CN117560015A - Efficient processing system for air conditioner installation engineering data - Google Patents
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Abstract
The invention relates to the technical field of efficient data processing, in particular to an efficient data processing system for an air conditioner installation project, which comprises the following components: the first acquisition module, the second acquisition module and the third acquisition module take the first data point and the last data point on the time sequence data sequence of each parameter as separation points, take the data point corresponding to the maximum value of the distances from the data points to the approximate line segments of the two adjacent separation points as determination separation points, adjust the distances from each data point to the approximate line segments according to the distribution condition of the distances from the plurality of adjacent data points to the approximate line segments of each data point, screen the plurality of separation points from all the data points between the two adjacent separation points according to the adjusted distances of each data point, and take all the separation points obtained by multiple iterations as compression processing results of the time sequence data sequence of each parameter. The invention improves the compression processing efficiency of the DP algorithm on the time sequence data sequence of the parameters and realizes the efficient compression processing of the air conditioner installation engineering data.
Description
Technical Field
The invention relates to the technical field of efficient data processing, in particular to an efficient data processing system for an air conditioner installation project.
Background
In order to ensure that the air conditioner can provide optimal performance and comfort in different installation scenes, test running and debugging are required in the air conditioner installation engineering; in the debugging stage, the test operation data of various parameters are analyzed through the debugging equipment to obtain the air conditioner operation state, technicians adjust the various parameters according to the air conditioner operation state so as to ensure that the installed air conditioner can reach the optimal working state, the accuracy of parameter debugging depends on the data volume of the test operation data, and therefore, in the test operation stage, a large amount of test operation data of various parameters are required to be acquired; because the sensor for collecting the test run data has a simple structure, and the storage space and the battery power are limited, the test run data of various parameters need to be compressed efficiently in order to collect and store more test run data as much as possible.
The Targelas-Prak algorithm (DP algorithm) is a lossy compression method, and the algorithm screens a plurality of division points from all data, so that the compression effect is good as the compression result of all data, but in the screening process, the conventional DP algorithm only screens one division point at a time, so that the compression processing efficiency of the parameter test run data by the conventional DP algorithm is low.
In view of the foregoing, there is a need for a system for efficiently compressing air conditioner installation engineering data.
Disclosure of Invention
In order to solve the above problems, the present invention provides an air conditioner installation engineering data efficient processing system, the system comprising:
the first acquisition module is used for acquiring test operation data of various air conditioning parameters at each moment, wherein the air conditioning parameters comprise pressure parameters, wind speed parameters, temperature parameters and humidity parameters, the test operation data of each air conditioning parameter at all moments form a time sequence data sequence of each air conditioning parameter, and a plurality of data points are distributed on the time sequence data sequence;
the second acquisition module is used for taking a first data point and a last data point on the time sequence data sequence of each parameter as separation points; screening a plurality of segmentation points from all data points between two adjacent segmentation points, including: taking a connecting line between two adjacent dividing points as an approximate line segment, and taking a data point corresponding to the maximum value of the distance between the data point and the approximate line segment as a determining dividing point if the maximum value of the distance between the data point and the approximate line segment is larger than or equal to a preset error distance threshold value; according to the distribution condition of the distances from a plurality of adjacent data points to the approximate line segment, the distances from each data point to the approximate line segment are adjusted, and according to the adjusted distances of each data point and the size relation between the distances from the dividing points to the approximate line segment, a plurality of dividing points are screened from all the data points between the two adjacent dividing points;
And the third acquisition module is used for iteratively screening a plurality of segmentation points from all data points between every two adjacent segmentation points on the time sequence data sequence of each parameter until the maximum value of the distance from the data point between every two adjacent segmentation points to the approximate line segment on the time sequence data sequence of each parameter is smaller than a preset error distance threshold value, stopping iteration, and taking all the obtained segmentation points as compression processing results of the time sequence data sequence of each parameter.
In an embodiment of the present application, the adjusting the distance between each data point and the approximate line segment according to the distribution of the distances between the adjacent data points and the approximate line segment, and screening the plurality of division points from all the data points between the two adjacent division points according to the adjusted distance between each data point and the size relationship between the determined division point and the approximate line segment, includes:
between two adjacent dividing points, taking any data point except the dividing point as a target data point, and determining the possibility that the target data point belongs to the dividing point according to the difference between the corresponding time of the target data point and the dividing point, the distance between the target data point and the approximate line segment and the change trend of the distance between the adjacent data point of the target data point and the approximate line segment;
Similarly, the probability that all data points between two adjacent segmentation points belong to the segmentation points is determined;
taking the data points with the possibility of being more than a preset threshold Y among all the data points between two adjacent dividing points as the dividing points to be determined;
respectively carrying out linear fitting on distances from the dividing points to the approximate line segments to be determined on a plurality of data points adjacent to the left side and a plurality of data points adjacent to the right side of each dividing point to be determined, obtaining two distance fitting results, and determining a distance adjustment coefficient of each dividing point to be determined according to the difference of the two distance fitting results and the possibility that the dividing point to be determined belongs to the dividing point;
adjusting the distance from the dividing point to be determined to the approximate line segment according to the distance adjustment coefficient to obtain the adjusted distance of the dividing point to be determined;
and the data points with the adjusted distance being larger than or equal to the distance from the determined dividing point to the approximate line segment and the determined dividing point are used as dividing points.
