CN117807375A - Ultrasonic water meter noise processing method, system and equipment based on Internet of Things - Google Patents
Ultrasonic water meter noise processing method, system and equipment based on Internet of Things Download PDFInfo
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Abstract
The embodiment of the application discloses an ultrasonic water meter noise processing method, system and equipment based on the Internet of things, which relate to the technical field of data processing and aim at solving the problem of poor processing effect on noise data acquired by an ultrasonic water meter in the prior art.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an ultrasonic water meter noise processing method, system and equipment based on the Internet of things.
Background
The ultrasonic water meter is a novel water meter, water flow is measured by utilizing time difference generated by speed change when ultrasonic waves are propagated forward and backward in water, and the ultrasonic water meter is connected with the Internet of things through the Internet of things system, so that the management of flow data can be realized. However, noise data is inevitably mixed in a large amount of data, and the prior art generally only uses a basic means of data cleaning to denoise some obviously discrete and erroneous data, and does not pay attention to the preservation of effective information, so that the noise data processing effect is poor.
Disclosure of Invention
The main aim of the application is to provide an ultrasonic water meter noise processing method, system and equipment based on the Internet of things, and aims to solve the problem that in the prior art, the processing effect of noise data acquired by an ultrasonic water meter is poor.
In order to achieve the above purpose, the technical solution adopted in the embodiments of the present application is as follows:
in a first aspect, an embodiment of the present application provides an ultrasonic water meter noise processing method based on the internet of things, which is applied to an internet of things system, where the internet of things system includes: the method comprises the following steps of:
according to the discrete distance and the standard flow data, performing discrete data rejection on the flow data uploaded by the ultrasonic water meter to obtain first flow data;
adding the first flow data into a first data queue and sequencing to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter;
obtaining an adjustment weight according to a probability density function based on Gaussian distribution;
and multiplying the adjustment weight with each flow data in the second data queue respectively to obtain target flow data.
In one possible implementation manner of the first aspect, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and before the first flow data is obtained, the method further includes:
obtaining a first adjustment coefficient according to the current special event;
and adjusting the preset discrete distance according to the first adjustment coefficient to obtain the discrete distance.
In one possible implementation manner of the first aspect, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and before the first flow data is obtained, the method further includes:
obtaining a second adjustment coefficient according to the current temperature data;
and adjusting the preset discrete distance according to the second adjustment coefficient to obtain the discrete distance.
In one possible implementation manner of the first aspect, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and after the first flow data is obtained, the method further includes:
acquiring forward time data and reverse time data of the collected data of the ultrasonic water meter according to the first flow data;
acquiring forward and reverse time difference data according to the forward time data and the reverse time data;
adding the first traffic data into a first data queue and sequencing to obtain a second data queue, wherein the method comprises the following steps:
and adding the forward and reverse time difference data into the first data queue and sequencing to obtain a second data queue.
In one possible implementation manner of the first aspect, before adding the first traffic data to the first data queue and ordering the first traffic data to obtain the second data queue, the method further includes:
creating a data queue;
inputting the flow data uploaded by the ultrasonic water meter into a data queue according to the time sequence to obtain a data queue to be sequenced;
and sequencing the data queues to be sequenced according to the size of the forward and reverse time difference data of the flow data to obtain a first data queue.
In a possible implementation manner of the first aspect, before obtaining the adjustment weight according to the probability density function based on the gaussian distribution, the method further includes:
generating random numbers meeting Gaussian distribution;
and calculating a probability density function corresponding to the random number to obtain a probability density function based on Gaussian distribution.
In one possible implementation manner of the first aspect, adding the first traffic data to the first data queue and ordering to obtain the second data queue includes:
adding first flow data into a first data queue to obtain a first data queue to be sequenced;
and sequencing the first data queue to be sequenced according to the bubbling sequencing method to obtain a second data queue.
In a second aspect, an embodiment of the present application provides an ultrasonic water meter noise processing system based on the internet of things, which is applied to the internet of things system, and the internet of things system includes: the object platform, sensing network platform and management platform of mutual in proper order, object platform are used for inserting the ultrasonic wave water gauge, and ultrasonic wave water gauge noise processing system based on the thing networking includes:
the eliminating module is used for eliminating discrete data of flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain first flow data;
the sequencing module is used for adding the first flow data into the first data queue and sequencing the first flow data to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter;
the adjusting module is used for obtaining adjusting weights according to probability density functions based on Gaussian distribution;
and the product module is used for multiplying the adjusting weight with each flow data in the second data queue respectively to obtain target flow data.
