Safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform

Abstract

The present invention provides a safe and reliable centralized air-conditioning cloud, edge, and terminal intelligent optimization energy-saving control platform, which relates to the technical field of control systems and is used to execute an energy-saving control method, which includes: collecting the operating status data of the central air-conditioning system, preprocessing the operating status data, and building an intelligent optimization model based on the law of the jungle algorithm. According to the preprocessed operating status data, the energy consumption of the central air-conditioning system is minimized, and the optimal control parameters are output through the intelligent optimization model. The optimal control parameters are sent to each sub-PLC controller through the main PLC controller to perform energy-saving control on the central air-conditioning system. The energy consumption of the central air-conditioning system is reduced, and the stability of the central air-conditioning control system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to a safe and reliable centralized air-conditioning cloud, edge, and end intelligent optimization and energy-saving control platform. Background Art

[0002] With the construction of a large number of public buildings, the energy consumption of HVAC systems continues to grow. By optimizing and controlling the existing HVAC systems, the energy consumed by buildings during operation can be greatly reduced. In the operation of central air-conditioning systems, it is of great significance to implement safe, reliable and stable optimized energy-saving control. This control can not only minimize the safety risks and failure rates during the operation of the system, and ensure the safety of personnel and equipment inside the building; at the same time, it can also improve the stability of the system and ensure stable operation under various working conditions, thereby maintaining the comfort of the indoor environment. In addition, optimized energy-saving control can also effectively reduce the energy consumption of the system, reduce energy consumption, and achieve the goal of energy conservation and emission reduction, which is of great significance to promoting sustainable development.

[0003] However, the prior art does not have effective solutions when a control system fails, which results in low stability of the control system and increased energy consumption. Summary of the invention

[0004] In order to solve the technical problem that the prior art has no effective solution when the control system fails, resulting in low stability of the control system and increased energy consumption, the present invention provides a safe and reliable centralized air-conditioning cloud, edge and end intelligent optimization energy-saving control platform.

[0005] The technical solution provided by the embodiment of the present invention is as follows:

[0006] A first aspect of the present invention provides a safe and reliable centralized air conditioning cloud, edge, and terminal intelligent optimization energy-saving control platform for executing an energy-saving control method, the energy-saving control method comprising:

[0007] S1: Collect the operating status data of the central air-conditioning system;

[0008] S2: preprocessing the operating status data;

[0009] S3: Building an intelligent optimization model based on the law of the jungle algorithm;

[0010] S4: Based on the pre-processed operating status data, with the goal of minimizing the energy consumption of the central air-conditioning system, the optimal control parameters are output through the intelligent optimization model;

[0011] S5: The optimal control parameters are sent to each sub-PLC controller through the main PLC controller to perform energy-saving control on the central air-conditioning system.

[0012] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0013] In the present invention, based on the law of the jungle algorithm, an intelligent optimization model is constructed, and according to the pre-processed operating status data, the minimum energy consumption of the central air-conditioning system is taken as the goal. Through the intelligent optimization model, the optimal control parameters are output, and by optimizing the control parameters, the system can operate in the best way under various working conditions, thereby reducing energy consumption and reducing the energy consumption of the central air-conditioning system; the optimal control parameters are sent to each sub-PLC controller through the main PLC controller, and the sending of the optimal control parameters is the best solution obtained according to the optimization algorithm, which can realize energy-saving control of the central air-conditioning system. At the same time, it can reduce energy consumption, reduce energy waste, improve the stability of the control system, and reduce the energy consumption of the central air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0015] Figure 1 A schematic diagram of a flow chart of an energy-saving control method provided by an embodiment of the present invention;

[0016] Figure 2 A structural schematic diagram of a safe and reliable centralized air-conditioning cloud, edge, and end intelligent optimization energy-saving control platform provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0018] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0019] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0020] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0021] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0022] Reference Manual Attached Figure 1 , which shows a flow chart of an energy-saving control method provided by an embodiment of the present invention.

[0023] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a safe and reliable centralized air-conditioning cloud, edge, and end intelligent optimization energy-saving control platform provided by an embodiment of the present invention.

[0024] The embodiment of the present invention provides a safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, wherein cloud refers to a cloud platform, edge refers to an edge end, and end refers to a terminal.

[0025] A safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform is used to execute the energy-saving control method. The processing flow of the energy-saving control method may include the following steps:

[0026] S1: Collect the operating status data of the central air-conditioning system.

