A method and system for optimizing and scheduling energy-saving operation of air conditioner

Abstract

The present invention discloses an air conditioning energy-saving operation optimization scheduling method and system, comprising the following steps: obtaining the position coordinate data of all air conditioning units in a factory workshop; dividing the air conditioning units into a basic air conditioning group and an adjacent air conditioning group according to the position coordinates and relative positions of the air conditioning units; constructing an influence relationship diagram between the air conditioning units according to the spatial distribution relationship of the air conditioning units; determining the heat load of each area based on the heat generated in the workshop; setting the operating parameters of each air conditioning unit based on the heat analysis results and the mutual influence relationship between the air conditioning units; scheduling and optimizing the operating state of the air conditioning units based on the operating parameters of the air conditioning units; the air conditioning energy-saving operation optimization scheduling method and system solve the problems of low energy efficiency, energy waste, unreasonable scheduling, lack of flexibility, etc. in the air conditioning system. Through intelligent optimization scheduling, the operating efficiency of the air conditioning system can be effectively improved, energy consumption can be reduced, and the purpose of energy saving and emission reduction can be achieved.

Description

Technical Field

[0001] The present invention specifically relates to an air-conditioning energy-saving operation optimization scheduling method and system. Background Art

[0002] In modern factory workshops, office buildings and large commercial buildings, air conditioning systems are important facilities for maintaining indoor environmental comfort and work efficiency. However, with the rapid development of industrialization and modernization, the operating scale and number of air conditioning systems are increasing, and energy consumption issues are becoming increasingly prominent. Especially in some large production workshops and open spaces, the distribution of air conditioning units is often highly complex, and the existing air conditioning scheduling methods generally have certain technical defects, resulting in low energy efficiency, serious waste of resources, and even affecting the normal operation of the system.

[0003] Most existing air conditioning systems use a fixed scheduling strategy based on time, or simply rely on the independent operation settings of each air conditioning unit, and fail to effectively consider the mutual influence between air conditioning units. This method often cannot flexibly adjust the operating status of the air conditioning unit according to the actual indoor environment and heat load demand. For example, some air conditioning units may continue to operate when not needed, while others may shut down due to insufficient load, resulting in low overall energy efficiency of the air conditioning system. Especially in a complex environment where multiple air conditioning units work together, a single control method cannot efficiently adjust the system, resulting in a large amount of energy waste.

[0004] In many industrial workshops and commercial buildings, the heat load conditions in different areas vary significantly. Some areas generate a lot of heat due to the operation of production equipment, personnel activities or machine equipment, while other areas may have almost no heat load. However, traditional air conditioning scheduling solutions usually fail to monitor in real time and dynamically adjust the on and off status of air conditioning units according to changes in regional heat load. Due to the lack of accurate heat load analysis and regional management, air conditioning systems often fail to flexibly allocate the workload of air conditioning units according to actual needs, resulting in low energy efficiency, poor comfort and excessive operation of air conditioning equipment.

[0005] The operation of air conditioning units is often affected by spatial layout, physical location, and surrounding environment. For example, some air conditioning units may require higher cooling loads due to their proximity to heat sources or other equipment, while adjacent air conditioning units may interact with each other due to similar environmental conditions. Traditional air conditioning scheduling methods usually ignore the spatial distribution and mutual influence between these units, resulting in the inability of air conditioning units to work effectively together. The lack of such a scheduling strategy for mutual influence not only makes it impossible to achieve optimal operating efficiency of air conditioning units, but may also increase the overall burden of the system and lead to unnecessary energy consumption.

[0006] Most traditional air conditioning systems use preset timing or temperature control schemes to manage the switching of air conditioning units. This method lacks the ability to dynamically adapt to environmental changes. The heat load in a workshop or building is a dynamically changing process, which is affected by many factors, such as production activities, personnel flow, equipment operation, etc. Existing air conditioning scheduling strategies are usually unable to respond to these changes in real time, resulting in the inability to adjust the operating status of the air conditioning unit according to the real-time heat and load requirements. Therefore, the air conditioning system often cannot achieve optimal energy-saving operation, resulting in energy waste and increased operating costs. Summary of the invention

[0007] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an air-conditioning energy-saving operation optimization scheduling method and system, which solves the problems of low energy efficiency, energy waste, unreasonable scheduling, lack of flexibility, etc. in the air-conditioning system by comprehensively considering the location, spatial distribution, mutual influence relationship, heat load analysis and other factors of the air-conditioning unit. Through intelligent optimization scheduling, the operating efficiency of the air-conditioning system can be effectively improved, energy consumption can be reduced, and the purpose of energy saving and emission reduction can be achieved.

