CN116608549A - Intelligent detection method for energy-saving efficiency of heating ventilation air conditioner - Google Patents
Intelligent detection method for energy-saving efficiency of heating ventilation air conditioner 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
- F24F11/46—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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- 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
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Abstract
The invention relates to the technical field of air conditioning, and provides an intelligent detection method for energy saving efficiency of a heating ventilation air conditioner, which comprises the following steps of obtaining multiple groups of monitoring data of the heating ventilation air conditioner system during operation; evaluating the difference between the multiple groups of monitoring data and the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system; and judging the air conditioner energy saving efficiency of the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data according to the difference evaluation result. According to the invention, through analyzing each data in the working process of the heating ventilation air conditioner, the intelligent detection of the energy-saving efficiency of the heating ventilation air conditioner is realized, so that a reference is provided for troubleshooting of the heating ventilation air conditioner, and when the heating ventilation air conditioner has a fault which cannot be directly perceived, a maintainer is timely reminded, and the high-efficiency operation of the heating ventilation air conditioner is ensured.
Description
Technical Field
The invention relates to the technical field of air conditioning, in particular to an intelligent detection method for energy-saving efficiency of a heating ventilation air conditioner.
Background
Heating ventilation air conditioning refers to a system or related equipment in a room or a vehicle that is responsible for heating, ventilation, and air conditioning. The heating ventilation air conditioning system can control the temperature and the humidity of air, improves the indoor comfort level, is an important facility in medium-and large-sized industrial buildings or office buildings (such as skyscrapers), and generally has the energy consumption accounting for 50% -60% of the total energy consumption of the office buildings. In order to meet the requirement of reasonably controlling the temperature and humidity of air, a heating ventilation air conditioning system in an office building is complex, equipment is numerous, and hundreds of faults are easy to occur. When a fault occurs, the heating ventilation air conditioning system can not reasonably control the indoor temperature and humidity, reduce the energy saving efficiency of the heating ventilation air conditioning, waste energy, shorten the service life of equipment, fire and other problems, so that the fault of the heating ventilation air conditioning system needs to be monitored and removed in time. In the actual fault monitoring and removing process, due to the problems of various faults, too scattered and disordered equipment and the like, each fault can not be detected, so that a fault alarm or a situation that the heating ventilation air conditioner can not normally operate can be frequently detected, and a user and a fault maintenance person can only find the fault and repair the fault. When a fault still capable of keeping the air conditioner in an operation state occurs, the operation efficiency and the energy-saving efficiency of the heating ventilation air conditioner can be reduced due to the existence of the fault, so that the purpose of monitoring the fault of the heating ventilation air conditioner can be achieved by detecting the operation and the energy-saving efficiency of the heating ventilation air conditioner, and when the condition that the obvious energy-saving efficiency is suddenly reduced occurs, the fault is timely checked.
The existing heating ventilation air conditioner energy-saving efficiency detection method is mainly effective in energy efficiency standard detection, aerodynamic test bed test and heating ventilation air conditioner simulation analysis technology. The energy efficiency standard detection and the aerodynamic test bed have accurate and fair detection results, can dynamically evaluate the performance of the air conditioner, but require special equipment and sites, and cannot reflect the actual use condition of the heating ventilation air conditioner and the influence of the building environment. The calculation result of the heating ventilation air conditioning simulation analysis technology has higher precision, and can evaluate the energy saving performance under different scenes, but modeling and operation all require a large amount of time and calculation resources, and the precision of model establishment and the accuracy of model parameters have larger influence on the calculation result. Therefore, a method for intelligently detecting the energy-saving efficiency of the heating ventilation air conditioner is needed.
Disclosure of Invention
In order to solve the problems, the invention relates to an intelligent detection method for energy saving efficiency of a heating ventilation air conditioner, which comprises the following steps:
s100: acquiring multiple groups of monitoring data of a heating ventilation air conditioning system during operation;
s200: evaluating the difference between the multiple groups of monitoring data and the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system;
s300: and judging the air conditioner energy saving efficiency of the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data according to the difference evaluation result.
