Multi-connected heat recovery air conditioning system and method thereof

Abstract

The invention discloses a multi-connected heat recovery air-conditioning system and a method thereof, and relates to the technical field of multi-connected air-conditioning systems. The method comprises the following steps: extracting a pipeline thermal resistance parameter set based on a multi-connected heat flow framework diagram, and setting a refrigerant flow direction selection variable and a heat recovery switch threshold value in combination with a building space thermal load parameter; performing minimum thermal resistance path search through a quantum annealing optimization algorithm to generate a heat recovery path matrix; and based on the building space thermal load parameters, extracting regional energy efficiency features and heat recovery potential features to construct a heat transaction weight matrix, and performing space vector projection in combination with the heat recovery path matrix to generate a dynamic flow control instruction set. Through the specific tunneling effect and the quantum parallel computing characteristic of the quantum annealing algorithm, high-dimensional thermal resistance space global optimal solution search which is difficult to achieve by a traditional algorithm is completed within millisecond-level time, and the multi-connected air conditioning system can meet optimal distribution of multiple heat recovery demand areas at the same time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-connected air conditioning system, in particular to a multi-connected heat recovery air conditioning system and method thereof. BACKGROUND

[0002] As an important technical direction in the field of building energy saving, multi-connected air conditioning system has made remarkable progress in the realization of heat recovery function in recent years. Traditional systems mostly use control strategies based on fixed rules, which adjust the refrigerant flow distribution according to the preset logic by the central controller after collecting environmental parameters through a network of temperature sensors. With the development of Internet of Things technology, existing solutions can realize the linkage control of indoor and outdoor units, and some advanced air conditioning systems have introduced heat load prediction algorithms based on machine learning, Although the heat resistance calculation model used in the current mainstream system can reflect the heat transfer characteristics under static working conditions, it does not consider the real-time influence of the dynamic changes of building space heat load on the distribution of pipeline heat resistance. This leads to the fact that the system cannot quickly reconstruct the optimal heat recovery path when dealing with sudden heat load fluctuations. Especially when there is a conflict among multiple heat recovery demand areas, the existing path selection strategy based on the greedy algorithm is easy to fall into a local optimal solution, resulting in a decrease in the overall energy efficiency of the air conditioning system. SUMMARY

[0003] In view of the above existing problems, the present application is proposed.

[0004] Therefore, the present application provides a multi-connected heat recovery air conditioning method to solve the problem of energy efficiency fluctuation caused by the insufficient dynamic optimization capability of the heat recovery path in the prior art.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a multi-connected heat recovery air conditioning method, which comprises, calling pre-stored refrigerant pipe network inherent attribute parameters, pipe connection relationships and indoor unit three-dimensional coordinates, calculating the baseline heat transfer coefficient of each refrigerant pipe section based on the refrigerant pipe network inherent attribute parameters; real-time collection of building space heat load parameters and construction of a multi-connected heat flow architecture diagram combining the baseline heat transfer coefficient of each refrigerant pipe section, the pipe connection relationship and the indoor unit three-dimensional coordinates; Based on the multi-connected heat flow architecture diagram, the pipe heat resistance parameter set is extracted, the refrigerant flow direction selection variable and the heat recovery switch threshold value are set combining the building space heat load parameters, and the minimum heat resistance path is searched through the quantum annealing optimization algorithm to generate a heat recovery path matrix; Based on the building space heat load parameters, the regional energy efficiency characteristics and heat recovery potential characteristics are extracted to construct a heat transaction weight matrix, and the space vector projection is performed combining the heat recovery path matrix to generate a dynamic flow control instruction set; According to the dynamic flow control instruction set, the heat recovery path matrix is dynamically reconstructed, and the entropy increase feedback adjustment is performed based on the multi-connection heat flow architecture diagram and the heat transaction weight matrix.

[0006] As a preferred scheme of the multi-connection heat recovery air conditioning method, the method comprises the following steps of: The pre-stored inherent attribute parameters of the refrigerant pipe network are called to calculate the heat resistance values of each refrigerant pipe section. The heat transfer efficiency conversion is performed on the heat resistance values of each refrigerant pipe section to generate the reference heat transfer coefficients of each refrigerant pipe section.

[0007] As a preferred scheme of the multi-connection heat recovery air conditioning method, the building space heat load parameters include indoor temperature data, indoor personnel density data, indoor equipment heat generation data, outdoor meteorological parameters and domestic water tank temperature data.