[0027] Specifically, data is collected through various data collection devices of the central air-conditioning system, including ambient temperature and humidity sensors, water flow sensors, water temperature sensors, electric power meters, etc.

[0028] In the present invention, by collecting various data, the operating status of the central air-conditioning system can be monitored in real time, including parameters such as ambient temperature and humidity, water flow, water temperature and electric power, etc. This allows the system administrator to understand the working status of the system in a timely manner and make necessary adjustments and controls to ensure the normal operation of the system.

[0029] S2: Preprocess the running status data.

[0030] In a possible implementation manner, the preprocessing specifically includes:

[0031] Abnormal data were eliminated and missing data were filled for parameter identification.

[0032] Specifically, the specific methods of eliminating abnormal data include:

[0033] Calculate the first and third quartiles of the run status data.

[0034] Difference between the third quartile and the first quartile gives the interquartile range:

[0035] IQR=Q3-Q1

[0036] Among them, IQR represents interquartile range, Q3 represents the third quartile, and Q1 represents the first quartile.

[0037] Subtract 1.5 times IQR from the first quartile and the third quartile respectively to obtain the lower limit and upper limit of the normal data value:

[0038] lb=Q1-1.5IQR

[0039] ub=Q3+1.5IQR

[0040] Among them, lb represents the lower limit of the normal data value, and ub represents the upper limit of the normal data value.

[0041] Parameters that are lower than the lower limit of normal parameter values ​​and higher than the upper limit of normal parameter values ​​are identified as abnormal data.

[0042] Eliminate abnormal data.

[0043] The specific method to fill in the missing data is:

[0044] Assign values ​​to abnormal data according to the normal value closest to the abnormal data.

[0045] In the present invention, the quality and accuracy of data can be improved by eliminating abnormal data and filling missing data. Abnormal data and missing data will affect the results of subsequent data analysis and modeling. Preprocessing can effectively reduce these effects and improve data credibility.

[0046] The specific method of system parameter identification is:

[0047] With the goal of minimizing the parameter value, the parameter identification is performed using the least squares method:

[0048]

[0049] Where n represents the total number of parameters, x i represents the model input observation value of the i-th parameter, y irepresents the model output observation value of the i-th parameter, ω represents the unknown parameter, f() represents the least squares error, and h() represents the model prediction value.

[0050] In the present invention, for the identification of system parameters, more accurate parameter estimation can be obtained by cleaning and preprocessing data.

[0051] In summary, preprocessing of operating status data can improve data quality, analysis efficiency and system stability, and provide a reliable data foundation for subsequent data analysis, modeling and decision-making.

[0052] S3: Build an intelligent optimization model based on the law of the jungle algorithm.

[0053] Among them, the law of the jungle algorithm divides the population into predators and prey. The identities of predators and prey can be interchanged. The predator changes from the prey with the highest fitness value after each round of iteration. It is worth noting that although the prey is prey to the predator, it will also perform its own predation behavior. The predator and prey share the responsibility of finding the highest point of food density.

[0054] It should be noted that the cloud processor and the main PLC (Programmable Logic Controller) controller communicate via 5G. The cloud processor has built-in optimization algorithms and fault prediction algorithms that use big data learning to improve the effect, and uses the optimization algorithm to obtain the current system's optimized energy-saving control parameters. At the same time, the built-in fault prediction algorithm predicts whether the current central air-conditioning system is about to fail or has already failed based on the current system operating parameters.

[0055] Among them, the slicing technology in 5G improves the security of the network and adopts a strict network disconnection and networking mechanism. During the data interaction between the main PLC controller and the cloud processor, the cloud processor is temporarily disconnected from the main PLC controller, during which the main PLC controller performs optimized control.

[0056] In the present invention, the data collected from the sensor can be used for central air-conditioning system model training after the data processing stage. The model can be used as the optimization object of the optimization algorithm to calculate the optimal control parameters of the current system. As the central air-conditioning system continues to run, new data is constantly used for model training, and the central air-conditioning system model used by the optimization algorithm will become more accurate.

[0057] S4: Based on the pre-processed operating status data, with the goal of minimizing the energy consumption of the central air-conditioning system, the optimal control parameters are output through the intelligent optimization model.

[0058] In the present invention, the energy consumption of the central air-conditioning system is minimized as the goal, and the energy consumption of the system can be effectively reduced by optimizing the control parameters through the intelligent optimization model. By optimizing the control parameters, the system can operate in the best way under various working conditions, thereby reducing energy consumption.

