Thermal radiation type electrochemical refrigeration system control method and device and temperature adjusting equipment

Abstract

The invention provides a thermal radiation type electrochemical refrigeration system control method and device and temperature adjusting equipment. The refrigerating system comprises a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve, the method comprises the steps that operation parameters and environment parameters of the refrigerating system at the current moment are obtained to serve as the state of the refrigerating system at the current moment; based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, the operation mode of the refrigeration system at the next moment is determined; and on the basis of the operation mode of the refrigeration system at the next moment and a preset optimization item priority, operation parameters of the refrigeration system at the next moment are determined, and the operation parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment are correspondingly adjusted. Personalized requirements of different users on the refrigerating system can be more accurately and flexibly met, and the use experience of the users is improved.

Description

Technical Field

[0001] The present invention relates to the field of refrigeration control technology, and in particular to a control method, device and temperature regulating equipment for a heat radiation type electrochemical refrigeration system. Background Art

[0002] At present, most air conditioners, especially household air conditioners, use steam compression refrigeration. This traditional steam compression refrigeration system consumes a lot of energy, and the refrigerant of the traditional steam compression refrigeration system is mostly fluoride, which can easily harm the environment if released or leaked.

[0003] As energy conservation and environmental protection become the theme of the times, thermal radiation electrochemical refrigeration systems have begun to replace traditional vapor compression refrigeration systems. The thermal radiation electrochemical refrigeration systems on the market all use two pairs of metal hydride heat exchangers to form two systems, which are operated in reverse phase to achieve continuous refrigeration. This thermal radiation electrochemical refrigeration system also includes a circulating water pipe for cooling or heating, which is usually installed on the wall. In order to prevent the wall where the circulating water pipe is installed from getting damp, the operating parameters of the refrigeration system are usually optimized based on the wall humidity.

[0004] However, the refrigeration system has multiple operating modes. Since different users have different requirements for the operating performance of the refrigeration system, if a unified and fixed parameter control and optimization logic is adopted, it can often only meet the usage needs of some users, resulting in a poor user experience for other users. Summary of the invention

[0005] The embodiments of the present invention provide a control method, device and temperature control equipment for a heat radiation type electrochemical refrigeration system to solve the problem that the unified and fixed parameter control logic of the refrigeration system is difficult to meet the use requirements of all users.

[0006] In a first aspect, an embodiment of the present invention provides a control method for a heat radiation electrochemical refrigeration system, wherein the refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump, and an electromagnetic three-way valve; the method includes:

[0007] Obtaining the operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment;

[0008] Based on the current state of the refrigeration system and the trained reinforcement learning network, determine the operation mode of the refrigeration system at the next moment;

[0009] Based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority, the operating parameters of the refrigeration system at the next moment are determined, and the operating parameters of the electrochemical hydrogen pump, circulating water pump and electromagnetic three-way valve at the next moment are adjusted accordingly.

[0010] In one possible implementation, the state space of the reinforcement learning network includes hydride content, wall temperature, ambient temperature, ambient humidity, carbon dioxide concentration and PM2.5 concentration, the action space includes cooling, heating, dehumidification, fresh air and air purification, and the reward function is R = f (energy efficiency, comfort, safety).

[0011] In a possible implementation, before determining the operation mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, the method further includes:

[0012] Based on the reward function, the reward values ​​of the refrigeration system performing different actions in various states are calculated and stored as training samples. This step is repeated to obtain a training sample set for the refrigeration system.

[0013] The initial reinforcement learning network is trained based on the training sample set to obtain a trained reinforcement learning network.

[0014] In a possible implementation, based on the operation mode of the refrigeration system at the next moment and the preset optimization item priority, determining the operation parameters of the refrigeration system at the next moment includes:

[0015] The operation of the refrigeration system is controlled based on standard operating parameters corresponding to the operating mode of the refrigeration system at the next moment, and the operating parameters of the refrigeration system are adjusted in real time based on preset optimization item priorities.

[0016] In a possible implementation, the priorities of the optimization items are, from high to low, hydride content control, wall humidity control, wall temperature control, circulating water pipe pressure control, energy efficiency ratio control, and ambient temperature control;

[0017] Real-time adjustment of the operating parameters of the refrigeration system based on the preset optimization item priorities, including:

[0018] Determine whether the hydride content of the heat exchanger is abnormal. If the hydride content of the heat exchanger is abnormal, control the electrochemical hydrogen pump to stop running. Otherwise, determine whether the wall humidity is abnormal.

[0019] If the wall humidity is abnormal, adjust the cooling temperature, otherwise determine whether the wall temperature is abnormal;

[0020] If the wall temperature is abnormal, adjust the cooling temperature, otherwise determine whether the circulating water pipe pressure is abnormal;

[0021] If the circulating water pipe pressure is abnormal, adjust the circulating water pump power, otherwise determine whether the energy efficiency ratio is abnormal;

[0022] If the energy efficiency ratio is abnormal, the cooling temperature is adjusted, otherwise the cooling temperature is optimized based on the ambient temperature and the user-set temperature.

