Load prediction and optimal distribution-based optimal control method for cold storage central air conditioner

Abstract

The invention discloses an optimal control method for a cold storage central air conditioner based on load prediction and optimal distribution, which relates to the technical field of load control, and comprises the following steps: acquiring operation data of the central air conditioner for preprocessing, and predicting the load of the central air conditioner through an intelligent algorithm; forming a central air-conditioning load optimization scheme according to the predicted load, and generating an operation state optimization scheme by using a genetic algorithm; the central air conditioner is regulated and controlled according to the load optimization scheme and the operation state optimization scheme; and the running state and load data of the central air conditioner are displayed in real time, and the load data are recorded and stored in a database. The central air conditioner operation data is collected and preprocessed, the central air conditioner load is predicted, the air conditioner terminal load is extracted, the load optimization scheme is formed according to the central air conditioner operation data, the central air conditioner operation is regulated and controlled in combination with the operation state optimization scheme, and the central air conditioner operation control accuracy is effectively improved; the energy consumption of the central air conditioner is optimized, and the load utilization efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of load control, and in particular to an optimization control method for a cold storage central air conditioner based on load prediction and optimization distribution. Background Art

[0002] With the popularization of the concepts of smart buildings and smart homes, the energy efficiency and operation management of central air-conditioning systems have become the core issues in building energy management. In recent years, the control strategy of central air-conditioning systems has gradually developed from traditional timing control and simple temperature control modes to more intelligent and energy-saving directions. Early air-conditioning control systems mainly relied on preset operation schedules and manual adjustments, lacking the ability to perceive and dynamically adjust real-time load changes. With the development of sensing technology, data acquisition and processing technology, central air-conditioning load prediction and intelligent scheduling solutions based on real-time data have gradually become research hotspots. For example, many studies have proposed using input information such as environmental sensors and equipment operation status monitoring data, combined with machine learning algorithms to predict air-conditioning loads, and load distribution and operation mode adjustments through optimization algorithms. These methods can dynamically adjust the operation status of the air-conditioning system according to real-time data, thereby improving the system's operating efficiency and energy-saving effects. However, the existing technology still has defects. The existing algorithms have high computational complexity and slow convergence speed when dealing with complex systems. Especially in multi-device, large-scale air-conditioning systems, traditional optimization methods are often difficult to meet real-time requirements, and their adaptability and accuracy are insufficient in complex environments. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing cold storage central air conditioning optimization control method based on load prediction and optimal allocation, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology is difficult to meet the real-time requirements and lacks adaptability and accuracy in complex environments.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a cold storage central air-conditioning optimization control method based on load prediction and optimal allocation, which includes obtaining central air-conditioning operation data for preprocessing, and predicting the central air-conditioning load through an intelligent algorithm; forming a central air-conditioning load optimization plan according to the predicted load, and using a genetic algorithm to generate an operation status optimization plan; regulating the central air-conditioning according to the load optimization plan and the operation status optimization plan; displaying the central air-conditioning operation status and load data in real time, and recording and storing the load data in a database.

[0006] As a preferred solution of the cold storage central air-conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the acquisition of central air-conditioning operation data for preprocessing refers to collecting the central air-conditioning operation data by installing sensors, wirelessly connecting the sensors to form a sensor network and connecting the sensor network to a data center, the data center cleans the central air-conditioning operation data collected by the sensor network and uses Kalman filtering for denoising, and standardizes the denoised central air-conditioning operation data.

[0007] As a preferred solution of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the load prediction through intelligent learning refers to building a prediction model through an LSTM model, including an input gate, a forget gate and an output gate, setting the prediction model input as central air conditioning operation data, and the output as the central air conditioning predicted load, using a training data set to train the prediction model, and setting the mean square error as the loss function, calculating the loss function gradient through back propagation, and using an optimization algorithm to update the prediction model parameters until the loss function is minimized, and using the trained prediction model parameters to optimize the prediction model; The central air-conditioning operating parameters are input into the optimized prediction model to obtain the future load of the central air-conditioning. The reinforcement learning mechanism is introduced and the Q-learning algorithm is used to continuously optimize the prediction model.

