Air conditioning equipment optimizing control method and system based on artificial intelligence

Abstract

The invention discloses an air conditioning equipment optimizing control method and system based on artificial intelligence. The method comprises the steps that area sensing data of a plurality of sub-areas in a target park area are obtained; according to a preset data correction rule, correcting the regional sensing data to obtain corrected sensing data corresponding to each sub-region; according to the corrected sensing data, predicting regional temperature control parameters corresponding to each sub-region based on a neural network algorithm; and according to the regional temperature control parameters, screening out a plurality of preferable air conditioning equipment from the air conditioning equipment in all the sub-regions, and sending a corresponding regional temperature control strategy to the preferable air conditioning equipment for control. It can be seen that air conditioner optimizing control under the multi-equipment scene in the park can be achieved based on more accurate sensing data, the efficiency and precision of air conditioner control are improved, and the temperature control effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an optimization control method and system for air-conditioning equipment based on artificial intelligence. Background Art

[0002] With the increasing demand for intelligent management and the increase in air-conditioning equipment in parks, more and more parks are beginning to face the need to introduce intelligent technology to achieve optimal air-conditioning control. Among them, how to improve the control effect and ensure the temperature comfort of the park has become a more important technical issue. In the existing technical solutions, most of the air-conditioning equipment is controlled automatically based on the controller control of the setting area or the temperature information of the area. It does not take into account the data correction and optimal control between multiple devices as a whole. The air-conditioning control efficiency and accuracy in multi-device scenarios are both lacking, and the temperature control effect is difficult to meet user requirements. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an air-conditioning equipment optimization control method and system based on artificial intelligence, which can realize the optimal control of air conditioning in multi-equipment scenarios in the park based on more accurate sensor data, improve the efficiency and accuracy of air-conditioning control, and improve the temperature control effect.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an optimal control method for air conditioning equipment based on artificial intelligence, the method comprising: Acquire regional sensor data of multiple sub-areas in the target park area; According to a preset data correction rule, the regional sensor data is corrected to obtain corrected sensor data corresponding to each of the sub-regions; According to the corrected sensor data, based on a neural network algorithm, predict the regional temperature control parameters corresponding to each of the sub-regions; According to the regional temperature control parameters, a plurality of preferred air-conditioning devices are screened out from the air-conditioning devices in all the sub-areas, and corresponding regional temperature control strategies are sent to the preferred air-conditioning devices for control.

[0005] As an optional implementation, in the first aspect of the present invention, the area sensing data includes infrared sensing data, temperature data, humidity data, image data and air pressure data.

[0006] As an optional implementation, in the first aspect of the present invention, correcting the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each of the sub-regions includes: For any two sub-regions whose regional distances are less than a first distance threshold, an average value of the numerical differences between all first data in the regional sensor data of the two sub-regions is calculated to obtain an average difference parameter; the first data is infrared sensor data, temperature data, humidity data or air pressure data; Calculating the difference between the image data of the two sub-areas; According to the area distance, the average difference parameter and the difference degree, judging whether the difference between the two sub-areas is too large, and obtaining a first judgment result; If the first judgment result is yes, calculating the product of the average value of each corresponding type of data in the regional sensing data of the two sub-regions and the adjustment weight to obtain the corrected sensing data corresponding to the two sub-regions; If the first judgment result is no, the regional sensing data of the two sub-regions remain unchanged and are determined as corrected sensing data.

[0007] As an optional implementation manner, in the first aspect of the present invention, judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining a first judgment result includes: Determine whether the average difference parameter is greater than a preset parameter threshold, and obtain a first determination result; Determine whether the difference is greater than a preset difference threshold, and obtain a third determination result; Determine whether the area distance is greater than a preset second distance threshold, and obtain a fourth determination result; It is determined whether the first judgment result is yes and whether the third judgment result and the fourth judgment result are both no, to obtain a first judgment result.

[0008] As an optional embodiment, in the first aspect of the present invention, the adjustment weight is proportional to the data difference between the regional sensor data of the two sub-regions; the data difference is the product of the average difference parameter and the difference between the regional sensor data of the two sub-regions.

