WO2020211436A1 - 空调数据分析方法、装置和计算机可读存储介质 - Google Patents
空调数据分析方法、装置和计算机可读存储介质 Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- the present disclosure relates to the field of air conditioning technology, in particular to an air conditioning data analysis method, device and computer readable storage medium.
- an air-conditioning data analysis method including: acquiring air-conditioning data information, the air-conditioning data information includes one of device information, setting information, environmental information, status information, or fault information, or Various; disassemble air-conditioning data information through semantic analysis to obtain air-conditioning data in multiple dimensions; determine the relationship between air-conditioning data and energy consumption in each dimension; adjust air-conditioning parameters or guidance according to the relationship between air-conditioning data and energy consumption in each dimension Product design to reduce energy consumption.
- the air-conditioning data analysis method further includes: coupling air-conditioning data of specified relevant dimensions; determining the relationship between the air-conditioning data of each dimension and energy consumption includes: determining the relationship between the air-conditioning data of the different dimensions and the energy consumption. relationship.
- the air-conditioning data analysis method further includes: dividing the air-conditioning data information into data blocks, and the data blocks include air-conditioning data of multiple associated dimensions; determining the data blocks to which the air-conditioning data of the specified relevant dimensions belong; The air-conditioning data of the data block is coupled; determining the relationship between the air-conditioning data of each dimension and the energy consumption includes: determining the relationship between the air-conditioning data and the energy consumption of the coupled different data blocks.
- determining the relationship between air-conditioning data and energy consumption in each dimension includes: extracting air-conditioning data within a predetermined value range; determining the relationship between air-conditioning data and energy consumption within a predetermined value range.
- adjusting parameters or guiding product design according to the relationship between air conditioning data and energy consumption in various dimensions includes at least one of the following: adjusting air conditioning parameters to reduce energy consumption for dimensions that can be adjusted by adjusting air conditioning parameters; or The dimension of the category, adjust the process design according to the relationship between air conditioning data and energy consumption.
- the air conditioning data analysis method further includes: specifying a predetermined coupling relationship between the dimensions.
- the air conditioning data analysis method further includes: traversing the calculation coupling relationship between two or more specified dimensions; obtaining the relationship between air conditioning data and energy consumption based on the calculation coupling relationship coupling; if the correlation of the relationship is greater than a predetermined correlation threshold , Then use the calculative coupling relationship to update the predetermined coupling relationship.
- an air-conditioning data analysis device including: a data acquisition unit configured to acquire air-conditioning data information, the air-conditioning data information including equipment information, setting information, environmental information, and status information Or one or more of the fault information; the data disassembly unit is configured to disassemble the air-conditioning data information through semantic analysis to obtain air-conditioning data in multiple dimensions; the relationship determination unit is configured to determine the air-conditioning data in each dimension and The relationship between energy consumption; the adjustment unit is configured to adjust air conditioning parameters or guide product design according to the relationship between air conditioning data and energy consumption in various dimensions to reduce energy consumption.
- the air-conditioning data analysis device further includes: a dimensional data coupling unit configured to couple air-conditioning data of a specified relevant dimension; and the relationship determining unit is configured to determine the coupling between air-conditioning data belonging to different dimensions and energy consumption. Relationship.
- the air-conditioning data analysis device further includes: a data block dividing unit configured to divide the air-conditioning data information into data blocks, the data blocks including multiple associated air-conditioning data in multiple dimensions; and a data block extraction unit configured to To determine the data blocks to which the air-conditioning data of the specified relevant dimensions belong; the data block data coupling unit is configured to couple the air-conditioning data of the attributed data blocks; the relationship determining unit is configured to: determine the air-conditioning data of the coupled data blocks belonging to different data blocks The relationship with energy consumption.
- the relationship determining unit is configured to: extract air-conditioning data within a predetermined numerical range; and determine the relationship between air-conditioning data and energy consumption within the predetermined numerical range.
- the adjustment unit is configured to perform at least one of the following: adjust air conditioning parameters to reduce energy consumption for dimensions that can be adjusted by adjusting air conditioning parameters; or for process dimensions, according to the relationship between air conditioning data and energy consumption Adjust the process design.
- the air conditioning data analysis device further includes: a coupling relationship determining unit configured to specify a predetermined coupling relationship between the dimensions.
- the coupling relationship determining unit is further configured to: traverse the calculation coupling relationship of air-conditioning data between two or more dimensions; determine whether the correlation between the air-conditioning data coupled based on the calculation coupling relationship and the energy consumption is greater than Predetermined correlation threshold; in the case that the correlation degree of the relationship is greater than the predetermined correlation threshold, the coupling relationship of the predetermined air-conditioning data is updated by the calculation coupling relationship.
- an air conditioner data analysis device including: a memory; and a processor coupled to the memory, the processor configured to execute any of the above based on instructions stored in the memory Air conditioning data analysis method.
- a computer-readable storage medium on which computer program instructions are stored, and when the instructions are executed by a processor, the steps of any of the above air conditioning data analysis methods are realized.
- FIG. 1 is a flowchart of some embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 2 is a flowchart of other embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 3 is a schematic diagram of some embodiments of air-conditioning data collection of the present disclosure.
- FIG. 4 is a flowchart of still other embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 5 is a schematic diagram of other embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 6 is a schematic diagram of still other embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 7 is a flowchart of still other embodiments of the air conditioning data analysis method of the present disclosure.