In one embodiment of the present application, the determining the likelihood that the target data point belongs to the division point according to the difference between the corresponding moments of the target data point and the determination division point, the distance between the target data point and the approximate line segment, and the change trend of the distance between the adjacent data points of the target data point and the approximate line segment includes:
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In the method, in the process of the invention,representing the likelihood that the target data point belongs to a partition point,/->、/>、/>Representing the distance from the left adjacent data point of the target data point, the target data point and the right adjacent data point of the target data point to the approximate line segment respectively, +.>Representing the difference between the target data point and the moment corresponding to the determined segmentation point,/->Representing the difference between the corresponding moments of the target data point and the adjacent division points of the target data point, +.>Representing the difference between adjacent segmentation points of the target data point and the moment corresponding to the determined segmentation point,representing determining the distance of the division point to the approximate line segment,/->Representing a discriminant function, when->In the time-course of which the first and second contact surfaces,when->When (I)>。
In one embodiment of the present application, the method for acquiring the adjacent division points of the target data point includes:
and recording the absolute value of the difference between the corresponding moments of the target data point and the two dividing points as the difference between the corresponding moments of the target data point and the two dividing points, and taking the dividing point corresponding to the minimum value of the difference between the corresponding moments as the adjacent dividing point of the target data point.
In an embodiment of the present application, the determining, by performing linear fitting on distances from the to-be-determined partition point to the approximate line segment for the plurality of data points adjacent to the left side and the plurality of data points adjacent to the right side of each partition point to be determined, respectively, obtains two distance fitting results, and determines a distance adjustment coefficient of each partition point to be determined according to a difference between the two distance fitting results and a probability that the partition point to be determined belongs to the partition point, where the determining includes:
A left adjacent interval of each to-be-determined partition point is formed by A adjacent data points on the left side of each to-be-determined partition point; a right adjacent interval of each to-be-determined dividing point is formed by adjacent A data points on the right side of each to-be-determined dividing point, wherein A represents preset quantity; respectively carrying out linear fitting on the distances from all the data points in the left adjacent section and the right adjacent section of each to-be-determined dividing point to the approximate line segments, respectively obtaining distance fitting results of the left adjacent section and the right adjacent section of each to-be-determined dividing point, wherein the distance fitting results of the left adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the left adjacent section of each to-be-determined dividing point, and the distance fitting results of the right adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the right adjacent section of each to-be-determined dividing point;
and adjusting the possibility that the partition point to be determined belongs to the partition point according to the difference of the distance fitting results of the left adjacent interval and the right adjacent interval of each partition point to be determined, determining the possibility that the partition point to be determined belongs to the partition point after adjustment, and determining the distance adjustment coefficient of each partition point to be determined according to the possibility that the partition point to be determined belongs to the partition point after adjustment.
In an embodiment of the present application, the adjusting the probability that the partition point to be determined belongs to the partition point according to the difference of the distance fitting results between the left adjacent section and the right adjacent section of each partition point to be determined, and determining the probability that the partition point to be determined belongs to the partition point after adjustment includes:
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in the method, in the process of the invention,representing the probability of belonging to the division point after adjustment of each division point to be determined, +.>Error representing distance fitting result of left adjacent section of each division point to be determined, +.>Error representing distance fitting result of right adjacent section of each division point to be determined, +.>Slope of distance fitting result representing left adjacent section of each division point to be determined, +.>Slope of distance fitting result representing right adjacent section of each division point to be determined, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Representing the likelihood that each partition point to be determined belongs to the partition point.
In an embodiment of the present application, the method for obtaining an error of a distance fitting result of a left adjacent section of each to-be-determined partition point includes:
taking the absolute value of the difference value of the distance fitting value between each data point in the left adjacent interval of each to-be-determined dividing point and each data point as the fitting error of each data point in the left adjacent interval of each to-be-determined dividing point, and taking the sum of the fitting errors of all data points in the left adjacent interval of each to-be-determined dividing point as the error of the distance fitting result of the left adjacent interval of each to-be-determined dividing point;
And so on, obtaining the error of the distance fitting result of the right adjacent interval of each to-be-determined dividing point.
In an embodiment of the present application, the determining, according to the probability of the partition point to be determined after the adjustment of the partition point, the distance adjustment coefficient of each partition point to be determined includes:
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in the method, in the process of the invention,distance adjustment coefficient representing each partition point to be determined, +.>Each to-be-determined possibility of the segmentation point belonging to the segmentation point after the adjustment.
In an embodiment of the present application, the adjusting, according to a distance adjustment coefficient, a distance from a to-be-determined dividing point to an approximate line segment to obtain an adjusted distance from the to-be-determined dividing point includes:
and taking the product of the distance adjustment coefficient of the to-be-determined dividing point and the distance from the to-be-determined dividing point to the approximate line segment as the adjusted distance of the to-be-determined dividing point.