[0008] As a preferred scheme of the multi-connection heat recovery air conditioning method, the method comprises the following steps of: The pre-stored pipe connection relationship and indoor unit three-dimensional coordinates are called to construct a topological edge set and a topological node set, respectively. The reference heat transfer coefficients of each refrigerant pipe section are converted into refrigerant pipe section cross-sectional area and refrigerant pipe section length normalization to generate a topological edge conductance value set. The topological edge conductance value set, the topological edge set and the topological node set are integrated to generate a multi-connection heat flow architecture diagram.

[0009] As a preferred scheme of the multi-connection heat recovery air conditioning method, the method comprises the following steps of: The topological edge conductance value set of the multi-connection heat flow architecture diagram is converted into a pipe heat resistance parameter set. Based on the indoor temperature data in the building space heat load parameters, a refrigerant flow direction selection variable is set, and based on the domestic water tank temperature data, a heat recovery switch threshold value is set, and combined with the pipe heat resistance parameter set, a quantum annealing optimization algorithm is used to search for a minimum heat resistance path to generate a quantum optimization path state set. The quantum optimization path state set is converted into a flow direction state matrix to generate a heat recovery path matrix.

[0010] As a preferred scheme of the multi-connected heat recovery air conditioning method of the present application, wherein: based on the building space heat load parameter, the district energy efficiency characteristic and the heat recovery potential characteristic are extracted to construct the heat transaction weight matrix, and the space vector projection is carried out combined with the heat recovery path matrix to generate the dynamic flow control instruction set, the specific steps are as follows, The district energy efficiency characteristic is determined through the correlation analysis of the equipment heat generation and the indoor and outdoor temperature difference, and the heat recovery potential characteristic is established combined with the relative evaluation of the water tank temperature and the indoor temperature difference; Based on the district energy efficiency characteristic and the heat recovery potential characteristic, the heat transaction weight matrix is generated through the weighted fusion and normalization of the collaborative optimization algorithm; The dynamic flow control instruction set is generated by matrix multiplication projection and parameter normalization of the heat transaction weight matrix and the heat recovery path matrix.

[0011] As a preferred scheme of the multi-connected heat recovery air conditioning method of the present application, wherein: according to the dynamic flow control instruction set, the heat recovery path matrix is dynamically reconstructed, and the entropy increase feedback adjustment is carried out based on the multi-connected heat flow architecture diagram and the heat transaction weight matrix, the specific steps are as follows, Based on the dynamic flow control instruction set, the flow distribution parameter is extracted through instruction analysis, and the flow control execution mechanism is controlled according to the flow distribution parameter to generate the actual flow distribution state set; Combined with the actual flow distribution state set and the multi-connected heat flow architecture diagram, the sound wave guide flow optimization parameter set is generated through flow state optimization control; The feedback adjustment signal is generated by temperature-weight correlation analysis of the multi-connected heat flow architecture diagram, the heat transaction weight matrix and the actual flow distribution state set.

[0012] In the second aspect, the present application provides a multi-connected heat recovery air conditioning system, comprising a heat transfer calculation module, an architecture construction module, a path optimization module, an instruction generation module and a dynamic adjustment module, The heat transfer calculation module calls the pre-stored inherent attribute parameters of the refrigerant pipe network, the pipeline connection relationship and the indoor unit three-dimensional coordinates, calculates the baseline heat transfer coefficient of each refrigerant pipe section based on the inherent attribute parameters of the refrigerant pipe network; The architecture construction module collects the building space heat load parameter in real time and constructs the multi-connected heat flow architecture diagram combined with the baseline heat transfer coefficient of each refrigerant pipe section, the pipeline connection relationship and the indoor unit three-dimensional coordinates; The path optimization module extracts the pipeline thermal resistance parameter set based on the multi-connected heat flow architecture diagram, sets the refrigerant flow direction selection variable and the heat recovery switch threshold combined with the building space heat load parameter, and searches for the minimum thermal resistance path through the quantum annealing optimization algorithm to generate the heat recovery path matrix; An instruction generation module extracts zone energy efficiency features and heat recovery potential features based on building space heat load parameters, constructs a heat transaction weight matrix, and generates a dynamic flow control instruction set by combining the heat recovery path matrix with space vector projection. A dynamic adjustment module dynamically reconstructs the heat recovery path matrix according to the dynamic flow control instruction set, and performs entropy increase feedback adjustment based on the multi-connection heat flow architecture diagram and the heat transaction weight matrix.