[0059] In a possible implementation, S4 specifically includes:

[0060] S401: The inverse of the energy consumption of the central air-conditioning system is used as the fitness function of the law of the jungle algorithm.

[0061] S402: Initialize the population, which includes multiple individuals, each of which represents a feasible control parameter set:

[0062] X0=X min +rand(X max -X min )

[0063] Among them, X0 represents the initial position, X min represents the lower bound of the problem to be optimized, X max Represents the upper bound of the problem to be optimized, and rand() represents a random number in the interval [0,1].

[0064] S403: Construct a prey matrix based on the position vectors of each individual:

[0065]

[0066] Where Prey represents the prey matrix, X uv Represents the position of the u-th prey in dimension v, u = 1, 2…n, v = 1, 2…d.

[0067] S404: Copy the prey with the best fitness n times to form an n×d predator matrix:

[0068]

[0069] Among them, Elite represents the predator matrix, Represents the position of the pth prey in the q dimension, p = 1, 2…n, q = 1, 2…d.

[0070] S405: According to the speed ratio of the prey and the predator, the optimization process is divided into a high speed ratio stage, a medium speed ratio stage and a low speed ratio stage.

[0071] S406: In the high speed ratio stage, searching is performed in the form of Brownian motion:

[0072]

[0073] in, represents the step length of the i-th prey, represents the d-dimensional vector of Brownian motion, Represents the operation of multiplying corresponding positions of vectors, represents the i-th predator, represents the i-th prey, P represents the scaling factor, represents a d-dimensional vector, Indicates the stride length of the prey.

[0074] Among them, Brownian motion is usually described as the random movement of tiny particles in liquid or gas. It is characterized by randomness, irregularity and disorder. In liquid or gas, Brownian particles are constantly collided with molecules and disturbed by the external environment, causing them to present irregular motion trajectories in space.

[0075] In the present invention, searching through Brownian motion can bring the advantages of randomness, diversity, rapid exploration and adaptation to irregularly shaped search spaces at a high speed ratio stage, which helps to improve the efficiency of the optimization algorithm and the global search capability.

[0076] S407: In the medium speed ratio stage, half of the population continues to do Brownian motion, and the other half moves by Levy flight:

[0077]

[0078]

[0079] in, Represents the d-dimensional vector of Levy flight, Iter represents the current iteration number, Iter max It represents the maximum number of iterations in the optimization process, and CF represents the adaptive parameter that controls the predator's moving step size.

[0080] Among them, Lévy flight is a random walk model based on Lévy distribution, which is often used to describe the long-range correlation and tail heaviness in random processes.

[0081] In the present invention, the long-range correlation and tail heavy-tailedness of the Levy flight help the algorithm avoid falling into the local optimal solution and quickly explore more potential solutions in the search space.

[0082] S408: At low speed ratios, all predators move in Levy flight:

[0083]

[0084] S409: After each speed ratio stage, the fitness of prey and predator is updated according to the effect of gene mutation:

[0085]

[0086] Among them, the effects of gene mutation represents a d-dimensional vector, r FADs The probability of gene mutation is represented by r1 and r2, which represent two preys randomly selected from the prey matrix.

[0087] In the present invention, the gene mutation effect introduces a certain degree of randomness, which makes the individuals in the population more diverse by changing the position of the prey. This helps to avoid the population from falling into the local optimal solution and increases the possibility of global search. At the same time, the mutation effect makes the individuals in the population move faster in the search space, thereby accelerating the optimization process. Especially when the search space is large or complex, the gene mutation effect can help the algorithm find potential optimization solutions faster.

[0088] S410: Determine whether the termination condition is met. If so, take the control parameter represented by the particle with the highest fitness as the optimal control parameter and output it. Otherwise, return to continue iteration.

[0089] Optionally, the termination condition is that the number of iterations reaches a maximum number of iterations.

[0090] In the present invention, the energy consumption of the central air-conditioning system is minimized as the goal, and the energy consumption of the system can be effectively reduced by optimizing the control parameters through the intelligent optimization model. By optimizing the control parameters, the system can operate in the best way under various working conditions, thereby reducing energy consumption. At the same time, the intelligent optimization model is adaptive and can adjust the control parameters according to the actual operating conditions of the system. This enables the system to maintain efficient and stable operation under different working conditions, improving the adaptability and robustness of the system.