- FIG. 8 is a schematic diagram of some embodiments of the air conditioning data analysis device of the present disclosure.
- FIG. 9 is a schematic diagram of other embodiments of the air-conditioning data analysis device of the present disclosure.
- FIG. 10 is a schematic diagram of still other embodiments of the air conditioning data analysis device of the present disclosure.
- inverter air conditioners, water-cooled air conditioners and other air conditioners that have emerged at the historic moment can take certain energy-saving measures in terms of consumables and power to a certain extent, and can also achieve certain effects, but this effect is in the equipment It is internally coupled and linked, but in fact, the elements that generate air-conditioning energy consumption are multi-dimensional, and many external reasons will lead to an increase in air-conditioning energy consumption.
- FIG. 1 The flowchart of some embodiments of the air conditioning data analysis method of the present disclosure is shown in FIG. 1.
- air-conditioning data information is acquired.
- the air-conditioning data information includes one or more of device information, setting information, environmental information, status information, or fault information.
- statistics are performed on the data obtained from real-time reporting or detection of air-conditioning conditions, or information collected or manually reported through the network, such as obtaining air-conditioning operating status through air-conditioning detection and real-time reporting, and collecting various information through the network. Weather conditions at all times, collecting manually reported fault data, etc.
- the air-conditioning data information is disassembled through semantic analysis to obtain air-conditioning data in multiple dimensions.
- semantic distinction is performed on the element attributes of the collected air conditioning data.
- features of data of different dimensions are pre-stored, and the dimension to which the air conditioning data belongs is determined by means of feature matching.
- the dimensions of disassembly include temperature dimensions, humidity dimensions, boot time dimensions, address dimensions, weather dimensions, operating cycle dimensions, and the like.
- step 103 the relationship between air conditioning data and energy consumption in each dimension is determined.
- a machine learning algorithm is used to deduce the relationship between the change trend of air conditioning data in each dimension and the energy consumption change trend, determine the correlation between the data of each dimension and the change in energy consumption, and determine under what circumstances the energy can be reduced. Consumption.
- step 104 the air conditioning parameters are adjusted or the product design is guided according to the relationship between the air conditioning data and energy consumption in various dimensions to reduce energy consumption.
- FIG. 2 The flowchart of other embodiments of the air conditioning data analysis method of the present disclosure is shown in FIG. 2.
- step 201 air conditioning data information is acquired.
- the air-conditioning data information is disassembled through semantic analysis to obtain air-conditioning data in multiple dimensions.
- the air-conditioning project or the air-conditioning unit is used as a unit, and the unit is first performed to form point and surface data.
- step 203 the air conditioning data of the specified relevant dimensions are coupled.
- the dimensions to be coupled are specified in advance, such as coupling indoor temperature and outdoor temperature, coupling temperature and humidity, and so on.
- the relationship between the coupled air conditioning data belonging to different dimensions and energy consumption is determined.
- the resource model is pre-configured to obtain the linear and non-linear relationship between the coupled air conditioning data and energy consumption. For example, if two-dimensional data are coupled, the linear and non-linear relationship between the two-dimensional data and the energy consumption is determined. For example, the energy consumption and the coupled two-dimensional data are formed into three-dimensional data, and the point cloud in the three-dimensional space is determined, and then drawn The point cloud data obtains the relationship between the coupled air conditioning data and energy consumption.
- the coupling method in addition to fusing two-dimensional data to form two-dimensional data, the coupling method also includes using a predetermined algorithm, such as normalizing the data and calculating the mean value, or calculating the sum of squares.
- a predetermined algorithm such as normalizing the data and calculating the mean value, or calculating the sum of squares.
- the relationship between data of different dimensions and the energy consumption of the air conditioner is displayed in a visual graphic manner, so that the relationship is intuitive and convenient for effective use.
- calculations are performed through linear algorithms such as statistical analysis and support vector machines, and the relationship between air conditioning data and energy consumption is mined through linear algorithms.
- step 205 the air conditioning parameters are adjusted or the product design is guided according to the relationship between air conditioning data and energy consumption in various dimensions to reduce energy consumption.
- the air-conditioning data information is divided into a variety of data blocks, as shown in Figure 3, including basic information, environmental information, setting information and other data blocks.
- the air-conditioning project or unit is used as the unit to store and Call each data block and data of different dimensions in each data block to facilitate data transfer.
- FIG. 4 The flowcharts of still other embodiments of the air conditioning data analysis method of the present disclosure are shown in FIG. 4.
- step 401 air conditioning data information is acquired.
- step 402 the air-conditioning data information is disassembled through semantic analysis to obtain air-conditioning data in multiple dimensions.
- step 403 the air-conditioning data information is divided into data blocks, and the data blocks include associated air-conditioning data of multiple dimensions.
- step 404 the data block to which the air conditioning data of the specified relevant dimension belongs is determined.
- step 405 the air conditioning data of the attributable data block is coupled.
- different data models are preset, and data coupling is performed in advance according to hotspots.
- step 406 the relationship between the coupled air conditioning data belonging to different data blocks and energy consumption is determined.
- a visualized data graph can be output based on the calculated energy consumption relationship.
- step 407 the relationship between air-conditioning data and energy consumption is classified according to energy-saving implementation means.