In a third aspect, an embodiment of the present application provides a computer readable storage medium, storing a computer program, where the computer program when loaded and executed by a processor implements the method for processing noise of an ultrasonic water meter based on the internet of things provided in any one of the first aspects.
In a fourth aspect, embodiments of the present application provide an electronic device comprising a processor and a memory, wherein,
the memory is used for storing a computer program;
the processor is configured to load and execute a computer program to cause the electronic device to execute the method for processing noise of the ultrasonic water meter based on the internet of things provided in any one of the first aspect.
Compared with the prior art, the beneficial effects of this application are:
the embodiment of the application provides an ultrasonic water meter noise processing method, system and equipment based on the Internet of things, wherein the method comprises the following steps: according to the discrete distance and the standard flow data, performing discrete data rejection on the flow data uploaded by the ultrasonic water meter to obtain first flow data; adding the first flow data into a first data queue and sequencing to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter; obtaining an adjustment weight according to a probability density function based on Gaussian distribution; and multiplying the adjustment weight with each flow data in the second data queue respectively to obtain target flow data. According to the method, the discrete degree of uploaded flow data relative to standard flow data is determined through the discrete distance, so that some obvious discrete data are removed firstly, then the rest data are added into an existing data queue for sorting, the probability density function meeting Gaussian distribution is used as adjustment weight to multiply the sorted queue data, the weight of normal data is amplified, the weight of abnormal data is reduced, the abnormal data with smaller occupation is further separated from effective data through normal distribution, filtering and denoising of the data are achieved, the output data is closer to an actual flow value, and the noise processing effect on collected data of an ultrasonic water meter is improved.
Drawings
FIG. 1 is a schematic diagram of an electronic device in a hardware operating environment according to an embodiment of the present application;
fig. 2 is a flow chart of an ultrasonic water meter noise processing method based on the internet of things according to an embodiment of the present application;
fig. 3 is a schematic diagram of a frame of an internet of things system in the method for processing noise of an ultrasonic water meter based on the internet of things according to the embodiment of the present application;
the marks in the figure: 101-processor, 102-communication bus, 103-network interface, 104-user interface, 105-memory.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device of a hardware operating environment according to an embodiment of the present application, where the electronic device may include: a processor 101, such as a central processing unit (Central Processing Unit, CPU), a communication bus 102, a user interface 104, a network interface 103, a memory 105. Wherein the communication bus 102 is used to enable connected communication between these components. The user interface 104 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 104 may also include standard wired, wireless interfaces. The network interface 103 may alternatively comprise a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 105 may alternatively be a storage device independent of the foregoing processor 101, where the Memory 105 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or may be a stable Non-Volatile Memory (NVM), such as at least one magnetic disk Memory; the processor 101 may be a general purpose processor including a central processing unit, a network processor, etc., as well as a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and an ultrasonic water meter noise processing system based on the internet of things.
In the electronic device shown in fig. 1, the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; the processor 101 and the memory 105 in the application can be arranged in an electronic device, and the electronic device calls the ultrasonic water meter noise processing system based on the internet of things stored in the memory 105 through the processor 101 and executes the ultrasonic water meter noise processing method based on the internet of things.
Referring to fig. 2, based on the hardware device of the foregoing embodiment, an embodiment of the present application provides an ultrasonic water meter noise processing method based on the internet of things, which is applied to the internet of things system, where the internet of things system includes: the method comprises the following steps of:
s10: and carrying out discrete data elimination on the flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain first flow data.
In the specific implementation process, the standard flow data represents theoretical flow data in the water pipeline under the conditions that no special condition occurs, the temperature is 25 ℃ at normal temperature and the equipment works normally. The discrete distance represents the difference between the flow data uploaded by the ultrasonic water meter and the standard flow data, if the flow data can be directly compared in a numerical form, the discrete distance can be measured by the magnitude of the difference, if the flow data cannot be measured by the similarity of the data, the higher the similarity is, the smaller the discrete distance is, through the clamping control of the discrete distance, some data which are obviously far away from the standard data are removed, and the flow data after the discrete data are removed are the first flow data.
In one embodiment, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and before the first flow data is obtained, the method further includes:
obtaining a first adjustment coefficient according to the current special event;
and adjusting the preset discrete distance according to the first adjustment coefficient to obtain the discrete distance.
In the implementation process, the occurrence of some special events can affect the flow data in actual situations, and if the same discrete distance is measured with the standard flow data, some data in a possibly normal range can be removed by mistake. For example, the current special event is flow restriction, at this time, the standard flow data is correspondingly reduced, an adjustment coefficient can be correspondingly obtained according to the reduced amount to adjust the preset discrete distance, the preset discrete distance can be understood as a discrete distance correspondingly set under the standard flow data, and under the condition that the standard flow data is reduced, the corresponding discrete distance also needs to be reduced, and the reduction can be multiplied by a coefficient larger than zero and smaller than one, or can be a fixed value, and the preset discrete distance is obtained by subtracting the fixed value.