Further, the step S100 specifically includes: corresponding sensors are arranged at different equipment of the heating ventilation air conditioning system with energy saving efficiency to be monitored, and each interval timeAcquiring corresponding kind data once, continuously acquiring +.>The secondary corresponding type data is used as a group of monitoring data; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data.
Further, the method for acquiring the data set of the normal running state of the hvac system in step S200 includes:
when the heating ventilation air conditioning system is in normal operation, each interval timeAcquiring corresponding kind data once, continuously acquiring +.>The sub-corresponding kind of data is used as a group of monitoring data, and the sub-corresponding kind of data is respectively acquired in different working modes in different working periods>The group monitoring data are used as a data set of the normal running state of the heating ventilation air conditioning system; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data.
Further, in step S200, the method for acquiring the data set of the efficient operation state of the hvac system includes: when the heating ventilation air conditioning system is in high-efficiency operation, each interval timeAcquiring corresponding kind data once, continuously acquiring +.>The sub-corresponding kind of data is used as a group of monitoring data, and the sub-corresponding kind of data is respectively acquired in different working modes in different working periods>The group monitoring data are used as a data set of the efficient running state of the heating ventilation air conditioning system; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data.
Further, step S200 is specifically:
determining a working time period and a working mode corresponding to each group of monitoring data to be evaluated;
comparing each group of monitoring data with the data set in the normal running state and the data set in the high-efficiency running state in the same working time period and the data in the same working mode respectively;
and acquiring the difference of each group of monitoring data and corresponding data in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system through a DTW algorithm.
In another preferred embodiment, step S200 specifically includes:
acquiring data values of corresponding types of data in multiple groups of monitoring data；
Acquiring data valuesCorresponding membership->And deviation tolerance +.>；
Analyzing and obtaining the difference influence degree corresponding to each data in multiple groups of monitoring dataWherein the difference affects the degree->The calculation formula of (2) is as follows:
。
further, the data valueCorresponding membership->The acquisition method of (1) comprises the following steps:
equidistantly segmenting the data set of the normal running state of the heating ventilation air conditioning system and the numerical value of each corresponding type of data in the data set of the high-efficiency running state of the heating ventilation air conditioning system to obtain a frequency table, obtaining a frequency distribution histogram and a probability density function according to the frequency table, and obtaining an accumulated probability density function according to the probability density function; acquiring the cumulative probability density as according to the cumulative probability density functionIndex value +.>；
Acquiring data values of corresponding types of data in multiple groups of monitoring dataDetermining a data value +.>Position in probability density function, data value +.>Index value of position and each corresponding type of data in probability density function +.>The absolute value of the difference between them is noted as data value +.>Center distance of>Will->Marked as data value +.>Is defined by a central range of (2);
obtaining data set and data value of normal running state of heating ventilation air conditioning system and high-efficiency running state of heating ventilation air conditioning systemData number for the same kind of data>Data value->Number of data contained in the center rangeAnd data value +.>The number of data contained in the corresponding bin in the frequency distribution histogram +.>The method comprises the steps of carrying out a first treatment on the surface of the Calculating data valuesCorresponding membership->Wherein the membership calculation formula is:
。
further, tolerance to deviationThe acquisition method of (1) comprises the following steps:
obtaining the average value of the data distance corresponding to each corresponding type of data of the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning systemAnd (2) extremely bad->The method comprises the steps of carrying out a first treatment on the surface of the The data distance is the absolute value of the difference value between the numerical value of one data in the group of data and the numerical value of the rest data, and the minimum value is taken as the data distance corresponding to the group of data;
obtaining the extremely bad data of each corresponding type of data set of the normal running state of the heating ventilation air conditioning system and the high-efficiency running state of the heating ventilation air conditioning system；
Acquiring data values of corresponding types of data in multiple groups of monitoring dataCorresponding data distance in each data contained in each corresponding type of data in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system>；
Calculating data valuesCorresponding tolerance to deviation->Wherein the deviation tolerance calculation formula is:
。
further, evaluating the difference between the plurality of sets of monitoring data and the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system comprises the following steps:
obtaining the difference distance of the running indexes of each data value in multiple groups of monitoring dataDistance of difference of running index->The calculation formula of (2) is as follows:
wherein ,data value for each corresponding category of data in the plurality of sets of monitoring data +.>Middle->A data value, wherein,，/>data value for each corresponding category of data in the plurality of sets of monitoring data +.>The total number of medium values; />Data set and data value for normal running state of heating ventilation air conditioning system and high-efficiency running state of heating ventilation air conditioning systemEach data value being the same kind of data, wherein +.>，/>The total number of data values in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system;is->The corresponding differences affect the degree.