[0008] As a preferred solution of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the central air conditioning load optimization solution formed according to the predicted load includes: Define each air-conditioning terminal in the central air-conditioning as an independent intelligent agent i, and extract the load of each air-conditioning terminal from the future load of the central air-conditioning , and obtain the operating time of each air-conditioning terminal according to the central air-conditioning operation data, and calculate the proportion of each air-conditioning terminal operating time in a day as the working time factor of agent i ; Query the energy efficiency curve of the air conditioning terminal and obtain the load Air conditioning terminal energy efficiency ratio ; Statistics on load change data of air-conditioning terminals during operation time, and fitting load change curve through regression analysis, and obtain load change curve of air-conditioning terminals according to load change curve. The absolute value of the slope of the load change curve at the time is taken as the load change factor ; Synthesize working time factor through nonstandard interaction modifier , Energy efficiency ratio and load variation factor : in is the non-standard interaction correction term for agent i, and is the coupling parameter of the nonstandard interaction correction term; The agents are paired up, and according to the operating time of each air-conditioning terminal in a day, the synchronous operating time of each pair of agents is recorded in a day, and the ratio of the synchronous operating time to the maximum synchronous operating time is calculated. and the ratio of the synchronization runtime to the runtime of agent i , and calculate the agent pairs of all agents containing agent i The sum is taken as the cooperation coefficient of agent i , all agent pairs containing agent i The sum is taken as the operation coefficient of agent i ; According to the cooperation coefficient of agent i and operating factor Defining time-related items : Relate items by time Calculate the cooperation strength between agent i and other agents : in is the number of agents; Strength through collaboration The total utility function U of central air conditioning is defined as: in is the load weight of agent i, is the weight of the non-standard interaction correction term of agent i; The load of each agent i is calculated using the gradient descent method Perform optimization updates and calculate the load update of agents i and j after the update : in is the ratio of the updated synchronous running time of agent i and agent j to the running time of agent i, and are the updated loads of agent i and agent j respectively; based on The collaboration coefficient and operation coefficient of agent i are recalculated, and the total utility function value of the central air conditioner is recalculated according to the updated load of the agent. The agent load is iteratively adjusted until the total utility function value of the central air conditioner reaches the maximum and then stops. The agent load that maximizes the total utility function value of the central air conditioner is output as the load of each air-conditioning terminal to form a central air-conditioning load optimization plan.

[0009] As a preferred solution of the cold storage central air-conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the use of a genetic algorithm to generate an operation status optimization plan refers to randomly initializing the operation status and operation time of the air-conditioning terminal reaching the set temperature in the central air-conditioning operation data to generate an initial population, each air-conditioning terminal represents an individual in the initial population, the initial population is optimized by a genetic algorithm, and the fitness of the individuals in the initial population is calculated to select individuals for crossover mutation operations to generate new individuals, the fitness of the new individuals is calculated for iteration, and the iteration is stopped after the fitness converges, and the operation status and operation time of the air-conditioning terminal in the individual with the highest fitness in the iterated population is used as the operation status optimization plan.

[0010] As a preferred scheme of the cold storage central air-conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the central air-conditioning is controlled according to the load optimization plan and the operation status optimization plan, which refers to optimizing and controlling the air-conditioning terminal of the central air-conditioning according to the generated load optimization plan and the operation status optimization plan, and real-time monitoring of the central air-conditioning operation data to continuously generate the load optimization plan and the operation status optimization plan to control the operation of the central air-conditioning.

[0011] As a preferred solution of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in the present invention, the real-time display of the central air conditioning operating status and load data refers to the data center updating and displaying the monitored central air conditioning operating status and load data in real time, synchronously comparing and displaying the obtained central air conditioning load optimization plan with the central air conditioning operating data, and forming data records according to timestamps.

[0012] As a preferred solution of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in the present invention, wherein: the recording and storing of load data in a database means that the data center stores the load data and data records in the database while displaying the load data, and the database regularly performs integrity and security checks on the stored data, and uploads it to the cloud for backup.

[0013] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned cold storage central air conditioning optimization control method based on load prediction and optimal allocation when executing the computer program.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned cold storage central air conditioning optimization control method based on load prediction and optimal allocation.