For example, if the special event is a pipeline leakage, the detected flow rate of the ultrasonic water meter is increased or decreased according to the position of the ultrasonic water meter, or if the special event is a change of the transmitted water quality, it is understood that the water quality is changed, such as excessive impurities and many bubbles, which affect the detection precision of the ultrasonic water meter, and if the discrete data are judged by the same standard, more data are removed or more data which should be removed are reserved. In short, the standard flow data needs to be adjusted along with the influence of the special event, so that the discrete distance is obtained through the adjustment of the corresponding adjustment coefficient.
In one embodiment, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and before the first flow data is obtained, the method further includes:
obtaining a second adjustment coefficient according to the current temperature data;
and adjusting the preset discrete distance according to the second adjustment coefficient to obtain the discrete distance.
In the specific implementation process, the same principle as the adjustment of the discrete distance under the special event, the temperature is a factor influencing the flow data, the change of the temperature can directly influence the flow property of water in a pipeline, and the flow data is further changed, so that the current temperature is changed compared with the normal temperature on the basis of the normal temperature of 25 ℃, and the preset discrete distance is adjusted by correspondingly obtaining an adjustment coefficient, thereby improving the accuracy of discrete data elimination and avoiding the false elimination of effective information.
In one embodiment, according to the discrete distance and the standard flow data, discrete data rejection is performed on the flow data uploaded by the ultrasonic water meter, and after the first flow data is obtained, the method further includes:
acquiring forward time data and reverse time data of the collected data of the ultrasonic water meter according to the first flow data;
and obtaining forward and reverse time difference data according to the forward time data and the reverse time data.
In the specific implementation process, forward and reverse time difference data, namely time difference generated by speed change when ultrasonic wave water meter detects forward and reverse time difference in water, different water flow conditions and pipeline environments can be better adapted through adjusting the forward and reverse time difference data, measurement adaptability and stability are improved, and forward and reverse time difference is a more visual numerical value, so that sequencing speed and sequencing accuracy can be improved.
Based on the foregoing steps, adding the first traffic data to the first data queue and ordering to obtain a second data queue, including:
and adding the forward and reverse time difference data into the first data queue and sequencing to obtain a second data queue.
S20: adding the first flow data into a first data queue and sequencing to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter.
In the implementation process, the uploaded flow data is stored in the first data queue in sequence, when the data is added again, the data queue is required to be reordered, and a second data queue is obtained after reordering, so that normal data can be arranged at one end of the queue, and abnormal data is arranged at the other end of the queue, and the subsequent filtering of abnormal information and the extraction of effective information are facilitated.
In one embodiment, the method further comprises, before adding the first traffic data to the first data queue and ordering to obtain the second data queue:
creating a data queue;
inputting the flow data uploaded by the ultrasonic water meter into a data queue according to the time sequence to obtain a data queue to be sequenced;
and sequencing the data queues to be sequenced according to the size of the forward and reverse time difference data of the flow data to obtain a first data queue.
In the specific implementation process, under the condition of adopting forward and reverse time difference data for sorting, a data queue can be created firstly, for example, a data queue is created in an embedded development system heap area, then the forward and reverse time differences which are sequentially and continuously output by an ultrasonic water meter in the working process are collected according to a time sequence and are input into the data queue, and finally the data in the queue are sorted according to the size of the forward and reverse time differences, so that a first data queue can be obtained. The sorting when the first data queue and the second data queue are established may be performed by using a sorting algorithm, which is a method for arranging records according to requirements, such as bubble sorting, selecting sorting, merging sorting, and the like, and in this embodiment of the present application, bubble sorting is taken as an example, that is: adding the first traffic data into a first data queue and sequencing to obtain a second data queue, wherein the method comprises the following steps:
adding first flow data into a first data queue to obtain a first data queue to be sequenced;
and sequencing the first data queue to be sequenced according to the bubbling sequencing method to obtain a second data queue.
In the specific implementation process, the bubbling ordering is a simple but low-efficiency ordering algorithm, the algorithm is very simple, easy to understand and realize, the ordering is realized by continuously comparing the sizes of adjacent elements and exchanging positions, and each ordering only involves the comparison and exchange operation between the adjacent elements. In the embodiment of the present application, since two orders are involved, the first order is already performed by the uploaded data, the second order is to add new elements to an ordered queue for ordering, and the characteristics of the bubbling algorithm make the efficiency higher than that of other ordering algorithms in the scene of the embodiment of the present application.
