Clean air conditioner energy consumption optimization method and system based on Internet of Things

Abstract

The invention relates to the technical field of energy consumption optimization and energy conservation, in particular to a clean air conditioner energy consumption optimization method and system based on the Internet of Things, and the method specifically comprises the steps that air supply path data is mapped into a digital twinborn model of a clean air conditioner; air supply path fluctuation simulation of the clean air conditioner is conducted, and a first energy consumption fluctuation coefficient is obtained through calculation; the linear distance between the marked sudden pollution point and the air outlet of the clean air conditioner is recorded; meanwhile, actual working state data of the clean air conditioner are collected, and a second energy consumption fluctuation coefficient is obtained through calculation; calculating an energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and performing feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value; the problem that in the prior art, when the cleaning standard of the cleaning air conditioner aiming at the sudden pollution source or the cleaning area in the product production line changes, the energy consumption fluctuation of the cleaning air conditioner cannot be controlled and optimized in real time is solved.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption optimization and energy-saving technology, and is a clean air conditioning energy consumption optimization method and system based on the Internet of Things. Background Art

[0002] In modern industrial and medical environments, clean air conditioning systems are key equipment to ensure the cleanliness of the production environment and product quality. Existing clean air conditioning systems have some defects in energy consumption management. First, the clean air conditioning in the existing technology relies on fixed parameter settings and lacks dynamic monitoring and analysis of the actual working status, resulting in insufficient accuracy in energy consumption prediction; secondly, when faced with sudden pollution events, the clean air conditioning has a slow response speed and cannot adapt to the new working status in time, further leading to energy waste and performance degradation, thereby increasing energy consumption costs and affecting the cleanliness of the production environment. It is urgent to introduce a clean air conditioning energy consumption optimization method that can solve the above technical problems.

[0003] In the existing disclosed invention technologies, for example, a Chinese patent with application publication number CN118640562A discloses a clean air conditioning control method based on a neural network and an MPC algorithm, including: collecting multiple dimensions of indoor and outdoor environmental data, and obtaining multiple dimensions of status data and operation data of the air conditioning system; constructing an LSTM neural network model, and obtaining the power change trend of the air conditioning system by training the LSTM neural network model based on the predicted values ​​of environmental parameters and environmental parameters at adjacent moments; setting the target environmental parameter range of each dimension based on the indoor cleanliness conditions, and constructing the energy consumption cost function of each dimension in the future time, and then optimizing and solving to obtain the optimal control input sequence of each dimension; and controlling the air conditioning system based on the optimal control input sequence of each dimension.

[0004] The above patent cannot achieve real-time monitoring and optimization of energy consumption when faced with the clean treatment of sudden pollution points, and has the problems described in the background technology. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, when a sudden pollution source occurs in a product production line or the cleanliness standard of a clean area changes, the energy consumption fluctuation of the clean air conditioner cannot be controlled and optimized in real time. A clean air conditioner energy consumption optimization method and system based on the Internet of Things are proposed.

[0006] In order to achieve the above object, the technical solution of a clean air conditioning energy consumption optimization method based on the Internet of Things of the present invention comprises the following steps:

[0007] S1: extracting preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and mapping the air supply path data to the digital twin model of the clean air conditioner;

[0008] S2: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient;

[0009] S3: Through IoT sensors, the sudden pollution points in the product production line area covered by the clean air conditioner are screened and marked in real time, and the straight-line distance between the marked sudden pollution points and the air outlet of the clean air conditioner is recorded;

[0010] S4: monitoring the air supply path of the clean air conditioner in real time, collecting actual working status data of the clean air conditioner, and calculating and obtaining the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner;

[0011] S5: Calculate the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and perform feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

[0012] Specifically, S2 includes the following specific steps:

[0013] S21: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, wherein the air supply path fluctuation simulation specifically includes: extracting the maximum coverage distance and the minimum coverage distance in the product production line area covered by the clean air conditioner, the distance between the maximum coverage distance and the minimum coverage distance constitutes the effective working distance of the clean air conditioner, the effective working distance is D, and the distance from the minimum coverage distance of 0.25D is taken as the first simulation sampling point, the distance from the minimum coverage distance of 0.5D is taken as the second simulation sampling point, and the distance from the minimum coverage distance of 0.75D is taken as the third simulation sampling point;