[0023] In a possible implementation, the operating parameter includes vibration data; before determining the operating mode of the refrigeration system at a next moment based on the state of the refrigeration system at a current moment and the trained reinforcement learning network, the method further includes:

[0024] determining a predicted failure probability of the refrigeration system based on vibration data of the refrigeration system;

[0025] If the predicted fault probability is greater than the threshold, the fault diagnosis mode is entered;

[0026] If the predicted failure probability is not greater than the threshold, the operating mode of the refrigeration system at the next moment is determined based on the state of the refrigeration system at the current moment and the trained reinforcement learning network.

[0027] In one possible implementation, determining a predicted failure probability of a refrigeration system based on vibration data includes:

[0028] The similarity between the vibration data of the refrigeration system and the historical fault vibration data is calculated as the predicted failure probability of the refrigeration system.

[0029] In a second aspect, an embodiment of the present invention provides a heat radiation electrochemical refrigeration system control device, the refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve; the device includes:

[0030] An acquisition module, used to acquire the operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment;

[0031] A mode determination module, used for determining an operation mode of the refrigeration system at a next moment based on a state of the refrigeration system at a current moment and a trained reinforcement learning network;

[0032] The parameter determination module is used to determine the operating parameters of the refrigeration system at the next moment based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority, and to adjust the operating parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment accordingly.

[0033] In a third aspect, an embodiment of the present invention provides a temperature control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0034] In a fourth aspect, an embodiment of the present invention 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 of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0035] The embodiments of the present invention provide a control method, device and temperature control equipment for a thermal radiation electrochemical refrigeration system. Through a reinforcement learning network, the current operating parameters and environmental parameters of the refrigeration system are comprehensively evaluated to select the most suitable operating mode, such as the highest safety, the best system energy efficiency, the highest user comfort or taking into account multiple needs. Then, based on this operating mode, the operating parameters are further optimized in combination with the preset optimization item priority, so as to more accurately and flexibly meet the personalized needs of different users for the refrigeration system and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0037] Figure 1 This is an application scenario diagram of a heat radiation type electrochemical refrigeration system control method provided by an embodiment of the present invention;

[0038] Figure 2 is a flow chart of a control method for a heat radiation type electrochemical refrigeration system provided by an embodiment of the present invention;

[0039] Figure 3 It is a schematic diagram of the data interaction relationship between various parts in a refrigeration system provided by an embodiment of the present invention;

[0040] Figure 4 is a flowchart for implementing real-time adjustment of operating parameters provided by an embodiment of the present invention;

[0041] Figure 5 This is a flow chart of a control method for a heat radiation type electrochemical refrigeration system according to another embodiment of the present invention.

[0042] Figure 6 is a schematic structural diagram of a heat radiation type electrochemical refrigeration system control device provided by an embodiment of the present invention;

[0043] Figure 7 It is a schematic diagram of a temperature control device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] 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 invention. However, it should be clear to those skilled in the art that the present invention may 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 invention.

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0046] Figure 1 This is an application scenario diagram of the heat radiation electrochemical refrigeration system control method provided in an embodiment of the present invention. Figure 1 As shown, the refrigeration system is an electrochemical refrigeration system. The core equipment is an electrochemical hydrogen pump. Two indoor and outdoor metal hydride heat exchangers 1 and 2 are connected through two hydrogen pipelines. The heat exchange system is a radiation heat exchange. The cooled water is sent into the room through a circulating water pipe through a pressure pump (only one of the pressure pumps 1 and 2 is turned on when working) for radiation heat exchange. The electromagnetic three-way reversing valve is controlled according to the positive and negative pressure of the power supply, and the heat-absorbing circulating water is sent to the heat exchanger for cooling, and continuous thermal radiation cooling (or heating) is performed. The specific implementation of the refrigeration process is introduced below:

[0047] When positive pressure is applied to the electrochemical hydrogen pump, heat exchanger 2 absorbs hydrogen and releases heat as a condenser, and heat exchanger 1 dehydrogenates and absorbs heat as an evaporator. Pressure pump 1 starts to work, and the circulating water cooled from heat exchanger 1 is pressurized by pressure pump 1 and enters the customer's home through the circulating water pipe for heat radiation absorption and refrigeration. At this time, solenoid valve 1 opens A1B1, solenoid valve 4 opens B4C4, solenoid valve 3 opens B3C3, and solenoid valve 2 opens C2A2, and then enters heat exchanger 1 for cooling. The remaining valves and power equipment are closed, and refrigeration under positive pressure is achieved at this time;

[0048] When negative pressure is applied to the electrochemical hydrogen pump, heat exchanger 1 absorbs hydrogen and releases heat as a condenser, and heat exchanger 2 dehydrogenates and absorbs heat as an evaporator. Pressure pump 1 stops and pressure pump 2 starts working. At this time, the circulating water is cooled by heat exchanger 2 and pressurized by pressure pump 2. Solenoid valve 3 opens A3C3, solenoid valve 2 opens C2B2, solenoid valve 1 opens C1B1, and solenoid valve 4 opens B4A4. After absorbing heat in the form of heat radiation through the circulating water pipe, it returns to heat exchanger 2 for recooling, and indoor cooling under negative pressure is achieved. By alternately applying positive and negative pressures at certain time intervals, the air conditioning system achieves continuous cooling.

[0049] See also Figure 2 , which shows a flow chart of the implementation of the control method of the thermal radiation electrochemical refrigeration system provided by an embodiment of the present invention, which is described in detail as follows:

[0050] The refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve; the method includes:

[0051] Step 201, obtaining the operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment.