- setting parameters can be adjusted by modifying settings
- some environmental parameters can be adjusted by updating the installation environment, and parameters affected by different device models need to change the device design.
- the relationship between air conditioning data and energy consumption is summarized and classified according to energy-saving implementation methods. As shown in Figure 6, the relationship between air-conditioning data and energy consumption based on mining is effectively applied.
- the energy consumption rule If it belongs to the range of air-conditioning operating parameter settings, label the energy consumption rule as an air-conditioning operating setting category, generate appropriate energy-saving operating parameter settings according to the energy-consumption rule, and push them to the air conditioner as an energy-saving measure, and perform step 408 .
- the label is converted into a process design category, and is pushed to the designer, and step 409 is executed.
- step 408 for the dimensions that can be adjusted by adjusting the air-conditioning parameters, the air-conditioning parameters are adjusted to reduce energy consumption.
- the process design is adjusted according to the relationship between air-conditioning data and energy consumption for the dimensions of the process category. For example, in multi-connection, single internal machine and multiple internal machines have the same energy consumption and power. There is excess power and waste of resources in the operation of single internal machine. Designers carry out relevant energy-saving design for this: the number of internal machines is different, and the external machine The power should also be different. Reduce unnecessary energy consumption during air-conditioning operation and reduce resource waste through improvements.
- the data block to which it belongs can be determined based on the specified relevant dimensions, and the relationship between the data of each dimension in the data block and the energy consumption after being coupled, so as to further explore more factors that affect energy consumption. Regularly, improve the utilization of air-conditioning data and improve energy-saving effects.
- the designated relevant dimensions can be pre-configured to facilitate the realization of directional analysis.
- data couplings of different dimensions are randomly designated or traversed to designate data couplings of different dimensions, thereby increasing the probability of mining the association relationship.
- FIG. 7 The flowcharts of still other embodiments of the air conditioning data analysis method of the present disclosure are shown in FIG. 7.
- step 701 the calculation coupling relationship of air conditioning data between two or more dimensions is specified.
- step 702 the relationship between air conditioning data and energy consumption coupled based on the calculation coupling relationship is obtained.
- the correlation coupling of data of different dimensions in the embodiment shown in FIG. 2 or FIG. 4 is performed based on the computational coupling relationship, or the data of the data block to which the data of different dimensions belongs is coupled.
- step 703 the correlation between the air conditioning data and the energy consumption is calculated based on a predetermined algorithm, and it is determined whether the correlation between the air conditioning data and the energy consumption is greater than a predetermined correlation threshold. If it is greater than the predetermined threshold, step 704 is executed; if it is not greater than the predetermined threshold, the current calculation coupling relationship is discarded.
- step 704 the predetermined coupling relationship of the air-conditioning data is updated using the computational coupling relationship, so that the updated coupling relationship is used in the subsequent air-conditioning data mining to process the air-conditioning data.
- the air-conditioning data analysis method described above can improve the energy-saving performance of the air-conditioning, so it can also be called an air-conditioning energy-saving processing method.
- the data acquisition unit 801 can acquire air-conditioning data information, and the air-conditioning data information includes one or more of device information, setting information, environmental information, status information, or fault information.
- the data disassembly unit 802 can disassemble air-conditioning data information through semantic analysis, and obtain air-conditioning data in multiple dimensions.
- semantic distinction is performed on the element attributes of the collected air conditioning data.
- features of data of different dimensions are pre-stored, and the dimension to which the air conditioning data belongs is determined by means of feature matching.
- the relationship determining unit 803 can determine the relationship between air conditioning data and energy consumption in various dimensions.
- a machine learning algorithm is used to deduce the relationship between the change trend of air conditioning data in each dimension and the energy consumption change trend, determine the correlation between the data of each dimension and the change in energy consumption, and determine under what circumstances the energy can be reduced. Consumption.
- the adjustment unit 804 can adjust air conditioning parameters or guide product design according to the relationship between air conditioning data and energy consumption in various dimensions, so as to reduce energy consumption.
- Such an air-conditioning data analysis device can obtain and disassemble air-conditioning data information, and obtain the relationship between air-conditioning data and energy consumption in various dimensions, and then adjust air-conditioning parameters and guide product design, so as to make full use of air-conditioning data from parameter adjustment and equipment itself
- the design aspect reduces energy consumption and improves product energy saving effect.
- the air conditioning data analysis device further includes a dimension data coupling unit 805, which can couple air conditioning data of a specified relevant dimension.
- the dimensions to be coupled are specified in advance, such as coupling indoor temperature and outdoor temperature, coupling temperature and humidity, and so on.
- the relationship determining unit 803 can determine the relationship between the coupled air conditioning data belonging to different dimensions and the energy consumption.
- the resource model is pre-configured to obtain the linear and non-linear relationship between the coupled air conditioning data and energy consumption.
- Such a device can couple data of different dimensions and analyze the relationship with energy consumption, and use common sense and experience to specify part of the data to be associated, which is convenient for mining more factors and laws that affect energy consumption, improving the utilization of air conditioning data, and further Improve energy saving effect.
- the air conditioning data analysis device further includes a data block dividing unit 806, a data block extracting unit 807 and a data block data coupling unit 808.