[0014] S22: Extract the standard values ​​of blade resistance {K1, K2...K n} and the standard value of wind duct resistance {P1,P2...P n}, where K n represents the standard value of the blade resistance of the guide vane at the nth elbow area; P n Indicates the standard value of the air duct resistance at the nth elbow area;

[0015] S23: According to S22, the actual air supply distance is kept unchanged, and the position of the clean air conditioner damper at different simulated sampling points is adaptively adjusted, wherein the actual air supply distance is the sum of the internal air duct distance between the damper and the clean air conditioner outlet and the external air duct distance between the clean air conditioner outlet and different simulated sampling points;

[0016] At the same time, in each elbow area, the blade resistance and duct resistance are kept unchanged, and the angle between the guide blades in each elbow area and the center line of the air supply path is adaptively adjusted according to the internal duct distance between the damper and the clean air conditioner outlet;

[0017] Furthermore, the calculation strategy of the wind duct resistance is:

[0018]

[0019] L i is the internal air duct distance between the damper and the clean air conditioner outlet during the simulation process of the i-th simulation sampling point;

[0020] L0 is the baseline distance of the internal air duct between the damper and the air outlet of the clean air conditioner when it leaves the factory;

[0021] θ 0,n is the baseline angle between the guide vane in the nth elbow area and the center line of the air supply path when the clean air conditioner leaves the factory; ρ is the air density of the fresh air;

[0022] S24: Extracting working status fluctuation data of the clean air conditioner, wherein the working status fluctuation data includes the number N of elbow areas in the air supply path of the clean air conditioner, the angle formed by the guide vanes in each elbow area and the center line of the air supply path, the air duct distance between the guide vanes in each elbow area and the fan damper, and the wind speed of the fresh air in each elbow area after passing through the guide vanes.

[0023] Specifically, S2 also includes the following specific steps:

[0024] S25: Calculate the energy consumption fluctuation sub-coefficient of each simulated sampling point according to the working state change data. The calculation strategy of the energy consumption fluctuation sub-coefficient of each simulated sampling point is:

[0025] q i =ε1×c1+ε2×c2+ε3×c3;

[0026] Where i is a subscript, indicating the i-th simulation sampling point; q i represents the energy consumption fluctuation coefficient of the clean air conditioner in the simulation process at the i-th simulation sampling point; i∈{1,2,3};

[0027] c 1,i is the angle fluctuation factor of the ith simulation sampling point, is the angle between the guide vane in the nth elbow area and the center line of the air supply path during the simulation of the i-th simulation sampling point;

[0028] c 2,i is the wind duct distance fluctuation factor of the ith simulation sampling point, is the air duct distance between the guide vane and the fan damper in the nth elbow area during the simulation of the i-th simulation sampling point;

[0029] c 3,i is the wind speed fluctuation factor of the ith simulation sampling point, It represents the wind speed of the fresh air in the nth elbow area after passing through the guide vane during the simulation process of the i-th simulation sampling point;

[0030] ε1, ε2, and ε3 are the proportional coefficients of the angle, duct distance, and wind speed, respectively;

[0031] S26: According to S25, the energy consumption fluctuation coefficients of the three simulated sampling points are introduced into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient Q1. The first energy consumption fluctuation prediction strategy is specifically: Among them, Q0 is the energy consumption baseline coefficient of the clean air conditioner in the factory state.

[0032] Specifically, S3 includes the following specific steps:

[0033] S31: Use IoT sensors to screen and mark sudden pollution points in the product production line area covered by the clean air conditioner in real time, preset sudden pollution point screening thresholds corresponding to different types of sensors, and mark the data collection location as a sudden pollution point when the return data of no less than two types of sensors exceeds the sudden pollution point screening threshold. The IoT sensors include: particulate matter sensors, VOCs sensors, carbon dioxide sensors, and carbon monoxide sensors;

[0034] S32: Record the straight-line distance between the marked sudden pollution point and the clean air-conditioning outlet and the marking time;

[0035] S33: Obtaining the internal air duct distance between the air door and the clean air outlet at the current marking time t according to the actual air supply distance and the straight-line distance between the marked sudden pollution point and the clean air outlet;

[0036] S34: While keeping the actual air supply distance, blade resistance and duct resistance unchanged, the clean air conditioner uses an adaptive fuzzy control module to adaptively adjust the air supply path data of the clean air conditioner, and carry out targeted air supply cleaning treatment for sudden pollution points.