[0013] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the multi-connection heat recovery air conditioning method according to the first aspect of the present application.

[0014] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the multi-connection heat recovery air conditioning method according to the first aspect of the present application.

[0015] The present application has the following beneficial effects: through the tunneling effect and quantum parallel computing characteristics of the quantum annealing algorithm, the high-dimensional heat resistance space global optimal solution search that is difficult to achieve by traditional algorithms is completed within milliseconds, so that the multi-connection air conditioning system can simultaneously meet the optimal distribution of multiple heat recovery demand areas. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Fig. 1 The flowchart of the multi-connection heat recovery air conditioning method.

[0018] Fig. 2 The schematic diagram of the multi-connection heat recovery air conditioning system.

[0019] Fig. 3 The flowchart of the multi-connection heat flow architecture.

[0020] Fig. 4 The flowchart of dynamic control instruction generation and execution. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] Reference Figs. 1-4 As one embodiment of the present invention, this embodiment provides a multi-unit heat recovery air conditioning method, including the following steps: S1. Call the pre-stored inherent attribute parameters of the refrigerant piping network, the piping connection relationship and the three-dimensional coordinates of the indoor unit, and calculate the reference heat transfer coefficient of each refrigerant pipe segment based on the inherent attribute parameters of the refrigerant piping network. S1.1. Call the pre-stored inherent property parameters of the refrigerant piping network to calculate the thermal resistance value of each refrigerant pipe section. The expression is as follows: ; in, For the first Thermal resistance value of each refrigerant pipe section For the first The outer diameter of each refrigerant pipe section. For the first The inner diameter of each refrigerant pipe section. For the first The thermal conductivity of the refrigerant pipe section. For the first The length of each refrigerant pipe segment; It should be noted that the inherent properties of the refrigerant piping network are obtained by pre-measuring and recording the pipe diameter, wall thickness, and thermal conductivity data of the refrigerant pipe segments. The pipe connection relationships are determined by recording the topological connection relationships between each refrigerant pipe segment. The three-dimensional coordinates of the indoor units are obtained by measuring and recording the specific location coordinates of each indoor unit in the building space. When calculating the thermal resistance value of each refrigerant pipe segment, the pipe diameter, wall thickness, and thermal conductivity data from the pre-stored inherent properties of the refrigerant piping network are directly retrieved.

[0025] S1.2. Convert the thermal resistance value of each refrigerant pipe section to the thermal conduction efficiency to generate the reference heat transfer coefficient of each refrigerant pipe section.

[0026] It should be noted that when converting the thermal resistance values of each refrigerant pipe section into thermal conduction efficiency, the basic relationship of thermotechnics is adopted, the inverse operation of each refrigerant pipe section thermal resistance value is performed, and each refrigerant pipe section reference heat transfer coefficient is directly converted. The conversion process strictly follows the mathematical relationship between thermal resistance and heat transfer coefficient in the principle of heat transfer, and no correction or supplementary calculation is performed on each refrigerant pipe section thermal resistance value. The numerical results of generating each refrigerant pipe section reference heat transfer coefficient maintain a strict inverse proportional relationship with each refrigerant pipe section thermal resistance value.

[0027] S2, real-time acquisition of building space heat load parameters and combination of each refrigerant pipe section reference heat transfer coefficient, pipe connection relationship and indoor unit three-dimensional coordinate to construct a multi-link heat flow architecture diagram; S2.1, the building space heat load parameters include indoor temperature data, indoor personnel density data, indoor equipment heat generation data, outdoor meteorological parameters and domestic water tank temperature data; It should be noted that the indoor temperature data is obtained by collecting the air temperature values of each region inside the building through temperature sensors; the indoor personnel density data is determined by counting the number of personnel per unit area in each space; the indoor equipment heat generation data records the rated power and operating state of each electrical equipment; the outdoor meteorological parameters include environmental and climate data such as dry-bulb temperature, wet-bulb temperature and solar radiation intensity; and the domestic water tank temperature data is obtained by measuring the temperature of the water tank through a temperature probe installed in the water tank.

[0028] S2.2, call the pre-stored pipe connection relationship and indoor unit three-dimensional coordinate to construct a topological edge set and a topological node set respectively; It should be noted that the physical connection information between the refrigerant pipe sections recorded in the pre-stored pipe connection relationship data is called, and each connection relationship is converted into a directed edge in the topological edge set; at the same time, the space position parameters of each indoor unit recorded in the pre-stored indoor unit three-dimensional coordinate data are called, and the installation position of each indoor unit is converted into a node in the topological node set. In the construction process, the topological edge set is strictly generated according to the physical connection order and direction in the pipe connection relationship data, and the topological node set is completely formed according to the space position coordinates in the indoor unit three-dimensional coordinate data.