[0009] As an optional implementation, in the first aspect of the present invention, the regional temperature control parameters include a temperature control necessity parameter and a temperature control amplitude parameter.

[0010] As an optional implementation, in the first aspect of the present invention, predicting the regional temperature control parameters corresponding to each of the sub-regions based on the corrected sensor data and a neural network algorithm includes: Inputting the corrected sensor data corresponding to each of the sub-areas into a pre-trained temperature control necessity prediction neural network to obtain a temperature control necessity parameter corresponding to each of the sub-areas; the temperature control necessity prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding temperature control necessity annotations; The corrected sensor data corresponding to each of the sub-areas is input into a pre-trained temperature control amplitude prediction neural network to obtain the temperature control amplitude parameters corresponding to each of the sub-areas; the temperature control amplitude prediction neural network is trained by a training data set including multiple training sensor data and corresponding temperature control amplitude annotations.

[0011] As an optional implementation, in the first aspect of the present invention, the method of selecting a plurality of preferred air-conditioning devices from the air-conditioning devices in all the sub-areas according to the regional temperature control parameters, and sending the corresponding regional temperature control strategies to the preferred air-conditioning devices for control, includes: Screening out the sub-regions whose temperature control necessity is higher than a preset necessity threshold, and obtaining a plurality of preferred sub-regions; For each air-conditioning device in each of the preferred sub-areas, calculating the shortest distance between the air-conditioning device and other nearby preferred sub-areas; Calculate the product of the shortest distance and the temperature control amplitude parameter corresponding to the preferred sub-area to obtain the device priority corresponding to the air-conditioning device; Filter out the air-conditioning devices whose device priority is higher than a preset priority threshold, and obtain a plurality of preferred air-conditioning devices; Determine a regional temperature control strategy corresponding to each of the preferred air conditioning devices; the regional temperature control strategy includes a startup duration and a blowing temperature; the blowing temperature is inversely proportional to the temperature control amplitude parameter corresponding to the sub-region where the preferred air conditioning device is located; The regional temperature control strategy corresponding to each of the preferred air-conditioning devices is sent to the corresponding preferred air-conditioning device for control.

[0012] A second aspect of an embodiment of the present invention discloses an air conditioning equipment optimization control system based on artificial intelligence, the system comprising: An acquisition module, used to acquire regional sensor data of multiple sub-areas in the target park area; A correction module, used for correcting the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each of the sub-regions; A prediction module, used for predicting the regional temperature control parameters corresponding to each of the sub-regions based on the corrected sensor data and a neural network algorithm; The control module is used to select a plurality of preferred air-conditioning devices from the air-conditioning devices in all the sub-areas according to the regional temperature control parameters, and send the corresponding regional temperature control strategies to the preferred air-conditioning devices for control.

[0013] As an optional implementation, in the second aspect of the present invention, the area sensing data includes infrared sensing data, temperature data, humidity data, image data and air pressure data.

[0014] As an optional implementation, in the second aspect of the present invention, the correction module corrects the regional sensor data according to a preset data correction rule to obtain the corrected sensor data corresponding to each sub-region, including: For any two sub-regions whose regional distances are less than a first distance threshold, an average value of the numerical differences between all first data in the regional sensor data of the two sub-regions is calculated to obtain an average difference parameter; the first data is infrared sensor data, temperature data, humidity data or air pressure data; Calculating the difference between the image data of the two sub-areas; According to the area distance, the average difference parameter and the difference degree, judging whether the difference between the two sub-areas is too large, and obtaining a first judgment result; If the first judgment result is yes, calculating the product of the average value of each corresponding type of data in the regional sensing data of the two sub-regions and the adjustment weight to obtain the corrected sensing data corresponding to the two sub-regions; If the first judgment result is no, the regional sensing data of the two sub-regions remain unchanged and are determined as corrected sensing data.