[0037] Specifically, S4 includes the following specific steps:

[0038] S41: According to the locations of the three simulated sampling points, select the simulated sampling point closest to the sudden pollution point as a comparison sampling point;

[0039] S42: Real-time monitoring of the air supply path of the clean air conditioner is performed, and actual working status data of the clean air conditioner is collected at the same time. According to the second energy consumption fluctuation calculation strategy of the clean air conditioner, a second energy consumption fluctuation coefficient Q2 is calculated and obtained. The second energy consumption fluctuation calculation strategy of the clean air conditioner is specifically:

[0040]

[0041] Among them, the clean air conditioner carries out targeted air supply cleaning treatment for the current sudden pollution point, q(t) is the energy consumption fluctuation coefficient of the clean air conditioner at the current marking time t; q x is the energy consumption fluctuation coefficient of the clean air conditioner at the comparison sampling point, x∈{1,2,3}.

[0042] Specifically, in S42, the calculation strategy of the energy consumption fluctuation coefficient q(t) of the clean air conditioner at the current marking time t is specifically as follows:

[0043] q(t)=ε1×c1(t)+ε2×c2(t)+ε3×c3(t);

[0044] Among them, c1(t) is the angle fluctuation factor of the clean air conditioner at the current marking time t,

[0045] θ n (t) is the angle between the guide vane in the nth elbow area and the center line of the air supply path at the current marking time t;

[0046] c2(t) is the air duct distance fluctuation factor of the clean air conditioner at the current marking time t, l n (t) is the air duct distance between the guide vane and the fan damper in the nth elbow area at the current marking time t;

[0047] c3(t) is the wind speed fluctuation factor of the clean air conditioner at the current marking time t, It indicates the wind speed of the fresh air in the nth elbow area after passing through the guide vane at the current marking time t.

[0048] Specifically, the calculation strategy of the energy consumption optimization cost value of the clean air conditioner in S5 is as follows:

[0049]

[0050] Among them, CB is the energy consumption optimization cost value of clean air conditioning.

[0051] Specifically, in S5, the feedback optimization process includes:

[0052] When CB is less than 0.5, the fan speed and wind pressure are increased at the same time, and targeted air supply cleaning treatment is continued for sudden pollution points;

[0053] When 0.5≤CB<1, the adaptive adjustment of the air supply path data of the current clean air conditioner is maintained, and targeted air supply clean treatment is continued for sudden pollution points;

[0054] When CB>1, the adaptive adjustment of the clean air conditioner by the adaptive fuzzy control module is marked as an adjustment failure, and the air supply path data of the clean air conditioner is manually adjusted by the clean air conditioner manager.

[0055] In addition, the present invention provides a clean air conditioning energy consumption optimization system based on the Internet of Things, which includes the following modules:

[0056] Data collection and mapping module, first energy consumption fluctuation coefficient prediction module, sudden pollution point marking module, second energy consumption fluctuation coefficient calculation module and energy consumption feedback optimization module;

[0057] The data acquisition and mapping module is used to extract the preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and map the air supply path data to the digital twin model of the clean air conditioner;

[0058] The first energy consumption fluctuation coefficient prediction module is used to simulate the air supply path fluctuation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient;

[0059] The sudden pollution point marking module uses the Internet of Things sensor to perform real-time screening and marking of sudden pollution points in the product production line area covered by the clean air conditioner, and records the straight-line distance between the marked sudden pollution point and the air outlet of the clean air conditioner;

[0060] The second energy consumption fluctuation coefficient calculation module is used to monitor the air supply path of the clean air conditioner in real time, collect the actual working state data of the clean air conditioner, and calculate and obtain the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner;

[0061] The energy consumption feedback optimization module calculates the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and performs feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

[0062] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the method for optimizing clean air conditioning energy consumption based on the Internet of Things.

[0063] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for optimizing clean air conditioning energy consumption based on the Internet of Things is implemented.

[0064] Compared with the prior art, the technical effects of the present invention are as follows:

[0065] 1. The present invention uses a digital twin model to simulate the fluctuation of the air supply path, combines the first energy consumption fluctuation prediction strategy and the second energy consumption fluctuation calculation strategy, and can accurately predict the energy consumption fluctuation of the clean air conditioner under different working conditions, and constitute comparative sampling points. Especially when dealing with sudden pollution points, feedback optimization processing can be performed according to the energy consumption optimization cost value to ensure the comprehensiveness and effectiveness of energy consumption optimization and achieve energy-saving goals.