[0091] Furthermore, optimizing control parameters can improve the overall performance of the central air conditioning system. By making the system run in the best state, it can not only reduce energy consumption, but also improve the stability, reliability and comfort of the system, thereby improving the overall performance of the system.

[0092] S5: The optimal control parameters are sent to each sub-PLC controller through the main PLC controller to perform energy-saving control on the central air-conditioning system.

[0093] In a possible implementation, S5 specifically includes:

[0094] S501: The PLC controller sends the energy-saving control parameter value corresponding to each device to the sub-PLC controller corresponding to each device.

[0095] S502: When the sub-PLC controller receives the energy-saving control parameters sent by the PLC, it performs energy-saving control on the central air-conditioning system according to the energy-saving control parameters.

[0096] S503: When the sub-PLC controller does not receive the control parameters sent by the PLC, the sub-PLC controller performs energy-saving control on the central air-conditioning system according to the fixed value of the energy-saving control parameter set by the redundant control algorithm.

[0097] Among them, redundant control algorithms and UPS are set in the main PLC controller and each sub-PLC controller. When the cloud processor fails, the redundant control algorithm set in the main PLC controller is used to control the central air-conditioning system; when the main PLC controller fails, the redundant control algorithm set in the refrigeration unit sub-PLC, the chilled water pump sub-PLC, the cooling water pump sub-PLC, the cooling tower sub-PLC and the AHU air-conditioning terminal sub-PLC is used to control the central air-conditioning system; when the central air-conditioning system has a power supply failure, UPS is used to power each sub-PLC controller, and each sub-PLC controller controls the central air-conditioning system according to the built-in redundant control logic.

[0098] Among them, the redundant control algorithm is used. When the cloud processor fails, the main PLC controller cannot receive the signal from the cloud processor or the received signal is abnormal. The strategy of controlling the chilled water return temperature is adopted to control the energy saving of the central air-conditioning system. The set value of the chilled water return temperature can be set according to the outdoor ambient temperature and humidity and the building cooling load. In order to make the chilled water return temperature reach the set value, it is necessary to adjust the chilled water pump frequency and the chilled water outlet temperature of the chiller; the chilled water pump frequency is adjusted first. When the chilled water pump frequency reaches the upper and lower limits and the chilled water return temperature still does not meet the requirements, the chiller is adjusted. The chilled water outlet temperature is adjusted to make the chilled water return temperature reach the set value; PID regulation is used to set the chilled water pump frequency and the chilled water outlet temperature of the chiller; the redundant control algorithm sets the fixed operating parameters of the corresponding equipment of each sub-PLC controller according to the most unfavorable working conditions. The fixed operating parameters can meet the load requirements of the central air-conditioning system under different environmental parameters to ensure the safety, stability and reliability of the central air-conditioning system; when each sub-PLC controller cannot receive the control signal of the main PLC, the redundant control algorithm sends fixed operating parameters to the corresponding equipment of each sub-PLC controller to ensure stable operation of the system.

[0099] In the present invention, the control parameters are sent by the main PLC controller, so that the unified management and control of the entire central air-conditioning system can be achieved. This can ensure the coordinated operation of various parts of the system and improve the overall efficiency and performance of the system. At the same time, sending the best control parameters is the best solution obtained according to the optimization algorithm, which can achieve energy-saving control of the central air-conditioning system. This can reduce energy consumption and energy waste, thereby achieving the purpose of energy saving and emission reduction.

[0100] Furthermore, when the sub-PLC controller does not receive the control parameters, it uses the fixed value set by the redundant control algorithm to control, ensuring the stable operation of the system. In this way, even in the event of communication failure or other abnormal conditions, the normal operation of the central air-conditioning system can be guaranteed.

[0101] In a possible implementation manner, after S5, the method further includes:

[0102] S6: When a power supply failure occurs in the control system, the UPS supplies power to the sub-PLC controller and performs energy-saving control on the central air-conditioning system.

[0103] UPS supplies power to each sub-PLC controller in case of power outage. Specifically, when the power supply of the control system fails, UPS is used to supply power to each sub-PLC controller, and each sub-PLC controller performs energy-saving control of the central air-conditioning system according to the built-in redundant control logic.