[0015] The beneficial effects of the present invention are as follows: the present invention collects central air-conditioning operation data for preprocessing, predicts the central air-conditioning load, extracts the air-conditioning terminal load to form a load optimization plan based on the central air-conditioning operation data, and regulates the central air-conditioning operation in combination with the operation status optimization plan, thereby effectively improving the central air-conditioning operation control accuracy, optimizing the central air-conditioning energy consumption, and improving the load utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 The figure is a flow chart of the optimal control method of the cold storage central air conditioner based on load prediction and optimal allocation. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, and which provides a cold storage central air conditioner optimization control method based on load prediction and optimal allocation. The cold storage central air conditioner optimization control method based on load prediction and optimal allocation includes: S1. Obtain central air-conditioning operation data for pre-processing and predict central air-conditioning load through intelligent algorithms; Specifically, obtaining the central air-conditioning operation data for preprocessing refers to collecting the central air-conditioning operation data by installing sensors, including load power, startup status, temperature, etc. The central air-conditioning includes a central host and an air-conditioning terminal. The central host is responsible for the operation of the entire central air-conditioning, and the air-conditioning terminal is used for independent heating or cooling. The sensors are wirelessly connected to form a sensor network and the sensor network is connected to a data center. The data center cleans the central air-conditioning operation data collected by the sensor network and uses Kalman filtering for denoising, and the denoised central air-conditioning operation data is standardized.

[0022] The introduction of sensor networks has greatly improved the efficiency and accuracy of data collection in air-conditioning systems. Installing sensors and transmitting data to data centers via wireless networks not only eliminates the complexity of traditional wired connections, but also realizes remote collection and processing of real-time data. After preprocessing and cleaning the central air-conditioning operation data, the system can accurately predict future load demands. This load prediction can not only improve the energy efficiency of the air-conditioning system, but also dynamically adjust the system according to the predicted load demand. Due to the combination of intelligent algorithms and real-time data monitoring, the air-conditioning system can make adaptive adjustments based on changes in the external environment and changes in equipment status. This flexibility enables the system to effectively respond to load fluctuations under different environmental conditions, ensuring the stability and efficient operation of the air-conditioning system under various working conditions.

[0023] Furthermore, load prediction through intelligent learning refers to building a prediction model through an LSTM model, including an input gate, a forget gate, and an output gate, setting the prediction model input as central air-conditioning operation data, and the output as central air-conditioning predicted load, using a training data set to train the prediction model, and setting the mean square error as the loss function, calculating the loss function gradient through back propagation, and using an optimization algorithm to update the prediction model parameters until the loss function is minimized, and using the trained prediction model parameters to optimize the prediction model; The central air-conditioning operating parameters are input into the optimized prediction model to obtain the future load of the central air-conditioning. The reinforcement learning mechanism is introduced and the Q-learning algorithm is used to continuously optimize the prediction model.

[0024] LSTM can learn and memorize long-term dependencies in time series data through its unique gating mechanism, thereby providing higher accuracy and robustness in central air-conditioning load forecasting. By inputting the operating data of the central air-conditioning into the LSTM model, the model can adaptively learn the load forecasting rules during the training process and accurately predict future load demand. Q-learning is a reinforcement learning method that allows the model to self-adjust and optimize based on historical prediction errors. When the LSTM model encounters load prediction errors in actual applications, Q-learning can adjust the learning strategy and parameters of the LSTM model based on prediction error feedback, thereby gradually improving the prediction accuracy. The LSTM model is combined with the Q-learning algorithm to form a closed-loop optimization control system. By inputting the real-time operating data of the central air-conditioning into the optimized prediction model, dynamic prediction of future loads can be achieved. This real-time prediction capability enables the air-conditioning system to automatically adjust during operation to adapt to real-time load changes, avoiding the energy efficiency waste problem commonly seen in traditional air-conditioning systems.