[0066] 2. The present invention dynamically adjusts the air supply path of the clean air conditioner according to real-time monitoring data, and can respond quickly, especially when dealing with sudden pollution points. The air supply path is optimized and adjusted through an adaptive fuzzy control module. The air supply path can be optimized under different simulated sampling points and actual working conditions to ensure the uniformity of air distribution and minimized energy consumption.

[0067] 3. Through the data acquisition and mapping module, the present invention can extract the preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and map it to the digital twin model. It can perform dynamic adjustments through the feedback optimization module to realize the data-driven decision-making process and provide data support for subsequent energy consumption prediction and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] 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 labor.

[0069] in:

[0070] Figure 1 A schematic diagram of a flow chart of a clean air conditioning energy consumption optimization method based on the Internet of Things of the present invention;

[0071] Figure 2 It is a structural schematic diagram of a clean air conditioning energy consumption optimization system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0073] 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.

[0074] 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 does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0075] Embodiment 1:

[0076] like Figure 1 As shown, a clean air conditioning energy consumption optimization method based on the Internet of Things in an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0077] S1: extracting preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and mapping the air supply path data to the digital twin model of the clean air conditioner;

[0078] S2: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient;

[0079] S2 includes the following specific steps:

[0080] S21: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, wherein the air supply path fluctuation simulation specifically includes: extracting the maximum coverage distance and the minimum coverage distance in the product production line area covered by the clean air conditioner, the distance between the maximum coverage distance and the minimum coverage distance constitutes the effective working distance of the clean air conditioner, the effective working distance is D, and the distance from the minimum coverage distance of 0.25D is taken as the first simulation sampling point, the distance from the minimum coverage distance of 0.5D is taken as the second simulation sampling point, and the distance from the minimum coverage distance of 0.75D is taken as the third simulation sampling point;

[0081] S22: Extract the standard values ​​of blade resistance {K1, K2...K n} and the standard value of wind duct resistance {P1,P2...P n}, where K n represents the standard value of the blade resistance of the guide vane at the nth elbow area; Pn Indicates the standard value of the air duct resistance at the nth elbow area;

[0082] S23: According to S22, the actual air supply distance is kept unchanged, and the position of the clean air conditioner damper at different simulated sampling points is adaptively adjusted, wherein the actual air supply distance is the sum of the internal air duct distance between the damper and the clean air conditioner outlet and the external air duct distance between the clean air conditioner outlet and different simulated sampling points;

[0083] At the same time, in each elbow area, the blade resistance and the duct resistance are kept unchanged, and the angle between the guide blades in each elbow area and the center line of the air supply path is adaptively adjusted according to the internal duct distance between the damper and the clean air conditioner outlet; wherein, the calculation strategy of the duct resistance is:

[0084]

[0085] L i is the internal air duct distance between the damper and the clean air conditioner outlet during the simulation process of the i-th simulation sampling point;

[0086] L0 is the baseline distance of the internal air duct between the damper and the air outlet of the clean air conditioner when it leaves the factory;

[0087] θ 0,n is the baseline angle between the guide vane in the nth elbow area and the center line of the air supply path when the clean air conditioner leaves the factory; ρ is the air density of the fresh air;

[0088] S24: Extracting working status fluctuation data of the clean air conditioner, wherein the working status fluctuation data includes the number N of elbow areas in the air supply path of the clean air conditioner, the angle formed by the guide vanes in each elbow area and the center line of the air supply path, the air duct distance between the guide vanes in each elbow area and the fan damper, and the wind speed of the fresh air in each elbow area after passing through the guide vanes.

[0089] S2 also includes the following specific steps:

[0090] S25: Calculate the energy consumption fluctuation sub-coefficient of each simulated sampling point according to the working state change data. The calculation strategy of the energy consumption fluctuation sub-coefficient of each simulated sampling point is:

[0091] q i =ε1×c1+ε2×c2+ε3×c3;

[0092] Where i is a subscript, indicating the i-th simulation sampling point; q i represents the energy consumption fluctuation coefficient of the clean air conditioner in the simulation process at the i-th simulation sampling point; i∈{1,2,3};

[0093] c1,i is the angle fluctuation factor of the ith simulation sampling point, is the angle between the guide vane in the nth elbow area and the center line of the air supply path during the simulation of the i-th simulation sampling point;