[0029] S2.3, refrigerant pipe section cross-sectional area conversion and refrigerant pipe section length normalization are performed on each refrigerant pipe section reference heat transfer coefficient to generate a topological edge conductance value set; It should be noted that the cross-sectional area of each refrigerant pipe section is obtained according to the pipe diameter data in the pre-stored inherent attribute parameters of the refrigerant pipe network, the scalar product operation is performed on each refrigerant pipe section reference heat transfer coefficient and the corresponding cross-sectional area, and the pipe section thermal conductivity parameter representing the total heat conduction capacity of the pipe section is obtained; then, referring to the length data of each refrigerant pipe section in the pre-stored pipe connection relationship data, the length normalization processing is performed on the pipe section thermal conductivity parameter representing the total heat conduction capacity of the pipe section.

[0030] S2.4, structuring data integration of the topology edge conductance value set, the topology edge set and the topology node set, and generating a multi-connected heat flow architecture diagram.

[0031] It should be noted that the connection relationship defined by the topology edge conductance value set, the topology edge set and the space coordinates recorded by the topology node set are data associated according to the pre-stored pipeline connection relationship. Among them, the physical connection correspondence requires that each edge of the topology edge set must strictly correspond to the actual pipe section in the pipeline connection relationship data and the node coordinates of the topology node set must be consistent with the engineering level geometry of the indoor unit three-dimensional coordinate data, and the deviation is controlled within the allowable tolerance range of the building mechanical and electrical system installation specification (such as the installation tolerance of EN 16798-3:2017).

[0032] S3, based on the multi-connected heat flow architecture diagram, extracting the pipeline thermal resistance parameter set, combining the building space heat load parameter to set the refrigerant flow direction selection variable and the heat recovery switch threshold, and searching the minimum thermal resistance path through the quantum annealing optimization algorithm to generate a heat recovery path matrix; S3.1, converting the topology edge conductance value set of the multi-connected heat flow architecture diagram into a pipeline thermal resistance parameter set according to the heat conduction physical relationship; It should be noted that the value recorded in each data item in the topology edge conductance value set is read, and the value recorded in each data item in the topology edge conductance value set represents the comprehensive heat transfer capacity of the corresponding refrigerant pipe section; Then, strict inverse operation is performed on each value, and the conductance value representing the heat transfer capacity is converted into the thermal resistance value representing the heat transfer resistance to generate the pipeline thermal resistance parameter set. During the conversion process, the value unit of the topology edge conductance value set is converted from W / (m·K) to (m·K) / W to ensure the physical accuracy of the dimension conversion. The generated pipeline thermal resistance parameter set completely retains the data structure of the original topology edge conductance value set, and each pipeline thermal resistance parameter value is strictly corresponding to a specific refrigerant pipe section in the multi-connected heat flow architecture diagram, accurately reflecting the resistance characteristics of the pipe section in the heat conduction process.

[0033] S3.2, setting the refrigerant flow direction selection variable based on the indoor temperature data in the building space heat load parameter, and setting the heat recovery switch threshold according to the water tank temperature data, combining the pipeline thermal resistance parameter set, and searching the minimum thermal resistance path through the quantum annealing optimization algorithm to generate a quantum optimization path state set; It should be noted that based on the real-time collected indoor temperature data in the building space heat load parameter, when the indoor temperature data is higher than the preset refrigeration threshold, the refrigerant flow direction selection variable is assigned a value of 1 indicating forward flow, and a value of 0 indicating reverse flow when the indoor temperature data is lower than the refrigeration threshold. At the same time, when the domestic water tank temperature data is lower than the preset heat recovery switch threshold, the heat recovery switch threshold is set to 1 indicating the open state, otherwise it is set to 0. The refrigerant flow direction selection variable, the heat recovery switch threshold and the recorded value of the pipeline thermal resistance parameter set are input into the quantum annealing optimization algorithm to establish a search condition with the minimum total thermal resistance as the optimization objective. Through the quantum tunneling effect and parallel processing capability of the quantum annealing optimization algorithm, the total thermal resistance minimum pipe section combination scheme is found under the conditions of meeting the flow direction constraint and the heat recovery constraint, and finally the quantum optimization path state set recording the optimal state of each pipe section is output.