Further, the step S300 specifically includes:
each data value contained in a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system is respectively obtained, the running index difference distance between the data value and the other data set is obtained, and the average value of the running index difference distances is taken as a threshold value of the running index difference distance when the heating ventilation air conditioning system is in the normal running state and the high-efficiency running state；
According to the difference distance of the running index between the data set of each data value in each group of monitoring data relative to the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning systemJudging the air conditioner energy saving efficiency of the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data;
wherein when the difference distance between the monitored data and the operation index corresponding to the data set of the normal operation state of the heating ventilation air conditioning system is smaller than or equal to a threshold valueWhen the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a normal running state; when the difference distance between the monitoring data and the operation index corresponding to the data set of the high-efficiency operation state of the heating ventilation air conditioning system is less than or equal to a threshold value +.>When the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a high-efficiency running state; when the difference distance between the monitored data and the running index corresponding to the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system is larger than a threshold value +.>And when the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is considered to be in a low-efficiency running state.
The invention has the beneficial effects that:
according to the invention, through analyzing each data in the working process of the heating ventilation air conditioner, the intelligent detection of the energy-saving efficiency of the heating ventilation air conditioner is realized, so that a reference is provided for troubleshooting of the heating ventilation air conditioner, and when the heating ventilation air conditioner has a fault which cannot be directly perceived, a maintainer is timely reminded, and the high-efficiency operation of the heating ventilation air conditioner is ensured.
Firstly, acquiring membership degrees corresponding to data to be monitored according to the concentration degree of numerical distribution of each monitored data and the overall distribution condition of the data, and comparing the data with densely distributed positions of the data when the operation state of the heating ventilation air conditioner is good; then, the difference influence degree corresponding to the data is obtained according to the normal deviation range of each kind of data and the deviation degree of the data value and the actual value, so that the problem that non-abnormal data with larger difference degree, which is caused by different difference degrees and different dimensions of different kinds of data, are misjudged to be abnormal data and abnormal data with smaller difference degree are misjudged to be non-abnormal data is solved; and finally, obtaining the deviation tolerance corresponding to each data value according to the analysis, and obtaining the data difference evaluation of the air conditioner in the actual running state and the good running state according to the deviation tolerance, thereby improving the accuracy of the detection of the energy-saving efficiency of the heating ventilation air conditioner.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent detection method for energy saving efficiency of a heating ventilation air conditioner;
fig. 2 is a schematic diagram of a central range of data values of each corresponding type of data in the plurality of sets of monitoring data.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1, an embodiment of the present invention provides an intelligent detection method for energy saving efficiency of a heating ventilation air conditioner, which includes the following steps:
s100: acquiring multiple groups of monitoring data of a heating ventilation air conditioning system during operation;
specifically, corresponding sensors are arranged at different devices of the heating, ventilation and air conditioning system needing to monitor the energy saving efficiency to acquire corresponding kinds of data, and the data needing to be acquired include, but are not limited to, temperature data, humidity data, pressure data, flow data, energy data and the like. The temperature data comprise the indoor and outdoor temperature, the return air inlet temperature, the air supply outlet temperature and the like of the air conditioner, and can reflect the refrigerating and heating effects of the air conditioner system; the humidity data comprise the indoor and outdoor relative humidity, the air return port humidity, the air supply port humidity and the like of the air conditioner, and can reflect the dehumidification effect of the air conditioner and the suitability of the indoor air humidity; the pressure data comprise the outlet pressure of the condenser of the air conditioner, the inlet pressure of the evaporator and the like, and can reflect the efficiency and the resistance of the compression and expansion process of the air conditioner; the flow data comprise the flow of the air supply outlet and the return air outlet of the air conditioner, the air ventilation quantity and the like, and can reflect the ventilation effect and the air flow condition of an air conditioning system; the energy data comprise the electricity consumption, the refrigerating capacity, the heating capacity and the like of the air conditioner, and can reflect the energy consumption condition and the energy saving effect of an air conditioning system.