[0008] The technical solution adopted by the present invention to solve its technical problem is:

[0009] An air conditioning energy-saving operation optimization scheduling method comprises the following steps:

[0010] Obtain the location coordinate data of all air conditioning units in the factory workshop;

[0011] According to the position coordinates and relative positions of the air-conditioning units, the air-conditioning units are divided into basic air-conditioning groups and adjacent air-conditioning groups, wherein the basic air-conditioning groups are air-conditioning units in the same indoor environment, and the adjacent air-conditioning groups are air-conditioning units in adjacent environments;

[0012] According to the spatial distribution relationship of the air-conditioning units, an impact relationship diagram between the air-conditioning units is constructed, wherein the impact relationship is determined by the physical distance and relative position between the air-conditioning units, and represents the environmental impact intensity between the air-conditioning units;

[0013] Based on the heat generated in the workshop, determine the heat load of each area, where the first influencing factor of heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group;

[0014] Based on the heat analysis results and the mutual influence between air-conditioning units, the operating parameters of each air-conditioning unit are set;

[0015] The operating status of the air-conditioning unit is scheduled and optimized based on the operating parameters of the air-conditioning unit.

[0016] As a preferred method, the method of obtaining the position coordinate data of all air-conditioning units in the factory workshop is:

[0017] Installing a position sensor in the workshop, wherein the position sensor is a GPS positioning module, an RFID tag, or a sensor based on wireless positioning technology such as Wi-Fi and Bluetooth;

[0018] The spatial position coordinate data of each air conditioning unit is collected in real time by the position sensor;

[0019] The collected position coordinate data of the air-conditioning units are transmitted to the central control system for grouping, scheduling and optimization of the air-conditioning units;

[0020] If there are areas of obstruction or signal interference in the workshop, use auxiliary positioning technology, such as lidar or infrared positioning system, to enhance the accuracy of position data;

[0021] The location coordinate data of all air conditioning units are filtered and corrected.

[0022] Preferably, the method of dividing the air-conditioning groups into basic air-conditioning groups and adjacent air-conditioning groups according to the position coordinates and relative positions of the air-conditioning groups is:

[0023] Calculate the spatial distance between each air conditioning unit and other surrounding air conditioning units based on the actual position coordinate data of the air conditioning unit;

[0024] According to the set distance threshold, the air-conditioning units whose distance is less than the threshold are divided into the same adjacent air-conditioning group;

[0025] Select an air conditioning unit with the best location or the most representative in the workshop as the basic air conditioning unit, and divide it into the basic air conditioning group;

[0026] According to the relative position relationship, other air-conditioning units whose distances to the basic air-conditioning unit meet the threshold are divided into adjacent air-conditioning groups.

[0027] Preferably, according to the spatial distribution relationship of the air-conditioning units, a method for constructing an influence relationship diagram between the air-conditioning units is:

[0028] According to the spatial distribution coordinates and relative position relationship of the air-conditioning units, determine the influence range between each air-conditioning unit and the surrounding air-conditioning units;

[0029] Based on factors such as the physical distance between air-conditioning units, air flow, and heat exchange area, an influence matrix between air-conditioning units is established to indicate the degree of influence of each air-conditioning unit on other air-conditioning units.

[0030] The air-conditioning units and their mutual influence relationships are mapped into a graph model, where the air-conditioning units are nodes of the graph, the edges between the nodes represent the influence relationships between the air-conditioning units, and the weights of the edges represent the influence strength;

[0031] According to the working status, operation mode and environmental changes of the air-conditioning units in the workshop, the edge weights in the influence relationship graph are dynamically adjusted.

[0032] Preferably, based on the heat generated in the workshop, the method for determining the heat load of each area is:

[0033] According to the nature of work and equipment distribution in each area of ​​the workshop, collect heat source data for each area, including heat emissions from production equipment and heat from lighting equipment;

[0034] The temperature and humidity sensors in the workshop collect data to monitor the temperature changes in each area in real time, and estimate the heat load of each area based on the collected environmental data and the preset heat emission model;

[0035] According to the area and equipment power factor of each area in the workshop, the unit area heat load of each area is calculated to generate the regional heat load distribution map.