[0052] In this embodiment, the operating parameters may include the hydride content in the heat exchanger, which is used to indicate the real-time operating status of the refrigeration system. The environmental parameters may include information such as ambient temperature, temperature and humidity of the wall where the circulating water pipe is installed, which can reflect the impact of the operation of the refrigeration system on the environment. In addition to temperature, wall humidity is also an important factor in wall moisture. Add a wall humidity sensor to more comprehensively monitor the moisture condition of the wall. Monitor the pressure in the circulating water pipe to prevent aging and damage of the pipe. Real-time monitoring of the system's energy efficiency ratio (Coefficient of Performance, COP) to ensure that the system operates in an efficient state.

[0053] The detection components that need to be placed to collect the above parameters are as follows:

[0054] A hydride content detection device EJ1, EJ2 is installed on each of the two heat exchangers;

[0055] Indoor temperature T detection component TJ;

[0056] Temperature detection component TxJ of the circulation pipe in the wall;

[0057] Temperature sensor: monitors indoor temperature and wall temperature;

[0058] Humidity sensor: monitors indoor humidity and wall humidity;

[0059] Carbon dioxide sensor: monitors carbon dioxide concentration;

[0060] PM2.5 sensor: monitors PM2.5 concentration in the air;

[0061] Vibration sensor: monitor equipment operating status and fault diagnosis;

[0062] Pressure sensor: monitors the pressure of the circulating water pipe.

[0063] Step 202: Based on the current state of the refrigeration system and the trained reinforcement learning network, determine the operation mode of the refrigeration system at the next moment.

[0064] In this embodiment, in order to further improve the system performance, a self-learning algorithm is added, and a reinforcement learning network is used to select the operating mode. The self-learning control algorithm is implemented through reinforcement learning (RL). The system continuously optimizes the control strategy through interaction with the environment to achieve the best system operation state. Specifically, the system collects sensor data, analyzes historical data and real-time data, and autonomously learns how to select the best control action under different conditions to improve the system's energy efficiency and user comfort. The specific process of mode selection based on the reinforcement learning network is as follows:

[0065] The sensor data collected in step 201 is input into the system to obtain the current environment state St. Based on the reinforcement learning algorithm, the system selects the best action At according to the current state St and executes the action. For example:

[0066] If the temperature is higher than the set value, the system will increase the cooling power; if the humidity is higher than the set value, the system will start the dehumidification function; if the pressure is too high, the system will reduce the water pump power.

[0067] After the action is executed, the system collects sensor data again, obtains the new state St+1, and calculates the reward Rt based on the new state.

[0068] During operation, the system updates the reinforcement learning strategy and optimizes the control parameters according to the current state St, action At, reward Rt and next state St+1, so that the system can adjust the environmental parameters more efficiently and intelligently in future operations.

[0069] In addition, the system monitors the data of each sensor in real time during operation. If abnormal vibration is detected, the system automatically enters fault diagnosis mode and sends maintenance notifications to ensure stable operation of the system.

[0070] Step 203, based on the operation mode of the refrigeration system at the next moment and the preset optimization item priority, determine the operation parameters of the refrigeration system at the next moment, and adjust the operation parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment accordingly.

[0071] In this embodiment, the principle of setting the control logic priority is based on the safety, reliability, energy efficiency and user comfort of the system operation. The priority setting ensures that the system can make the best decision and maintain a stable, efficient and safe operation state when facing complex environments and multiple influencing factors. The following is a specific setting principle and its reasons. The corresponding relationship between the parameters corresponding to each optimization item and each part of the refrigeration system is as follows: Figure 3 As shown:

[0072] 1. Safety First

[0073] in principle:

[0074] Ensure the security of system operation and avoid any situation that may cause system damage or endanger user safety.

[0075] reason:

[0076] Hydride content control: The hydride content is directly related to the safety of the system. When the hydride content is too low, it may cause system damage or performance degradation. Therefore, hydride content control is set as the highest priority to ensure that the system operates within a safe range.

[0077] 2. Structural Protection

[0078] in principle:

[0079] Protect the physical structure of the system, prevent damage caused by environmental factors, and extend the service life of the system.

[0080] reason:

[0081] Wall temperature and humidity control: The temperature and humidity of the wall directly affect the long-term stability of the system and the safety of the building structure. Excessive humidity can cause the wall to get damp, moldy, and even damage the building structure. Therefore, wall temperature and humidity control is set as the second priority to prevent the wall from getting damp and structural damage.

[0082] 3. System Efficiency

[0083] in principle:

[0084] Ensure that the system operates in an efficient state, optimize energy consumption, and improve the energy efficiency ratio of the system.

[0085] reason:

[0086] Circulating water pipe pressure control: Excessive pressure will cause pipe aging and rupture, thus affecting the efficiency and life of the system. By monitoring and controlling the water pipe pressure, ensure that the system operates within a safe pressure range. Energy efficiency ratio (COP) monitoring: Real-time monitoring of the system's energy efficiency ratio to ensure that the system operates in an efficient state. When the energy efficiency ratio is lower than the set value, the system needs to adjust the operating parameters to improve energy efficiency.