[0015] As an optional implementation, in the second aspect of the present invention, the correction module determines whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and a specific manner of obtaining the first judgment result includes: Determine whether the average difference parameter is greater than a preset parameter threshold, and obtain a first determination result; Determine whether the difference is greater than a preset difference threshold, and obtain a third determination result; Determine whether the area distance is greater than a preset second distance threshold, and obtain a fourth determination result; It is determined whether the first judgment result is yes and whether the third judgment result and the fourth judgment result are both no, to obtain a first judgment result.

[0016] As an optional embodiment, in the second aspect of the present invention, the adjustment weight is proportional to the data difference between the regional sensing data of the two sub-regions; the data difference is the product of the average difference parameter and the difference between the regional sensing data of the two sub-regions.

[0017] As an optional implementation, in the second aspect of the present invention, the regional temperature control parameters include a temperature control necessity parameter and a temperature control amplitude parameter.

[0018] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the specific manner of the regional temperature control parameter corresponding to each of the sub-regions based on the corrected sensor data and a neural network algorithm, including: Inputting the corrected sensor data corresponding to each of the sub-areas into a pre-trained temperature control necessity prediction neural network to obtain a temperature control necessity parameter corresponding to each of the sub-areas; the temperature control necessity prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding temperature control necessity annotations; The corrected sensor data corresponding to each of the sub-areas is input into a pre-trained temperature control amplitude prediction neural network to obtain the temperature control amplitude parameters corresponding to each of the sub-areas; the temperature control amplitude prediction neural network is trained by a training data set including multiple training sensor data and corresponding temperature control amplitude annotations.

[0019] As an optional implementation, in the second aspect of the present invention, the control module selects a plurality of preferred air-conditioning devices from the air-conditioning devices in all the sub-areas according to the regional temperature control parameters, and sends the corresponding regional temperature control strategy to the preferred air-conditioning device for control, including: Screening out the sub-regions whose temperature control necessity is higher than a preset necessity threshold, and obtaining a plurality of preferred sub-regions; For each air-conditioning device in each of the preferred sub-areas, calculating the shortest distance between the air-conditioning device and other nearby preferred sub-areas; Calculate the product of the shortest distance and the temperature control amplitude parameter corresponding to the preferred sub-area to obtain the device priority corresponding to the air-conditioning device; Filter out the air-conditioning devices whose device priority is higher than a preset priority threshold, and obtain a plurality of preferred air-conditioning devices; Determine a regional temperature control strategy corresponding to each of the preferred air conditioning devices; the regional temperature control strategy includes a startup duration and a blowing temperature; the blowing temperature is inversely proportional to the temperature control amplitude parameter corresponding to the sub-region where the preferred air conditioning device is located; The regional temperature control strategy corresponding to each of the preferred air-conditioning devices is sent to the corresponding preferred air-conditioning device for control.

[0020] The third aspect of the present invention discloses another air conditioning equipment optimization control system based on artificial intelligence, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the artificial intelligence-based air-conditioning equipment optimization control method disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the artificial intelligence-based air-conditioning equipment optimization control method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can correct the regional sensor data based on the data correction rules to obtain the accurate corrected sensor data corresponding to each sub-region, and then predict the regional temperature control parameters corresponding to each sub-region based on the neural network algorithm, so as to screen out multiple preferred air-conditioning equipment for control based on the regional temperature control parameters, thereby realizing optimal control of the air-conditioning in multi-device scenarios in the park based on more accurate sensor data, improving the efficiency and accuracy of air-conditioning control, and improving the temperature control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of an air conditioning equipment optimization control method based on artificial intelligence disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of an air-conditioning equipment optimization control system based on artificial intelligence disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another air conditioning equipment optimization control system based on artificial intelligence disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses an air conditioning equipment optimization control method and system based on artificial intelligence, which can correct regional sensor data based on data correction rules to obtain accurate corrected sensor data corresponding to each sub-region, and then predict regional temperature control parameters corresponding to each sub-region based on a neural network algorithm, so as to screen out multiple preferred air conditioning equipment for control based on regional temperature control parameters, thereby realizing air conditioning optimization control in multiple equipment scenarios in a park based on more accurate sensor data, improving the efficiency and accuracy of air conditioning control, and improving the temperature control effect. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of an air conditioning equipment optimization control method based on artificial intelligence disclosed in an embodiment of the present invention. Figure 1 The described method for optimizing the control of air-conditioning equipment based on artificial intelligence can be applied to a data processing system / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the air conditioning equipment optimization control method based on artificial intelligence may include the following operations: 101. Acquire regional sensor data of multiple sub-areas in a target park area.