In the embodiment, when the heating ventilation air conditioning system works daily, the time interval isAcquiring corresponding kind data once, continuously acquiring +.>The secondary corresponding type data is used as a group of monitoring data, and the working time period and the working mode of the heating ventilation air conditioner are recorded when the group of monitoring data is acquired, wherein the interval time is ∈ ->Preferably 30s, the number of times data is continuously acquired in each set of monitoring data +.>Preferably 60 times, the working modes comprise ventilation, heating, refrigeration, automation, dehumidification and the like. And repeating the steps to obtain a plurality of groups of detection data, and recording the detection data as a data set C.
S200, evaluating the difference between a plurality of groups of monitoring data and a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system;
before evaluating the difference between a plurality of groups of monitoring data and a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system, collecting data of the heating ventilation air conditioning system in the normal running state and the high-efficiency running state as analysis data;
in the present embodiment, when the hvac system is operating normally, every interval timeAcquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>The group analysis data is used as a data set of the normal running state of the heating ventilation and air conditioning system and is recorded as a data set A. When the heating ventilation air conditioning system is in high-efficiency operation, every interval time is +>Acquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>The group analysis data is used as a data set of the efficient running state of the heating ventilation and air conditioning system and is recorded as a data set B. Wherein the interval is->Preferably 30s, the number of times data is continuously acquired in each set of monitoring data +.>Preferably 60 times, and the amount of data in each data set is preferably 500 sets. By selecting different working modes of different working periods to collect analysis data, different data characteristics of the heating ventilation air conditioning system at different moments in the operation process can be covered, and the comprehensiveness of the analysis data is improved.
In this embodiment, evaluating the difference between the multiple sets of monitoring data and the data set of the normal running state of the hvac system and the data set of the high-efficiency running state of the hvac system specifically includes: determining a working time period and a working mode corresponding to each group of monitoring data to be evaluated; comparing each group of monitoring data with the data set in the normal running state and the data set in the high-efficiency running state in the same working time period and the data in the same working mode respectively; and acquiring the difference of each group of monitoring data and corresponding data in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system through a DTW algorithm.
S300, judging the air conditioner energy saving efficiency of the heating ventilation air conditioning system working period corresponding to each group of monitoring data according to the difference evaluation result;
the DTW algorithm can measure the difference between two sets of data, and when the DTW distance between the two sets of data is larger, the difference between the two sets of data is considered to be larger. In this embodiment, the DTW algorithm is used to analyze corresponding types of data in the same working period and the same working mode, that is, analyze and obtain the difference between the monitored data and the data in the normal operation state and the efficient operation state in the past, when the difference is large, consider that the operation efficiency of the hvac to which the monitored data points is reduced, and when the operation efficiency is reduced repeatedly, consider that the hvac being monitored is abnormal.
