Air conditioning equipment group energy-saving control method and system based on big data driving

Abstract

The invention discloses an air conditioning equipment group energy-saving control method and system based on big data driving, and relates to the technical field of air conditioning equipment group control. According to a target environment where an air conditioning equipment group is located, arrangement information corresponding to the target environment is obtained; the temperature can be adjusted by X1 DEG C within the set time to determine the initial coverage area of any air conditioner, then the initial coverage area of the air conditioner is updated according to the cooling speed for the areas which are not divided, and therefore the approved coverage area of each air conditioner is obtained, and the number of seats in the approved coverage area is synchronously obtained; and then the number of started air conditioners is determined according to the number of students in the next class of the current time, the corresponding number of started air conditioners is determined, and the started objects are determined, so that the number of started air conditioners and the specific number of started air conditioners are reasonably arranged.

Description

Technical Field

[0001] The present invention belongs to the technical field of air conditioning equipment group control, and specifically relates to an energy-saving control method and system for an air conditioning equipment group driven by big data. Background Art

[0002] In the prior art, the air conditioner itself is provided with an energy-saving mode. When the user turns on the energy-saving mode, the air conditioner will limit the operating current to reduce the operating power of the compressor, thereby achieving the purpose of energy saving.

[0003] However, most of the air conditioners currently used in various public buildings are decentralized and independently controlled, which are greatly affected by human factors and are extensively controlled and managed, resulting in great energy consumption of air conditioners. For example, Chinese patent CN102734893A discloses a linkage control device to achieve more energy-efficient air conditioning control. In a linkage control device that controls multiple devices via a network, it is characterized in that setting information for linkage control is used, and the setting information as a corresponding information includes linkage source device information, linkage source condition number information, linkage source condition action information, linkage target device information, and linkage target control action information.

[0004] However, for the air-conditioning equipment groups in school classrooms, especially according to different teaching numbers and different teaching arrangements, how to reasonably analyze and control the opening of air-conditioning equipment is a difficult problem; based on this, the present application provides a solution. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art;

[0006] To this end, the present invention proposes an energy-saving control method for an air-conditioning equipment group based on big data drive, which specifically includes the following steps:

[0007] According to the target environment where the air-conditioning equipment group is located, the corresponding arrangement information is obtained, and the arrangement information is used to indicate the specific arrangement of the class time and the number of students for each class in the corresponding target environment;

[0008] According to the location of the air conditioners in the target environment, the temperature can be adjusted by X1 degrees Celsius within the set time to determine the preliminary coverage area of ​​any air conditioner, and then the preliminary coverage area of ​​the air conditioner is updated according to the cooling speed for the areas that are not divided, so as to obtain the approved coverage area of ​​each air conditioner and the number of seats in the approved coverage area simultaneously;

[0009] Then, the number of air conditioners to be turned on is determined according to the number of students attending the next class at the current time, and the corresponding number of air conditioners is turned on to determine the objects to be turned on.

[0010] Furthermore, the target environment is the classroom where the controlled air-conditioning equipment group is located.

[0011] Furthermore, the specific method of determining the preliminary coverage area of ​​the air conditioner is as follows:

[0012] Perform environmental simulation. In the same target environment, with doors and windows closed, turn on the air conditioner at any position, and then use the position directly below the center of the air conditioner as the center of the circle.

[0013] Obtain the area range where the temperature changes by X1 degrees Celsius within the set time T2 when the air conditioner is turned on for cooling or heating, and mark it as a preliminary area; X1 is a preset value;

[0014] Then all the desks in the preliminary area are marked as the preliminary coverage area of ​​the corresponding air-conditioning equipment;

[0015] Then treat the rest of the air conditioners in the same way;

[0016] Furthermore, the preliminary coverage area is updated and the specific method for determining the approved coverage area is as follows:

[0017] Get the preliminary coverage areas of adjacent air conditioners. When there are overlapping areas in the preliminary coverage areas of any air conditioner, get all the air conditioners corresponding to the overlapping areas, and determine which air conditioner the overlapping areas belong to according to the time required to adjust the temperature change value of the overlapping areas to X1 degrees Celsius. When there are areas that are not divided into any preliminary coverage areas, mark the undivided areas as blank areas, get all the air conditioners whose straight-line distances from the center points of the blank areas are within a preset value L1, and determine which air conditioner the blank areas belong to according to the time required to adjust the temperature change value of the blank areas to X1 degrees Celsius. If the required time corresponding to the overlapping areas or the blank areas is the same, randomly divide the overlapping areas or the blank areas to which air conditioner they belong, and the division process ensures that the square difference of the areas covered by all air conditioners does not exceed X2, where X2 is a preset value.