[0032] 102. According to a preset data correction rule, correct the regional sensor data to obtain corrected sensor data corresponding to each sub-region. 103. According to the corrected sensor data, based on the neural network algorithm, the regional temperature control parameters corresponding to each sub-region are predicted. 104. According to the regional temperature control parameters, multiple preferred air-conditioning devices are selected from the air-conditioning devices in all sub-areas, and corresponding regional temperature control strategies are sent to the preferred air-conditioning devices for control.

[0033] It can be seen that the above-mentioned embodiments of the invention can correct the regional sensor data based on the data correction rules to obtain the accurate corrected sensor data corresponding to each sub-region, and then predict the regional temperature control parameters corresponding to each sub-region based on the neural network algorithm, so as to screen out multiple preferred air-conditioning equipment for control based on the regional temperature control parameters, thereby realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, improving the efficiency and accuracy of air-conditioning control, and improving the temperature control effect.

[0034] As an optional embodiment, in the above steps, the regional sensing data includes infrared sensing data, temperature data, humidity data, image data and air pressure data.

[0035] It can be seen that through the above optional embodiments, the content of the regional sensor data is clarified, which can more accurately and comprehensively characterize the situation of the sub-area of ​​the park, and assist in realizing the optimal control of the air conditioning in multiple equipment scenarios in the park based on more precise sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0036] As an optional embodiment, in the above steps, correcting the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each sub-region includes: For any two sub-regions whose regional distance is less than a first distance threshold, calculate the average of the numerical differences between all first data in the regional sensor data of the two sub-regions to obtain an average difference parameter; optionally, the first data is infrared sensor data, temperature data, humidity data or air pressure data; Calculating the difference between the image data of the two sub-regions; Judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining a first judgment result; If the first judgment result is yes, the product of the average value of each corresponding type of data in the regional sensing data of the two sub-regions and the adjustment weight is calculated to obtain the corrected sensing data corresponding to the two sub-regions; If the first judgment result is no, the regional sensing data of the two sub-regions remain unchanged and are determined as corrected sensing data.

[0037] It can be seen that through the above optional embodiments, it is possible to judge whether the differences between adjacent areas are too large based on the regional distance, the mean difference parameter of the data and the difference of the image. If so, it is corrected by adjusting the weights, otherwise it remains unchanged, thereby assisting in realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, improving the efficiency and accuracy of air conditioning control, and improving the temperature control effect.

[0038] As an optional embodiment, in the above step, judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining the first judgment result includes: Determine whether the average difference parameter is greater than a preset parameter threshold, and obtain a first determination result; Determine whether the difference is greater than a preset difference threshold, and obtain a third determination result; Determine whether the area distance is greater than a preset second distance threshold, and obtain a fourth determination result; It is determined whether the first judgment result is yes and whether the third judgment result and the fourth judgment result are both no, to obtain the first judgment result.

[0039] It can be seen that through the above-mentioned optional embodiments, it is possible to judge whether the differences between adjacent areas are still too large when the image differences and position differences are normal based on the size judgment relationship between the regional distance, the average difference parameter of the data and the image difference and the preset threshold, so as to facilitate subsequent accurate correction by adjusting the weights, and assist in realizing the optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0040] As an optional embodiment, in the above steps, the adjustment weight is proportional to the data difference between the regional sensing data of the two sub-regions; the data difference is the product of the average difference parameter and the difference between the regional sensing data of the two sub-regions.

[0041] It can be seen that through the above optional embodiments, the correlation between the adjustment weight and the degree of difference in the sensor data between the two areas is defined, so as to accurately correct the sensor data by adjusting the weight, and assist in realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0042] As an optional embodiment, in the above steps, the regional temperature control parameters include temperature control necessity parameters and temperature control amplitude parameters.