- the data block dividing unit 805 can divide the air-conditioning data information into data blocks, and the data blocks include associated air-conditioning data in multiple dimensions.
- the data block extraction unit 806 can determine the data block to which the air conditioning data of the specified relevant dimension belongs.
- the data block data coupling unit 807 can couple the air conditioning data of the belonging data block.
- Such an air conditioning data analysis device can determine the data block to which it belongs based on the specified relevant dimensions, and further obtain the relationship between the data of each dimension in the data block and the energy consumption after coupling, so as to further explore more factors affecting energy consumption , Regularity, improve the utilization of air conditioning data, and improve energy-saving effects.
- the air-conditioning data analysis device further includes a coupling relationship determining unit 809, which can randomly specify or traversely specify data couplings of different dimensions to generate a calculus coupling relationship.
- the calculus coupling relationship is used by the dimensional data coupling unit In 804, data of different dimensions are correlated and coupled, and a data block data coupling unit 807 couples data of data blocks to which the data of different dimensions belongs. If the correlation between the air conditioning data and the energy consumption is greater than the predetermined correlation threshold, the calculated coupling relationship is used to update the predetermined coupling relationship.
- Such a device can dig out the relationship between hidden data, and further improve the possibility of improving energy saving performance.
- the air conditioning data analysis device includes a memory 901 and a processor 902.
- the memory 901 is a magnetic disk, flash memory or any other non-volatile storage medium.
- the memory is used to store the instructions in the corresponding embodiment of the above air conditioning data analysis method.
- the processor 902 is coupled to the memory 901 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller.
- the processor 902 is used to execute instructions stored in the memory, and can make full use of air-conditioning data to reduce energy consumption in terms of parameter adjustment and the design of the device itself, and improve product energy saving effects.
- the air conditioning data analysis device 1000 includes a memory 1001 and a processor 1002.
- the processor 1002 is coupled to the memory 1001 through the BUS bus 1003.
- the air conditioning data analysis device 1000 can also be connected to an external storage device 1005 through the storage interface 1004 to call external data, and can also be connected to the network or another computer system (not shown) through the network interface 1006. No more detailed introduction here.