[0036] Preferably, based on the heat analysis results and in combination with the mutual influence relationship between the air-conditioning units, the method for setting the operating parameters of each air-conditioning unit is:

[0037] Assume that the heat load of the i-th region is Qi, and the influence coefficient of air-conditioning unit j on region i is αij. The relationship between the heat load and the air-conditioning unit can be expressed as:

[0038]

[0039] Where Pj is the cooling or heating power of air conditioning unit j, and N is the total number of air conditioning units;

[0040] Assuming the heat exchange coefficient between air-conditioning unit i and air-conditioning unit j is βij, the mutual influence between air-conditioning units is:

[0041]

[0042] Among them, ΔTij is the temperature difference between air-conditioning units i and j, Ti and Tj are the set temperatures of air-conditioning units i and j;

[0043] Assume that the target load of each air conditioning unit is P j target , then according to the heat load distribution and mutual influence, the load of each air conditioning unit is adjusted as follows:

[0044]

[0045] Where M is the number of zones, αij is the influence coefficient of air conditioning unit j on zone i, and Qi is the heat load of zone i;

[0046] The operating parameters Xj of each air-conditioning unit are set, and the adjustment of the operating parameters is achieved through the following optimization objective function:

[0047]

[0048] Among them, Cj(Xj) is the energy consumption function of air-conditioning unit j, and Xj is the operating parameter of air-conditioning unit j.

[0049] Another technical problem to be solved by the present invention is to provide an air conditioning energy-saving operation optimization scheduling system, comprising:

[0050] Data acquisition module: used to obtain the location coordinate data of all air-conditioning units in the factory workshop;

[0051] Air conditioning grouping module: according to the position coordinates and relative positions of the air conditioning units, the air conditioning units are divided into basic air conditioning groups and adjacent air conditioning groups, wherein the basic air conditioning groups are air conditioning units in the same indoor environment, and the adjacent air conditioning groups are air conditioning units in adjacent environments;

[0052] Impact relationship diagram construction module: constructs an impact relationship diagram between air-conditioning units according to the spatial distribution relationship of the air-conditioning units. The impact relationship is determined by the physical distance and relative position between the air-conditioning units, indicating the environmental impact intensity between the air-conditioning units.

[0053] Heat load calculation module: based on the heat generated in the workshop, determine the heat load of each area, the first influencing factor of the heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group;

[0054] Operation parameter setting module: Based on the heat analysis results and the mutual influence between air-conditioning units, the operation parameters of each air-conditioning unit are set;

[0055] Scheduling optimization module: Based on the operating parameters of the air-conditioning units, the operating status of the air-conditioning units is scheduled and optimized to control the start, stop, wind speed, and cooling / heating power of each air-conditioning unit.

[0056] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an air conditioning energy-saving operation optimization scheduling method as described above is implemented.

[0057] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, an air conditioning energy-saving operation optimization scheduling method is implemented.

[0058] The beneficial effects of the present invention are:

[0059] By optimizing the scheduling strategy of the air-conditioning units, combined with the location, spatial distribution and mutual influence between adjacent air-conditioning units, the air-conditioning units can be reasonably scheduled to ensure that the relevant units are enabled only when needed, thereby reducing the ineffective operation of the air-conditioning system and reducing overall energy consumption. Through thermal analysis and the mutual influence between air-conditioning units, the heat load conditions of each area can be dynamically monitored and evaluated, and the operating parameters of the air-conditioning units can be intelligently adjusted, so that the air-conditioning units can be optimized and scheduled according to the heat load requirements of the specific area, ensuring maximum air-conditioning energy efficiency.

[0060] By constructing an influence relationship diagram between air-conditioning units and taking factors such as physical distance and relative position into consideration, a more accurate scheduling model can be established, which can optimize the coordination between air-conditioning units, avoid mutual interference and ineffective consumption between air-conditioning units, and improve the energy-saving effect of the overall system; through the analysis of the heat generated in the workshop and the negative heat of adjacent air-conditioning units, combined with the real-time changes in heat load, the operating status of the air-conditioning units can be dynamically adjusted. Through intelligent scheduling of the switching and power adjustment of air-conditioning units, real-time optimization can be achieved, further improving the energy efficiency of the air-conditioning system.

