P corresponding to the current time point. usage With a preset energy-saving threshold θ p Perform continuous comparisons. When P is detected... usage <θ pWhen the conditions are met, the main controller 100 determines that the equipment is about to enter a water usage downtime period and immediately sends a control signal to the relay controlling the heating module 120 or cooling module 130 to disconnect it. This signal causes the relay to cut off the power supply to the heating module 120 or cooling module 130, thereby stopping their operation to reduce energy consumption. When the subsequently received water usage probability prediction value P... usage Restored to a value greater than or equal to the energy-saving threshold θ p At this time, the main controller 100 sends a control signal to close the relay to restore power supply to the corresponding module.

[0046] The specific execution process of the dynamic water quality assurance strategy is as follows: The main controller 100 analyzes the received sequence of water use probability prediction values ​​for a future period to identify whether there is a continuous time window in which all water use probability prediction values ​​are lower than a preset water exchange threshold. If such a long water use gap is identified, the main controller 100 starts an internal timer to count the duration of the gap.

[0047] When the timer records a preset duration, the main controller 100 automatically performs a water change operation. This operation is accomplished by sending a set of timing control signals to the solenoid valve 140: First, the solenoid valve connecting the water storage container and the drain pipe is opened to drain the stale water in the container; after the preset emptying time is completed, the solenoid valve is closed, and the solenoid valve connecting the inlet pipe and the water storage container is opened to inject new water; the end of the water injection process can be determined by the internal timer reaching the preset duration or the metering data of the high-precision flow meter 150 reaching the preset volume.

[0048] The aforementioned energy management strategy and water quality assurance strategy are executed collaboratively by the main controller 100. Both share the same water usage behavior prediction data, thereby achieving unified control over the energy consumption and water quality of the pipeline machine.

[0049] Please see the appendix Figure 6 The present invention also provides a control method for a low-energy billing pipeline machine, comprising the following steps: Obtain historical water usage data for users of the pipeline machine; Based on the user's historical water usage data, a water usage behavior prediction result is generated; Based on the water usage behavior prediction results, the operating status of the heating or cooling module of the pipeline machine is controlled in a coordinated manner, as is the opening and closing of the solenoid valves used for the water circuit of the pipeline machine.

[0050] The method in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so it will not be described again here.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-energy billing pipeline machine, characterized in that, include: body; The main controller, communication module, heating module, cooling module, solenoid valve for controlling the water circuit, and high-precision flow meter are installed inside the body. The main controller is configured as follows: Obtain users' historical water usage data; The communication module interacts with the cloud service platform to obtain water use behavior prediction results generated based on the user's historical water use data. Based on the predicted water usage behavior, the system coordinates the operation of the heating module or the cooling module, and coordinates the control of the solenoid valve to perform water exchange operations or respond to the user's water supply request.

2. The low-energy billing pipeline machine according to claim 1, characterized in that, The water use behavior prediction results include a quantitative prediction value of the probability of water use in the future time period; the quantitative prediction value is generated by the main controller or the cloud service platform by performing time-series analysis on the user's historical water use data to establish a water use behavior model.

3. The low-energy billing pipeline machine according to claim 1, characterized in that, The pipeline machine also includes a first power management circuit for supplying power to the main controller, and a second power management circuit for independently supplying power to the communication module via a step-up / step-down circuit; the second power management circuit provides a stable power supply to the communication module to ensure the reliability of data interaction between the main controller and the cloud service platform.

4. The low-energy billing pipeline machine according to claim 1, characterized in that, The main controller coordinates the operation of the heating module or the cooling module, specifically including: the main controller compares the water use probability prediction value in the water use behavior prediction result with a preset energy-saving threshold, and automatically shuts down the heating module or the cooling module when the water use probability prediction value is lower than the energy-saving threshold.

5. A low-energy billing pipeline machine according to claim 1, characterized in that, The main controller coordinates the control of the solenoid valve, specifically including: the main controller identifies an upcoming long period of water usage gap based on the water usage behavior prediction results, and when the gap reaches a preset duration, controls the solenoid valve to perform an automatic water replacement operation to discharge the stagnant water in the water storage container.

6. A low-energy billing pipeline machine according to claim 1, characterized in that, The pipeline machine also includes a local storage device; the main controller is configured to temporarily store the generated user consumption data in the local storage device when communication with the cloud service platform is interrupted, so as to ensure the integrity of the user's historical water consumption data used to generate the water consumption behavior prediction results.

7. A low-energy billing pipeline machine according to claim 1, characterized in that, The main controller is also configured to receive a billing mode instruction from the cloud service platform, and selectively perform billing based on the metering data of the high-precision flow meter or its internal timing according to the instruction, so as to achieve accurate deduction by metering or by timing.

8. A low-energy billing pipeline machine according to claim 1, characterized in that, When the main controller interacts with the cloud service platform via the communication module, it employs a preset data encryption protocol and cyclic redundancy check to ensure the secure transmission of the water usage behavior prediction results and the confidentiality and integrity of the user's historical water usage data.

9. A low-energy billing pipeline machine according to claim 1, characterized in that, The communication module includes a cellular communication unit and a Bluetooth communication unit; the main controller is configured to automatically switch to the Bluetooth communication unit to communicate with the user's mobile terminal to complete consumption authorization when the cellular communication unit is in communication failure, thereby ensuring the continuity of user interaction and the continuous acquisition of the user's historical water consumption data.

10. A control method for a low-energy billing pipeline machine, applied to a low-energy billing pipeline machine as described in any one of claims 1-9, characterized in that, Includes the following steps: Obtain historical water usage data for users of the pipeline machine; Based on the user's historical water usage data, a water usage behavior prediction result is generated; Based on the water usage behavior prediction results, the operating status of the heating or cooling module of the pipeline machine is controlled in a coordinated manner, as is the opening and closing of the solenoid valves used for the water circuit of the pipeline machine.

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