[0087] 4. User Comfort

[0088] in principle:

[0089] Adjust the indoor ambient temperature according to user needs to improve user comfort.

[0090] reason:

[0091] Ambient temperature control: The user's demand for ambient temperature directly affects their comfort. By accurately controlling the indoor temperature, the system can provide a more comfortable living environment. Therefore, ambient temperature control is set as the third priority to ensure user comfort.

[0092] The embodiment of the present invention uses a reinforcement learning network to comprehensively evaluate the current operating parameters and environmental parameters of the refrigeration system, selects the most appropriate operating mode, such as the highest safety, the best system energy efficiency, the highest user comfort, or taking into account multiple needs, and then further optimizes the operating parameters based on this operating mode in combination with the preset optimization item priority, so as to more accurately and flexibly meet the personalized needs of different users for the refrigeration system and improve the user experience.

[0093] In one possible implementation, the state space of the reinforcement learning network includes hydride content, wall temperature, ambient temperature, ambient humidity, carbon dioxide concentration and PM2.5 concentration, the action space includes cooling, heating, dehumidification, fresh air and air purification, and the reward function is R = f (energy efficiency, comfort, safety).

[0094] In this embodiment, the strategy is optimized through reinforcement learning algorithms such as Q-learning or DQN, so that the system selects the optimal action in each state.

[0095] 1. State Space S: defines the state space of the system, including environmental parameters such as temperature, humidity, carbon dioxide concentration, PM2.5 concentration, and equipment vibration. S = {CH, TW, T, H, CO2, PM2.5}

[0096] 2. Action Space A: Defines the action space of the system, including cooling, heating, dehumidification, fresh air, air purification and other operations. A = {Start / stop cooling, start / stop dehumidification, start / stop fresh air, start / stop air purification}

[0097] 3. Reward Function R: Design a reward function to give rewards or penalties based on the performance of the system. For example, a positive reward is given when the system energy efficiency is improved and the user comfort level is satisfied; a negative reward is given when the system energy efficiency is low or the user comfort level is not satisfied. R = f (energy efficiency, comfort, safety)

[0098] 4. Policy Optimization: Use reinforcement learning algorithms (such as Q-learning, Deep Q-Network, DQN) to optimize system control strategies by continuously interacting with the environment.

[0099]

[0100] In a possible implementation, before determining the operation mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, the method further includes:

[0101] Based on the reward function, the reward values ​​of the refrigeration system performing different actions in various states are calculated and stored as training samples. This step is repeated to obtain a training sample set for the refrigeration system.

[0102] The initial reinforcement learning network is trained based on the training sample set to obtain a trained reinforcement learning network.

[0103] In this embodiment, the specific steps for training the initial reinforcement learning network are as follows:

[0104] 1. Initialization: Initialize the state space, action space, and reward function.

[0105] 2. State monitoring: Monitor the environmental state in real time through sensors to obtain the current state St.

[0106] 3. Strategy selection: Select action At based on the current state St and strategy.

[0107] 4. Execute action: Execute action At, and the system adjusts the operating parameters according to the action.

[0108] 5. Get rewards: Calculate the reward value Rt based on the execution results.

[0109] 6. State transfer: Get the state St+1 at the next moment.

[0110] 7. Update strategy: Update the strategy and optimize the control parameters based on the current state, action, reward and next state.

[0111] 8. Iteration: Repeat steps 2-7 and continuously optimize the control strategy until the optimal strategy is reached.

[0112] Take the temperature control mode as an example, the initial state: the set temperature T0 = 25°C. The current temperature T is 28°C.

[0113] Action selection: The system selects cooling mode and adjusts cooling power.

[0114] Execution action: The system starts cooling and the temperature gradually drops.

[0115] Get rewards: When the temperature drops to 25℃ and the energy efficiency is high, the system gives positive rewards; when the temperature is too low or the energy efficiency is low, negative rewards are given.

[0116] State transfer: The system monitors temperature changes and obtains a new state.

[0117] Strategy update: Update the cooling strategy based on the reward and new status.

[0118] In a possible implementation, based on the operation mode of the refrigeration system at the next moment and the preset optimization item priority, determining the operation parameters of the refrigeration system at the next moment includes:

[0119] The operation of the refrigeration system is controlled based on standard operating parameters corresponding to the operating mode of the refrigeration system at the next moment, and the operating parameters of the refrigeration system are adjusted in real time based on preset optimization item priorities.

[0120] In this embodiment, the operation mode includes cooling, heating, dehumidification, fresh air, air purification and other operations, which correspond to the state of the environment in which the refrigeration system is located, but each operation mode can be based on a variety of operation parameters, and different operation parameters can produce different operation effects. Therefore, after adjusting the operation mode of the refrigeration system, it is first operated with standard operation parameters, which are preset values ​​and can be the most used operation parameters of the refrigeration system in each operation mode, and then the operation parameters are continuously adjusted in real time based on the preset optimization item priority, so as to achieve the safety and efficiency of the refrigeration system in the entire operation process and ensure the comfort of users.

[0121] In a possible implementation, the priorities of the optimization items are, from high to low, hydride content control, wall humidity control, wall temperature control, circulating water pipe pressure control, energy efficiency ratio control, and ambient temperature control;

[0122] Real-time adjustment of the operating parameters of the refrigeration system based on the preset optimization item priorities, including:

[0123] Determine whether the hydride content of the heat exchanger is abnormal. If the hydride content of the heat exchanger is abnormal, control the electrochemical hydrogen pump to stop running. Otherwise, determine whether the wall humidity is abnormal.