[0104] Among them, for the occurrence of condensing pressure too high, compressor exhaust pressure too high, condensing pressure too low, compressor exhaust pressure too low, condensing temperature too high, compressor exhaust temperature too high, evaporating pressure too low, compressor suction pressure too low, evaporating pressure too high, compressor suction pressure too high, compressor suction temperature too high, compressor suction temperature too low, chilled water outlet temperature too high, chilled water outlet temperature too low, filter inlet and outlet temperature difference, oil system bearing temperature too high, oil system oil pressure too low, oil system oil pressure too high, oil temperature too high, lubricating oil consumption When there are faults such as large flow rate, blocked thermal expansion valve passage, excessive opening of thermal expansion valve, ice or oil blockage in expansion valve, failure of solenoid valve to open, no water discharge from water pump, sudden stop of water discharge during operation, overheating of water pump bearing, water pump flow rate failing to reach rated value, excessive power consumption of water pump motor, too high outlet water temperature of cooling tower, too high inlet water temperature of cooling tower, insufficient ventilation of cooling tower, low water level in water collection pan of cooling tower, etc., each sub-PLC controller handles the fault and sends prompt information, including fault cause analysis and troubleshooting suggestions, to the cloud processor through MQTT.

[0105] In the present invention, the UPS provides a backup power supply, which can promptly supply power to the sub-PLC controller when the power supply fails, ensuring the continuous operation of the control system. This helps to avoid system downtime and production interruption caused by power outages, and improves the stability and reliability of the system.

[0106] Furthermore, even in the event of a power failure, the sub-PLC controller can continue to operate and perform energy-saving control of the equipment according to the built-in redundant control logic. This helps maintain energy-saving control of the central air-conditioning system, reduce energy consumption, and thus achieve the goal of energy saving and emission reduction.

[0107] In one possible implementation, a secure and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform includes:

[0108] Processor 301.

[0109] The memory 302 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 301, an energy-saving control method is implemented.

[0110] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0111] In the present invention, based on the law of the jungle algorithm, an intelligent optimization model is constructed, and according to the pre-processed operating status data, the minimum energy consumption of the central air-conditioning system is taken as the goal. Through the intelligent optimization model, the optimal control parameters are output, and by optimizing the control parameters, the system can operate in the best way under various working conditions, thereby reducing energy consumption and reducing the energy consumption of the central air-conditioning system; the optimal control parameters are sent to each sub-PLC controller through the main PLC controller, and the sending of the optimal control parameters is the best solution obtained according to the optimization algorithm, which can realize energy-saving control of the central air-conditioning system. At the same time, it can reduce energy consumption, reduce energy waste, improve the stability of the control system, and reduce the energy consumption of the central air-conditioning system.

[0112] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0114] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0115] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0116] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0117] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0118] 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.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the 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 device, 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.

[0121] 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.

[0122] 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.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0124] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, a safe and reliable centralized air-conditioning cloud, edge, and end intelligent optimization energy-saving control platform is implemented as described in the method embodiment.

[0125] A computer-readable storage medium provided by the present invention can implement the steps and effects of the safe and reliable centralized air-conditioning cloud, edge, and end intelligent optimization energy-saving control platform of the above-mentioned method embodiment. To avoid repetition, the present invention will not go into details.

[0126] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0127] In the present invention, based on the law of the jungle algorithm, an intelligent optimization model is constructed, and according to the pre-processed operating status data, the minimum energy consumption of the central air-conditioning system is taken as the goal. Through the intelligent optimization model, the optimal control parameters are output, and by optimizing the control parameters, the system can operate in the best way under various working conditions, thereby reducing energy consumption and reducing the energy consumption of the central air-conditioning system; the optimal control parameters are sent to each sub-PLC controller through the main PLC controller, and the sending of the optimal control parameters is the best solution obtained according to the optimization algorithm, which can realize energy-saving control of the central air-conditioning system. At the same time, it can reduce energy consumption, reduce energy waste, improve the stability of the control system, and reduce the energy consumption of the central air-conditioning system.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0129] There are a few points to note:

[0130] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0131] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0132] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0133] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform. It is characterized in that Used to execute an energy-saving control method, the energy-saving control method comprising: S1: Collect the operating status data of the central air-conditioning system; S2: preprocessing the operating status data; S3: Building an intelligent optimization model based on the law of the jungle algorithm; S4: Based on the pre-processed operating status data, with the goal of minimizing the energy consumption of the central air-conditioning system, the optimal control parameters are output through the intelligent optimization model; S5: Sending the optimal control parameters to each sub-PLC controller through the main PLC controller to perform energy-saving control on the central air-conditioning system; The S4 specifically includes: S401: Taking the inverse of the energy consumption of the central air-conditioning system as the fitness function of the law of the jungle algorithm; S402: Initialize a population, the population includes a plurality of individuals, each of which represents a feasible control parameter set: X 0 =X min +rand(X max -X min ) Among them, X 0 represents the initial position, X min represents the lower bound of the problem to be optimized, X max represents the upper bound of the problem to be optimized, and rand() represents a random number in the interval [0,1]; S403: Construct a prey matrix based on the position vectors of each individual: Where Prey represents the prey matrix, X uv represents the position of the u-th prey in dimension v, u = 1, 2…n, v = 1, 2…d; S404: Copy the prey with the best fitness n times to form an n×d predator matrix: Among them, Elite represents the predator matrix, represents the position of the pth prey in the q dimension, p = 1, 2…n, q = 1, 2…d; S405: dividing the optimization process into a high speed ratio stage, a medium speed ratio stage and a low speed ratio stage according to the speed ratio of the prey and the predator; S406: In the high speed ratio stage, the individual searches in the form of Brownian motion: in, represents the step length of the i-th prey, represents the d-dimensional vector of Brownian motion, Represents the operation of multiplying corresponding positions of vectors, represents the i-th predator, represents the i-th prey, P represents the scaling factor, represents a d-dimensional vector, represents the stride length of the prey; S407: In the middle speed ratio stage, half of the individuals in the population continue to do Brownian motion, and the other half of the individuals move by Levy flight: in, Represents the d-dimensional vector of Levy flight, Iter represents the current iteration number, Iter max represents the maximum number of iterations of the optimization process, CF represents the adaptive parameter, which controls the predator's moving step size; S408: In the low speed ratio stage, all predators move in the manner of Levy flight: S409: After each speed ratio stage, the fitness of prey and predator is updated according to the effect of gene mutation: Among them, the effects of gene mutation represents a d-dimensional vector, r FADs represents the probability of gene mutation, r 1 、r 2 represents two prey items randomly selected from the prey matrix; S410: Determine whether the termination condition is met. If so, take the control parameter represented by the particle with the highest fitness as the optimal control parameter and output it. Otherwise, return to continue iteration.

2. According to the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform described in claim 1, It is characterized in that The pre-processing specifically includes: Abnormal data were eliminated and missing data were filled for parameter identification.

3. According to the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform described in claim 2, It is characterized in that The specific method of removing abnormal data includes: Calculating the first quartile and the third quartile of the operating status data; Subtract the third quartile from the first quartile to obtain the interquartile range: IQR / Q 3 -Q 1 Among them, IQR represents interquartile range, Q 3 Indicates the third quartile Q 1 represents the first quartile; Subtract 1.5 times the IQR from the first quartile and the third quartile, respectively, to obtain the lower limit and upper limit of the normal data value: <h2 style=";text-align:left;direction:ltr">lb=Q<h2 style=";text-align:left;direction:ltr"> 1 <h2 style=";text-align:left;direction:ltr"> -1.5 IQR ub6Q 3 +1.5IQR Among them, lb represents the lower limit of normal data value, and ub represents the upper limit of normal data value; Identify parameters that are lower than the lower limit of normal parameter values ​​and higher than the upper limit of normal parameter values ​​as abnormal data; The abnormal data are eliminated.

4. According to claim 2, the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, It is characterized in that The specific method of filling missing data is: The abnormal data is assigned a value according to the normal value closest to the abnormal data.

5. According to claim 2, the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, It is characterized in that The specific method of parameter identification is: With the goal of minimizing the parameter value, the parameter identification is performed using the least squares method: Where n represents the total number of parameters, x i represents the model input observation value of the i-th parameter, y i represents the model output observation value of the i-th parameter, ω represents the unknown parameter, f() represents the least squares error, and h() represents the model prediction value.

6. According to claim 1, the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, It is characterized in that The control parameter set includes: Chiller start / stop status, chiller chilled water outlet temperature, chilled water pump start / stop status, chilled water pump frequency, cooling water pump start / stop status, cooling water pump frequency, cooling tower start / stop status, cooling tower fan frequency, AHU start / stop status, and AHU fan frequency.