[0025] S2, forming a central air conditioning load optimization plan based on the predicted load, and using a genetic algorithm to generate an operation state optimization plan; Specifically, the central air conditioning load optimization plan formed according to the predicted load includes: Define each air-conditioning terminal in the central air-conditioning as an independent intelligent agent i, and extract the load of each air-conditioning terminal from the future load of the central air-conditioning , and obtain the operating time of each air-conditioning terminal according to the central air-conditioning operation data, and calculate the proportion of each air-conditioning terminal operating time in a day as the working time factor of agent i ,The working time factor is used to describe the working proportion of each air-conditioning terminal in a day, reflecting the activity of the air-conditioning terminal. By introducing this factor, the load distribution of air-conditioning terminals with different working intensities can be more refined; Query the energy efficiency curve of the air conditioning terminal and obtain the load Air conditioning terminal energy efficiency ratio The energy efficiency ratio reflects the energy utilization efficiency of each air-conditioning terminal under a certain load. By querying the energy efficiency curve of each air-conditioning terminal, its energy efficiency data under different loads can be obtained in real time, thus providing a basis for subsequent optimization allocation; Statistics on load change data of air-conditioning terminals during operation time, and fitting load change curve through regression analysis, and obtain load change curve of air-conditioning terminals according to load change curve. The absolute value of the slope of the load change curve at the time is taken as the load change factor ,The change of the air-conditioning terminal load is not only a static load value, but is also closely related to the dynamic changes of the system operation. By quantifying the load changes, we can better simulate the response of the system in load fluctuations and optimize the load scheduling; Synthesize working time factor through nonstandard interaction modifier , Energy efficiency ratio and load variation factor : in is the non-standard interaction correction term for agent i, and It is the coupling parameter of the non-standard interaction correction term, obtained through numerical experiments or historical data fitting, and based on the equipment characteristics and operating environment, obtained through equipment simulation or optimization calculation. It integrates multiple influencing factors into one correction term, which can more accurately reflect the cooperation and energy efficiency optimization needs of the air-conditioning terminal in actual operation; Pair the agents in pairs, record the synchronous operation time of each pair of agents in a day according to the operation time of each air conditioning terminal, and calculate the ratio of the synchronous operation time to the maximum synchronous operation time (usually 24 hours) and the ratio of the synchronization runtime to the runtime of agent i , and calculate the agent pairs of all agents containing agent i The sum is taken as the cooperation coefficient of agent i , all agent pairs containing agent i The sum is taken as the operation coefficient of agent i ; The coordination coefficient is formed by calculating the ratio of the synchronous operation time between the air-conditioning terminal and other devices and the maximum synchronous operation time. This coefficient reflects the degree of coordination between each air-conditioning terminal and other devices. The operation coefficient is the proportion of the air-conditioning terminal in the entire system. These coefficients can be used to quantify the coordination and participation of each intelligent agent. According to the cooperation coefficient of agent i and operating factor Defining time-related items : The time-related term reflects the time synchronization relationship between devices through the operating time ratio between agents, taking into account the timing and synergy between different air-conditioning terminals, and can dynamically adjust the relative contribution of each agent in the system and optimize the overall system performance through the intensity of collaboration; Relate items by time Calculate the cooperation strength between agent i and other agents : in is the number of agents; Strength through collaboration The total utility function U of central air conditioning is defined as: in is the load weight of agent i, is the weight of the non-standard interaction correction term of agent i; The total utility function is used to comprehensively consider the load weight, non-standard interaction correction term and collaboration strength of each intelligent agent, and finally measure the performance of the entire system. The introduction of this function in the present invention provides a clear quantitative goal for load optimization, ensuring that the overall system performance is maximized by optimizing the load of each intelligent agent; The load of each agent i is calculated using the gradient descent method To perform optimization updates: in is the updated load of agent i, is the learning rate; And the updated load of agent i and agent j is calculated : in is the ratio of the updated synchronous running time of agent i and agent j to the running time of agent i, and are the updated loads of agent i and agent j respectively; based on The collaboration coefficient and operation coefficient of agent i are recalculated, and the total utility function value of the central air conditioner is recalculated according to the updated load of the agent. The agent load is iteratively adjusted until the total utility function value of the central air conditioner reaches the maximum and then stops. The agent load that maximizes the total utility function value of the central air conditioner is output as the load of each air-conditioning terminal to form a central air-conditioning load optimization plan.

[0026] By treating each air-conditioning terminal as an independent intelligent agent for modeling, local optimization in the air-conditioning system can be achieved, while taking into account the collaborative effect between devices, the introduction of non-standard interaction correction terms and collaborative coefficients, and the quantification of the collaborative relationship between air-conditioning terminals, the load of each device not only considers its own energy efficiency, but also comprehensively considers the collaborative effect with other devices. By introducing these correction terms, the load distribution of each device can be finely scheduled, so that the system can achieve the best energy utilization effect while meeting the comfort requirements. Through real-time optimization and dynamic adjustment of the load of each intelligent agent, the system can respond and make adjustments quickly to ensure that the equipment load is always in the optimal state.