- the data instructions are stored in the memory, and the above instructions are processed by the processor, which can make full use of the air conditioning data to reduce energy consumption in terms of parameter adjustment and the design of the device itself, and improve the energy-saving effect of the product.
- the air-conditioning data analysis device described above can improve the energy-saving performance of the air-conditioning, so it can also be called an air-conditioning energy-saving processing device.
- a computer-readable storage medium has computer program instructions stored thereon, and when the instructions are executed by a processor, the steps of the method in the corresponding embodiment of the air conditioning data analysis method are realized.
- the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.
- the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. .
- These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
- the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
- These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
- the instructions provide steps for implementing functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in the block diagram.
- the method and device of the present disclosure can be implemented in many ways.
- the method and apparatus of the present disclosure are implemented by software, hardware, firmware or any combination of software, hardware, and firmware.
- the above-mentioned order of the steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless specifically stated otherwise.
- the present disclosure is implemented as programs recorded in a recording medium, and these programs include machine-readable instructions for implementing the method according to the present disclosure.
- the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
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Abstract
本公开提出一种空调数据分析方法、装置和计算机可读存储介质，涉及空调技术领域。本公开的一种空调数据分析方法，包括：获取空调数据信息，空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种；通过语义分析拆解空调数据信息，获取多个维度的空调数据；确定各个维度的空调数据与能耗间的关系；根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。通过这样的方法，能够获取并拆解空调数据信息，并得到各个维度的空调数据与能耗之间的关系，进而调节空调参数和指导产品设计，从而充分利用空调数据从参数调节和设备本身的设计方面降低能耗，提高产品节能效果。
Description
相关申请的交叉引用
本申请是以CN申请号为201910311705.8，申请日为2019年4月18日的申请为基础，并主张其优先权，该CN申请的公开内容在此作为整体引入本申请中。
本公开涉及空调技术领域，特别是一种空调数据分析方法、装置和计算机可读存储介质。
随着空调设备装机量的增加，几乎成为每家每户必备的大家电之一；作为日常生活中纳凉取暖的电器，其使用率极高且使用周期长，伴随而来的是能耗增加。据相关研究调查表明，建筑能耗占据社会总能耗的20％；在建筑能耗部分，空调的能耗占比达40％～50％。
发明内容
根据本公开的一些实施例的一个方面，提出一种空调数据分析方法，包括：获取空调数据信息，空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种；通过语义分析拆解空调数据信息，获取多个维度的空调数据；确定各个维度的空调数据与能耗间的关系；根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
在一些实施例中，空调数据分析方法还包括：将指定相关维度的空调数据耦合；确定各个维度的空调数据与能耗间的关系包括：确定耦合的归属不同维度的空调数据与能耗间的关系。
在一些实施例中，空调数据分析方法还包括：将空调数据信息划分数据块，数据块中包括相关联的多个维度的空调数据；确定指定相关维度的空调数据各自归属的数据块；将归属的数据块的空调数据耦合；确定各个维度的空调数据与能耗间的关系包括：确定耦合的归属不同数据块的空调数据与能耗间的关系。
在一些实施例中，确定各个维度的空调数据与能耗间的关系包括：提取预定数值 范围内的空调数据；确定预定数值范围内的空调数据与能耗之间的关系。
在一些实施例中，根据各个维度的空调数据与能耗间的关系调节参数或指导产品设计包括以下至少一项：针对通过调整空调参数能够调整的维度，调整空调参数以降低能耗；或针对工艺类的维度，根据空调数据与能耗间的关系调整工艺设计。