[0018] The preliminary coverage area of ​​the adjusted area is marked as the approved coverage area, and the approved coverage area of ​​each air conditioner is obtained.

[0019] Furthermore, the specific method for determining the number of air conditioners to be turned on according to the number of students in class is as follows:

[0020] Acquire the approved coverage areas and the number of seats of all air conditioners, combine the number of seats, select at least one approved coverage area of ​​the air conditioner, add the number of seats of the selected approved coverage areas to obtain the total number of seats, obtain a combination of the approved coverage areas of several air conditioners, and mark it as a coverage group;

[0021] Subtract the number of students in class from the total number of seats, and mark the resulting value as the seat difference of the corresponding coverage group;

[0022] Then the classroom seats are divided into front row seats, middle seats and back row seats. The front row seats refer to the seats in the first third of the classroom, the back row seats refer to the seats in the last third of the classroom, and the rest are middle seats.

[0023] Then, the number of front seats, middle seats, and back seats in each coverage group is obtained, and marked as the front row number, middle number, and back row number respectively, and the preferred value is calculated using the formula:

[0024] Optimal value = 2*number of front rows + number of middle rows;

[0025] Get the optimal value of each covering group, and then use the formula to calculate the confirmed value of each covering group. The specific formula is:

[0026] Confirmed value = 0.58*preferred value + 0.42 / seat difference;

[0027] In the formula, 0.58 and 0.42 are preset weights;

[0028] The air conditioner corresponding to the approved coverage area in the coverage group with the largest selected value is marked as an on object.

[0029] Further, after obtaining the activation object, the air conditioner is activated according to the activation object;

[0030] When the get out of class is over, the scheduling information of the corresponding target environment will be automatically obtained. If there are no classes scheduled within T3 time, all air conditioners will be automatically turned off. T3 here is a preset value.

[0031] Furthermore, the value of T2 can also be determined in the following manner:

[0032] In a room with the same air conditioner installed, get the time required for the temperature to change by X1 degree Celsius;

[0033] Then, the time required for the same air-conditioning equipment in several different rooms to adjust the temperature change to X1 degrees Celsius is obtained; several times are obtained and marked as adjustment duration Ci, i=1, ..., n, indicating that the simulation is performed in n rooms;

[0034] Then obtain the mean value P of Ci, obtain the mean of the values ​​greater than P in Ci, mark it as a transcendental number, and mark the mean of the values ​​less than P in Ci as a submean number;

[0035] When the exceedance number is less than the average lower number, the P value is marked as the value of T2;

[0036] Otherwise, the median of the P value and the transcendental number is automatically marked as the value of T2.

[0037] Furthermore, T2 is a preset value.

[0038] An energy-saving control system for an air-conditioning equipment group driven by big data, the system adopts the aforementioned energy-saving control method to achieve energy-saving control of the air-conditioning equipment.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present application obtains the corresponding arrangement information according to the target environment in which the air-conditioning equipment group is located, and according to the air-conditioning arrangement position of the target environment, the temperature can be adjusted by X1 degree Celsius within the set time to determine the preliminary coverage area of ​​any air-conditioner, and then the preliminary coverage area of ​​the air-conditioner is updated according to the cooling speed for the area that has not been divided, so as to obtain the approved coverage area of ​​each air-conditioner, and simultaneously obtain the number of seats in the approved coverage area; then, according to the number of students in the next class at the current time, the number of air-conditioners to be turned on is determined, the corresponding number of air-conditioners is turned on, and the objects to be turned on are determined, so as to reasonably arrange the number of air-conditioners to be turned on and how to turn them on; the present invention is simple, effective, and easy to use.