The technical scheme of the invention has the beneficial effects that: the invention takes the connecting line between two adjacent dividing points as an approximate line segment, takes the data point corresponding to the maximum value of the distance from the data point to the approximate line segment as a dividing point, and based on the characteristic that the data point belonging to the dividing point does not change along with the selected dividing point, adjusts the distance from each data point to the approximate line segment according to the distribution condition of the distance from a plurality of adjacent data points to the approximate line segment, and screens a plurality of dividing points from all the data points between the two adjacent dividing points each time according to the size relation between the adjusted distance of each data point and the distance from the dividing point to the approximate line segment, thereby improving the compression processing efficiency of the time sequence data sequence of the air conditioner parameter by the DP algorithm, overcoming the problem that the storage space and the battery electric quantity of the sensor for collecting the test operation data are limited, and realizing the efficient compression processing of the air conditioner installation engineering data.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a system block diagram of an air conditioner installation engineering data efficient processing system according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of an air conditioner installation engineering data efficient processing system according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the efficient processing system for air conditioner installation engineering data provided by the invention with reference to the accompanying drawings.
The climate environment, the space size, the space layout and the like in different installation scenes are different, and in order to ensure that the air conditioner can provide optimal performance and comfort in different installation scenes, the parameters of the air conditioner need to be adjusted to adapt to the characteristics of temperature, humidity, air quality and the like in different installation scenes; therefore, in the air conditioner installation engineering, after the installation stage, commissioning and debugging are required, wherein in the commissioning stage, commissioning data of various parameters are required to be acquired through various sensors, and in the debugging stage, the commissioning data of various parameters are analyzed through debugging equipment to obtain an air conditioner operation state in the commissioning stage, and technicians adjust various parameters according to the air conditioner operation state so as to ensure that the installed air conditioner can reach an optimal working state.
Analyzing the test running data of various parameters through the debugging equipment, and when the air conditioner running state of the test running stage is obtained, the more the data quantity of the input test running data is, the more accurate the air conditioner running state of the test running stage is obtained, and the higher the accuracy of adjusting various parameters according to the air conditioner running state is; therefore, the accuracy of parameter debugging depends on the data amount of the test run data, while the sensor for collecting the test run data of various parameters has a simple structure, and the storage space and the battery power of the sensor are limited.
The DP algorithm is a lossy compression method, and the compression process of the algorithm specifically includes:
(1) Taking the first data point and the last data point on the time sequence as separation points;
(2) Screening a segmentation point from all data points between every two adjacent segmentation points, comprising: taking a connecting line between every two adjacent dividing points as an approximate line segment, calculating the distance from a data point between every two adjacent dividing points to the approximate line segment, and taking the data point corresponding to the maximum value of the distance from the data point to the approximate line segment as the dividing point if the maximum value of the distance from the data point to the approximate line segment is larger than or equal to an error distance threshold value, wherein the error distance threshold value is predefined;
(4) And (3) repeating the step (2), screening one division point from all data points between every two adjacent division points until the distance from all the data points between the two adjacent division points to an approximate line segment is smaller than a predefined error distance threshold value, wherein all the obtained division points are compression results of the data time sequence, and fold lines formed by sequentially connecting all the division points are decompression results of the time sequence data sequence.
In summary, the conventional DP algorithm performs screening of the partition points according to the relationship between the distance from the data point to the approximate line segment and the error distance threshold, and uses all the partition points as compression processing results of the test run data, so that the compression effect is better, and therefore, the DP algorithm compresses the test run data of various parameters, so that more test run data can be stored in a limited storage space of the sensor, but only one partition point can be screened out by the DP algorithm at a time, and the compression processing efficiency is lower; in order to improve the compression processing efficiency of a DP algorithm, the invention uses the first data point and the last data point on a time sequence data sequence of an air conditioner parameter as separation points based on the characteristic that the data points belonging to the separation points are not changed along with the selected separation points, uses a connecting line between two adjacent separation points as an approximate line segment, uses a data point corresponding to the maximum value of the distance from the data point to the approximate line segment as a definite separation point, adjusts the distance from each data point to the approximate line segment according to the distribution condition of the distances from a plurality of adjacent data points to the approximate line segment, and screens a plurality of separation points from all the data points between two adjacent separation points according to the size relation between the adjusted distance from each data point and the distance from the definite separation point to the approximate line segment.
Referring to fig. 1, an efficient processing system for air conditioner installation engineering data according to an embodiment of the present invention is shown, where the system includes the following acquisition modules:
the first obtaining module 101 is configured to collect test operation data of various air conditioning parameters at each time, where the test operation data of each air conditioning parameter at all times constitutes a time sequence data sequence of each air conditioning parameter.