在一些实施例中，空调数据分析方法还包括：指定维度间的预定的耦合关系。
在一些实施例中，空调数据分析方法还包括：遍历指定两个以上维度间的演算耦合关系；获取基于演算耦合关系耦合的空调数据与能耗间的关系；若关系的相关度大于预定相关门限，则利用演算耦合关系更新预定的耦合关系。
根据本公开的另一些实施例的一个方面，提出一种空调数据分析装置，包括：数据获取单元，被配置为获取空调数据信息，空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种；数据拆解单元，被配置为通过语义分析拆解空调数据信息，获取多个维度的空调数据；关系确定单元，被配置为确定各个维度的空调数据与能耗间的关系；调整单元，被配置为根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
在一些实施例中，空调数据分析装置还包括：维度数据耦合单元，被配置为将指定相关维度的空调数据耦合；关系确定单元被配置为：确定耦合的归属不同维度的空调数据与能耗间的关系。
在一些实施例中，空调数据分析装置还包括：数据块划分单元，被配置为将空调数据信息划分数据块，数据块中包括相关联的多个维度的空调数据；数据块提取单元，被配置为确定指定相关维度的空调数据各自归属的数据块；数据块数据耦合单元，被配置为将归属的数据块的空调数据耦合；关系确定单元被配置为：确定耦合的归属不同数据块的空调数据与能耗间的关系。
在一些实施例中，关系确定单元被配置为：提取预定数值范围内的空调数据；确定预定数值范围内的空调数据与能耗之间的关系。
在一些实施例中，调整单元被配置为执行以下至少一项：针对通过调整空调参数能够调整的维度，调整空调参数以降低能耗；或针对工艺类的维度，根据空调数据与能耗间的关系调整工艺设计。
在一些实施例中，空调数据分析装置还包括：耦合关系确定单元，被配置为指定维度间的预定的耦合关系。
在一些实施例中，耦合关系确定单元还被配置为：遍历指定两个以上维度间的空 调数据的演算耦合关系；判断基于演算耦合关系耦合的空调数据与能耗间的关系的相关度是否大于预定相关门限；在关系的相关度大于预定相关门限的情况下，利用演算耦合关系更新预定的空调数据的耦合关系。
根据本公开的又一些实施例的一个方面，提出一种空调数据分析装置，包括：存储器；以及耦接至存储器的处理器，处理器被配置为基于存储在存储器的指令执行上文中任意一种空调数据分析方法。
根据本公开的再一些实施例的一个方面，提出一种计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现上文中任意一种空调数据分析方法的步骤。
此处所说明的附图用来提供对本公开的进一步理解，构成本公开的一部分，本公开的示意性实施例及其说明用于解释本公开，并不构成对本公开的不当限定。在附图中：
图1为本公开的空调数据分析方法的一些实施例的流程图。
图2为本公开的空调数据分析方法的另一些实施例的流程图。
图3为本公开的空调数据采集的一些实施例的示意图。
图4为本公开的空调数据分析方法的又一些实施例的流程图。
图5为本公开的空调数据分析方法的另一些实施例的示意图。
图6为本公开的空调数据分析方法的又一些实施例的示意图。
图7为本公开的空调数据分析方法的再一些实施例的流程图。
图8为本公开的空调数据分析装置的一些实施例的示意图。
图9为本公开的空调数据分析装置的另一些实施例的示意图。
图10为本公开的空调数据分析装置的又一些实施例的示意图。
下面通过附图和实施例，对本公开的技术方案做进一步的详细描述。
在目前的空调市场中，应运而生的变频空调、水冷等等空调，在一定程度上能够从耗材、功率等方面进行一定的节能措施，也能达到一定的效果，但这种效果是在设备本身内部耦合关联进行开展，而实际上产生空调能耗的元素是多维度的，很多外部 原因均会导致空调能耗的增加。
随着空调DTU(Data Transfer unit，数据传输单元)的标配化，空调的各级运行数据源源不断的采集，海量的空调数据慢慢的形成规模，包含着各种珍贵的数据，然而空调大数据相对杂乱多，难以形成有效应用。
本公开的空调数据分析方法的一些实施例的流程图如图1所示。
在步骤101中，获取空调数据信息，空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种。在一些实施例中，对空调实时上报或探测空调情况所得到的数据进行统计，或通过网络收集或采集人工上报的信息，如通过空调探测和实时上报的信息获取空调运行状态，通过网络采集各个时刻的天气情况，采集人工上报的故障情况数据等。
在步骤102中，通过语义分析拆解空调数据信息，获取多个维度的空调数据。在一些实施例中，对采集的空调数据的元素属性进行语义区分。在一些实施例中，预存不同维度数据的特征，通过特征匹配的方式确定空调数据所属的维度。在一些实施例中，拆解的维度包括温度维度、湿度维度、开机时间维度、地址维度、天气维度、运行周期维度等。
在步骤103中，确定各个维度的空调数据与能耗间的关系。在一些实施例中，通过机器学习算法推演各个维度的空调数据的变化趋势与能耗变化趋势之间的关系，确定各个维度数据与能耗变化的相关度，以及确定在什么情况下能够降低能耗。
在步骤104中，根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
通过这样的方法，能够获取并拆解空调数据信息，并得到各个维度的空调数据与能耗之间的关系，进而调节空调参数和指导产品设计，从而充分利用空调数据从参数调节和设备本身的设计方面降低能耗，提高产品节能效果。
本公开的空调数据分析方法的另一些实施例的流程图如图2所示。
在步骤201中，获取空调数据信息。
在步骤202中，通过语义分析拆解空调数据信息，获取多个维度的空调数据。在一些实施例中，以空调工程或者空调机组为单位，先按照单位进行，进而形成点面数据。
在步骤203中，将指定相关维度的空调数据耦合。在一些实施例中，预先指定需要耦合的维度，如将室内温度和室外温度耦合、温度与湿度耦合等。
在步骤204中，确定耦合的归属不同维度的空调数据与能耗间的关系。在一些实施例中，预先配置资源模型，获取耦合后的空调数据与能耗间的线性、非线性关系。例如，若将两个维度的数据耦合，则确定二维数据与能耗的线性、非线性关系，如将能耗与耦合的二维数据形成三维数据，确定在三维空间的点云，进而绘制点云数据得到耦合后的空调数据与能耗的关系。
在一些实施例中，耦合的方法除了将两个维度的数据融合形成二维数据，还包括采用预定算法，如将数据归一化处理后计算均值，或计算平方和等。
在一些实施例中，采用可视化图形的方式显示不同维度数据与空调能耗间的关系，从而使关系直观，便于有效利用。
在一些实施例中，通过统计分析、支持向量机等线性算法进行演算，通过线性算法挖掘空调数据与能耗的关系。
在步骤205中，根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