S30: the adjustment weights are obtained from a probability density function based on a gaussian distribution.
In the specific implementation process, the Gaussian distribution has symmetry, the probability density function of the Gaussian distribution takes the mean value as the center and is in bell-shaped curve distribution, the variance of the Gaussian distribution determines the width of the curve, and the larger the variance is, the wider the curve is, the smaller the variance is, and the narrower the curve is. Therefore, by adjusting the parameters of the Gaussian distribution, different data distribution conditions can be flexibly adapted. Since the abnormal data is usually few in data distribution, the probability density is lower, and the probability density of the normal data is higher, and the abnormal data can be distinguished from the normal data by adopting a probability density function of Gaussian distribution as an adjustment weight.
The probability density function based on gaussian distribution can be generated from random numbers, namely: before obtaining the adjustment weight according to the probability density function based on the Gaussian distribution, the method further comprises:
generating random numbers meeting Gaussian distribution;
and calculating a probability density function corresponding to the random number to obtain a probability density function based on Gaussian distribution.
In practice, the gaussian function, also known as a normal distribution function, is used to generate a probability density function that satisfies the gaussian distribution. When generating the probability density function meeting the Gaussian distribution, a mean value and a standard deviation are required to be set, then the probability density function value corresponding to each value is calculated according to the mathematical expression formula of the Gaussian function, and the values are connected to obtain the probability density function meeting the Gaussian distribution. In computer programming, random numbers that satisfy a gaussian distribution may be generated using a random number generator, and then probability density function values corresponding to the random numbers are calculated, thereby generating a probability density function that satisfies the gaussian distribution.
S40: and multiplying the adjustment weight with each flow data in the second data queue respectively to obtain target flow data.
In the specific implementation process, the probability density function meeting Gaussian distribution is used as the adjustment weight, so that the weight of normal data can be amplified, and the weight of abnormal data can be reduced, thereby realizing filtering and denoising of the data. The probability density function meeting Gaussian distribution is used as the adjustment weight to be multiplied by the ordered queue data, and effective data and abnormal data can be separated, so that the output target flow data is closer to the actual flow value.
In this embodiment, the discrete degree of the uploaded flow data relative to the standard flow data is determined through the discrete distance, so that some obvious discrete data are removed, then the rest data are added into the existing data queue for sorting, the probability density function meeting the gaussian distribution is used as the adjustment weight to multiply with the sorted queue data, the weight of the normal data is amplified, the weight of the abnormal data is reduced, the abnormal data with smaller occupation is further separated from the effective data through the normal distribution, filtering and denoising of the data are realized, the output data is closer to the actual flow value, and the noise processing effect on the collected data of the ultrasonic water meter is improved.
The framework of the internet of things system provided in the embodiment of the application in the application scene is shown in fig. 3, and may include a user platform, a service platform, a management platform, a sensor network platform and an object platform which interact in sequence to form a basic five-platform framework, wherein the object platform may include an ultrasonic water meter object sub-platform; the sensor network platform can comprise a device management module and a data transmission management module, wherein the device management module can comprise a network management unit, an instruction management unit and a device state management unit, and the data transmission management module can comprise a data protocol management unit, a data analysis unit, a data classification unit, a data transmission monitoring unit and a data transmission safety unit; the management platform can comprise a device management sub-platform, a service management sub-platform and a data center, wherein the device management sub-platform and the service management sub-platform can interact with the data center respectively, the device management sub-platform can comprise a device running state monitoring management unit, a metering data monitoring management unit, a device parameter management unit and a device life cycle management unit, and the service management sub-platform can comprise a revenue management unit, a business and business management unit, a reporting management unit, a message management unit, a scheduling management unit, a purchase and sale difference management unit, a running analysis management unit and a comprehensive service management unit; the service platform may include a water service module, an operation service module, and a security service module; the user platform can comprise a common user module, a government user module and a supervision user module, and through interaction among all functional platforms of the five-platform-based internet of things system, perfect closed-loop information operation logic is established, ordered operation of perception information and control information is ensured, and intelligent management of equipment local operation safety is realized.
Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides an ultrasonic water meter noise processing system based on the internet of things, which is applied to the internet of things system, where the internet of things system includes: the object platform, sensing network platform and management platform of mutual in proper order, object platform are used for inserting the ultrasonic wave water gauge, and ultrasonic wave water gauge noise processing system based on the thing networking includes:
the eliminating module is used for eliminating discrete data of flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain first flow data;
the sequencing module is used for adding the first flow data into the first data queue and sequencing the first flow data to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter;
the adjusting module is used for obtaining adjusting weights according to probability density functions based on Gaussian distribution;
and the product module is used for multiplying the adjusting weight with each flow data in the second data queue respectively to obtain target flow data.