[0124] If the wall humidity is abnormal, adjust the cooling temperature, otherwise determine whether the wall temperature is abnormal;

[0125] If the wall temperature is abnormal, adjust the cooling temperature, otherwise determine whether the circulating water pipe pressure is abnormal;

[0126] If the circulating water pipe pressure is abnormal, adjust the circulating water pump power, otherwise determine whether the energy efficiency ratio is abnormal;

[0127] If the energy efficiency ratio is abnormal, the cooling temperature is adjusted, otherwise the cooling temperature is optimized based on the ambient temperature and the user-set temperature.

[0128] In this embodiment, the judgment process of each optimization item is as follows: Figure 4 As shown, the details are as follows:

[0129] 1. Hydride content control (highest priority)

[0130] Logic description:

[0131] When it is detected that the hydride content CH is less than or equal to the safety threshold CHmin, the system must be shut down immediately to prevent system damage or efficiency reduction.

[0132] Formula and calculation:

[0133] The safety threshold CHmin is set to 5%.

[0134] If CH≤CHmin, the system is shut down.

[0135] If CH>CHmin, the system operates normally.

[0136] 2. Wall temperature and humidity control (second priority)

[0137] Logic description:

[0138] When the wall temperature TW is lower than the minimum safe temperature TWmin or the wall humidity HW exceeds the set threshold HWmax, the system must adjust to prevent the wall from getting damp and the structure from being damaged.

[0139] Formula and calculation:

[0140] Set the minimum safe temperature TWmin=15°C and the wall humidity threshold HWmax=60%.

[0141] If TW≤TWmin or HW>HWmax, the system stops the electrochemical hydrogen pump and the circulating water pump, and closes the electromagnetic three-way valve.

[0142] If TW>TWmin and HW≤HWmax, the system operates normally.

[0143] 3. Circulating water pipe pressure and energy efficiency ratio control (third priority)

[0144] Logic description:

[0145] When the circulating water pipe pressure PC exceeds the set threshold PCmax or the energy efficiency ratio COP is lower than the set value COPmin, the system needs to adjust the operating parameters to improve efficiency and save energy. By monitoring and optimizing the flow and temperature of circulating water, the stress of the pipeline is reduced and the service life of the system is extended. If the circulating water temperature is detected to be too high, the system automatically reduces the water pump power and reduces the flow to prevent pipeline aging. If the system detects that the current environmental load is low, it automatically enters the energy-saving mode to reduce the cooling and heating power.

[0146] Formula and calculation:

[0147] Set the circulating water pipe pressure threshold PCmax = 3 MPa and the coefficient of performance COPmin = 3.0.

[0148] If PC > PCmax, the system reduces the pump power to lower the pressure.

[0149] If COP < COPmin, the system optimizes the operating parameters to improve energy efficiency.

[0150] 4. Ambient Temperature Control (Fourth Priority)

[0151] Logical Explanation:

[0152] According to the ambient temperature T0 set by the user, adjust the indoor temperature T to improve comfort. By precisely controlling the temperature sensor and the environmental adjustment equipment, ensure the stability of the indoor temperature and improve user comfort. The system automatically adjusts the cooling and heating power according to the temperature range set by the user to keep the temperature fluctuating within the set range.

[0153] Formulas and Calculations:

[0154] Assume the user's desired temperature T0 = 25 °C.

[0155] If T > T0, the system starts the cooling mode.

[0156] If T ≤ T0, the system stops the cooling mode.

[0157] When powered on, taking positive pressure as an example, at this time, heat exchanger 1 serves as an evaporator, and the detection components EJ1, TJ, and TxJ start power-on detection simultaneously (EJ2 does not operate), and each detects data (TxJ is set to prevent the wall from getting cold and damp and protect the wall: that is, it is set only when the circulating water pipe is laid in the wall. If it is arranged outside the wall, this control mode is not set):

[0158] a. When EJ1 detects that the hydride content > 5%, the system operates normally; when the hydride content ≤ 5%, the system powers off and shuts down. At this time, the electrochemical pump shuts down, the circulating water pump 1 stops, and all electromagnetic three-way valves close. When the system is powered off for 1 min, the three-way valve starts to switch to the negative pressure water supply direction. When the power is off for 3 min, the system powers on to negative pressure, the electrochemical pump starts, the circulating water pump 2 operates, and heat exchanger 2 serves as an evaporator. At this time, the detection component EJ2 detects (Note: The detected hydride content (5%) is an approximate number, and the power-off time is also an approximate number, which needs to be determined according to the specific working conditions and models);

[0159] b. When TJ detects that the indoor temperature T> the set temperature T0, the system operates normally; when T≤T0, the electrochemical pump stops running, and the circulating water pump 1 and the electromagnetic three-way valve still operate normally. When TJ detects that T≥T0+3, the electrochemical pump starts cooling (Note: T≥T0+3 detected by TJ is an approximate number and needs to be determined according to the specific working conditions and models);

[0160] c. When the circulating water pipe temperature Tx detected by TxJ ≥ the lowest humidity temperature Tx0 that the wall can withstand (Txo depends on the actual wall structure and material), the system operates normally; when Tx<Tx0, the electrochemical hydrogen pump is shut down, the circulating water pump is shut down, and the three-way valve is still open; when Tx≥Tx0+3, the circulating water pump is turned on; when Tx≥Tx0+5, the electrochemical pump is turned on and runs (Note: Tx≥Tx0+3 and Tx≥Tx0+5 are approximate numbers and need to be determined according to specific working conditions and machine models).