Embodiment two:
as shown in fig. 1, an embodiment of the present invention provides an intelligent detection method for energy saving efficiency of a heating ventilation air conditioner, which includes the following steps:
s100: acquiring multiple groups of monitoring data of a heating ventilation air conditioning system during operation;
specifically, corresponding sensors are arranged at different devices of the heating, ventilation and air conditioning system needing to monitor the energy saving efficiency to acquire corresponding kinds of data, and the data needing to be acquired include, but are not limited to, temperature data, humidity data, pressure data, flow data, energy data and the like. The temperature data comprise the indoor and outdoor temperature, the return air inlet temperature, the air supply outlet temperature and the like of the air conditioner, and can reflect the refrigerating and heating effects of the air conditioner system; the humidity data comprise the indoor and outdoor relative humidity, the air return port humidity, the air supply port humidity and the like of the air conditioner, and can reflect the dehumidification effect of the air conditioner and the suitability of the indoor air humidity; the pressure data comprise the outlet pressure of the condenser of the air conditioner, the inlet pressure of the evaporator and the like, and can reflect the efficiency and the resistance of the compression and expansion process of the air conditioner; the flow data comprise the flow of the air supply outlet and the return air outlet of the air conditioner, the air ventilation quantity and the like, and can reflect the ventilation effect and the air flow condition of an air conditioning system; the energy data comprise the electricity consumption, the refrigerating capacity, the heating capacity and the like of the air conditioner, and can reflect the energy consumption condition and the energy saving effect of an air conditioning system.
In the embodiment, when the heating ventilation air conditioning system works daily, the time interval isAcquiring corresponding kind data once, continuously acquiring +.>The secondary corresponding type data is used as a group of monitoring data, and the working time period and the working mode of the heating ventilation air conditioner are recorded when the group of monitoring data is acquired, wherein the interval time is ∈ ->Preferably 30s, the number of times data is continuously acquired in each set of monitoring data +.>Preferably 60 times, the working mode comprises ventilation and heatingRefrigerating, automatic, dehumidifying, etc. And repeating the steps to obtain a plurality of groups of detection data, and recording the detection data as a data set C.
S200, evaluating the difference between a plurality of groups of monitoring data and a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system;
before evaluating the difference between a plurality of groups of monitoring data and a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system, collecting data of the heating ventilation air conditioning system in the normal running state and the high-efficiency running state as analysis data;
in the present embodiment, when the hvac system is operating normally, every interval timeAcquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>The group analysis data is used as a data set of the normal running state of the heating ventilation and air conditioning system and is recorded as a data set A. When the heating ventilation air conditioning system is in high-efficiency operation, every interval time is +>Acquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>The group analysis data is used as a data set of the efficient running state of the heating ventilation and air conditioning system and is recorded as a data set B. Wherein the interval is->Preferably 30s, the number of times data is continuously acquired in each set of monitoring data +.>Preferably 60 times, and the amount of data in each data set is preferably 500 sets. By selecting different working modes of different working periods to collect analysis data, different data characteristics of the heating ventilation air conditioning system at different moments in the operation process can be covered, and the comprehensiveness of the analysis data is improved.
In this embodiment, step S200 further specifically includes:
s201, obtaining data values of corresponding types of data in multiple groups of monitoring data；
The multiple sets of monitoring data obtained in step S100, i.e. data set C, respectively count the data value of each corresponding type of data in data set CSuch as the data value of the indoor and outdoor temperature of the air conditioner and the data value of the temperature of the return air inlet.
S202: acquiring data valuesCorresponding membership->And deviation tolerance +.>；
Data value for each corresponding category of dataThe membership degree of the data set (namely the data set A) relative to the normal running state of the heating ventilation air conditioning system and the data set (namely the data set B) relative to the high-efficiency running state of the heating ventilation air conditioning system are calculated respectivelyAnd respectivelyCalculate its tolerance of deviation from dataset A and dataset B>. For convenience of description, +.>The category data refers to one of the respective corresponding category data, and is expressed by +.>Category data details membership degree +.>And deviation tolerance +.>Is provided.
Wherein the degree of membershipThe acquisition method of (1) comprises the following steps:
will be in data set A or data set BThe numerical value of the category data is subjected to equidistant segmentation to obtain a frequency table, a frequency distribution histogram and a probability density function are obtained according to the frequency table, and an accumulated probability density function is obtained according to the probability density function; acquiring the cumulative probability density as +.>Index value +.>The method comprises the steps of carrying out a first treatment on the surface of the Preferably, the method comprises the steps of,0.5.