[0043] It can be seen that through the above-mentioned optional embodiments, the content of the regional temperature control parameters is clarified, which can more accurately and comprehensively characterize the necessity and amplitude of temperature control in the sub-areas of the park, and assist in realizing optimal control of air conditioning in multi-device scenarios within the park based on more precise sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0044] As an optional embodiment, in the above steps, predicting the regional temperature control parameters corresponding to each sub-region based on the corrected sensor data and the neural network algorithm includes: Inputting the corrected sensor data corresponding to each sub-region into a pre-trained temperature control necessity prediction neural network to obtain a temperature control necessity parameter corresponding to each sub-region; optionally, the temperature control necessity prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding temperature control necessity annotations; The corrected sensor data corresponding to each sub-area is input into a pre-trained temperature control amplitude prediction neural network to obtain the temperature control amplitude parameters corresponding to each sub-area; the temperature control amplitude prediction neural network is trained by a training data set including multiple training sensor data and corresponding temperature control amplitude annotations.

[0045] It can be seen that through the above-mentioned optional embodiments, the pre-trained temperature control necessity prediction neural network and the temperature control amplitude prediction neural network can be used to predict the precise temperature control necessity parameters and temperature control amplitude parameters corresponding to the sub-area according to the corrected sensor data, so as to facilitate the subsequent precise air-conditioning optimal control, and assist in realizing the air-conditioning optimal control in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air-conditioning control and improving the temperature control effect.

[0046] As an optional embodiment, in the above steps, according to the regional temperature control parameters, multiple preferred air-conditioning devices are screened out from the air-conditioning devices in all sub-areas, and the corresponding regional temperature control strategies are sent to the preferred air-conditioning devices for control, including: Screen out sub-areas whose temperature control necessity is higher than a preset necessity threshold, and obtain multiple preferred sub-areas; For each air conditioning device in each preferred sub-area, calculating the shortest distance between the air conditioning device and other nearby preferred sub-areas; Calculate the product of the shortest distance and the temperature control amplitude parameter corresponding to the preferred sub-area to obtain the device priority corresponding to the air-conditioning device; Filter out air-conditioning devices whose device priority is higher than a preset priority threshold, and obtain multiple preferred air-conditioning devices; Determine the regional temperature control strategy corresponding to each preferred air conditioning device; optionally, the regional temperature control strategy includes a startup duration and a blowing temperature; the blowing temperature is inversely proportional to the temperature control amplitude parameter corresponding to the sub-region where the preferred air conditioning device is located; The regional temperature control strategy corresponding to each preferred air-conditioning device is sent to the corresponding preferred air-conditioning device for control.

[0047] It can be seen that through the above optional embodiments, it is possible to screen out sub-areas that need to be controlled based on the necessity of temperature control and screen out preferred equipment for control based on the distance of the air-conditioning equipment, thereby realizing optimal control of air conditioning in multi-equipment scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air-conditioning control and improving the temperature control effect.

[0048] Embodiment 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of an air conditioning equipment optimization control system based on artificial intelligence disclosed in an embodiment of the present invention. Figure 2 The described air conditioning equipment optimization control system based on artificial intelligence can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the air conditioning equipment optimization control system based on artificial intelligence may include: The acquisition module 201 is used to acquire regional sensor data of multiple sub-areas in the target park area.

[0049] The correction module 202 is used to correct the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each sub-region. The prediction module 203 is used to predict the regional temperature control parameters corresponding to each sub-region based on the corrected sensor data and the neural network algorithm. The control module 204 is used to select a plurality of preferred air-conditioning devices from the air-conditioning devices in all sub-areas according to the regional temperature control parameters, and send the corresponding regional temperature control strategies to the preferred air-conditioning devices for control.

[0050] It can be seen that the above-mentioned embodiments of the invention can correct the regional sensor data based on the data correction rules to obtain the accurate corrected sensor data corresponding to each sub-region, and then predict the regional temperature control parameters corresponding to each sub-region based on the neural network algorithm, so as to screen out multiple preferred air-conditioning equipment for control based on the regional temperature control parameters, thereby realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, improving the efficiency and accuracy of air-conditioning control, and improving the temperature control effect.