In some implementations, in order to ensure that the air conditioner can provide optimal performance and comfort in different installation scenes, commissioning and debugging are required after the air conditioner is installed, in a debugging stage, technicians need to adjust various parameters according to the running state of the air conditioner, the running state of the air conditioner needs to be obtained by analyzing the commissioning data of the various parameters through debugging equipment, and therefore, in the commissioning stage, the commissioning data of the various parameters need to be acquired through various sensors.
Optionally, the air conditioning parameters include pressure parameters, wind speed parameters, temperature parameters and humidity parameters, and in a commissioning phase of the air conditioner, the commissioning data of various parameters at each moment are collected through various sensors, the commissioning data of each parameter at all moments form a time sequence data sequence of each parameter, a plurality of data points are distributed on the time sequence data sequence of each parameter, and each data point is the commissioning data of each moment.
The time interval for acquiring the test run data is 1 minute, the time length for acquiring the test run data is 2 hours, and the test run data at all moments acquired in one hour form a time sequence data sequence.
Preferably, the collecting test run data of various parameters at each moment by various sensors includes: the pressure meter and the wind speed meter are used for respectively acquiring test operation data of the pressure parameter and the wind speed parameter at each moment, and the hygrothermograph is used for acquiring test operation data of the temperature parameter and the humidity parameter at each moment; the pressure gauge is a pressure sensor and is arranged on a pressure pipe of the air conditioner and used for measuring the pressure on the pressure pipe of the air conditioner; the anemometer is a speed sensor and is arranged at an air outlet of the air conditioner and used for measuring the wind speed at the air outlet of the air conditioner; the hygrothermograph integrates a temperature sensor and a humidity sensor, is arranged at the air outlet of the air conditioner and is used for measuring the temperature and the humidity at the air outlet of the air conditioner.
The second obtaining module 102 is configured to take a first data point and a last data point on the time sequence data sequence of each parameter as separation points, take a connecting line between two adjacent division points as an approximate line segment, and screen a plurality of division points from all data points between two adjacent division points according to a distance between each data point between two adjacent division points to the approximate line segment if a maximum value of a distance between the data point and the approximate line segment is greater than or equal to a preset error distance threshold.
In some implementations, the accuracy of parameter debugging depends on the data volume of the test run data, and the storage space of a sensor for collecting the test run data is limited, so that in order to store more test run data, compression processing is required to be performed on the test run data of various parameters; the DP algorithm is a lossy compression method, and the conventional DP algorithm only can screen out one segmentation point at a time according to the relation between the distance from a data point to an approximate line segment and an error distance threshold value, so that the compression processing efficiency is low; in order to improve the compression processing efficiency of the DP algorithm, the present embodiment screens out a plurality of segmentation points from all data points between two adjacent segmentation points each time by improving the DP algorithm.
Optionally, for a time series data sequence of any one parameter, taking a first data point and a last data point on the time series data sequence as separation points; taking a connecting line between two adjacent dividing points as an approximate line segment, if the maximum value of the distance from the data point to the approximate line segment is greater than or equal to a preset error distance threshold value, screening a plurality of dividing points from all the data points between the two adjacent dividing points according to the distance from each data point between the two adjacent dividing points to the approximate line segment, wherein the method comprises the following steps: taking a data point corresponding to the maximum value of the distance from the data point to the approximate line segment as a determined division point; according to the distribution condition of the distances from a plurality of adjacent data points to the approximate line segment, the distances from each data point to the approximate line segment are adjusted, and according to the adjusted distances from each data point and the size relation between the distances from the dividing points to the approximate line segment, the plurality of dividing points are screened from all the data points between the two adjacent dividing points.
Wherein, the operator can set the error distance threshold according to the actual implementation condition, and the error distance threshold of each parameter Wherein->Representing preset parameters,/->、/>Representing the maximum and minimum values of each parameter, respectively.
It will be appreciated that when the first data point and the last data point on the time series data sequence are used as the separation points, since there are only two division points at this time, the two division points are two adjacent division points.
In some implementations, the approximate line segment between two adjacent dividing points can approximate the distribution situation of the data points between two adjacent dividing points under ideal conditions, when the distance from the data point to the approximate line segment is larger, the distribution situation of the data points between two adjacent dividing points under ideal conditions is more inconsistent with the distribution situation of the data points between two adjacent dividing points under actual conditions, which means that the selected dividing points cannot completely represent the distribution situation of the data points between two adjacent dividing points under actual conditions, therefore, a plurality of dividing points need to be further selected according to the distribution situation of the data points between two adjacent dividing points under actual conditions; the distance from the data point to the approximate line segment is adjusted based on the characteristic that the data point does not change with the selected dividing point when a plurality of dividing points are selected from all the data points, so that the distance from the data point after the adjustment can accurately represent whether the data point is taken as the dividing point.