通过这样的方法，能够将不同维度的数据耦合后分析与能耗的关系，利用常识和经验指定部分数据相关联，便于挖掘更多的影响能耗的因素、规律，提高对空调数据的利用率，进一步提高节能效果。
在一些实施例中，将空调数据信息分为多种数据块，如图3所示，包括基本信息、环境信息、设定信息等数据块，在数据存储时以空调工程或机组为单位存储和调用各个数据块、各个数据块中不同维度的数据，方便数据的调用。
本公开的空调数据分析方法的又一些实施例的流程图如图4所示。
在步骤401中，获取空调数据信息。
在步骤402中，通过语义分析拆解空调数据信息，获取多个维度的空调数据。
在步骤403中，将空调数据信息划分数据块，数据块中包括相关联的多个维度的空调数据。
在步骤404中，确定指定相关维度的空调数据各自归属的数据块。
在步骤405中，将归属的数据块的空调数据耦合。
如图5所示，例如需要耦合环境温度与设备开机累计时间，该两维度属于设定信息、环境信息、运行数据等数据块，再将数据块内定义的相关参数：设定温度、室内温度、室外温度、运行周期等各个维度数据进行关联耦合。
在一些实施例中，预先设定不同的数据模型，根据热点提前进行数据耦合。
在一些实施例中，由于不同型号的空调数据区间和数据规律不同，因此能够根据需要分析的空调型号，提取属于该机型的数据范围内的数据，从而降低运算量，也使运算结果有针对性。
在步骤406中，确定耦合的归属不同数据块的空调数据与能耗间的关系。在一些实施例中，如图5所示，基于演算出的能耗关系能够输出可视化数据图形。
在步骤407中，将空调数据与能耗的关系按照节能实施手段分类。在一些实施例中，由于能够影响到空调数据的因素多种多样，如设定参数能够通过修改设置调整，部分环境参数能够通过更新安装环境调整，以及设备型号不同影响的参数需要更改设备设计。将空调数据与能耗的关系按照节能实施手段进行归纳分类。如图6所示，基于挖掘的空调数据与能耗的关系实现有效应用。
若属于空调运行参数设定的范围，则将该能耗规律标签化为空调运行设定类，根据能耗规律，生成适宜的节能的运行参数设定，作为节能措施推送给空调，执行步骤408。
若无法通过控制空调完成，则标签化为工艺设计类，推送至设计员，执行步骤409。
在步骤408中，针对通过调整空调参数能够调整的维度，调整空调参数以降低能耗。
在步骤409中，针对工艺类的维度，根据空调数据与能耗间的关系调整工艺设计。例如在多联机中，单内机和多内机使用，其能耗功率相同，单内机运行存在功率过剩、资源浪费，设计员对此进行相关节能设计：内机工作数量不同，其外机功率也应有所不同。通过改进减少空调运行过程中的不必要的能耗，减少资源浪费。
通过这样的方法，能够基于指定相关维度确定其归属的数据块，进一步得到归属的数据块中各个维度数据相耦合后与能耗之间的关系，从而进一步挖掘更多的影响能耗的因素、规律，提高对空调数据的利用率，提高节能效果。
在一些实施例中，指定相关维度能够通过预先配置，从而便于实现定向分析。在另一些实施例中，为了便于挖掘隐藏的关系，随机指定或遍历指定不同维度的数据耦合，从而提高关联关系的挖掘概率。
本公开的空调数据分析方法的再一些实施例的流程图如图7所示。
在步骤701中，遍历指定两个以上维度间的空调数据的演算耦合关系。
在步骤702中，获取基于演算耦合关系耦合的空调数据与能耗间的关系。在一些 实施例中，基于演算耦合关系执行如图2或图4所示实施例中将不同维度的数据相关耦合，或将不同维度的数据归属的数据块的数据相耦合。
在步骤703中，基于预定算法计算空调数据与能耗间的相关度，判断空调数据与能耗间关系的相关度是否大于预定相关门限。若大于预定门限，则执行步骤704；若不大于预定门限，则抛弃当前的演算耦合关系。
在步骤704中，利用演算耦合关系更新预定的空调数据的耦合关系，以便在之后的空调数据挖掘中利用更新后的耦合关系处理空调数据。
通过这样的方法，能够挖掘出隐藏的数据间的关联关系，进一步提高节能的可能性。
在一些实施例中，上文中所述的空调数据分析方法能够提高空调的节能性能，因此也可以称为空调节能处理方法。
本公开的空调数据分析装置的一些实施例的示意图如图8所示。数据获取单元801能够获取空调数据信息，空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种。
数据拆解单元802能够通过语义分析拆解空调数据信息，获取多个维度的空调数据。在一些实施例中，对采集的空调数据的元素属性进行语义区分。在一些实施例中，预存不同维度数据的特征，通过特征匹配的方式确定空调数据所属的维度。
关系确定单元803能够确定各个维度的空调数据与能耗间的关系。在一些实施例中，通过机器学习算法推演各个维度的空调数据的变化趋势与能耗变化趋势之间的关系，确定各个维度数据与能耗变化的相关度，以及确定在什么情况下能够降低能耗。
调整单元804能够根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
这样的空调数据分析装置能够获取并拆解空调数据信息，并得到各个维度的空调数据与能耗之间的关系，进而调节空调参数和指导产品设计，从而充分利用空调数据从参数调节和设备本身的设计方面降低能耗，提高产品节能效果。
在一些实施例中，如图8所示，空调数据分析装置还包括维度数据耦合单元805，能够将指定相关维度的空调数据耦合。在一些实施例中，预先指定需要耦合的维度，如将室内温度和室外温度耦合、温度与湿度耦合等。
关系确定单元803能够确定耦合的归属不同维度的空调数据与能耗间的关系。在一些实施例中，预先配置资源模型，获取耦合后的空调数据与能耗间的线性、非线性 关系。
这样的装置能够将不同维度的数据耦合后分析与能耗的关系，利用常识和经验指定部分数据相关联，便于挖掘更多的影响能耗的因素、规律，提高对空调数据的利用率，进一步提高节能效果。
在一些实施例中，如图8所示，空调数据分析装置还包括数据块划分单元806、数据块提取单元807和数据块数据耦合单元808。数据块划分单元805能够将空调数据信息划分数据块，数据块中包括相关联的多个维度的空调数据。数据块提取单元806能够确定指定相关维度的空调数据各自归属的数据块。数据块数据耦合单元807能够将归属的数据块的空调数据耦合。
这样的空调数据分析装置能够基于指定相关维度确定其归属的数据块，进一步得到归属的数据块中各个维度数据相耦合后与能耗之间的关系，从而进一步挖掘更多的影响能耗的因素、规律，提高对空调数据的利用率，提高节能效果。
在一些实施例中，如图8所示，空调数据分析装置还包括耦合关系确定单元809，能够随机指定或遍历指定不同维度的数据耦合，生成演算耦合关系，利用演算耦合关系由维度数据耦合单元804将不同维度的数据相关耦合，由数据块数据耦合单元807将不同维度的数据归属的数据块的数据相耦合。若空调数据与能耗间关系的相关度大于预定相关门限，则用演算耦合关系更新预定的耦合关系。
这样的装置能够挖掘出隐藏的数据间的关联关系，进一步提高提升节能性能的可能性。