[0061] Through the mutual influence relationship between the heat analysis results and the air-conditioning units, the operating parameters of each air-conditioning unit can be dynamically set. According to the environmental changes in the workshop, the operating status of the air-conditioning system can be flexibly adjusted, the system's adaptability can be improved, and the air-conditioning scheduling process can be optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flow chart of an air conditioning energy-saving operation optimization scheduling method. DETAILED DESCRIPTION

[0063] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0065] Example

[0066] An air conditioning energy-saving operation optimization scheduling method comprises the following steps:

[0067] Obtain the location coordinate data of all air conditioning units in the factory workshop;

[0068] According to the position coordinates and relative positions of the air-conditioning units, the air-conditioning units are divided into basic air-conditioning groups and adjacent air-conditioning groups, wherein the basic air-conditioning groups are air-conditioning units in the same indoor environment, and the adjacent air-conditioning groups are air-conditioning units in adjacent environments;

[0069] According to the spatial distribution relationship of the air-conditioning units, an impact relationship diagram between the air-conditioning units is constructed, wherein the impact relationship is determined by the physical distance and relative position between the air-conditioning units, and represents the environmental impact intensity between the air-conditioning units;

[0070] Based on the heat generated in the workshop, determine the heat load of each area, where the first influencing factor of heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group;

[0071] Based on the heat analysis results and the mutual influence between air-conditioning units, the operating parameters of each air-conditioning unit are set;

[0072] The operating status of the air-conditioning unit is scheduled and optimized based on the operating parameters of the air-conditioning unit.

[0073] This method can intelligently adjust the operating status of air-conditioning units by accurately analyzing the heat load of different areas in the workshop and combining the mutual influence of air-conditioning units. For example, some air-conditioning units may not need to run at full capacity due to low ambient temperature, while other areas with heavier heat loads can increase their operating loads. This precise scheduling can minimize unnecessary energy consumption, improve the overall energy efficiency of the air-conditioning system, and thus reduce energy costs.

[0074] Traditional air conditioning systems often ignore the interaction between air conditioning units, but this method can fully consider the synergy between units during the scheduling process by constructing an influence relationship diagram between air conditioning units. Adjacent air conditioning units can cooperate and complement each other to avoid the situation where some units are over-operated while other units are not effectively scheduled, reducing resource waste in the air conditioning system and improving operating efficiency.

[0075] The heat load of the air conditioning system changes dynamically with time, personnel activities, equipment operation and other factors, while the traditional air conditioning system scheduling method often uses fixed time periods or temperature settings, which lacks sufficient flexibility. Based on real-time heat load analysis, this method can dynamically adjust the operating parameters of the air conditioning unit to adapt to the changes in heat load under different time and environmental conditions, thereby ensuring that the system always operates in the optimal state.

[0076] This method can more accurately determine the heat load of each area by considering the heat sources in the workshop (such as production equipment, personnel activities, etc.) and the negative heat effects between air-conditioning units. When the heat loads of different areas vary greatly, the scheduling of air-conditioning units can be adjusted accordingly to avoid unnecessary over-cooling or over-heating, further improving energy efficiency.

[0077] The method to obtain the location coordinate data of all air conditioning units in the factory workshop is:

[0078] Installing a position sensor in the workshop, wherein the position sensor is a GPS positioning module, an RFID tag, or a sensor based on wireless positioning technology such as Wi-Fi and Bluetooth;

[0079] The spatial position coordinate data of each air conditioning unit is collected in real time by the position sensor;

[0080] The collected position coordinate data of the air-conditioning units are transmitted to the central control system for grouping, scheduling and optimization of the air-conditioning units;

[0081] If there are areas of obstruction or signal interference in the workshop, use auxiliary positioning technology, such as lidar or infrared positioning system, to enhance the accuracy of position data;

[0082] The location coordinate data of all air conditioning units are filtered and corrected.

[0083] Using wireless positioning technologies such as GPS, RFID, Wi-Fi, and Bluetooth, the spatial location of each air-conditioning unit can be tracked in real time and dynamically. This method ensures the high real-time nature of the location data, accurately reflects the specific location of the air-conditioning unit, and avoids errors that may be caused by manual recording or static solutions.

[0084] By transmitting the real-time location data of the air-conditioning units to the central control system, intelligent scheduling and optimization can be performed based on information such as the environmental distribution in the workshop, heat load, and mutual influence between air-conditioning units. This method can achieve accurate grouping and scheduling of air-conditioning units, ensure more efficient operation of air-conditioning units, and reduce waste caused by excessive operation or uneven distribution of air-conditioning units.

[0085] In areas of the workshop where there may be obstructions or signal interference, auxiliary positioning technologies (such as lidar or infrared positioning) can solve the problem of limited signal transmission. This supplementary solution ensures that the position data can still maintain high accuracy even in complex environments, thereby improving the reliability and accuracy of the entire system.