Preferably, the adjusting the distance between each data point and the approximate line segment according to the distribution of the distances between the adjacent data points and the approximate line segment, and screening the plurality of division points from all the data points between the adjacent two division points according to the adjusted distance between each data point and the size relationship between the distances between the division points and the approximate line segment, including: between two adjacent dividing points, taking any data point except the dividing point as a target data point, and determining the possibility that the target data point belongs to the dividing point according to the difference between the corresponding time of the target data point and the dividing point, the distance between the target data point and the approximate line segment and the change trend of the distance between the adjacent data point of the target data point and the approximate line segment; similarly, determining the possibility that all data points between two adjacent dividing points belong to dividing points, wherein among all data points between two adjacent dividing points, the data points with the possibility of belonging to dividing points larger than a preset threshold value Y are used as dividing points to be determined, so as to screen out a plurality of dividing points to be determined from all data points between two adjacent dividing points, and an implementer can set the threshold value Y according to actual implementation conditions, for example, Y=0.7; respectively carrying out linear fitting on distances from the dividing points to the approximate line segments to be determined on a plurality of data points adjacent to the left side and a plurality of data points adjacent to the right side of each dividing point to be determined, obtaining two distance fitting results, and determining a distance adjustment coefficient of each dividing point to be determined according to the difference of the two distance fitting results and the possibility that the dividing point to be determined belongs to the dividing point; and adjusting the distance from the to-be-determined dividing point to the approximate line segment according to the distance adjustment coefficient to obtain the adjusted distance from the to-be-determined dividing point, and using the data points with the adjusted distance being greater than or equal to the distance from the determined dividing point to the approximate line segment and the determined dividing point as dividing points to realize screening of a plurality of dividing points from all the data points between two adjacent dividing points.
Preferably, the determining the probability that the target data point belongs to the division point according to the difference between the corresponding moments of the target data point and the determination division point, the distance between the target data point and the approximate line segment, and the change trend of the distance between the adjacent data points of the target data point and the approximate line segment includes:
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in the method, in the process of the invention,representing the likelihood that the target data point belongs to a partition point,/->、/>、/>Representing the distance from the left adjacent data point of the target data point, the target data point and the right adjacent data point of the target data point to the approximate line segment respectively, +.>Representing the difference between the target data point and the moment corresponding to the determined segmentation point, i.e. the absolute value of the difference between the target data point and the moment corresponding to the determined segmentation point +.>Representing the difference between the corresponding moments of the target data point and the adjacent division points of the target data point, +.>Representing the difference between the adjacent division point of the target data point and the moment corresponding to the determined division point, namely the absolute value of the difference between the adjacent division point of the target data point and the moment corresponding to the determined division point, +.>Representing determining the distance of the division point to the approximate line segment,/->Representing a discriminant function, when-> When (I)>When->When (I)> 。
The acquisition method of the adjacent division points of the target data point comprises the following steps: and recording the absolute value of the difference between the corresponding moments of the target data point and the two dividing points as the difference between the corresponding moments of the target data point and the two dividing points, and taking the dividing point corresponding to the minimum value of the difference between the corresponding moments as the adjacent dividing point of the target data point.
It will be appreciated that since the test run data of the air conditioning parameters has a local correlation in time series, the data points adjacent to the division points are identical to the distribution of the division points, so the data points adjacent to the division points do not serve as new division points, whereas the data points farther from the division points are different from the distribution of the division points, so the probability that the data points farther from the division points belong to the division points is greater,and->Respectively representing the difference between the corresponding moments of the target data point and the determined division point and the adjacent division point of the target data point, and also representing the distance between the target data point and the determined division point and the adjacent division point of the target data point, therefore +.>The greater the likelihood that the target data point belongs to the partition point, the greater the +.>For aligningNormalization is performed, thus, ">At [0,1]Within the range; />、/>The positive and negative signs of (1) respectively represent the trend of the distance from the left adjacent data point of the target data point to the approximate line segment and the trend of the distance from the right adjacent data point of the target data point to the approximate line segment, when>Description of->And->The positive and negative signs of the target data points are different, the change trend of the distance from the adjacent data points on the left side and the right side of the target data points to the approximate line segment is different, the probability that the target data points belong to the division points is higher, and the probability that the target data points belong to the division points is higher Larger; distance of target data point to approximate line segment +.>The larger the target data point is, the less compliant the distribution of the data points between two adjacent dividing points in the ideal case is, and at this time, the greater the probability that the target data point belongs to the dividing point is, the more>For->Normalization is performed, thus, ">At [0,1]Within the range.
In some implementations, as the sensor collects data and is affected by noise, the distribution situation of a plurality of data points around a division point fluctuates, so that part of data points around the division point show the characteristics of the division point, which are called false division points, but the distribution of a plurality of data points around the false division points still keeps the same trend, so that distances from a plurality of data points on the left side and the right side of the false division point to an approximate line segment obey the same distribution trend, and the distances from a plurality of data points on the left side and the right side of a real division point to the approximate line segment obey different distribution trends, therefore, the embodiment determines the distances from the division point to the approximate line segment to the adjacent data points on the left side and the right side of the division point to be determined respectively in a linear fitting mode, adjusts the possibility that the division point to be determined belongs to the division point according to the difference of two distance fitting results, and determines the distance adjustment coefficient of each division point to be determined according to the adjusted result.