Obtaining multiple groups of monitoring dataData value of category data->Determining a data value +.>The position in the probability density function, as shown in FIG. 2, will be data value +.>Position in probability Density function and +.>Index value->The absolute value of the difference between them is noted as data value +.>Center distance of>Will->Marked as data value +.>Is defined by a central range of (2);
calculating data values by means of a membership calculation formulaCorresponding membership->：
wherein For dataset +.>Middle->Data value of category data->Corresponding membership degree; />Is->The number of data contained within the central range of (2); />For the data set analyzed (i.e. data set +.>Or data set->) Middle->Total number of category data; />Is->The number of data included in the bin where the frequency distribution histogram is located.
Tolerance to deviationThe acquisition method of (1) comprises the following steps:
acquisition of data set A or data set BMean value of data distance of all data in category data +.>And (2) extremely bad->The method comprises the steps of carrying out a first treatment on the surface of the The data distance is the absolute value of the difference value between the numerical value of one data in the group of data and the numerical value of the rest data, and the minimum value is taken as the data distance corresponding to the group of data;
acquisition of data set A or data set B, respectivelyExtremely bad of category data>；
Acquisition of data set CData value of category data->Relative to data set A or data set B +.>Data distance ∈of category data>；
Calculating data value by using deviation tolerance calculation formulaCorresponding tolerance to deviation->：
。
S203: analyzing and obtaining the difference influence degree corresponding to each data in multiple groups of monitoring dataWherein the difference affects the degree->The calculation formula of (2) is as follows:
。
acquiring data values through steps S201 and S202Corresponding membership->And deviation tolerance +.>I.e. calculateDifferential influence degree of category data->。
S300, judging the energy-saving efficiency of the air conditioner in the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data according to the difference evaluation result, wherein the energy-saving efficiency comprises the following steps:
obtaining the difference distance of the running indexes of each data value in multiple groups of monitoring dataDistance of difference of running index->The calculation formula of (2) is as follows:
wherein ,for +.>Data value of category data->Middle->A data value, wherein,，/>for +.>Data value of category data->The total number of medium values; />For data set A or data set B and +.>Individual data values of the category data, wherein +.>，/>As in dataset A or dataset BThe total number of data values in the category data; />Is->The corresponding differences affect the degree.
In this embodiment, step S300 specifically further includes:
respectively for data set a and data set bEach data value contained in the data set B is obtained, the difference distance of the data value relative to the running index between the other data sets is obtained, and the average value of the difference distances of the running indexes is taken as the threshold value of the difference distance of the running indexes when the heating ventilation air conditioner is in the normal running state and the high-efficiency running state；
Based on the difference distance of each data value in data set C relative to the running index between data set A and data set BJudging the air conditioner energy saving efficiency of the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data;
wherein when the difference distance between the monitored data and the operation index corresponding to the data set A is less than or equal to the threshold valueWhen the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a normal running state; when the difference distance between the operation indexes of the monitoring data and the data set B is less than or equal to the threshold value +.>When the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a high-efficiency running state; when the difference distances between the monitored data and the running indexes corresponding to the data set A and the data set B are all greater than the threshold value +.>And when the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is considered to be in a low-efficiency running state.
Continuously acquiring new data sets according to the same stepsAnd judging the energy-saving efficiency of the heating ventilation air conditioning system, wherein when three continuous data sets exist>The sum of the Chinese medicines is more than or equal to->When the energy-saving efficiency of the heating ventilation air conditioner corresponding to the data values is in a low-efficiency running state, the energy-saving efficiency of the heating ventilation air conditioner is considered to be problematic, and the heating ventilation air conditioner system needs to be overhauled and removed in time. Preferably, the +>9.
According to the invention, through analyzing each data in the working process of the heating ventilation air conditioner, the intelligent detection of the energy-saving efficiency of the heating ventilation air conditioner is realized, so that a reference is provided for troubleshooting of the heating ventilation air conditioner, and when the heating ventilation air conditioner has a fault which cannot be directly perceived, a maintainer is timely reminded, and the high-efficiency operation of the heating ventilation air conditioner is ensured.