[0086] Traditional air conditioning management methods usually rely on manual records and manual inspections, while sensor-based automated positioning can eliminate errors caused by human factors and improve data accuracy and consistency. Accurate location data obtained in real time enables workshop managers to make decisions faster and more accurately, reducing the possibility of operational errors.

[0087] The integration of position sensors makes the operation of air conditioning systems more intelligent. Through the combination of central control systems and sensor data, air conditioning units can automatically adjust their working status according to actual needs and spatial location. This increase in automation not only optimizes the energy consumption of air conditioning systems, but also greatly reduces the need for manual intervention and improves management efficiency.

[0088] According to the position coordinates and relative positions of the air-conditioning units, the method of dividing the air-conditioning units into basic air-conditioning groups and adjacent air-conditioning groups is as follows:

[0089] Calculate the spatial distance between each air conditioning unit and other surrounding air conditioning units based on the actual position coordinate data of the air conditioning unit;

[0090] According to the set distance threshold, the air-conditioning units whose distance is less than the threshold are divided into the same adjacent air-conditioning group;

[0091] Select an air conditioning unit with the best location or the most representative in the workshop as the basic air conditioning unit, and divide it into the basic air conditioning group;

[0092] According to the relative position relationship, other air-conditioning units whose distances to the basic air-conditioning unit meet the threshold are divided into adjacent air-conditioning groups.

[0093] By dividing the air conditioner groups into basic air conditioner groups and adjacent air conditioner groups according to their actual location coordinates and spatial distances, scientific classification and optimized management of air conditioner groups can be achieved. As the core node, the basic air conditioner group can be more easily managed and scheduled centrally, while the adjacent air conditioner group helps to reasonably allocate resources and tasks and improve the overall operating efficiency of the system.

[0094] By dividing the basic air conditioning group and the adjacent air conditioning group, the interaction and coordinated scheduling between air conditioning units become more efficient. The units of adjacent air conditioning groups can be coordinated and adjusted according to each other's actual needs and loads. For example, when a certain air conditioning unit needs to increase its cooling capacity, the surrounding adjacent air conditioning units can work together to improve the scheduling efficiency of the entire system.

[0095] The grouping method of air conditioning units is based on accurate spatial location and distance calculation, so the configuration of each group can be flexibly adjusted in real time according to factors such as load changes of air conditioning units in the workshop and environmental changes. For example, when there is a temperature change or cooling load change in a certain area, the system can quickly identify adjacent air conditioning groups and make automatic adjustments to avoid energy waste or excessive operation of air conditioning units.

[0096] The distance division between air conditioning units ensures load balancing. For example, after selecting a "basic air conditioning unit", the system can reasonably dispatch based on the distance and load of adjacent air conditioning units to avoid overloading or overcooling of some air conditioning units due to being too far or too close. This helps reduce the energy consumption of the system and increase the service life of air conditioning equipment.

[0097] According to the spatial distribution relationship of air-conditioning units, the method of constructing the influence relationship diagram between air-conditioning units is as follows:

[0098] According to the spatial distribution coordinates and relative position relationship of the air-conditioning units, determine the influence range between each air-conditioning unit and the surrounding air-conditioning units;

[0099] Based on factors such as the physical distance between air-conditioning units, air flow, and heat exchange area, an influence matrix between air-conditioning units is established to indicate the degree of influence of each air-conditioning unit on other air-conditioning units.

[0100] The air-conditioning units and their mutual influence relationships are mapped into a graph model, where the air-conditioning units are nodes of the graph, the edges between the nodes represent the influence relationships between the air-conditioning units, and the weights of the edges represent the influence strength;

[0101] According to the working status, operation mode and environmental changes of the air-conditioning units in the workshop, the edge weights in the influence relationship graph are dynamically adjusted.

[0102] By considering factors such as the physical location of the air conditioning unit, air flow, heat exchange area, etc., the influence matrix between each air conditioning unit and the surrounding air conditioning units can be accurately established. This modeling method is more detailed than the traditional simple grouping or intuitive management method, and can fully reflect the actual interaction and influence between air conditioning units.

[0103] In the influence relationship diagram, the influence relationship between air conditioning units is represented by the weight of the edge. By calculating and adjusting these weights in real time, dynamic scheduling can be performed based on the interactive relationship between units. For example, when the load of an air conditioning unit increases, the system can optimize the overall energy consumption and avoid energy waste by adjusting the working mode or status of the adjacent units. This method can effectively improve the energy efficiency and responsiveness of the air conditioning system.