[0051] As an optional embodiment, the regional sensing data includes infrared sensing data, temperature data, humidity data, image data and air pressure data.

[0052] It can be seen that through the above optional embodiments, the content of the regional sensor data is clarified, which can more accurately and comprehensively characterize the situation of the sub-area of ​​the park, and assist in realizing the optimal control of the air conditioning in multiple equipment scenarios in the park based on more precise sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0053] As an optional embodiment, the correction module corrects the regional sensor data according to the preset data correction rule to obtain the corrected sensor data corresponding to each sub-region, including: For any two sub-regions whose regional distance is less than a first distance threshold, calculate the average of the numerical differences between all first data in the regional sensor data of the two sub-regions to obtain an average difference parameter; optionally, the first data is infrared sensor data, temperature data, humidity data or air pressure data; Calculating the difference between the image data of the two sub-regions; Judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining a first judgment result; If the first judgment result is yes, the product of the average value of each corresponding type of data in the regional sensing data of the two sub-regions and the adjustment weight is calculated to obtain the corrected sensing data corresponding to the two sub-regions; If the first judgment result is no, the regional sensing data of the two sub-regions remain unchanged and are determined as corrected sensing data.

[0054] It can be seen that through the above optional embodiments, it is possible to judge whether the differences between adjacent areas are too large based on the regional distance, the mean difference parameter of the data and the difference of the image. If so, it is corrected by adjusting the weights, otherwise it remains unchanged, thereby assisting in realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, improving the efficiency and accuracy of air conditioning control, and improving the temperature control effect.

[0055] As an optional embodiment, the correction module determines whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtains the first judgment result in a specific manner including: Determine whether the average difference parameter is greater than a preset parameter threshold, and obtain a first determination result; Determine whether the difference is greater than a preset difference threshold, and obtain a third determination result; Determine whether the area distance is greater than a preset second distance threshold, and obtain a fourth determination result; It is determined whether the first judgment result is yes and whether the third judgment result and the fourth judgment result are both no, to obtain the first judgment result.

[0056] It can be seen that through the above-mentioned optional embodiments, it is possible to judge whether the differences between adjacent areas are still too large when the image differences and position differences are normal based on the size judgment relationship between the regional distance, the average difference parameter of the data and the image difference and the preset threshold, so as to facilitate subsequent accurate correction by adjusting the weights, and assist in realizing the optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0057] As an optional embodiment, the adjustment weight is proportional to the data difference between the regional sensing data of the two sub-regions; the data difference is the product of the average difference parameter and the difference between the regional sensing data of the two sub-regions.

[0058] It can be seen that through the above optional embodiments, the correlation between the adjustment weight and the degree of difference in the sensor data between the two areas is defined, so as to accurately correct the sensor data by adjusting the weight, and assist in realizing optimal control of air conditioning in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0059] As an optional embodiment, the regional temperature control parameters include a temperature control necessity parameter and a temperature control amplitude parameter.

[0060] It can be seen that through the above-mentioned optional embodiments, the content of the regional temperature control parameters is clarified, which can more accurately and comprehensively characterize the necessity and amplitude of temperature control in the sub-areas of the park, and assist in realizing optimal control of air conditioning in multi-device scenarios within the park based on more precise sensor data, thereby improving the efficiency and accuracy of air conditioning control and improving the temperature control effect.

[0061] As an optional embodiment, the prediction module predicts the specific manner of the regional temperature control parameters corresponding to each sub-region based on the corrected sensor data and the neural network algorithm, including: Inputting the corrected sensor data corresponding to each sub-region into a pre-trained temperature control necessity prediction neural network to obtain a temperature control necessity parameter corresponding to each sub-region; optionally, the temperature control necessity prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding temperature control necessity annotations; The corrected sensor data corresponding to each sub-area is input into a pre-trained temperature control amplitude prediction neural network to obtain the temperature control amplitude parameters corresponding to each sub-area; the temperature control amplitude prediction neural network is trained by a training data set including multiple training sensor data and corresponding temperature control amplitude annotations.