Preferably, the determining the distance from the dividing point to the approximate line segment for the plurality of data points adjacent to the left side and the plurality of data points adjacent to the right side of each dividing point to be determined respectively performs linear fitting, obtains two distance fitting results, and determines a distance adjustment coefficient of each dividing point to be determined according to the difference between the two distance fitting results and the possibility that the dividing point to be determined belongs to the dividing point, including: a left adjacent interval of each to-be-determined partition point is formed by A adjacent data points on the left side of each to-be-determined partition point; a data points adjacent to the right side of each to-be-determined dividing point form a right adjacent interval of each to-be-determined dividing point, wherein a represents a preset number, and an implementation person can set the number a according to actual implementation conditions, for example, a=8; respectively carrying out linear fitting on the distances from all the data points in the left adjacent section and the right adjacent section of each to-be-determined dividing point to the approximate line segments, respectively obtaining distance fitting results of the left adjacent section and the right adjacent section of each to-be-determined dividing point, wherein the distance fitting results of the left adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the left adjacent section of each to-be-determined dividing point, and the distance fitting results of the right adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the right adjacent section of each to-be-determined dividing point; and adjusting the possibility that the partition point to be determined belongs to the partition point according to the difference of the distance fitting results of the left adjacent interval and the right adjacent interval of each partition point to be determined, determining the possibility that the partition point to be determined belongs to the partition point after adjustment, and determining the distance adjustment coefficient of each partition point to be determined according to the possibility that the partition point to be determined belongs to the partition point after adjustment.
The calculation formula of the possibility of each to-be-determined partition point belonging to the partition point after the adjustment is as follows:
；
in the method, in the process of the invention,representing the probability of belonging to the division point after adjustment of each division point to be determined, +.>Error representing distance fitting result of left adjacent section of each division point to be determined, +.>Error representing distance fitting result of right adjacent section of each division point to be determined, +.>Slope of distance fitting result representing left adjacent section of each division point to be determined, +.>Slope of distance fitting result representing right adjacent section of each division point to be determined, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Representing the likelihood that each partition point to be determined belongs to the partition point.
The method for acquiring the error of the distance fitting result of the left adjacent interval of each to-be-determined dividing point comprises the following steps: the absolute value of the difference value of the distance fitting value between each data point in the left adjacent interval of each to-be-determined dividing point and each data point is used as the fitting error of each data point in the left adjacent interval of each to-be-determined dividing point, and the sum of the fitting errors of all data points in the left adjacent interval of each to-be-determined dividing point is used as the error of the distance fitting result of the left adjacent interval of each to-be-determined dividing point.
And so on, obtaining the error of the distance fitting result of the right adjacent interval of each to-be-determined dividing point.
It will be appreciated that the number of components,、/>the slopes of the distance fitting results of the left adjacent section and the right adjacent section respectively representing the dividing point to be determined represent the distribution trend of the distances from a plurality of data points on the left side and the right side of the dividing point to be determined to the approximate line segment, and the slope of the distance fitting results of the left adjacent section and the right adjacent section represents the distribution trend of the distances from the plurality of data points on the left side and the right side of the dividing point to be determined to the approximate line segment, and the slope is->The larger the difference of the distribution trend representing the distances from a plurality of data points on the left side and the right side of the dividing point to be determined to the approximate line segment, the greater the possibility that the dividing point to be determined belongs to the dividing point after being adjusted; />The smaller the value representing the error of the distance fitting result of the left and right adjacent sections of each division point to be determined, the more reliable the distance fitting result of the left and right adjacent sections of each division point to be determined, thus according to ∈>For a pair ofWeighting is performed.
The calculation formula of the distance adjustment coefficient of each partition point to be determined is as follows:
；
in the method, in the process of the invention,distance adjustment coefficient representing each partition point to be determined, +.>Each to-be-determined possibility of the segmentation point belonging to the segmentation point after the adjustment.
Preferably, the adjusting the distance from the to-be-determined dividing point to the approximate line segment according to the distance adjustment coefficient to obtain the adjusted distance from the to-be-determined dividing point includes: and taking the product of the distance adjustment coefficient of the to-be-determined dividing point and the distance from the to-be-determined dividing point to the approximate line segment as the adjusted distance of the to-be-determined dividing point.
A third obtaining module 103, configured to iterate to screen a plurality of segmentation points from all data points between every two adjacent segmentation points on the time-series data sequence of each parameter, and take all obtained segmentation points as compression processing results of the time-series data sequence of each parameter.
Optionally, for the time series data sequence of each parameter, using the method in the second obtaining module 102, taking the connection line between every two adjacent dividing points as an approximate line segment, if the maximum value of the distance from the data point to the approximate line segment is greater than or equal to a preset error distance threshold, screening a plurality of dividing points from all the data points between every two adjacent dividing points according to the distance from each data point between every two adjacent dividing points, iterating the above process until the maximum value of the distance from the data point between every two adjacent dividing points to the approximate line segment is less than the preset error distance threshold, stopping iterating, and taking all the obtained dividing points as the compression processing result of the time series data sequence of each parameter.