[0104] Dynamically adjusting the edge weights in the influence relationship graph can adaptively adjust the mutual influence between air-conditioning units according to the working status, operation mode and environmental changes of the air-conditioning units. For example, when the temperature in a certain area is high, the surrounding air-conditioning units can automatically increase the cooling capacity, or some units can adjust the operation strategy during maintenance to ensure that the system is always in the best state under different environmental conditions.

[0105] Based on the heat generated in the workshop, the method to determine the heat load of each area is:

[0106] According to the nature of work and equipment distribution in each area of ​​the workshop, collect heat source data for each area, including heat emissions from production equipment and heat from lighting equipment;

[0107] The temperature and humidity sensors in the workshop collect data to monitor the temperature changes in each area in real time, and estimate the heat load of each area based on the collected environmental data and the preset heat emission model;

[0108] According to the area and equipment power factor of each area in the workshop, the unit area heat load of each area is calculated to generate the regional heat load distribution map.

[0109] By estimating the heat load based on the heat source data and real-time environmental data of each area of ​​the workshop, we can more accurately understand the heat load distribution of each area. This provides a more refined basis for the design of the air conditioning system, and can reasonably select the type and configuration of air conditioning equipment and make reasonable load distribution. The system can dynamically adjust according to the heat load requirements of different areas, optimize the operating efficiency of the air conditioning system, and reduce energy waste.

[0110] Using real-time temperature, humidity and heat source data, the cooling requirements of each area in the workshop can be dynamically monitored and adjusted. By accurately calculating the heat load of each area, the air conditioning system can avoid overcooling areas that do not need cooling, thereby achieving more efficient energy use. This method can help managers identify areas with high energy consumption and improve overall energy efficiency through local regulation or optimization solutions to achieve energy conservation.

[0111] Based on the data from temperature and humidity sensors and the heat emission model, real-time monitoring and dynamic adjustment of the heat load in each area of ​​the workshop can be achieved. This intelligent management method makes the environment in the workshop more controllable, and can adjust the operation strategy of the air-conditioning equipment in real time according to the actual operation conditions (such as production load, equipment heat emission changes, etc.), avoiding unnecessary equipment loss or reduced production efficiency due to excessively high or low temperatures, thereby improving the overall comfort of the workshop working environment and production efficiency.

[0112] Based on the results of heat analysis and the mutual influence between air-conditioning units, the method for setting the operating parameters of each air-conditioning unit is as follows:

[0113] Assume that the heat load of the i-th region is Qi, and the influence coefficient of air-conditioning unit j on region i is αij. The relationship between the heat load and the air-conditioning unit can be expressed as:

[0114]

[0115] Where Pj is the cooling or heating power of air conditioning unit j, and N is the total number of air conditioning units;

[0116] Assuming the heat exchange coefficient between air-conditioning unit i and air-conditioning unit j is βij, the mutual influence between air-conditioning units is:

[0117]

[0118] Among them, ΔTij is the temperature difference between air-conditioning units i and j, Ti and Tj are the set temperatures of air-conditioning units i and j;

[0119] Assume that the target load of each air conditioning unit is P j target , then according to the heat load distribution and mutual influence, the load of each air conditioning unit is adjusted as follows:

[0120]

[0121] Where M is the number of zones, αij is the influence coefficient of air conditioning unit j on zone i, and Qi is the heat load of zone i;

[0122] The operating parameters Xj of each air-conditioning unit are set, and the adjustment of the operating parameters is achieved through the following optimization objective function:

[0123]

[0124] Among them, Cj(Xj) is the energy consumption function of air-conditioning unit j, and Xj is the operating parameter of air-conditioning unit j.

[0125] By considering the heat load of each area and the mutual influence between air conditioning units, this method can accurately adjust the target load of each air conditioning unit. When multiple air conditioning units work together, the system can reasonably distribute the load to ensure that each air conditioning unit operates in the optimal working state, thereby avoiding overloading or underloading some units. This can not only improve cooling / heating efficiency, but also reduce idling and energy consumption of equipment, and improve the overall efficiency of the air conditioning system.