[0062] It can be seen that through the above-mentioned optional embodiments, the pre-trained temperature control necessity prediction neural network and the temperature control amplitude prediction neural network can be used to predict the precise temperature control necessity parameters and temperature control amplitude parameters corresponding to the sub-area according to the corrected sensor data, so as to facilitate the subsequent precise air-conditioning optimal control, and assist in realizing the air-conditioning optimal control in multi-device scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air-conditioning control and improving the temperature control effect.

[0063] As an optional embodiment, the control module selects multiple preferred air-conditioning devices from the air-conditioning devices in all sub-areas according to the regional temperature control parameters, and sends the corresponding regional temperature control strategy to the preferred air-conditioning device for control, including: Screen out sub-areas whose temperature control necessity is higher than a preset necessity threshold, and obtain multiple preferred sub-areas; For each air conditioning device in each preferred sub-area, calculating the shortest distance between the air conditioning device and other nearby preferred sub-areas; Calculate the product of the shortest distance and the temperature control amplitude parameter corresponding to the preferred sub-area to obtain the device priority corresponding to the air-conditioning device; Filter out air-conditioning devices whose device priority is higher than a preset priority threshold, and obtain multiple preferred air-conditioning devices; Determine the regional temperature control strategy corresponding to each preferred air conditioning device; optionally, the regional temperature control strategy includes a startup duration and a blowing temperature; the blowing temperature is inversely proportional to the temperature control amplitude parameter corresponding to the sub-region where the preferred air conditioning device is located; The regional temperature control strategy corresponding to each preferred air-conditioning device is sent to the corresponding preferred air-conditioning device for control.

[0064] It can be seen that through the above optional embodiments, it is possible to screen out sub-areas that need to be controlled based on the necessity of temperature control and screen out preferred equipment for control based on the distance of the air-conditioning equipment, thereby realizing optimal control of air conditioning in multi-equipment scenarios within the park based on more accurate sensor data, thereby improving the efficiency and accuracy of air-conditioning control and improving the temperature control effect.

[0065] Embodiment 3 See also Figure 3 , Figure 3 This is another air conditioning equipment optimization control system based on artificial intelligence disclosed in an embodiment of the present invention. Figure 3 The described air conditioning equipment optimization control system based on artificial intelligence is applied to a data processing system / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the air conditioning equipment optimization control system based on artificial intelligence may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the air conditioning equipment optimization control method based on artificial intelligence described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the artificial intelligence-based air conditioning equipment optimization control method described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the artificial intelligence-based air-conditioning equipment optimization control method described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the artificial intelligence-based air-conditioning equipment optimization control method and system disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An optimal control method for air conditioning equipment based on artificial intelligence, characterized in that: The method comprises: Acquire regional sensor data of multiple sub-areas in the target park area; According to a preset data correction rule, the regional sensor data is corrected to obtain corrected sensor data corresponding to each of the sub-regions; According to the corrected sensor data, based on a neural network algorithm, predict the regional temperature control parameters corresponding to each of the sub-regions; According to the regional temperature control parameters, a plurality of preferred air-conditioning devices are screened out from the air-conditioning devices in all the sub-areas, and corresponding regional temperature control strategies are sent to the preferred air-conditioning devices for control.

2. The method for optimizing the control of air-conditioning equipment based on artificial intelligence according to claim 1, characterized in that: The regional sensing data includes infrared sensing data, temperature data, humidity data, image data and air pressure data.

3. The method for optimizing the control of air-conditioning equipment based on artificial intelligence according to claim 2, characterized in that: The step of correcting the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each sub-region includes: For any two sub-regions whose regional distances are less than a first distance threshold, an average value of the numerical differences between all first data in the regional sensor data of the two sub-regions is calculated to obtain an average difference parameter; the first data is infrared sensor data, temperature data, humidity data or air pressure data; Calculating the difference between the image data of the two sub-areas; Judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining a first judgment result; If the first judgment result is yes, calculating the product of the average value of each corresponding type of data in the regional sensing data of the two sub-regions and the adjustment weight to obtain the corrected sensing data corresponding to the two sub-regions; If the first judgment result is no, the regional sensing data of the two sub-regions are kept unchanged and determined as corrected sensing data.