Firstly, acquiring membership degrees corresponding to data to be monitored according to the concentration degree of numerical distribution of each monitored data and the overall distribution condition of the data, and comparing the data with densely distributed positions of the data when the operation state of the heating ventilation air conditioner is good; then, the difference influence degree corresponding to the data is obtained according to the normal deviation range of each kind of data and the deviation degree of the data value and the actual value, so that the problem that non-abnormal data with larger difference degree, which is caused by different difference degrees and different dimensions of different kinds of data, are misjudged to be abnormal data and abnormal data with smaller difference degree are misjudged to be non-abnormal data is solved; and finally, obtaining the deviation tolerance corresponding to each data value according to the analysis, and obtaining the data difference evaluation of the air conditioner in the actual running state and the good running state according to the deviation tolerance, thereby improving the accuracy of the detection of the energy-saving efficiency of the heating ventilation air conditioner.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. An intelligent detection method for energy saving efficiency of a heating ventilation air conditioner is characterized by comprising the following steps:
s100, acquiring multiple groups of monitoring data of a heating ventilation air conditioning system during operation;
s200, evaluating the difference between a plurality of groups of monitoring data and a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system;
s300, judging the air conditioner energy saving efficiency of the heating ventilation air conditioning system working period corresponding to each group of monitoring data according to the difference evaluation result;
the step S100 specifically includes: corresponding sensors are arranged at different equipment of the heating ventilation air conditioning system with energy saving efficiency to be monitored, and the interval time is thatAcquiring corresponding kind data once, continuously acquiring +.>The secondary corresponding type data is used as a group of monitoring data; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data;
the method for acquiring the data set of the normal running state of the heating ventilation air conditioning system in the step S200 comprises the following steps:
when the heating ventilation air conditioning system is in normal operation, each interval timeAcquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>Group analysisThe data are used as a data set of the normal running state of the heating ventilation air conditioning system; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data;
in step S200, the method for acquiring the data set of the efficient operation state of the hvac system includes: when the heating ventilation air conditioning system is in high-efficiency operation, each interval timeAcquiring corresponding kind data once, continuously acquiring +.>Sub-corresponding category data is used as a set of analysis data, and the sub-corresponding category data is respectively acquired in different working modes in different working periods>The group analysis data are used as a data set of the efficient running state of the heating ventilation air conditioning system; the corresponding category data includes, but is not limited to, temperature data, humidity data, pressure data, flow data, and energy data.
2. The intelligent detection method for energy-saving efficiency of heating ventilation air conditioner according to claim 1, which is characterized by comprising the following steps: the step S200 specifically includes:
determining a working time period and a working mode corresponding to each group of monitoring data to be evaluated;
comparing each group of monitoring data with the data set in the normal running state and the data set in the high-efficiency running state in the same working time period and the data in the same working mode respectively;
and acquiring the difference of each group of monitoring data and corresponding data in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system through a DTW algorithm.