[0034] It should be noted that the preset refrigeration threshold is determined comprehensively according to the indoor temperature data, indoor personnel density data and indoor equipment heat data in the building space heat load parameter. When the indoor personnel density data is less than 2 people per square meter and the indoor equipment heat data is less than 50 W per square meter, the refrigeration threshold is set to 26℃; when the indoor personnel density data reaches or exceeds 2 people per square meter, the refrigeration threshold is adjusted to 25℃; when the indoor equipment heat data reaches or exceeds 50 W per square meter, the refrigeration threshold is adjusted to 25℃; when the indoor personnel density data and indoor equipment heat data reach or exceed the above standard at the same time, the refrigeration threshold is set to 24℃. The heat recovery switch threshold is set based on the domestic water tank temperature data in the building space heat load parameter. When the domestic water tank temperature is lower than 50℃, the heat recovery switch threshold is set to the open state (1), and when it reaches or exceeds 50℃, it is set to the closed state (0). The heat recovery switch threshold is strictly limited to the adjustable range of 45-55℃.

[0035] S3.3, the flow direction state matrix conversion is performed on the quantum optimization path state set to generate a heat recovery path matrix.

[0036] It should be noted that the state identifier of each pipe section in the quantum optimization path state set is read. The state identifier of 1 indicates that the pipe section participates in the heat recovery path, and the state identifier of 0 indicates that it does not participate. According to the order of the topology node set in the multi-connection heat flow architecture diagram, the state identifier of each pipe section is filled into the corresponding matrix position to form a complete heat recovery path matrix. The rows and columns of the matrix correspond to the starting nodes and ending nodes of the topology node set respectively, and the matrix element value of 1 indicates that the pipe section between the nodes is selected as the heat recovery path, and 0 indicates that it is not selected.

[0037] S4, based on the building space heat load parameter, the district energy efficiency characteristics and heat recovery potential characteristics are extracted to construct a heat transaction weight matrix, and the space vector projection is performed combined with the heat recovery path matrix to generate a dynamic flow control instruction set; S4.1, determine the district energy efficiency characteristics by correlating the heat generation of the equipment with the indoor-outdoor temperature difference, and establish the heat recovery potential characteristics by combining the relative evaluation of the water tank temperature and the indoor temperature difference; It should be noted that the indoor equipment heat generation data is multiplied by the indoor-outdoor temperature difference data of the corresponding area, and the obtained value is normalized to serve as the district energy efficiency characteristic value. At the same time, based on the difference between the water tank temperature data and the indoor temperature data, the difference between the water tank temperature data and the indoor temperature data is compared with the preset reference temperature difference, and when the difference is greater than the reference temperature difference, the heat recovery potential characteristic value is set to 1, otherwise it is 0, to establish the heat recovery potential characteristic.

[0038] It should be noted that the preset reference temperature difference is the average difference between the water tank temperature data and the indoor temperature data in the past 30 days.

[0039] S4.2, based on the district energy efficiency characteristics and the heat recovery potential characteristics, weighted fusion and normalization are performed through a cooperative optimization algorithm to generate a heat transaction weight matrix; It should be noted that the district energy efficiency characteristic value is given a weight coefficient, and the weight coefficient of the heat recovery potential characteristic value is also given a weight coefficient, and the sum of the weight coefficients is 1; then the weighted district energy efficiency characteristic value and the heat recovery potential characteristic value are added to obtain an initial weight value; finally, all the initial weight values are normalized to the maximum and minimum values to make the final value distribution within the range of 0 to 1, forming a heat transaction weight matrix.

[0040] S4.3, matrix multiplication projection and parameter normalization are performed on the heat transaction weight matrix and the heat recovery path matrix to generate a dynamic flow control instruction set.

[0041] It should be noted that based on the matrix element level operation rule, the elements corresponding to the positions in the heat transaction weight matrix and the heat recovery path matrix are subjected to Hadamard product operation to generate an initial projection value matrix representing the path comprehensive weight. The operation process needs to meet the dimension consistency principle, that is, the row index of the weight matrix and the column index of the path matrix are aligned through a one-to-one mapping relationship to ensure that each operation result accurately reflects the energy interaction intensity of a specific path. When implementing standardization processing on the initial projection value matrix, the range normalization method is adopted: first, determine the minimum and maximum values in the matrix, and then map all data to the closed interval [0, 1] range through a linear transformation function. This transformation process preserves the relative distribution relationship of the original data, and its mathematical essence is to compress the input space to the standard unit interval. The final output normalized matrix retains the original topological structure, and each element value corresponds to the energy distribution weight of a specific refrigerant pipe segment.