It can be understood that in this embodiment, by screening a plurality of partition points from all data points between two adjacent partition points each time, the compression processing efficiency of the DP algorithm on the time-series data sequence of the air conditioner parameters is improved, and efficient compression processing on the air conditioner installation engineering data is realized.
After the test operation stage is finished, a technician sequentially connects fold lines formed by two adjacent dividing points in the compression processing results of each time sequence data sequence according to the compression processing results of all time sequence data sequences stored in the pressure gauge, the anemometer and the hygrothermograph to obtain all time sequence data sequences of each parameter of the air conditioner, the test operation data of various parameters are analyzed through the debugging equipment to obtain the operation state of the air conditioner, and the technician adjusts various parameters according to the operation state of the air conditioner to ensure that the installed air conditioner can reach the optimal operation state.
The system comprises a first acquisition module, a second acquisition module and a third acquisition module. The invention takes the connecting line between two adjacent dividing points as an approximate line segment, takes the data point corresponding to the maximum value of the distance from the data point to the approximate line segment as a dividing point, and based on the characteristic that the data point belonging to the dividing point does not change along with the selected dividing point, adjusts the distance from each data point to the approximate line segment according to the distribution condition of the distance from a plurality of adjacent data points to the approximate line segment, and screens a plurality of dividing points from all the data points between the two adjacent dividing points each time according to the size relation between the adjusted distance of each data point and the distance from the dividing point to the approximate line segment, thereby improving the compression processing efficiency of the time sequence data sequence of the air conditioner parameter by the DP algorithm, overcoming the problem that the storage space and the battery electric quantity of the sensor for collecting the test operation data are limited, and realizing the efficient compression processing of the air conditioner installation engineering data.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. An efficient processing system for air conditioner installation engineering data, the system comprising:
the first acquisition module is used for acquiring test operation data of various parameters at each moment, wherein the parameters comprise pressure parameters, wind speed parameters, temperature parameters and humidity parameters, the test operation data of each parameter at all moments form a time sequence data sequence of each parameter, and a plurality of data points are distributed on the time sequence data sequence;
the second acquisition module is used for taking a first data point and a last data point on the time sequence data sequence of each parameter as separation points; screening a plurality of segmentation points from all data points between two adjacent segmentation points, including: taking a connecting line between two adjacent dividing points as an approximate line segment, and taking a data point corresponding to the maximum value of the distance between the data point and the approximate line segment as a determining dividing point if the maximum value of the distance between the data point and the approximate line segment is larger than or equal to a preset error distance threshold value; according to the distribution condition of the distances from a plurality of adjacent data points to the approximate line segment, the distances from each data point to the approximate line segment are adjusted, and according to the adjusted distances of each data point and the size relation between the distances from the dividing points to the approximate line segment, a plurality of dividing points are screened from all the data points between the two adjacent dividing points;
And the third acquisition module is used for iteratively screening a plurality of segmentation points from all data points between every two adjacent segmentation points on the time sequence data sequence of each parameter until the maximum value of the distance from the data point between every two adjacent segmentation points to the approximate line segment on the time sequence data sequence of each parameter is smaller than a preset error distance threshold value, stopping iteration, and taking all the obtained segmentation points as compression processing results of the time sequence data sequence of each parameter.
2. The system of claim 1, wherein the adjusting the distance between each data point and the approximate line segment according to the distribution of the distances between the adjacent data points and the approximate line segment, and the screening the plurality of division points from all the data points between the two adjacent division points according to the adjusted distance between each data point and the size relationship between the determined division point and the approximate line segment, comprises:
between two adjacent dividing points, taking any data point except the dividing point as a target data point, and determining the possibility that the target data point belongs to the dividing point according to the difference between the corresponding time of the target data point and the dividing point, the distance between the target data point and the approximate line segment and the change trend of the distance between the adjacent data point of the target data point and the approximate line segment;
Similarly, the probability that all data points between two adjacent segmentation points belong to the segmentation points is determined;
taking the data points with the possibility of being more than a preset threshold Y among all the data points between two adjacent dividing points as the dividing points to be determined;
respectively carrying out linear fitting on distances from the dividing points to the approximate line segments to be determined on a plurality of data points adjacent to the left side and a plurality of data points adjacent to the right side of each dividing point to be determined, obtaining two distance fitting results, and determining a distance adjustment coefficient of each dividing point to be determined according to the difference of the two distance fitting results and the possibility that the dividing point to be determined belongs to the dividing point;
adjusting the distance from the dividing point to be determined to the approximate line segment according to the distance adjustment coefficient to obtain the adjusted distance of the dividing point to be determined;
and the data points with the adjusted distance being larger than or equal to the distance from the determined dividing point to the approximate line segment and the determined dividing point are used as dividing points.