[0161] In a possible implementation, the operating parameter includes vibration data; before determining the operating mode of the refrigeration system at a next moment based on the state of the refrigeration system at a current moment and the trained reinforcement learning network, the method further includes:

[0162] determining a predicted failure probability of the refrigeration system based on vibration data of the refrigeration system;

[0163] If the predicted fault probability is greater than the threshold, the fault diagnosis mode is entered;

[0164] If the predicted failure probability is not greater than the threshold, the operating mode of the refrigeration system at the next moment is determined based on the state of the refrigeration system at the current moment and the trained reinforcement learning network.

[0165] In this embodiment, the predictive maintenance system combines vibration sensors and self-learning algorithms to monitor the operating status of equipment in real time, predict equipment failures in advance, reduce downtime, and improve system reliability. Vibration sensors can be set at multiple parts of the refrigeration system, and the data of each vibration sensor can be monitored in real time during the operation of the refrigeration system. If an abnormal situation is detected (such as abnormal vibration of a certain equipment), the system enters the fault diagnosis mode and sends a maintenance notification.

[0166] If the system operates normally, the current operating state is maintained, and mode control and parameter optimization are performed based on the steps in the above embodiment.

[0167] The implementation of vibration monitoring can specifically include the following steps:

[0168] 1. Vibration monitoring: Vibration sensors are used to monitor the operating status of equipment in real time and detect abnormal vibrations.

[0169] V=Vibration data

[0170] 2. Data analysis and model training: Self-learning algorithms analyze historical vibration data to identify potential failure modes.

[0171] Pfault=f(V,historical data)

[0172] 3. Fault warning: When abnormal vibration is detected, the system performs fault diagnosis, warns of possible equipment failures in advance, and sends maintenance notifications to users.

[0173] Example:

[0174] Equipment vibration monitoring: When the system is running, the vibration sensor detects abnormal vibration frequency of the equipment. The self-learning algorithm analyzes the vibration data and finds that the equipment may have mechanical failure.

[0175] Fault warning and maintenance notification: The system determines the potential cause of the fault, warns in advance, sends maintenance notifications to users, and recommends inspection and maintenance. Users can arrange maintenance in time based on the warning information to reduce equipment downtime.

[0176] In one possible implementation, determining a predicted failure probability of a refrigeration system based on vibration data includes:

[0177] The similarity between the vibration data of the refrigeration system and the historical fault vibration data is calculated as the predicted failure probability of the refrigeration system.

[0178] In this embodiment, the vibration data of the refrigeration system can come from multiple parts, such as the circulating water pipe, the electrochemical hydrogen pump, the circulating water pump, etc. The vibration data collected from each part can be used to calculate the similarity between each vibration data and the historical fault vibration data corresponding to the collected part. The higher the similarity, the greater the probability of failure of the part, so the similarity is used as the predicted failure probability.

[0179] In a specific embodiment, Figure 5 As shown in the figure, the complete process of controlling the refrigeration system includes:

[0180] 1. System startup (start):

[0181] After the system starts, it enters the initialization phase and starts collecting sensor data.

[0182] 2. Sensor data acquisition (sensor):

[0183] The current environmental data is collected through a multi-sensor system (temperature, humidity, carbon dioxide concentration, PM2.5 concentration and vibration sensors).

[0184] 3. Get the current state:

[0185] According to the data collected by the sensor, the current state St of the system is obtained.

[0186] 4. Select actions based on the strategy (policy):

[0187] Based on the current state St and the self-learning algorithm (reinforcement learning strategy), the best action At to be performed by the system is selected. The actions include adjusting cooling, heating, dehumidification, fresh air, air purification and other operations.

[0188] 5. Execute action:

[0189] The system adjusts the operating parameters according to the selected action At and performs the corresponding operation.

[0190] 6. Get the next state (newState):

[0191] After executing the action, the system re-collects data through sensors and obtains the new state St+1.

[0192] 7. Calculate reward:

[0193] According to the new state St+1 and the predefined reward function, the reward Rt for system operation is calculated. If the system is energy efficient and the user comfort is satisfied, a positive reward is given; otherwise, a negative reward is given.

[0194] 8. Update strategy (update):

[0195] According to the current state St, action At, reward Rt and next state St+1, the reinforcement learning strategy is updated and the control parameters are optimized.

[0196] 9. Loop iteration:

[0197] The system continuously executes the above steps in a cycle, optimizes the control strategy through continuous environmental interaction, and achieves the best operating state.

[0198] 10. Real-time monitoring (monitor):

[0199] During operation, the system monitors the data of each sensor in real time. If an abnormal situation is detected (such as abnormal equipment vibration), it will enter fault diagnosis and send a maintenance notification.

[0200] If the system is operating normally, maintain the current operating status.