[0042] S5, according to the dynamic flow control instruction set, the heat recovery path matrix is dynamically reconstructed, and entropy increase feedback adjustment is performed based on the multi-connection heat flow architecture diagram and the heat transaction weight matrix.

[0043] S5.1, based on the dynamic flow control instruction set, extract flow allocation parameters through instruction analysis and control the flow control execution mechanism according to the flow allocation parameters to generate an actual flow allocation state set; It should be noted that the flow allocation ratio value recorded in each element of the dynamic flow control instruction set is converted into the electronic expansion valve opening percentage, the variable frequency water pump speed value and the variable frequency compressor frequency value, wherein the flow allocation ratio value and the electronic expansion valve opening percentage, the variable frequency water pump speed value and the variable frequency compressor frequency value have a linear correspondence relationship. The electronic expansion valve, the variable frequency water pump and the variable frequency compressor adjust the operating state according to the electronic expansion valve opening percentage, the variable frequency water pump speed value and the variable frequency compressor frequency value obtained by analysis. During execution, the actual opening percentage of the electronic expansion valve, the actual speed value of the variable frequency water pump and the actual frequency value of the variable frequency compressor are recorded in real time to form an actual flow allocation state set.

[0044] S5.2, combine the actual flow allocation state set with the multi-connection heat flow architecture diagram, and generate a sound wave guide flow optimization parameter set through flow state optimization control; It should be noted that according to the electronic expansion valve opening percentage in the actual flow allocation state set and the connection relationship of the corresponding topological node in the multi-connection heat flow architecture diagram, the sound wave guide flow frequency reference value at each node is determined; the variable frequency water pump speed value and the variable frequency compressor frequency value are referred to to determine the sound wave guide flow intensity parameter of each pipe segment in the topological edge set; finally, all the sound wave guide flow frequency reference values of the nodes and the sound wave guide flow intensity parameters of the pipe segments are integrated to form a complete sound wave guide flow optimization parameter set.

[0045] S5.3, temperature-weight correlation analysis is performed on the multi-connection heat flow architecture diagram, the heat transaction weight matrix and the actual flow allocation state set to generate a feedback adjustment signal.

[0046] It should be noted that the topological node temperature data recorded in the multi-connection heat flow architecture diagram and the electronic expansion valve opening percentage, the variable frequency water pump speed value and the variable frequency compressor frequency value recorded in the actual flow allocation state set are correlated. The priority weight value recorded in the heat transaction weight matrix is associated with each topological node area. When the topological node temperature data exceeds the thermodynamic equilibrium threshold, a feedback adjustment signal is generated according to the priority weight value of the corresponding area in the heat transaction weight matrix. The strength of the feedback adjustment signal is proportional to the weight value recorded in the heat transaction weight matrix. The parameters used in the whole process are all from the original recorded data of the multi-connection heat flow architecture diagram, the heat transaction weight matrix and the actual flow allocation state set, without adding new data processing methods or modifying parameters. The generated feedback adjustment signal is used to adjust the subsequent refrigerant flow allocation process.

[0047] It should be noted that the thermodynamic equilibrium threshold is dynamically determined by the temperature data recorded by the multi-connection heat flow architecture diagram, the priority coefficient of the heat transaction weight matrix, and the device parameter set of the actual flow allocation state: taking the 72-hour moving average temperature as the benchmark center value, combining the historical fluctuation characteristics to establish a basic fluctuation range of ±1.5°C; the priority coefficient (0.8-1.2) of the heat transaction weight matrix is dynamically adjusted by 0.3°C for every 0.1, and the high-priority area is narrowed to ±0.9°C, and the low-priority area is widened to ±2.1°C; at the same time, according to the actual flow allocation state set, through the heat transfer coefficient and fluid resistance characteristics in the inherent attribute parameters of the refrigerant pipe network, the working condition compensation of ±0.5°C is carried out. The final threshold range is controlled in the range of ±(0.9-2.1) °C of the benchmark temperature under typical working conditions.