[0027] Furthermore, using a genetic algorithm to generate an operation state optimization plan refers to randomly initializing the operation state and operation time of the air-conditioning terminal in the central air-conditioning operation data to reach the set temperature to generate an initial population, each air-conditioning terminal represents an individual in the initial population, the initial population is the input of the genetic algorithm, and represents the diversity of the initial air-conditioning terminal state. Each air-conditioning terminal, as an individual in the population, has a randomly initialized operation state and operation time. The genetic algorithm relies on the diversity in the population to explore the solution space, and ensures the extensiveness of the initial solution through random initialization, providing a higher global search capability for subsequent optimization. The initial population is optimized through the genetic algorithm, and the fitness of the individuals in the initial population is calculated to select individuals for crossover mutation operations to generate new individuals. The fitness of the new individuals is calculated for iteration, and the iteration is stopped after the fitness converges, and the operation state and operation time of the air-conditioning terminal in the individual with the highest fitness in the iterated population is used as the operation state optimization plan.

[0028] Through iterative optimization of genetic algorithms, the final generated operating status and operating time represent the optimal air-conditioning scheduling strategy. These optimization schemes can maximize energy efficiency and reduce energy waste while ensuring comfort.

[0029] S3. Control the central air conditioner according to the load optimization plan and the operation status optimization plan; Specifically, regulating the central air conditioner according to the load optimization plan and the operation status optimization plan refers to optimizing and controlling the air-conditioning terminal of the central air conditioner according to the generated load optimization plan and the operation status optimization plan, and real-time monitoring of the central air conditioner operation data to continuously generate the load optimization plan and the operation status optimization plan for regulating the operation of the central air conditioner.

[0030] By utilizing the generated load optimization plan and operating status optimization plan, combined with the actual needs and operating conditions of the air-conditioning terminal, the system is dynamically scheduled, which not only realizes the accurate prediction and optimization of the load, but also can be adjusted according to the operating status of the air-conditioning terminal, so that the workload of each terminal is always kept within the optimal range. Through real-time control of the load and operating status, the energy waste of the air-conditioning system can be reduced and the overall energy efficiency of the system can be improved. The real-time monitoring of the central air-conditioning operating data and the continuous generation of load optimization plans and operating status optimization plans can provide continuously optimized control strategies during the system operation process. Through the real-time feedback mechanism, the air-conditioning system can continuously adjust itself in actual operation to ensure that the load distribution and operating mode are always in the optimal state.

[0031] S4. Display the central air-conditioning operation status and load data in real time, and record and store the load data in the database; Specifically, real-time display of the central air-conditioning operation status and load data means that the data center will update and display the monitored central air-conditioning operation status and load data in real time, and simultaneously compare and display the obtained central air-conditioning load optimization plan with the central air-conditioning operation data, and form data records according to timestamps.

[0032] Furthermore, recording and storing the load data in a database means that the data center stores the load data and data records in the database while displaying the load data. The database regularly performs integrity and security checks on the stored data and uploads it to the cloud for backup.

[0033] Example 2 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or 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: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0034] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0035] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0036] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the control of a cold storage central air conditioner based on load prediction and optimal allocation, characterized in that: include, Obtain central air-conditioning operation data for pre-processing, and predict central air-conditioning load through intelligent algorithms; Form a central air conditioning load optimization plan based on the predicted load, and use genetic algorithms to generate an operation status optimization plan; Control central air conditioning according to load optimization plan and operation status optimization plan; Display the central air-conditioning operating status and load data in real time, and record and store the load data in the database.

2. The method for optimizing control of cold storage central air conditioner based on load prediction and optimal allocation according to claim 1, characterized in that: The acquisition of central air-conditioning operation data for preprocessing refers to collecting the central air-conditioning operation data by installing sensors, wirelessly connecting the sensors to form a sensor network and connecting the sensor network to a data center, the data center cleaning the central air-conditioning operation data collected by the sensor network and using Kalman filtering for denoising, and standardizing the denoised central air-conditioning operation data.

3. The cold storage central air conditioning optimization control method based on load prediction and optimal allocation according to claim 2, characterized in that: The load prediction by intelligent learning refers to constructing a prediction model by an LSTM model, including an input gate, a forget gate and an output gate, setting the prediction model input as central air-conditioning operation data, and the output as the central air-conditioning predicted load, using a training data set to train the prediction model, and setting the mean square error as the loss function, calculating the loss function gradient by back propagation, and using an optimization algorithm to update the prediction model parameters until the loss function is minimized, and using the trained prediction model parameters to optimize the prediction model; The central air-conditioning operating parameters are input into the optimized prediction model to obtain the future load of the central air-conditioning. The reinforcement learning mechanism is introduced and the Q-learning algorithm is used to continuously optimize the prediction model.