[0201] From the above, it can be seen that the embodiment of the present invention increases judgment conditions such as wall humidity monitoring, circulating water pipe pressure monitoring and energy efficiency ratio monitoring, optimizes the flow and temperature of circulating water, reduces the stress of pipes, and extends the service life of the system. It dynamically adjusts system operating parameters, optimizes energy consumption, and reduces unnecessary energy consumption. By accurately controlling temperature sensors and environmental adjustment equipment, it ensures the stability of indoor temperature and improves user comfort. Combined with multi-sensor fusion technology, self-learning control algorithm and predictive maintenance system, this solution can achieve more efficient, smarter and more reliable system operation, and has broad application prospects and market value.

[0202] It should be understood that the order of execution of the steps in the above embodiment does not necessarily 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 embodiment of the present invention.

[0203] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0204] Figure 6 The structure diagram of the heat radiation type electrochemical refrigeration system control device provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0205] like Figure 6 As shown, the refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve, and the heat radiation electrochemical refrigeration system control device 6 includes:

[0206] An acquisition module 61 is used to acquire the operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment;

[0207] A mode determination module 62, configured to determine an operation mode of the refrigeration system at a next moment based on a state of the refrigeration system at a current moment and a trained reinforcement learning network;

[0208] The parameter determination module 63 is used to determine the operating parameters of the refrigeration system at the next moment based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority, and to adjust the operating parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment accordingly.

[0209] In one possible implementation, the state space of the reinforcement learning network includes hydride content, wall temperature, ambient temperature, ambient humidity, carbon dioxide concentration and PM2.5 concentration, the action space includes cooling, heating, dehumidification, fresh air and air purification, and the reward function is R = f (energy efficiency, comfort, safety).

[0210] In a possible implementation, the mode determination module 62 is further configured to:

[0211] Before determining the operation mode of the refrigeration system at the next moment based on the current state of the refrigeration system and the trained reinforcement learning network, the reward values ​​of the refrigeration system performing different actions in multiple states are calculated based on the reward function and stored as training samples. This step is repeated to obtain a training sample set of the refrigeration system.

[0212] The initial reinforcement learning network is trained based on the training sample set to obtain a trained reinforcement learning network.

[0213] In a possible implementation, the parameter determination module 63 is specifically configured to:

[0214] The operation of the refrigeration system is controlled based on standard operating parameters corresponding to the operating mode of the refrigeration system at the next moment, and the operating parameters of the refrigeration system are adjusted in real time based on preset optimization item priorities.

[0215] In a possible implementation, the priorities of the optimization items are, from high to low, hydride content control, wall humidity control, wall temperature control, circulating water pipe pressure control, energy efficiency ratio control, and ambient temperature control;

[0216] The parameter determination module 63 is specifically used for:

[0217] Determine whether the hydride content of the heat exchanger is abnormal. If the hydride content of the heat exchanger is abnormal, control the electrochemical hydrogen pump to stop running. Otherwise, determine whether the wall humidity is abnormal.

[0218] If the wall humidity is abnormal, adjust the cooling temperature, otherwise determine whether the wall temperature is abnormal;

[0219] If the wall temperature is abnormal, adjust the cooling temperature, otherwise determine whether the circulating water pipe pressure is abnormal;

[0220] If the circulating water pipe pressure is abnormal, adjust the circulating water pump power, otherwise determine whether the energy efficiency ratio is abnormal;

[0221] If the energy efficiency ratio is abnormal, the cooling temperature is adjusted, otherwise the cooling temperature is optimized based on the ambient temperature and the user-set temperature.

[0222] In a possible implementation, the operating parameters include vibration data; the mode determination module 62 is further configured to:

[0223] Before determining the operation mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, determining the predicted failure probability of the refrigeration system based on the vibration data of the refrigeration system;

[0224] If the predicted fault probability is greater than the threshold, the fault diagnosis mode is entered;

[0225] If the predicted failure probability is not greater than the threshold, the operating mode of the refrigeration system at the next moment is determined based on the state of the refrigeration system at the current moment and the trained reinforcement learning network.

[0226] In a possible implementation, the mode determination module 62 is specifically configured to:

[0227] The similarity between the vibration data of the refrigeration system and the historical fault vibration data is calculated as the predicted failure probability of the refrigeration system.

[0228] The embodiment of the present invention uses a reinforcement learning network to comprehensively evaluate the current operating parameters and environmental parameters of the refrigeration system, selects the most appropriate operating mode, such as the highest safety, the best system energy efficiency, the highest user comfort, or taking into account multiple needs, and then further optimizes the operating parameters based on this operating mode in combination with the preset optimization item priority, so as to more accurately and flexibly meet the personalized needs of different users for the refrigeration system and improve the user experience.

[0229] Figure 7 Schematic diagram of a temperature control device provided by an embodiment of the present invention. Figure 7 As shown, the temperature control device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. When the processor 70 executes the computer program 72, the steps in the above-mentioned various heat radiation type electrochemical refrigeration system control method embodiments are implemented, such as Figure 2 Alternatively, when the processor 70 executes the computer program 72, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 6 Functions of the modules / units 61 to 63 are shown.

[0230] Exemplarily, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 72 in the temperature control device 7. For example, the computer program 72 may be divided into Figure 6 Modules / units 61 to 63 are shown.