3. The intelligent detection method for energy-saving efficiency of heating ventilation air conditioner according to claim 1, which is characterized by comprising the following steps: the step S200 specifically includes:
acquiring multiple setsMonitoring data values of respective corresponding kinds of data in data；
Acquiring data valuesCorresponding membership->And deviation tolerance +.>；
Analyzing and obtaining the difference influence degree corresponding to each data in multiple groups of monitoring dataWherein the difference affects the degree->The calculation formula of (2) is as follows:
。
4. the intelligent detection method for energy saving efficiency of heating ventilation air conditioner according to claim 3, which is characterized by comprising the following steps: data valueCorresponding membership->The acquisition method of (1) comprises the following steps:
equidistant segmentation is carried out on the data set of the normal running state of the heating ventilation air conditioning system and the numerical value of each corresponding type of data in the data set of the high-efficiency running state of the heating ventilation air conditioning system to obtain a frequency table, a frequency distribution histogram and a probability density function are obtained according to the frequency table, and the frequency distribution histogram and the probability density function are obtained according to the probability densityThe function obtains an accumulated probability density function; acquiring the cumulative probability density as according to the cumulative probability density functionIndex value +.>；
Acquiring data values of corresponding types of data in multiple groups of monitoring dataDetermining a data value +.>Position in probability density function, data value +.>Index value of position and each corresponding type of data in probability density function +.>The absolute value of the difference between them is noted as data value +.>Center distance of>Will->Marked as data value +.>Is defined by a central range of (2);
obtaining data set and data value of normal running state of heating ventilation air conditioning system and high-efficiency running state of heating ventilation air conditioning systemData number for the same kind of data>Data value->The number of data contained in the central area +.>And data value +.>The number of data contained in the corresponding bin in the frequency distribution histogram +.>The method comprises the steps of carrying out a first treatment on the surface of the Calculating data value +.>Corresponding membership->Wherein the membership calculation formula is:
。
5. the intelligent detection method for energy-saving efficiency of heating ventilation air conditioner according to claim 4 is characterized in that: tolerance to deviationThe acquisition method of (1) comprises the following steps:
obtaining the average value of the data distance corresponding to each corresponding type of data of the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning systemAnd (2) extremely bad->The method comprises the steps of carrying out a first treatment on the surface of the The data distance is the absolute value of the difference value between the numerical value of one data in the group of data and the numerical value of the rest data, and the minimum value is taken as the data distance corresponding to the group of data;
obtaining the extremely bad data of each corresponding type of data set of the normal running state of the heating ventilation air conditioning system and the high-efficiency running state of the heating ventilation air conditioning system；
Acquiring data values of corresponding types of data in multiple groups of monitoring dataCorresponding data distance in each data contained in each corresponding type of data in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system>；
Calculating data valuesCorresponding tolerance to deviation->Wherein the deviation tolerance calculation formula is:
。
6. the intelligent detection method for energy-saving efficiency of heating ventilation air conditioner according to claim 5, which is characterized by comprising the following steps: step S200 further includes:
operation for obtaining each data value in multiple groups of monitoring dataLine index difference distanceDistance of difference of running index->The calculation formula of (2) is as follows:
wherein ,data value for each corresponding category of data in the plurality of sets of monitoring data +.>Middle->A data value, wherein,，/>data value for each corresponding category of data in the plurality of sets of monitoring data +.>The total number of medium values; />Data set and data value for normal running state of heating ventilation air conditioning system and high-efficiency running state of heating ventilation air conditioning systemEach data value being the same kind of data, wherein +.>，/>The total number of data values in the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system;is->The corresponding differences affect the degree.
7. The intelligent detection method for energy-saving efficiency of heating ventilation air conditioner according to claim 6, which is characterized in that: the step S300 specifically includes:
each data value contained in a data set of a normal running state of the heating ventilation air conditioning system and a data set of a high-efficiency running state of the heating ventilation air conditioning system is respectively obtained, the running index difference distance between the data value and the other data set is obtained, and the average value of the running index difference distances is taken as a threshold value of the running index difference distance when the heating ventilation air conditioning system is in the normal running state and the high-efficiency running state；
According to the difference distance of the running index between the data set of each data value in each group of monitoring data relative to the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning systemJudging the air conditioner energy saving efficiency of the working period of the heating ventilation air conditioning system corresponding to each group of monitoring data;
wherein when the difference distance between the monitored data and the operation index corresponding to the data set of the normal operation state of the heating ventilation air conditioning system is smaller than or equal to a threshold valueWhen the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a normal running state; when the difference distance between the monitoring data and the operation index corresponding to the data set of the high-efficiency operation state of the heating ventilation air conditioning system is less than or equal to a threshold value +.>When the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is in a high-efficiency running state; when the difference distance between the monitored data and the running index corresponding to the data set of the normal running state of the heating ventilation air conditioning system and the data set of the high-efficiency running state of the heating ventilation air conditioning system is larger than a threshold value +.>And when the energy-saving efficiency of the heating ventilation air conditioner corresponding to the monitoring data is considered to be in a low-efficiency running state.
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