[0094] c 2,i is the wind duct distance fluctuation factor of the ith simulation sampling point, is the air duct distance between the guide vane and the fan damper in the nth elbow area during the simulation of the i-th simulation sampling point;

[0095] c 3,i is the wind speed fluctuation factor of the ith simulation sampling point, It represents the wind speed of the fresh air in the nth elbow area after passing through the guide vane during the simulation process of the i-th simulation sampling point;

[0096] ε1, ε2, and ε3 are the proportional coefficients of the angle, duct distance, and wind speed, respectively;

[0097] S26: According to S25, the energy consumption fluctuation coefficients of the three simulated sampling points are introduced into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient Q1. The first energy consumption fluctuation prediction strategy is specifically: Among them, Q0 is the energy consumption baseline coefficient of the clean air conditioner in the factory state.

[0098] S3: Through IoT sensors, the sudden pollution points in the product production line area covered by the clean air conditioner are screened and marked in real time, and the straight-line distance between the marked sudden pollution points and the air outlet of the clean air conditioner is recorded;

[0099] S3 includes the following specific steps:

[0100] S31: Use IoT sensors to screen and mark sudden pollution points in the product production line area covered by the clean air conditioner in real time, preset sudden pollution point screening thresholds corresponding to different types of sensors, and mark the data collection location as a sudden pollution point when the return data of no less than two types of sensors exceeds the sudden pollution point screening threshold. The IoT sensors include: particulate matter sensors, VOCs sensors, carbon dioxide sensors, and carbon monoxide sensors;

[0101] S32: Record the straight-line distance between the marked sudden pollution point and the clean air-conditioning outlet and the marking time;

[0102] S33: Obtaining the internal air duct distance between the air door and the clean air outlet at the current marking time t according to the actual air supply distance and the straight-line distance between the marked sudden pollution point and the clean air outlet;

[0103] S34: While keeping the actual air supply distance, blade resistance and duct resistance unchanged, the clean air conditioner uses an adaptive fuzzy control module to adaptively adjust the air supply path data of the clean air conditioner, and carry out targeted air supply cleaning treatment for sudden pollution points.

[0104] S4: monitoring the air supply path of the clean air conditioner in real time, collecting actual working status data of the clean air conditioner, and calculating and obtaining the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner;

[0105] S4 includes the following specific steps:

[0106] S41: According to the locations of the three simulated sampling points, select the simulated sampling point closest to the sudden pollution point as a comparison sampling point;

[0107] S42: Real-time monitoring of the air supply path of the clean air conditioner is performed, and actual working status data of the clean air conditioner is collected at the same time. According to the second energy consumption fluctuation calculation strategy of the clean air conditioner, a second energy consumption fluctuation coefficient Q2 is calculated and obtained. The second energy consumption fluctuation calculation strategy of the clean air conditioner is specifically:

[0108]

[0109] Among them, the clean air conditioner carries out targeted air supply cleaning treatment for the current sudden pollution point, q(t) is the energy consumption fluctuation coefficient of the clean air conditioner at the current marking time t; q x is the energy consumption fluctuation coefficient of the clean air conditioner at the comparison sampling point, x∈{1,2,3}.

[0110] In S42, the calculation strategy of the energy consumption fluctuation coefficient q(t) of the clean air conditioner at the current marking time t is specifically as follows:

[0111] q(t)=ε1×c1(t)+ε2×c2(t)+ε3×c3(t);

[0112] Among them, c1(t) is the angle fluctuation factor of the clean air conditioner at the current marking time t,

[0113] θ n (t) is the angle between the guide vane in the nth elbow area and the center line of the air supply path at the current marking time t;

[0114] c2(t) is the air duct distance fluctuation factor of the clean air conditioner at the current marking time t, l n (t) is the air duct distance between the guide vane and the fan damper in the nth elbow area at the current marking time t;

[0115] c3(t) is the wind speed fluctuation factor of the clean air conditioner at the current marking time t, It indicates the wind speed of the fresh air in the nth elbow area after passing through the guide vane at the current marking time t.

[0116] S5: Calculate the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and perform feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

[0117] The calculation strategy of the energy consumption optimization cost value of the clean air conditioner described in S5 is as follows:

[0118]

[0119] Among them, CB is the energy consumption optimization cost value of clean air conditioning.