[0041] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 This is a flow chart of the energy-saving control method of an air-conditioning equipment group driven by big data according to the present invention;

[0044] Figure 2 Flow chart for determining a preliminary coverage area of ​​an air conditioner for the present invention. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0046] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0047] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0048] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0049] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0051] Embodiment 1:

[0052] See also Figure 1 As shown, the present application provides an energy-saving control method for an air-conditioning equipment group driven by big data;

[0053] As an embodiment 1 of the present application, the method specifically includes the following steps:

[0054] Step 1: Get the air conditioning equipment group and its target environment. The target environment is the environment where the corresponding air conditioning equipment group is located. For this application, it is the classroom, office or activity venue. The air conditioning equipment group includes several air conditioning equipment.

[0055] Step 2: Get the target environment. First, get the scheduling information of the target environment. The scheduling information is the planned air conditioning usage time and number of users in the target environment. Furthermore, the scheduling information includes the specific class time of each class and the number of students in the corresponding class time. When it is T1 time before the class time, T1 is the preset time value, generally 5 minutes, and energy-saving analysis will be automatically performed. The specific method of energy-saving analysis is as follows:

[0056] First, the number of students in the class will be automatically obtained, and then the number of seats in the target environment and the distribution information of the air-conditioning equipment will be synchronized. The distribution information refers to the location of the air-conditioning equipment, and then the coverage area of ​​the air-conditioning equipment will be analyzed, such as Figure 2 As shown, the analysis method is:

[0057] First, conduct environmental simulation. In the same target environment, with doors and windows closed, turn on the air conditioner at any position, and then observe its coverage area with the position directly below the center of the air conditioner as the center of the circle.

[0058] First, determine the preliminary area; obtain the area range where the temperature changes by X1 degrees Celsius within the set time T2 when the air conditioner is turned on for cooling or heating, and mark it as the preliminary area; X1 and T2 are both preset values, and X1 is generally set to 2 degrees Celsius;

[0059] Then all the desks in the preliminary area are marked as the preliminary coverage area of ​​the corresponding air-conditioning equipment;

[0060] Then treat the rest of the air conditioners in the same way;

[0061] Get the preliminary coverage areas of adjacent air conditioners. When there are overlapping areas in the preliminary coverage areas of any air conditioner, get all the air conditioners corresponding to the overlapping areas, and determine which air conditioner the overlapping areas belong to according to the time required to adjust the temperature change value of the overlapping areas to X1 degrees Celsius. When there are areas that are not divided into any preliminary coverage areas, mark the undivided areas as blank areas, get all the air conditioners whose straight-line distances from the center points of the blank areas are within a preset value L1, and determine which air conditioner the blank areas belong to according to the time required to adjust the temperature change value of the blank areas to X1 degrees Celsius. If the required time corresponding to the overlapping areas or the blank areas is the same, randomly divide the overlapping areas or the blank areas to which air conditioner they belong, and the division process ensures that the square difference of the areas covered by all air conditioners does not exceed X2, where X2 is a preset value.

[0062] Mark the preliminary coverage area of ​​the adjusted area as the approved coverage area, obtain the approved coverage area of ​​each air conditioner, and simultaneously approve the number of seats in the coverage area;

[0063] The number of air conditioners to be turned on is determined according to the number of students in class. The rule followed here is to turn on the least number of air conditioners to determine the number of seats that can cover a number greater than or equal to the number of students in class. The approved coverage areas and the number of seats of all air conditioners are obtained, the number of seats is combined, and the approved coverage area of ​​at least one air conditioner is selected. The number of seats in the selected approved coverage areas is added to obtain the total number of seats. The combination of the approved coverage areas of several air conditioners is obtained and marked as a coverage group;

[0064] Subtract the number of students in class from the total number of seats, and mark the resulting value as the seat difference of the corresponding coverage group;

[0065] Then the classroom seats are divided into front row seats, middle seats and back row seats. The front row seats refer to the seats in the first third of the classroom, the back row seats refer to the seats in the last third of the classroom, and the rest are middle seats.

[0066] Then, the number of front seats, middle seats, and back seats in each coverage group is obtained, and marked as the front row number, middle number, and back row number respectively, and the preferred value is calculated using the formula:

[0067] Optimal value = 2*number of front rows + number of middle rows;

[0068] Get the optimal value of each covering group, and then use the formula to calculate the confirmed value of each covering group. The specific formula is:

[0069] Confirmed value = 0.58*preferred value + 0.42 / seat difference;

[0070] In the formula, 0.58 and 0.42 are preset weights, which are used to highlight the different importance of different factors;

[0071] Mark the air conditioner corresponding to the approved coverage area in the coverage group with the largest selected value as an on object;

[0072] Step 3: After obtaining the start object, turn on the air conditioner according to the start object;

[0073] Step 4: When the get out of class is over, the schedule information of the corresponding target environment will be automatically obtained. If there is no class schedule within T3 time, all air conditioners will be automatically turned off. T3 here is a preset value. Generally, the specific value is 20 minutes.