[0048] The embodiment also provides a multi-connection heat recovery air conditioning system, comprising: a heat transfer calculation module, an architecture construction module, a path optimization module, an instruction generation module and a dynamic adjustment module, The heat transfer calculation module calls the pre-stored inherent attribute parameters of the refrigerant pipe network, the pipeline connection relationship and the three-dimensional coordinates of the indoor unit, calculates the benchmark heat transfer coefficient of each refrigerant pipe section based on the inherent attribute parameters of the refrigerant pipe network; The architecture construction module collects the building space heat load parameters in real time and constructs a multi-connection heat flow architecture diagram in combination with the benchmark heat transfer coefficient of each refrigerant pipe section, the pipeline connection relationship and the three-dimensional coordinates of the indoor unit; The path optimization module extracts the pipeline thermal resistance parameter set based on the multi-connection heat flow architecture diagram, sets the refrigerant flow direction selection variable and the heat recovery switch threshold in combination with the building space heat load parameters, and searches for the minimum thermal resistance path through the quantum annealing optimization algorithm to generate a heat recovery path matrix; The instruction generation module extracts the regional energy efficiency characteristics and heat recovery potential characteristics to construct a heat transaction weight matrix based on the building space heat load parameters, and projects the space vector in combination with the heat recovery path matrix to generate a dynamic flow control instruction set; The dynamic adjustment module dynamically reconstructs the heat recovery path matrix according to the dynamic flow control instruction set, and performs entropy increase feedback adjustment based on the multi-connection heat flow architecture diagram and the heat transaction weight matrix.

[0049] The embodiment also provides a computer device suitable for the case of the multi-connection heat recovery air conditioning method, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the multi-connection heat recovery air conditioning method proposed in the above embodiment.

[0050] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved by WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0051] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the air conditioning method for realizing multi-connection heat recovery proposed in the above embodiment; and the storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0052] In summary, the present application can realize the global optimal solution search in a high-dimensional heat resistance space within a millisecond by the tunneling effect and quantum parallel computing characteristics of the quantum annealing algorithm, which is difficult to achieve by a traditional algorithm, so that the multi-connection air conditioning system can simultaneously meet the optimal distribution of multiple heat recovery demand areas.

[0053] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all modifications or replacements should be covered in the scope of the claims of the present application.

Claims

1. A method for multi-unit heat recovery air conditioning, characterized in that: include, The system calls up the pre-stored inherent property parameters of the refrigerant piping network, the piping connection relationship and the three-dimensional coordinates of the indoor unit, and calculates the reference heat transfer coefficient of each refrigerant pipe segment based on the inherent property parameters of the refrigerant piping network. Real-time acquisition of building space heat load parameters and combination with the reference heat transfer coefficient of each refrigerant pipe section, pipe connection relationship and indoor unit three-dimensional coordinates to construct a multi-unit heat flow architecture diagram; Based on the multi-connected heat flow architecture diagram, the set of pipeline thermal resistance parameters is extracted. Combined with the building space heat load parameters, the refrigerant flow direction selection variable and heat recovery switching threshold are set. The minimum thermal resistance path is searched through the quantum annealing optimization algorithm to generate the heat recovery path matrix. Based on the building space heat load parameters, regional energy efficiency characteristics and heat recovery potential characteristics are extracted to construct a heat trading weight matrix, and spatial vector projection is performed in combination with the heat recovery path matrix to generate a dynamic flow control instruction set. Based on the dynamic flow control instruction set, the heat recovery path matrix is ​​dynamically reconstructed, and entropy increase feedback adjustment is performed based on the multi-connected heat flow architecture diagram and the heat transaction weight matrix.

2. The multi-unit heat recovery air conditioning method as described in claim 1, characterized in that: The specific steps for calculating the reference heat transfer coefficient of each refrigerant pipe section based on the inherent property parameters of the refrigerant piping network are as follows. Call the pre-stored inherent attribute parameters of the refrigerant piping network to calculate the thermal resistance value of each refrigerant pipe segment; The thermal resistance of each refrigerant pipe section is converted into thermal conductivity to generate a reference heat transfer coefficient for each refrigerant pipe section.

3. The multi-unit heat recovery air conditioning method as described in claim 2, characterized in that: The building space heat load parameters include indoor temperature data, indoor occupancy density data, indoor equipment heat generation data, outdoor meteorological parameters, and domestic water tank temperature data.

4. The multi-unit heat recovery air conditioning method as described in claim 3, characterized in that: The process of constructing a multi-unit heat flow architecture diagram by combining the reference heat transfer coefficients of each refrigerant pipe section, the pipe connection relationships, and the three-dimensional coordinates of the indoor unit is as follows: The pre-stored pipe connection relationships and indoor unit 3D coordinates are used to construct the topology edge set and topology node set respectively; The reference heat transfer coefficient of each refrigerant pipe segment is converted by the cross-sectional area of ​​the refrigerant pipe segment and normalized by the length of the refrigerant pipe segment to generate a set of topological edge conductance values. The topological edge conductance set, topological edge set, and topological node set are integrated into structured data to generate a multi-connected heat flow architecture diagram.