[0120] In S5, the feedback optimization process includes:

[0121] When CB is less than 0.5, the fan speed and wind pressure are increased at the same time, and targeted air supply cleaning treatment is continued for sudden pollution points;

[0122] When 0.5≤CB<1, the adaptive adjustment of the air supply path data of the current clean air conditioner is maintained, and targeted air supply clean treatment is continued for sudden pollution points;

[0123] When CB>1, the adaptive adjustment of the clean air conditioner by the adaptive fuzzy control module is marked as an adjustment failure, and the air supply path data of the clean air conditioner is manually adjusted by the clean air conditioner manager.

[0124] Embodiment 2:

[0125] like Figure 2 As shown, a clean air conditioning energy consumption optimization system based on the Internet of Things in an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0126] Data collection and mapping module, first energy consumption fluctuation coefficient prediction module, sudden pollution point marking module, second energy consumption fluctuation coefficient calculation module and energy consumption feedback optimization module;

[0127] The data acquisition and mapping module is used to extract the preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and map the air supply path data to the digital twin model of the clean air conditioner;

[0128] The first energy consumption fluctuation coefficient prediction module is used to simulate the air supply path fluctuation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient;

[0129] The sudden pollution point marking module uses the Internet of Things sensor to perform real-time screening and marking of sudden pollution points in the product production line area covered by the clean air conditioner, and records the straight-line distance between the marked sudden pollution point and the air outlet of the clean air conditioner;

[0130] The second energy consumption fluctuation coefficient calculation module is used to monitor the air supply path of the clean air conditioner in real time, collect the actual working state data of the clean air conditioner, and calculate and obtain the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner;

[0131] The energy consumption feedback optimization module calculates the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and performs feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

[0132] Embodiment three:

[0133] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0134] The processor executes the above-mentioned method for optimizing energy consumption of clean air conditioners based on the Internet of Things by calling a computer program stored in the memory.

[0135] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a clean air conditioning energy consumption optimization method based on the Internet of Things provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0136] Embodiment 4:

[0137] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0138] When the computer program runs on a computer device, the computer device executes the above-mentioned clean air conditioning energy consumption optimization method based on the Internet of Things.

[0139] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0140] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0141] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0142] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0143] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0144] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0149] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0150] 1. The present invention uses a digital twin model to simulate the fluctuation of the air supply path, combines the first energy consumption fluctuation prediction strategy and the second energy consumption fluctuation calculation strategy, and can accurately predict the energy consumption fluctuation of the clean air conditioner under different working conditions, and constitute comparative sampling points. Especially when dealing with sudden pollution points, feedback optimization processing can be performed according to the energy consumption optimization cost value to ensure the comprehensiveness and effectiveness of energy consumption optimization and achieve energy-saving goals.

[0151] 2. The present invention dynamically adjusts the air supply path of the clean air conditioner according to real-time monitoring data, and can respond quickly, especially when dealing with sudden pollution points. The air supply path is optimized and adjusted through an adaptive fuzzy control module. The air supply path can be optimized under different simulated sampling points and actual working conditions to ensure the uniformity of air distribution and minimized energy consumption.

[0152] 3. Through the data acquisition and mapping module, the present invention can extract the preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and map it to the digital twin model. It can perform dynamic adjustments through the feedback optimization module to realize the data-driven decision-making process and provide data support for subsequent energy consumption prediction and optimization.

[0153] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A clean air conditioning energy consumption optimization method based on the Internet of Things, characterized in that: The method comprises the following specific steps: S1: extracting preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and mapping the air supply path data to the digital twin model of the clean air conditioner; S2: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient; S3: Through IoT sensors, the sudden pollution points in the product production line area covered by the clean air conditioner are screened and marked in real time, and the straight-line distance between the marked sudden pollution points and the air outlet of the clean air conditioner is recorded; S4: monitoring the air supply path of the clean air conditioner in real time, collecting actual working status data of the clean air conditioner, and calculating and obtaining the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner; S5: Calculate the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and perform feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