[0231] The temperature control device 7 may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will appreciate that Figure 7It is only an example of the temperature control device 7 and does not constitute a limitation of the temperature control device 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the temperature control device may also include input and output devices, network access devices, buses, etc.

[0232] The processor 70 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.

[0233] The memory 71 may be an internal storage unit of the temperature control device 7, such as a hard disk or memory of the temperature control device 7. The memory 71 may also be an external storage device of the temperature control device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the temperature control device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the temperature control device 7. The memory 71 is used to store the computer program and other programs and data required by the temperature control device. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0234] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0235] 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.

[0236] Those skilled 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 the present invention.

[0237] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0238] 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.

[0239] In addition, each functional unit in each embodiment of the present invention 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.

[0240] 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 invention 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. When the computer program is executed by the processor, the steps of the above-mentioned thermal radiation electrochemical refrigeration system control method embodiments can be implemented. 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. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0241] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those 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 invention, and should all be included in the protection scope of the present invention.

Claims

1. A control method for a thermal radiation electrochemical refrigeration system, characterized in that: The refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve; the method includes: Acquiring operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment; Determining an operation mode of the refrigeration system at a next moment based on a state of the refrigeration system at a current moment and a trained reinforcement learning network; Based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority, the operating parameters of the refrigeration system at the next moment are determined, and the operating parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment are adjusted accordingly.

2. The control method of the thermal radiation electrochemical refrigeration system according to claim 1, characterized in that: The state space of the reinforcement learning network includes hydride content, wall temperature, ambient temperature, ambient humidity, carbon dioxide concentration and PM2.5 concentration, the action space includes cooling, heating, dehumidification, fresh air and air purification, and the reward function is R=f (energy efficiency, comfort, safety).

3. The control method of the thermal radiation electrochemical refrigeration system according to claim 2, characterized in that: Before determining the operation mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, the method further includes: Based on the reward function, the reward values ​​of the refrigeration system performing different actions in multiple states are calculated and stored as training samples, and this step is repeated to obtain a training sample set of the refrigeration system; The initial reinforcement learning network is trained based on the training sample set to obtain a trained reinforcement learning network.

4. The control method of the thermal radiation electrochemical refrigeration system according to claim 1, characterized in that: The determining the operating parameters of the refrigeration system at the next moment based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority includes: The operation of the refrigeration system is controlled based on standard operating parameters corresponding to the operating mode of the refrigeration system at the next moment, and the operating parameters of the refrigeration system are adjusted in real time based on preset optimization item priorities.

5. The control method of the thermal radiation electrochemical refrigeration system according to claim 4, characterized in that: The priorities of the optimization items from high to low are hydride content control, wall humidity control, wall temperature control, circulating water pipe pressure control, energy efficiency ratio control and ambient temperature control; The real-time adjustment of the operating parameters of the refrigeration system based on the preset optimization item priorities includes: Determine whether the hydride content of the heat exchanger is abnormal. If the hydride content of the heat exchanger is abnormal, control the electrochemical hydrogen pump to stop running. Otherwise, determine whether the wall humidity is abnormal. If the wall humidity is abnormal, the cooling temperature is adjusted, otherwise it is determined whether the wall temperature is abnormal; If the wall temperature is abnormal, the cooling temperature is adjusted, otherwise it is determined whether the circulating water pipe pressure is abnormal; If the pressure of the circulating water pipe is abnormal, the power of the circulating water pump is adjusted; otherwise, whether the energy efficiency ratio is abnormal is determined; If the energy efficiency ratio is abnormal, the cooling temperature is adjusted; otherwise, the cooling temperature is optimized based on the ambient temperature and the user-set temperature.

6. The control method of the thermal radiation electrochemical refrigeration system according to claim 1, characterized in that: The operating parameters include vibration data; before determining the operating mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network, the method further includes: determining a predicted failure probability of the refrigeration system based on vibration data of the refrigeration system; If the predicted fault probability is greater than a threshold, then entering a fault diagnosis mode; If the predicted failure probability is not greater than the threshold, the operation mode of the refrigeration system at the next moment is determined based on the state of the refrigeration system at the current moment and the trained reinforcement learning network.

7. The control method of the thermal radiation electrochemical refrigeration system according to claim 6, characterized in that: Determining the predicted failure probability of the refrigeration system based on the vibration data includes: The similarity between the vibration data of the refrigeration system and the historical fault vibration data is calculated as the predicted fault probability of the refrigeration system.

8. A heat radiation electrochemical refrigeration system control device, characterized in that: The refrigeration system includes a heat exchanger, an electrochemical hydrogen pump, a circulating water pump and an electromagnetic three-way valve; the device includes: An acquisition module, used to acquire operating parameters and environmental parameters of the refrigeration system at the current moment as the state of the refrigeration system at the current moment; A mode determination module, used to determine the operation mode of the refrigeration system at the next moment based on the state of the refrigeration system at the current moment and the trained reinforcement learning network; A parameter determination module is used to determine the operating parameters of the refrigeration system at the next moment based on the operating mode of the refrigeration system at the next moment and the preset optimization item priority, and to adjust the operating parameters of the electrochemical hydrogen pump, the circulating water pump and the electromagnetic three-way valve at the next moment accordingly.

9. A temperature control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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