7. According to claim 1, the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, It is characterized in that The S5 specifically includes: S501: The PLC controller sends the energy-saving control parameter value corresponding to each device to the sub-PLC controller corresponding to each device; S502: When the sub-PLC controller receives the energy-saving control parameter sent by the PLC, it performs energy-saving control on the central air-conditioning system according to the energy-saving control parameter; S503: When the sub-PLC controller does not receive the control parameter sent by the PLC, the sub-PLC controller performs energy-saving control on the central air-conditioning system according to the fixed value of the energy-saving control parameter set by the redundant control algorithm; Wherein, a redundant control algorithm and UPS are set in the main PLC controller and each sub-PLC controller. When the cloud processor fails, the redundant control algorithm set in the main PLC controller is used to control the central air-conditioning system; when the main PLC controller fails, the redundant control algorithm set in the refrigeration unit sub-PLC, the chilled water pump sub-PLC, the cooling water pump sub-PLC, the cooling tower sub-PLC and the AHU air-conditioning terminal sub-PLC is used to control the central air-conditioning system; when the central air-conditioning system fails in power supply, the UPS is used to power each sub-PLC controller, and each sub-PLC controller controls the central air-conditioning system according to the built-in redundant control logic; Wherein, the redundant control algorithm is used for, when the cloud processor fails, the main PLC controller cannot receive the signal of the cloud processor or the received signal is abnormal, and the strategy of controlling the chilled water return temperature is adopted to perform energy-saving control on the central air-conditioning system. The set value of the chilled water return temperature can be set according to the outdoor environment temperature and humidity and the building cooling load. In order to make the chilled water return temperature reach the set value, it is necessary to adjust the chilled water pump frequency and the chilled water outlet temperature of the chiller; the chilled water pump frequency is adjusted first, and when the chilled water pump frequency reaches the upper and lower limits and the chilled water return temperature still does not meet the requirements, the chiller is adjusted. The chilled water outlet temperature of the group is adjusted to make the chilled water return temperature reach the set value; the chilled water pump frequency and the chilled water outlet temperature of the chiller are set by using PID regulation; the redundant control algorithm sets the fixed operating parameters of the equipment corresponding to each sub-PLC controller according to the most unfavorable working conditions, and the fixed operating parameters can meet the load requirements of the central air-conditioning system under different environmental parameters to ensure the safety, stability and reliability of the central air-conditioning system; when each sub-PLC controller cannot receive the control signal of the main PLC, the redundant control algorithm sends the fixed operating parameters to the equipment corresponding to each sub-PLC controller to ensure stable operation of the system.

8. The safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform according to claim 7, It is characterized in that After S5, the method further includes: S6: When a power supply failure occurs in the control system, the UPS supplies power to each sub-PLC controller and performs energy-saving control on the central air-conditioning system; The UPS supplies power to each sub-PLC controller in case of power failure. Specifically, when the power supply of the control system fails, the UPS is used to supply power to each sub-PLC controller, and each sub-PLC controller performs energy-saving control on the central air-conditioning system according to the built-in redundant control logic; Among them, for the occurrence of condensing pressure is too high, compressor exhaust pressure is too high, condensing pressure is too low, compressor exhaust pressure is too low, condensing temperature is too high, compressor exhaust temperature is too high, evaporating pressure is too low, compressor suction pressure is too low, evaporating pressure is too high, compressor suction pressure is too high, compressor suction temperature is too high, compressor suction temperature is too low, chilled water outlet temperature is too high, chilled water outlet temperature is too low, there is a temperature difference between the filter inlet and outlet, oil system bearing temperature is too high, oil system oil pressure is too low, oil system oil pressure is too high, oil temperature is too high, and lubricating oil consumption is large. , the thermal expansion valve passage is blocked, the thermal expansion valve is opened too large, the expansion valve is blocked by ice or oil, the solenoid valve cannot be opened, the water pump does not discharge water at all, the water discharge suddenly stops during operation, the water pump bearing is overheated, the water pump flow does not reach the rated value, the water pump motor consumes too much power, the cooling tower outlet water temperature is too high, the cooling tower inlet water temperature is too high, the cooling tower ventilation volume is insufficient, the water level in the cooling tower water collection pan is low, etc., each sub-PLC controller handles the fault and sends the prompt information, including the fault cause analysis and troubleshooting suggestions, to the cloud processor through MQTT.

9. According to claim 1, the safe and reliable centralized air conditioning cloud, edge, and end intelligent optimization energy-saving control platform, It is characterized in that include: processor; A memory having computer-readable instructions stored therein, wherein the computer-readable instructions, when executed by the processor, implement an energy-saving control method.

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