2. According to the method for optimizing energy consumption of clean air conditioner based on Internet of Things in claim 1, it is characterized in that: S2 includes the following specific steps: S21: Perform air supply path fluctuation simulation of the clean air conditioner in the digital twin model, wherein the air supply path fluctuation simulation specifically includes: extracting the maximum coverage distance and the minimum coverage distance in the product production line area covered by the clean air conditioner, the distance between the maximum coverage distance and the minimum coverage distance constitutes the effective working distance of the clean air conditioner, the effective working distance is D, and the distance from the minimum coverage distance of 0.25D is taken as the first simulation sampling point, the distance from the minimum coverage distance of 0.5D is taken as the second simulation sampling point, and the distance from the minimum coverage distance of 0.75D is taken as the third simulation sampling point; S22: Extract the standard values ​​of blade resistance {K1, K2...K n } and the standard value of wind duct resistance {P1,P2...P n }, where K n represents the standard value of the blade resistance of the guide vane at the nth elbow area; P n Indicates the standard value of the air duct resistance at the nth elbow area; S23: According to S22, the actual air supply distance is kept unchanged, and the position of the clean air conditioner damper at different simulated sampling points is adaptively adjusted, wherein the actual air supply distance is the sum of the internal air duct distance between the damper and the clean air conditioner outlet and the external air duct distance between the clean air conditioner outlet and different simulated sampling points; At the same time, in each elbow area, the blade resistance and duct resistance are kept unchanged, and the angle between the guide blades in each elbow area and the center line of the air supply path is adaptively adjusted according to the internal duct distance between the damper and the clean air conditioner outlet; S24: Extracting working status fluctuation data of the clean air conditioner, wherein the working status fluctuation data includes the number N of elbow areas in the air supply path of the clean air conditioner, the angle formed by the guide vanes in each elbow area and the center line of the air supply path, the air duct distance between the guide vanes in each elbow area and the fan damper, and the wind speed of the fresh air in each elbow area after passing through the guide vanes.

3. According to the method for optimizing energy consumption of clean air conditioner based on Internet of Things in claim 2, it is characterized in that: S2 also includes the following specific steps: S25: Calculate the energy consumption fluctuation sub-coefficient of each simulated sampling point according to the working state change data. The calculation strategy of the energy consumption fluctuation sub-coefficient of each simulated sampling point is: q i =ε1×c1+ε2×c2+ε3×c3; Where i is a subscript, indicating the i-th simulation sampling point; q i represents the energy consumption fluctuation coefficient of the clean air conditioner in the simulation process at the i-th simulation sampling point; i∈{1,2,3}; c 1,i is the angle fluctuation factor of the ith simulation sampling point, is the angle between the guide vane in the nth elbow area and the center line of the air supply path during the simulation of the i-th simulation sampling point; c 2,i is the wind duct distance fluctuation factor of the ith simulation sampling point, is the air duct distance between the guide vane and the fan damper in the nth elbow area during the simulation of the i-th simulation sampling point; c 3,i is the wind speed fluctuation factor of the ith simulation sampling point, It represents the wind speed of the fresh air in the nth elbow area after passing through the guide vane during the simulation process of the i-th simulation sampling point; ε1, ε2, and ε3 are the proportional coefficients of the angle, duct distance, and wind speed, respectively; S26: According to S25, the energy consumption fluctuation coefficients of the three simulated sampling points are introduced into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient Q1. The first energy consumption fluctuation prediction strategy is specifically: Among them, Q0 is the energy consumption baseline coefficient of the clean air conditioner in the factory state.

4. The method for optimizing clean air conditioning energy consumption based on the Internet of Things according to claim 3 is characterized in that: S3 includes the following specific steps: S31: Use IoT sensors to screen and mark sudden pollution points in the product production line area covered by the clean air conditioner in real time, preset sudden pollution point screening thresholds corresponding to different types of sensors, and when the returned data of no less than two types of sensors exceeds the sudden pollution point screening threshold, mark the data collection location as a sudden pollution point. The IoT sensors include: particulate matter sensors, VOCs sensors, carbon dioxide sensors, and carbon monoxide sensors; S32: Record the straight-line distance between the marked sudden pollution point and the clean air-conditioning outlet and the marking time; S33: Obtaining the internal air duct distance between the air door and the clean air conditioner outlet at the current marking time t according to the actual air supply distance and the straight-line distance between the marked sudden pollution point and the clean air conditioner outlet; S34: While keeping the actual air supply distance, blade resistance and duct resistance unchanged, the clean air conditioner uses an adaptive fuzzy control module to adaptively adjust the air supply path data of the clean air conditioner, and carry out targeted air supply cleaning treatment for sudden pollution points.