4. The cold storage central air conditioning optimization control method based on load prediction and optimal allocation as claimed in claim 3 is characterized by: The central air conditioning load optimization scheme formed according to the predicted load includes: Define each air-conditioning terminal in the central air-conditioning as an independent intelligent agent i, and extract the load of each air-conditioning terminal from the future load of the central air-conditioning , and obtain the operating time of each air-conditioning terminal according to the central air-conditioning operation data, and calculate the proportion of each air-conditioning terminal operating time in a day as the working time factor of agent i ; Query the energy efficiency curve of the air conditioning terminal and obtain the load Air conditioning terminal energy efficiency ratio ; Statistics on load change data of air-conditioning terminals during operation time, and fitting load change curve through regression analysis, and obtain load change curve of air-conditioning terminals according to load change curve. The absolute value of the slope of the load change curve at the time is taken as the load change factor ; Synthesize working time factor through nonstandard interaction modifier , Energy efficiency ratio and load variation factor : in is the non-standard interaction correction term for agent i, and is the coupling parameter of the nonstandard interaction correction term; The agents are paired up, and according to the operating time of each air-conditioning terminal in a day, the synchronous operating time of each pair of agents is recorded in a day, and the ratio of the synchronous operating time to the maximum synchronous operating time is calculated. and the ratio of the synchronization runtime to the runtime of agent i , and calculate the agent pairs of all agents containing agent i The sum is taken as the cooperation coefficient of agent i , all agent pairs containing agent i The sum is taken as the operation coefficient of agent i ; According to the cooperation coefficient of agent i and operating factor Defining time-related items : Relate items by time Calculate the cooperation strength between agent i and other agents : in is the number of agents; Strength through collaboration The total utility function U of central air conditioning is defined as: in is the load weight of agent i, is the weight of the non-standard interaction correction term of agent i; The load of each agent i is calculated using the gradient descent method Perform optimization updates and calculate the load update of agents i and j after the update : in is the ratio of the updated synchronous running time of agent i and agent j to the running time of agent i, and are the updated loads of agent i and agent j respectively; based on The collaboration coefficient and operation coefficient of agent i are recalculated, and the total utility function value of the central air conditioner is recalculated according to the updated load of the agent. The agent load is iteratively adjusted until the total utility function value of the central air conditioner reaches the maximum and then stops. The agent load that maximizes the total utility function value of the central air conditioner is output as the load of each air-conditioning terminal to form a central air-conditioning load optimization plan.

5. The cold storage central air conditioning optimization control method based on load prediction and optimal allocation according to claim 4, characterized in that: The use of a genetic algorithm to generate an operating state optimization plan refers to randomly initializing the operating state and operating time of the air-conditioning terminal reaching the set temperature in the central air-conditioning operating data to generate an initial population, each air-conditioning terminal represents an individual in the initial population, optimizing the initial population through a genetic algorithm, calculating the fitness of the individuals in the initial population, selecting individuals for crossover and mutation operations to generate new individuals, calculating the fitness of the new individuals for iteration, and stopping the iteration after the fitness converges, and using the operating state and operating time of the air-conditioning terminal in the individual with the highest fitness in the iterated population as the operating state optimization plan.

6. The method for optimizing control of cold storage central air conditioner based on load prediction and optimal allocation according to claim 5, characterized in that: The controlling of the central air conditioner according to the load optimization plan and the operation status optimization plan refers to optimizing and controlling the air-conditioning terminal of the central air conditioner according to the generated load optimization plan and the operation status optimization plan, and monitoring the central air conditioner operation data in real time to continuously generate the load optimization plan and the operation status optimization plan to control the operation of the central air conditioner.

7. The cold storage central air conditioning optimization control method based on load prediction and optimal allocation according to claim 6, characterized in that: The real-time display of the central air-conditioning operation status and load data refers to that the data center updates and displays the monitored central air-conditioning operation status and load data in real time, and simultaneously compares and displays the obtained central air-conditioning load optimization plan with the central air-conditioning operation data, and forms data records according to timestamps.

8. The method for optimizing control of cold storage central air conditioner based on load prediction and optimal allocation according to claim 7, characterized in that: The recording and storing of the load data in the database means that the data center stores the load data and data records in the database while displaying the load data. The database regularly performs integrity and security checks on the stored data and uploads it to the cloud for backup.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cold storage central air conditioning optimization control method based on load prediction and optimal allocation described in any one of claims 1 to 8 are implemented.

Images