4. The method for optimizing the control of air-conditioning equipment based on artificial intelligence according to claim 3 is characterized in that: The step of judging whether the difference between the two sub-regions is too large according to the region distance, the average difference parameter and the difference degree, and obtaining a first judgment result includes: Determine whether the average difference parameter is greater than a preset parameter threshold, and obtain a first determination result; Determine whether the difference is greater than a preset difference threshold, and obtain a third determination result; Determine whether the area distance is greater than a preset second distance threshold, and obtain a fourth determination result; It is determined whether the first judgment result is yes and whether the third judgment result and the fourth judgment result are both no, to obtain a first judgment result.

5. The method for optimizing control of air conditioning equipment based on artificial intelligence according to claim 3 is characterized in that: The adjustment weight is proportional to the data difference between the regional sensing data of the two sub-regions; the data difference is the product of the average difference parameter and the difference between the regional sensing data of the two sub-regions.

6. The method for optimizing control of air conditioning equipment based on artificial intelligence according to claim 1, characterized in that: The regional temperature control parameters include temperature control necessity parameters and temperature control amplitude parameters.

7. The method for optimizing control of air conditioning equipment based on artificial intelligence according to claim 6, characterized in that: The predicting of the regional temperature control parameters corresponding to each of the sub-regions based on the corrected sensor data and a neural network algorithm includes: Inputting the corrected sensor data corresponding to each of the sub-areas into a pre-trained temperature control necessity prediction neural network to obtain a temperature control necessity parameter corresponding to each of the sub-areas; the temperature control necessity prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding temperature control necessity annotations; The corrected sensor data corresponding to each of the sub-areas is input into a pre-trained temperature control amplitude prediction neural network to obtain the temperature control amplitude parameters corresponding to each of the sub-areas; the temperature control amplitude prediction neural network is trained by a training data set including multiple training sensor data and corresponding temperature control amplitude annotations.

8. The method for optimizing control of air conditioning equipment based on artificial intelligence according to claim 6, characterized in that: The selecting a plurality of preferred air-conditioning devices from the air-conditioning devices in all the sub-areas according to the regional temperature control parameters, and sending corresponding regional temperature control strategies to the preferred air-conditioning devices for control, includes: Screening out the sub-regions whose temperature control necessity is higher than a preset necessity threshold, and obtaining a plurality of preferred sub-regions; For each air-conditioning device in each of the preferred sub-areas, calculating the shortest distance between the air-conditioning device and other nearby preferred sub-areas; Calculate the product of the shortest distance and the temperature control amplitude parameter corresponding to the preferred sub-area to obtain the device priority corresponding to the air-conditioning device; Filter out the air-conditioning devices whose device priority is higher than a preset priority threshold, and obtain a plurality of preferred air-conditioning devices; Determine a regional temperature control strategy corresponding to each of the preferred air conditioning devices; the regional temperature control strategy includes a startup duration and a blowing temperature; the blowing temperature is inversely proportional to the temperature control amplitude parameter corresponding to the sub-region where the preferred air conditioning device is located; The regional temperature control strategy corresponding to each of the preferred air-conditioning devices is sent to the corresponding preferred air-conditioning device for control.

9. An air conditioning equipment optimization control system based on artificial intelligence, characterized in that: The system comprises: An acquisition module, used to acquire regional sensor data of multiple sub-areas in the target park area; A correction module, used for correcting the regional sensor data according to a preset data correction rule to obtain corrected sensor data corresponding to each of the sub-regions; A prediction module, used for predicting the regional temperature control parameters corresponding to each of the sub-regions based on the corrected sensor data and a neural network algorithm; The control module is used to select a plurality of preferred air-conditioning devices from the air-conditioning devices in all the sub-areas according to the regional temperature control parameters, and send the corresponding regional temperature control strategies to the preferred air-conditioning devices for control.

10. An air conditioning equipment optimization control system based on artificial intelligence, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the artificial intelligence-based optimization control method for air-conditioning equipment as described in any one of claims 1-8.

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