5. The method for optimizing clean air conditioning energy consumption based on the Internet of Things according to claim 4 is characterized in that: S4 includes the following specific steps: S41: According to the locations of the three simulated sampling points, select the simulated sampling point closest to the sudden pollution point as a comparison sampling point; S42: Real-time monitoring of the air supply path of the clean air conditioner is performed, and actual working status data of the clean air conditioner is collected at the same time. According to the second energy consumption fluctuation calculation strategy of the clean air conditioner, a second energy consumption fluctuation coefficient Q2 is calculated and obtained. The second energy consumption fluctuation calculation strategy of the clean air conditioner is specifically: Among them, the clean air conditioner carries out targeted air supply cleaning treatment for the current sudden pollution point, q(t) is the energy consumption fluctuation coefficient of the clean air conditioner at the current marking time t; q x is the energy consumption fluctuation coefficient of the clean air conditioner at the comparison sampling point, x∈{1,2,3}.

6. The method for optimizing clean air conditioning energy consumption based on the Internet of Things according to claim 5 is characterized in that: In S42, the calculation strategy of the energy consumption fluctuation coefficient q(t) of the clean air conditioner at the current marking time t is specifically as follows: q(t)=ε1×c1(t)+ε2×c2(t)+ε3×c3(t); Among them, c1(t) is the angle fluctuation factor of the clean air conditioner at the current marking time t, θ n (t) is the angle between the guide vane in the nth elbow area and the center line of the air supply path at the current marking time t; c2(t) is the air duct distance fluctuation factor of the clean air conditioner at the current marking time t, l n (t) is the air duct distance between the guide vane and the fan damper in the nth elbow area at the current marking time t; c3(t) is the wind speed fluctuation factor of the clean air conditioner at the current marking time t, It indicates the wind speed of the fresh air in the nth elbow area after passing through the guide vane at the current marking time t.

7. The method for optimizing clean air conditioning energy consumption based on the Internet of Things according to claim 6, characterized in that: The calculation strategy of the energy consumption optimization cost value of the clean air conditioner described in S5 is as follows: Among them, CB is the energy consumption optimization cost value of clean air conditioning.

8. The method for optimizing clean air conditioning energy consumption based on the Internet of Things according to claim 7, characterized in that: In S5, the feedback optimization process includes: When CB is less than 0.5, the fan speed and wind pressure are increased at the same time, and targeted air supply cleaning treatment is continued for sudden pollution points; When 0.5≤CB<1, the adaptive adjustment of the air supply path data of the current clean air conditioner is maintained, and targeted air supply clean treatment is continued for sudden pollution points; When CB>1, the adaptive adjustment of the clean air conditioner by the adaptive fuzzy control module is marked as an adjustment failure, and the air supply path data of the clean air conditioner is manually adjusted by the clean air conditioner manager.

9. A clean air conditioning energy consumption optimization system based on the Internet of Things, which is implemented based on a clean air conditioning energy consumption optimization method based on the Internet of Things as described in any one of claims 1 to 8, characterized in that: The system includes the following modules: Data collection and mapping module, first energy consumption fluctuation coefficient prediction module, sudden pollution point marking module, second energy consumption fluctuation coefficient calculation module and energy consumption feedback optimization module; The data acquisition and mapping module is used to extract the preset air supply path data of the clean air conditioner from the layout diagram of the product production line, and map the air supply path data to the digital twin model of the clean air conditioner; The first energy consumption fluctuation coefficient prediction module is used to simulate the air supply path fluctuation of the clean air conditioner in the digital twin model, extract the working state fluctuation data of the clean air conditioner, and import the working state fluctuation data into the first energy consumption fluctuation prediction strategy of the clean air conditioner to calculate and obtain the first energy consumption fluctuation coefficient; The sudden pollution point marking module uses the Internet of Things sensor to perform real-time screening and marking of sudden pollution points in the product production line area covered by the clean air conditioner, and records the straight-line distance between the marked sudden pollution point and the air outlet of the clean air conditioner; The second energy consumption fluctuation coefficient calculation module is used to monitor the air supply path of the clean air conditioner in real time, collect the actual working state data of the clean air conditioner, and calculate and obtain the second energy consumption fluctuation coefficient according to the second energy consumption fluctuation calculation strategy of the clean air conditioner; The energy consumption feedback optimization module calculates the energy consumption optimization cost value of the clean air conditioner according to the first energy consumption fluctuation coefficient and the second energy consumption fluctuation coefficient, and performs feedback optimization processing on the working energy consumption of the clean air conditioner according to the energy consumption optimization cost value.

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