[0126] By optimizing the operating parameters of the air conditioning units, this method can minimize the energy consumption of the air conditioning units. According to the mutual influence coefficients between the air conditioning units and environmental changes, by adjusting the operating parameters, unnecessary energy consumption can be reduced, and the air conditioning system can be prevented from over-operating, thereby reducing energy consumption. This method can achieve dynamic adjustment and avoid over-cooling or heating caused by environmental changes, thereby effectively saving energy.

[0127] This method fully considers the heat exchange relationship and mutual influence between air-conditioning units, can respond to changes in heat load in different areas in real time, and optimize the working state of air-conditioning units. By setting the optimization objective function, the air-conditioning system can be intelligently adaptively adjusted to maintain system balance under different working environments. This flexible and dynamic adjustment capability not only improves the working accuracy of the air-conditioning system, but also copes with complex production environments and reduces working environment discomfort or equipment failures caused by uneven temperature.

[0128] An air conditioning energy-saving operation optimization scheduling system, comprising:

[0129] Data acquisition module: used to obtain the location coordinate data of all air-conditioning units in the factory workshop;

[0130] Air conditioning grouping module: according to the position coordinates and relative positions of the air conditioning units, the air conditioning units are divided into basic air conditioning groups and adjacent air conditioning groups, wherein the basic air conditioning groups are air conditioning units in the same indoor environment, and the adjacent air conditioning groups are air conditioning units in adjacent environments;

[0131] Impact relationship diagram construction module: constructs an impact relationship diagram between air-conditioning units according to the spatial distribution relationship of the air-conditioning units. The impact relationship is determined by the physical distance and relative position between the air-conditioning units, indicating the environmental impact intensity between the air-conditioning units.

[0132] Heat load calculation module: based on the heat generated in the workshop, determine the heat load of each area, the first influencing factor of the heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group;

[0133] Operation parameter setting module: Based on the heat analysis results and the mutual influence between air-conditioning units, the operation parameters of each air-conditioning unit are set;

[0134] Scheduling optimization module: Based on the operating parameters of the air-conditioning units, the operating status of the air-conditioning units is scheduled and optimized to control the start, stop, wind speed, and cooling / heating power of each air-conditioning unit.

[0135] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an air conditioning energy-saving operation optimization scheduling method as described above is implemented.

[0136] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the air-conditioning energy-saving operation optimization scheduling method as described above is implemented.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0139] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. An air conditioning energy-saving operation optimization scheduling method, characterized in that: The following steps are involved: Obtain the location coordinate data of all air conditioning units in the factory workshop; According to the position coordinates and relative positions of the air-conditioning units, the air-conditioning units are divided into basic air-conditioning groups and adjacent air-conditioning groups, wherein the basic air-conditioning groups are air-conditioning units in the same indoor environment, and the adjacent air-conditioning groups are air-conditioning units in adjacent environments; According to the spatial distribution relationship of the air-conditioning units, an impact relationship diagram between the air-conditioning units is constructed, wherein the impact relationship diagram is determined by the physical distance and relative position between the air-conditioning units, and represents the environmental impact intensity between the air-conditioning units; Based on the heat generated in the workshop, determine the heat load of each area, where the first influencing factor of heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group; Based on the heat analysis results and the mutual influence between air-conditioning units, the operating parameters of each air-conditioning unit are set; Scheduling and optimizing the operating status of the air-conditioning unit based on the operating parameters of the air-conditioning unit; Based on the heat generated in the workshop, the method to determine the heat load of each area is: According to the nature of work and equipment distribution in each area of ​​the workshop, collect heat source data for each area, including heat emissions from production equipment and heat from lighting equipment; The temperature and humidity sensors in the workshop collect data to monitor the temperature changes in each area in real time, and estimate the heat load of each area based on the collected environmental data and the preset heat emission model; According to the area and equipment power factor of each area in the workshop, the unit area heat load of each area is calculated to generate the regional heat load distribution map; Based on the results of heat analysis and the mutual influence between air-conditioning units, the method for setting the operating parameters of each air-conditioning unit is as follows: Assume that the heat load of the i-th region is Qi, and the influence coefficient of air-conditioning unit j on region i is αij. The relationship between the heat load and the air-conditioning unit can be expressed as: Where Pj is the cooling or heating power of air conditioning unit j, and N is the total number of air conditioning units; Assuming the heat exchange coefficient between air-conditioning unit i and air-conditioning unit j is βij, the mutual influence between air-conditioning units is: Among them, ΔTij is the temperature difference between air-conditioning units i and j, Ti and Tj are the set temperatures of air-conditioning units i and j; Assume that the target load of each air conditioning unit is P j target , then according to the heat load distribution and mutual influence, the load of each air conditioning unit is adjusted as follows: Where M is the number of zones, αij is the influence coefficient of air conditioning unit j on zone i, and Qi is the heat load of zone i; The operating parameters Xj of each air-conditioning unit are set, and the adjustment of the operating parameters is achieved through the following optimization objective function: Among them, Cj(Xj) is the energy consumption function of air-conditioning unit j, and Xj is the operating parameter of air-conditioning unit j.