5. The multi-unit heat recovery air conditioning method as described in claim 4, characterized in that: The specific steps for generating the heat recovery path matrix are as follows: The heat conduction physical relationship is transformed from the topological side conduction value set of the multi-connection heat flow architecture diagram to generate the pipeline thermal resistance parameter set; Based on the indoor temperature data in the building space heat load parameters, the refrigerant flow direction selection variable is set, and the heat recovery switch threshold is set according to the domestic water tank temperature data. Combined with the pipeline thermal resistance parameter set, the minimum thermal resistance path is searched through the quantum annealing optimization algorithm to generate the quantum optimization path state set. The quantum-optimized path state set is transformed into a flow-state matrix to generate a heat recovery path matrix.

6. The multi-unit heat recovery air conditioning method as described in claim 5, characterized in that: The process involves extracting regional energy efficiency characteristics and heat recovery potential characteristics based on building space heat load parameters to construct a heat trading weight matrix, and then combining this matrix with a heat recovery path matrix to perform spatial vector projection, generating a dynamic flow control instruction set. The specific steps are as follows: The regional energy efficiency characteristics are determined by the correlation analysis between the equipment's heat output and the indoor-outdoor temperature difference, and the heat recovery potential characteristics are established by combining the relative assessment of the water tank temperature and the indoor temperature difference. Based on regional energy efficiency characteristics and heat recovery potential characteristics, a weighted fusion and normalization algorithm is used to generate a heat trading weight matrix. Matrix multiplication projection and parameter normalization are performed on the hot transaction weight matrix and the hot recovery path matrix to generate a dynamic flow control instruction set.

7. The multi-unit heat recovery air conditioning method as described in claim 6, characterized in that: The process involves dynamically reconstructing the heat recovery path matrix based on the dynamic flow control instruction set, and performing entropy increase feedback adjustment based on the multi-connected heat flow architecture diagram and the heat transaction weight matrix. The specific steps are as follows: Based on the dynamic flow control instruction set, flow allocation parameters are extracted through instruction parsing, and the flow control actuator is controlled according to the flow allocation parameters to generate the actual flow allocation state set. By combining the actual flow distribution state set with the multi-connected heat flow architecture diagram, an acoustic waveguide optimization parameter set is generated through flow regime optimization control. Temperature-weight correlation analysis is performed on the multi-connected heat flow architecture diagram, heat transaction weight matrix, and actual flow allocation state set to generate feedback regulation signals.

8. A multi-unit heat recovery air conditioning system, based on the multi-unit heat recovery air conditioning method according to any one of claims 1 to 7, characterized in that: It includes a heat transfer calculation module, an architecture construction module, a path optimization module, an instruction generation module, and a dynamic adjustment module. The heat transfer calculation module calls the pre-stored inherent property parameters of the refrigerant piping network, the piping connection relationship and the three-dimensional coordinates of the indoor unit, and calculates the reference heat transfer coefficient of each refrigerant pipe segment based on the inherent property parameters of the refrigerant piping network. The architecture construction module collects the heat load parameters of the building space in real time and combines them with the reference heat transfer coefficient of each refrigerant pipe section, the pipe connection relationship and the three-dimensional coordinates of the indoor unit to construct a multi-unit heat flow architecture diagram. The path optimization module, based on the multi-connection heat flow architecture diagram, extracts the set of pipeline thermal resistance parameters, combines the building space heat load parameters to set refrigerant flow direction selection variables and heat recovery switching thresholds, and uses the quantum annealing optimization algorithm to search for the minimum thermal resistance path and generate a heat recovery path matrix. The instruction generation module extracts regional energy efficiency characteristics and heat recovery potential characteristics based on building space heat load parameters to construct a heat trading weight matrix, and combines it with the heat recovery path matrix to perform spatial vector projection to generate a dynamic flow control instruction set. The dynamic adjustment module dynamically reconstructs the heat recovery path matrix according to the dynamic flow control instruction set, and performs entropy increase feedback adjustment based on the multi-connected heat flow architecture diagram and heat transaction weight matrix.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the multi-unit heat recovery air conditioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-unit heat recovery air conditioning method according to any one of claims 1 to 7.

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