3. The system for efficiently processing data of an air conditioner installation project according to claim 2, wherein the determining the probability that the target data point belongs to the division point according to the difference between the corresponding time of the target data point and the determination division point, the distance from the target data point to the approximate line segment, and the trend of the distance from the adjacent data point to the approximate line segment, comprises:
；
In the method, in the process of the invention,representing the likelihood that the target data point belongs to a partition point,/->、/>、/>Representing the distance from the left adjacent data point of the target data point, the target data point and the right adjacent data point of the target data point to the approximate line segment respectively, +.>Representing the difference between the target data point and the moment corresponding to the determined segmentation point,/->Representing the difference between the corresponding moments of the target data point and the adjacent division points of the target data point, +.>Representing the difference between adjacent segmentation points of the target data point and the moment corresponding to the determined segmentation point,representing determining the distance of the division point to the approximate line segment,/->Representing discriminant functions whenWhen (I)>When (when)When (I)>。
4. The efficient processing system for air conditioner installation engineering data as claimed in claim 3, wherein the method for acquiring the adjacent division points of the target data points comprises the following steps:
and recording the absolute value of the difference between the corresponding moments of the target data point and the two dividing points as the difference between the corresponding moments of the target data point and the two dividing points, and taking the dividing point corresponding to the minimum value of the difference between the corresponding moments as the adjacent dividing point of the target data point.
5. The efficient processing system for air conditioner installation engineering data according to claim 2, wherein the determining the distance from the division point to the approximate line segment by respectively performing linear fitting on a plurality of data points adjacent to the left side and a plurality of data points adjacent to the right side of each division point to be determined, obtaining two distance fitting results, determining a distance adjustment coefficient of each division point to be determined according to the difference between the two distance fitting results and the possibility that the division point to be determined belongs to the division point, comprises:
A left adjacent interval of each to-be-determined partition point is formed by A adjacent data points on the left side of each to-be-determined partition point; a right adjacent interval of each to-be-determined dividing point is formed by adjacent A data points on the right side of each to-be-determined dividing point, wherein A represents preset quantity; respectively carrying out linear fitting on the distances from all the data points in the left adjacent section and the right adjacent section of each to-be-determined dividing point to the approximate line segments, respectively obtaining distance fitting results of the left adjacent section and the right adjacent section of each to-be-determined dividing point, wherein the distance fitting results of the left adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the left adjacent section of each to-be-determined dividing point, and the distance fitting results of the right adjacent section of each to-be-determined dividing point comprise distance fitting values of each data point in the right adjacent section of each to-be-determined dividing point;
and adjusting the possibility that the partition point to be determined belongs to the partition point according to the difference of the distance fitting results of the left adjacent interval and the right adjacent interval of each partition point to be determined, determining the possibility that the partition point to be determined belongs to the partition point after adjustment, and determining the distance adjustment coefficient of each partition point to be determined according to the possibility that the partition point to be determined belongs to the partition point after adjustment.
6. The efficient processing system for air conditioner installation engineering data according to claim 5, wherein the adjusting the possibility that the division point to be determined belongs to the division point according to the difference of the distance fitting results of the left adjacent section and the right adjacent section of each division point to be determined, determining the possibility that the division point to be determined belongs to the division point after adjustment, comprises:
；
in the method, in the process of the invention,representing the probability of belonging to the division point after adjustment of each division point to be determined, +.>Error representing distance fitting result of left adjacent section of each division point to be determined, +.>Error representing distance fitting result of right adjacent section of each division point to be determined, +.>Slope of distance fitting result representing left adjacent section of each division point to be determined, +.>Slope of distance fitting result representing right adjacent section of each division point to be determined, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Representing the likelihood that each partition point to be determined belongs to the partition point.
7. The efficient processing system for air conditioner installation engineering data according to claim 6, wherein the method for obtaining the error of the distance fitting result of the left adjacent section of each to-be-determined division point comprises the following steps:
Taking the absolute value of the difference value of the distance fitting value between each data point in the left adjacent interval of each to-be-determined dividing point and each data point as the fitting error of each data point in the left adjacent interval of each to-be-determined dividing point, and taking the sum of the fitting errors of all data points in the left adjacent interval of each to-be-determined dividing point as the error of the distance fitting result of the left adjacent interval of each to-be-determined dividing point;
and so on, obtaining the error of the distance fitting result of the right adjacent interval of each to-be-determined dividing point.
8. The system for efficiently processing data of an air conditioner installation project according to claim 5, wherein said determining a distance adjustment coefficient for each division point to be determined according to the possibility of belonging to the division point after adjustment of the division point to be determined comprises:
；
in the method, in the process of the invention,distance adjustment coefficient representing each partition point to be determined, +.>Each to-be-determined possibility of the segmentation point belonging to the segmentation point after the adjustment.
9. The efficient processing system for air conditioner installation engineering data according to claim 2, wherein the adjusting the distance from the to-be-determined dividing point to the approximate line segment according to the distance adjustment coefficient to obtain the adjusted distance from the to-be-determined dividing point comprises:
And taking the product of the distance adjustment coefficient of the to-be-determined dividing point and the distance from the to-be-determined dividing point to the approximate line segment as the adjusted distance of the to-be-determined dividing point.
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