本公开空调数据分析装置的一个实施例的结构示意图如图9所示。空调数据分析装置包括存储器901和处理器902。其中：存储器901是磁盘、闪存或其它任何非易失性存储介质。存储器用于存储上文中空调数据分析方法的对应实施例中的指令。处理器902耦接至存储器901，能够作为一个或多个集成电路来实施，例如微处理器或微控制器。该处理器902用于执行存储器中存储的指令，能够充分利用空调数据从参数调节和设备本身的设计方面降低能耗，提高产品节能效果。
在一个实施例中，如图10所示，空调数据分析装置1000包括存储器1001和处理器1002。处理器1002通过BUS总线1003耦合至存储器1001。该空调数据分析装置1000还能够通过存储接口1004连接至外部存储装置1005以便调用外部数据，还能够通过网络接口1006连接至网络或者另外一台计算机系统(未标出)。此处不再进行详细介绍。
在该实施例中，通过存储器存储数据指令，再通过处理器处理上述指令，能够充分利用空调数据从参数调节和设备本身的设计方面降低能耗，提高产品节能效果。
在一些实施例中，上文中所述的空调数据分析装置能够提高空调的节能性能，因此也可以称为空调节能处理装置。
在另一个实施例中，一种计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现空调数据分析方法对应实施例中的方法的步骤。本领域内的技术人员应明白，本公开的实施例可提供为方法、装置、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用非瞬时性存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开是参照根据本公开实施例的方法、设备(系统)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
至此，已经详细描述了本公开。为了避免遮蔽本公开的构思，没有描述本领域所公知的一些细节。本领域技术人员根据上面的描述，完全能够明白如何实施这里公开的技术方案。
本公开的方法以及装置能够以许多方式实现。例如，通过软件、硬件、固件或者软件、硬件、固件的任何组合来实现本公开的方法以及装置。用于所述方法的步骤的上述顺序仅是为了进行说明，本公开的方法的步骤不限于以上具体描述的顺序，除非以其它方式特别说明。此外，在一些实施例中，将本公开实施为记录在记录介质中的程序，这些程序包括用于实现根据本公开的方法的机器可读指令。因而，本公开还覆盖存储用于执行根据本公开的方法的程序的记录介质。
最后应当说明的是：以上实施例仅用以说明本公开的技术方案而非对其限制；尽管参照较佳实施例对本公开进行了详细的说明，所属领域的普通技术人员应当理解：依然能够对本公开的具体实施方式进行修改或者对部分技术特征进行等同替换；而不脱离本公开技术方案的精神，其均应涵盖在本公开请求保护的技术方案范围当中。
Claims (16)
- 一种空调数据分析方法，包括：获取空调数据信息，所述空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种；通过语义分析拆解所述空调数据信息，获取多个维度的空调数据；确定各个维度的空调数据与能耗间的关系；根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
- 根据权利要求1所述的方法，还包括：将指定相关维度的空调数据耦合；所述确定各个维度的空调数据与能耗间的关系包括：确定耦合的归属不同维度的空调数据与能耗间的关系。
- 根据权利要求2所述的方法，还包括：将空调数据信息划分数据块，所述数据块中包括相关联的多个维度的空调数据；确定指定相关维度的空调数据各自归属的数据块；将归属的数据块的空调数据耦合；所述确定各个维度的空调数据与能耗间的关系包括：确定耦合的归属不同数据块的空调数据与能耗间的关系。
- 根据权利要求1～3任意一项所述的方法，其中，所述确定各个维度的空调数据与能耗间的关系包括：提取预定数值范围内的所述空调数据；确定所述预定数值范围内的空调数据与能耗之间的关系。
- 根据权利要求1所述的方法，其中，所述根据各个维度的空调数据与能耗间的关系调节参数或指导产品设计包括以下至少一项：针对通过调整空调参数能够调整的维度，调整空调参数以降低能耗；或针对工艺类的维度，根据空调数据与能耗间的关系调整工艺设计。
- 根据权利要求2或3所述的方法，还包括：指定维度间的预定的空调数据的耦合关系。
- 根据权利要求6所述的方法，还包括：遍历指定两个以上维度间的空调数据的演算耦合关系；获取基于所述演算耦合关系耦合的空调数据与能耗间的关系；若关系的相关度大于预定相关门限，则利用所述演算耦合关系更新预定的空调数据的耦合关系。
- 一种空调数据分析装置，包括：数据获取单元，被配置为获取空调数据信息，所述空调数据信息包括设备信息、设定信息、环境信息、状态信息或故障信息中的一种或多种；数据拆解单元，被配置为通过语义分析拆解所述空调数据信息，获取多个维度的空调数据；关系确定单元，被配置为确定各个维度的空调数据与能耗间的关系；调整单元，被配置为根据各个维度的空调数据与能耗间的关系调节空调参数或指导产品设计，以降低能耗。
- 根据权利要求8所述的装置，还包括：维度数据耦合单元，被配置为将指定相关维度的空调数据耦合；所述关系确定单元被配置为：确定耦合的归属不同维度的空调数据与能耗间的关系。
- 根据权利要求9所述的装置，还包括：数据块划分单元，被配置为将空调数据信息划分数据块，所述数据块中包括相关联的多个维度的空调数据；数据块提取单元，被配置为确定指定相关维度的空调数据各自归属的数据块；数据块数据耦合单元，被配置为将归属的数据块的空调数据耦合；所述关系确定单元被配置为：确定耦合的归属不同数据块的空调数据与能耗间的关系。
- 根据权利要求8～10任意一项所述的装置，其中，所述关系确定单元被配置为：提取预定数值范围内的所述空调数据；确定所述预定数值范围内的空调数据与能耗之间的关系。
- 根据权利要求8所述的装置，其中，所述调整单元被配置为执行以下至少一项：针对通过调整空调参数能够调整的维度，调整空调参数以降低能耗；或针对工艺类的维度，根据空调数据与能耗间的关系调整工艺设计。
- 根据权利要求9或10所述的装置，还包括：耦合关系确定单元，被配置为指定维度间的预定的空调数据的耦合关系。
- 根据权利要求13所述的装置，其中，所述耦合关系确定单元还被配置为：遍历指定两个以上维度间的空调数据的演算耦合关系；判断基于所述演算耦合关系耦合的空调数据与能耗间的关系的相关度是否大于预定相关门限；在关系的相关度大于预定相关门限的情况下，利用所述演算耦合关系更新预定的空调数据的耦合关系。
- 一种空调数据分析装置，包括：存储器；以及耦接至所述存储器的处理器，所述处理器被配置为基于存储在所述存储器的指令执行如权利要求1至7任一项所述的方法。
- 一种计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现权利要求1至7任意一项所述的方法的步骤。
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