[0074] Embodiment 2:

[0075] As the second embodiment of the present application, the present application is implemented on the basis of the first embodiment, where T2 is determined by the following method: in a room where the same air conditioner is installed, the time required for the temperature to change by X1 degrees Celsius is obtained;

[0076] Then, the time required for the same air-conditioning equipment in several different rooms to adjust the temperature change to X1 degrees Celsius is obtained; several times are obtained and marked as adjustment duration Ci, i=1, ..., n, indicating that the simulation is performed in n rooms;

[0077] Then obtain the mean value P of Ci, obtain the mean of the values ​​greater than P in Ci, mark it as a transcendental number, and mark the mean of the values ​​less than P in Ci as a submean number;

[0078] When the exceedance number is less than the average lower number, the P value is marked as the value of T2;

[0079] Otherwise, the median of the P value and the transcendental number is automatically marked as the value of T2.

[0080] Embodiment three:

[0081] As the third embodiment of the present application, the present application is implemented on the basis of the first embodiment, and the difference from the first embodiment is that after the get out of class time is reached in step 4, before turning off all air conditioners, a continuation analysis is required, and the specific method of the continuation analysis is:

[0082] The number of students studying in the next class in the classroom is obtained through prediction and analysis, and positioning is performed according to the same month and specific day of the week. For example, if today is Monday in March, the number of people studying in the time period of the next class every day in the past year is obtained in a similar manner, and then the average of the number of students studying is automatically calculated. When the average is divided by the number of people who can sit in all the seats in the classroom, the obtained value is marked as the study ratio. If the study ratio exceeds B1, the air conditioner of the classroom will not be turned off, and the number of air conditioners turned on will be analyzed according to the average number of students studying in the same manner as in the aforementioned embodiment 1. When the class is over, it is automatically obtained whether there is a teaching arrangement for the next class. If so, it is analyzed in the manner of embodiment 1. If not, it is analyzed in the manner of this embodiment whether there is anyone studying, and the air conditioner control is arranged in a targeted manner.

[0083] Embodiment 4:

[0084] Of course, the present application provides an embodiment 4, which is used to integrate embodiments 1 to 3 for implementation. At the same time, based on this embodiment, the present application also provides an air-conditioning equipment group energy-saving control device driven by big data. The device adopts the air-conditioning equipment group energy-saving control method mentioned in this embodiment to realize the energy-saving control of the air-conditioning equipment group.

[0085] 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.

[0086] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An energy-saving control method for air-conditioning equipment groups driven by big data, characterized in that: The method comprises the following steps: Step S1: According to the target environment where the air-conditioning equipment group is located, corresponding arrangement information is obtained, where the arrangement information is the planned air-conditioning usage time and number of users in the target environment; Step S2: determining a preliminary coverage area of ​​any air conditioner according to the arrangement position of the air conditioner in the target environment and the degree Celsius to which the temperature can be adjusted within a set time; Step S3: for the overlapping area or the undivided area, the preliminary coverage area of ​​the air conditioner is updated according to the cooling speed, the approved coverage area of ​​each air conditioner is obtained, and the number of seats in the approved coverage area is simultaneously obtained; Step S4: Determine the number of air conditioners to be turned on according to the number of students attending the next class at the current time, determine the objects to be turned on, and turn on the corresponding air conditioners.

2. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 1 is characterized in that: The target environment is the classroom where the controlled air-conditioning equipment group is located; the arrangement information is used to indicate the class time and number of students for each class in the corresponding target environment.

3. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 1 is characterized in that: In step S2, the specific method of determining the preliminary coverage area of ​​the air conditioner is: Step S21: Perform environmental simulation. In the same target environment, with doors and windows closed, turn on the air conditioner at any position, and take the position directly below the center of the air conditioner as the center of the circle; Step S22: When the air conditioner is turned on to cool down or heat up, the area where the temperature change reaches X1 degrees Celsius within the set time T2 is circled, and the circled area is marked as a preliminary area; X1 is a preset value; Step S23: Mark all the desks in the preliminary area as the preliminary coverage area of ​​the corresponding air conditioner; Step S24: Process the air conditioners at other locations according to steps S21 to S23 to obtain preliminary coverage areas of all air conditioners.

4. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 1 is characterized in that: In step S3, the preliminary coverage area is updated, and the specific method of determining the approved coverage area is: Step S31: obtaining the preliminary coverage area of ​​adjacent air conditioners; Step S32: when there is an overlapping area in the preliminary coverage area of ​​any air conditioner, all air conditioners corresponding to the overlapping area are obtained, and the air conditioner to which the overlapping area belongs is determined according to the length of time required to adjust the temperature change value of the overlapping area to reach X1 degrees Celsius; Step S33: when there is an area that is not divided into any preliminary coverage area, the undivided area is marked as a blank area, and all air conditioners whose straight-line distance from the center point of the blank area is within the preset value L1 are obtained, and the blank area is determined to which air conditioner it belongs according to the length of time required to adjust the temperature change value of the blank area to reach X1 degrees Celsius; Step S34: In step S32 and step S33, if the required time corresponding to the overlapping area or the blank area is the same, the overlapping area or the blank area is randomly divided to determine which air conditioner it belongs to; During random division, ensure that the square difference of the initial coverage area of ​​all air conditioners does not exceed X2, where X1 and X2 are preset values; Step S35: Mark the updated and adjusted preliminary coverage area as the approved coverage area, and obtain the approved coverage area of ​​each air conditioner.

5. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 1 is characterized in that: In step S4, the specific method of determining the number of air conditioners to be turned on according to the number of students in class is: Obtain the approved coverage areas and corresponding seat numbers of all air conditioners, combine the seat numbers, select at least one approved coverage area of ​​the air conditioner, add the seat numbers of the selected approved coverage areas to obtain the total number of seats, obtain a combination of approved coverage areas of several air conditioners, and mark it as a coverage group; Subtract the number of students in class from the total number of seats, and mark the resulting value as the seat difference of the corresponding coverage group; Classroom seats are divided into front row seats, middle seats and back row seats. The front row seats refer to the seats in the first third of the classroom, the back row seats refer to the seats in the last third of the classroom, and the rest are middle seats. Then, the number of front seats, middle seats, and back seats in each coverage group is obtained, and marked as the front row number, middle number, and back row number respectively, and the preferred value is calculated using the formula: Optimal value = 2*number of front rows + number of middle rows; Get the optimal value of each covering group, and then use the formula to calculate the confirmed value of each covering group. The specific formula is: Confirmed value = 0.58*preferred value + 0.42 / seat difference; In the formula, 0.58 and 0.42 are preset weights; The air conditioner corresponding to the approved coverage area in the coverage group with the largest selected value is marked as an on object.

6. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 5 is characterized in that: After obtaining the activation object, the air conditioner is activated according to the activation object; When the get out of class is over, the scheduling information of the corresponding target environment will be automatically obtained. If there are no classes scheduled within T3 time, all air conditioners will be automatically turned off. T3 is the preset value.

7. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 3 is characterized in that: The value of T2 can also be determined in the following manner: In a room with the same air conditioner installed, get the time required for the temperature to change by X1 degree Celsius; Then, the time required for the same air-conditioning equipment in several different rooms to adjust the temperature to change by X1 degrees Celsius is obtained; the obtained several time marks are the adjustment time Ci, i=1, ..., n, indicating that the simulation is performed in n rooms; Then obtain the mean value P of Ci, obtain the mean of the values ​​in Ci that are greater than P, mark it as a transcendental number, and mark the mean of the values ​​in Ci that are less than P as a submean number; When the exceedance number is less than the average lower number, the P value is marked as the value of T2; Otherwise, the median of the P value and the transcendental number is automatically marked as the value of T2.

8. The energy-saving control method for air-conditioning equipment groups based on big data drive according to claim 3 is characterized in that: The T2 is a preset value.

9. An energy-saving control system for air-conditioning equipment groups driven by big data, characterized in that: The system adopts the energy-saving control method as described in any one of claims 1 to 8 to realize energy-saving control of air-conditioning equipment.

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