2. The air conditioning energy-saving operation optimization scheduling method according to claim 1 is characterized in that: The method to obtain the location coordinate data of all air conditioning units in the factory workshop is: Installing a position sensor in the workshop, wherein the position sensor is a GPS positioning module, an RFID tag, or a sensor based on Wi-Fi or Bluetooth wireless positioning technology; The spatial position coordinate data of each air conditioning unit is collected in real time by the position sensor; The collected position coordinate data of the air-conditioning units are transmitted to the central control system for grouping, scheduling and optimization of the air-conditioning units; If there are areas of obstruction or signal interference in the workshop, use auxiliary positioning technology, laser radar or infrared positioning system to enhance the accuracy of position data; The location coordinate data of all air conditioning units are filtered and corrected.

3. The air conditioning energy-saving operation optimization scheduling method according to claim 1 is characterized in that: According to the position coordinates and relative positions of the air-conditioning units, the method of dividing the air-conditioning units into basic air-conditioning groups and adjacent air-conditioning groups is as follows: Calculate the spatial distance between each air conditioning unit and other surrounding air conditioning units based on the actual position coordinate data of the air conditioning unit; According to the set distance threshold, the air-conditioning units whose distance is less than the threshold are divided into the same adjacent air-conditioning group; Select an air conditioning unit with the best location or the most representative in the workshop as the basic air conditioning unit, and divide it into the basic air conditioning group; According to the relative position relationship, other air-conditioning units whose distances to the basic air-conditioning unit meet the threshold are divided into adjacent air-conditioning groups.

4. The air conditioning energy-saving operation optimization scheduling method according to claim 1 is characterized in that: According to the spatial distribution relationship of air-conditioning units, the method of constructing the influence relationship diagram between air-conditioning units is as follows: According to the spatial distribution coordinates and relative position relationship of the air-conditioning units, determine the influence range between each air-conditioning unit and the surrounding air-conditioning units; Based on the physical distance, air flow, and heat exchange area between air conditioning units, an influence matrix between air conditioning units is established to indicate the degree of influence of each air conditioning unit on other air conditioning units. The air-conditioning units and their mutual influence relationships are mapped into a graph model, where the air-conditioning units are nodes of the graph, the edges between the nodes represent the influence relationships between the air-conditioning units, and the weights of the edges represent the influence strength; According to the working status, operation mode and environmental changes of the air-conditioning units in the workshop, the edge weights in the influence relationship graph are dynamically adjusted.

5. A system using the air conditioning energy-saving operation optimization scheduling method according to claim 1, characterized in that: Included are: Data acquisition module: used to obtain the location coordinate data of all air-conditioning units in the factory workshop; Air conditioning grouping module: according to the position coordinates and relative positions of the air conditioning units, the air conditioning units are divided into basic air conditioning groups and adjacent air conditioning groups, wherein the basic air conditioning groups are air conditioning units in the same indoor environment, and the adjacent air conditioning groups are air conditioning units in adjacent environments; Impact relationship diagram construction module: constructs an impact relationship diagram between air-conditioning units according to the spatial distribution relationship of the air-conditioning units. The impact relationship is determined by the physical distance and relative position between the air-conditioning units, indicating the environmental impact intensity between the air-conditioning units. Heat load calculation module: based on the heat generated in the workshop, determine the heat load of each area, the first influencing factor of the heat is the heat generated in the workshop, and the second influencing factor is the negative heat generated by the adjacent air conditioning group; Operation parameter setting module: Based on the heat analysis results and the mutual influence between air-conditioning units, the operation parameters of each air-conditioning unit are set; Scheduling optimization module: Based on the operating parameters of the air-conditioning units, the operating status of the air-conditioning units is scheduled and optimized to control the start, stop, wind speed, and cooling / heating power of each air-conditioning unit.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the air conditioning energy-saving operation optimization scheduling method as described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the air conditioning energy-saving operation optimization scheduling method as described in any one of claims 1 to 4 is implemented.

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