It should be understood by those skilled in the art that the division of each module in the embodiment is only a division of a logic function, and all or part of the modules may be integrated onto one or more actual carriers in practical application, and the modules may be implemented in a form of calling by a processing unit through all software, or may be implemented in a form of hardware, or in a form of combining software and hardware, and it should be noted that each module in the noise processing system of the ultrasonic water meter based on the internet of things in the embodiment is in one-to-one correspondence with each step in the noise processing method of the ultrasonic water meter based on the internet of things in the foregoing embodiment, so that a specific implementation of the embodiment may refer to an implementation of the noise processing method of the ultrasonic water meter based on the internet of things.
Based on the same inventive concept as in the foregoing embodiments, embodiments of the present application further provide a computer readable storage medium storing a computer program, where the computer program, when loaded and executed by a processor, implements an ultrasonic water meter noise processing method based on the internet of things as provided in the embodiments of the present application.
Based on the same inventive concepts as in the previous embodiments, embodiments of the present application also provide an electronic device, including a processor and a memory, wherein,
the memory is used for storing a computer program;
the processor is used for loading and executing the computer program so that the electronic equipment executes the ultrasonic water meter noise processing method based on the Internet of things.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, the executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located at one site or, alternatively, distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising several instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to perform the method described in the embodiments of the present application.
In summary, the method, system and equipment for processing noise of an ultrasonic water meter based on the internet of things provided by the application comprise the following steps: according to the discrete distance and the standard flow data, performing discrete data rejection on the flow data uploaded by the ultrasonic water meter to obtain first flow data; adding the first flow data into a first data queue and sequencing to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter; obtaining an adjustment weight according to a probability density function based on Gaussian distribution; and multiplying the adjustment weight with each flow data in the second data queue respectively to obtain target flow data. According to the method, the discrete degree of uploaded flow data relative to standard flow data is determined through the discrete distance, so that some obvious discrete data are removed firstly, then the rest data are added into an existing data queue for sorting, the probability density function meeting Gaussian distribution is used as adjustment weight to multiply the sorted queue data, the weight of normal data is amplified, the weight of abnormal data is reduced, the abnormal data with smaller occupation is further separated from effective data through normal distribution, filtering and denoising of the data are achieved, the output data is closer to an actual flow value, and the noise processing effect on collected data of an ultrasonic water meter is improved.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
Claims (10)
1. The ultrasonic water meter noise processing method based on the Internet of things is characterized by being applied to an Internet of things system, and the Internet of things system comprises the following steps: the method comprises the following steps of:
according to the discrete distance and the standard flow data, discrete data rejection is carried out on the flow data uploaded by the ultrasonic water meter, and first flow data is obtained;
adding the first flow data into a first data queue and sequencing to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter;
obtaining an adjustment weight according to a probability density function based on Gaussian distribution;
and multiplying the adjustment weight with each flow data in the second data queue respectively to obtain target flow data.
2. The method for processing noise of an ultrasonic water meter based on the internet of things according to claim 1, wherein the method further comprises, before performing discrete data rejection on the flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain the first flow data:
obtaining a first adjustment coefficient according to the current special event;
and adjusting a preset discrete distance according to the first adjustment coefficient to obtain the discrete distance.
3. The method for processing noise of an ultrasonic water meter based on the internet of things according to claim 1, wherein the method further comprises, before performing discrete data rejection on the flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain the first flow data:
obtaining a second adjustment coefficient according to the current temperature data;
and adjusting a preset discrete distance according to the second adjustment coefficient to obtain the discrete distance.
4. The method for processing noise of an ultrasonic water meter based on the internet of things according to claim 1, wherein the method further comprises, after performing discrete data rejection on the flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain the first flow data:
acquiring forward time data and reverse time data of the collected data of the ultrasonic water meter according to the first flow data;
acquiring forward and reverse time difference data according to the forward time data and the reverse time data;
the step of adding the first traffic data into a first data queue and sequencing the first traffic data to obtain a second data queue, including:
and adding the forward and reverse time difference data into a first data queue and sequencing to obtain a second data queue.
5. The method for processing noise of an ultrasonic water meter based on internet of things according to claim 4, wherein before adding the first traffic data to a first data queue and ordering the first traffic data to obtain a second data queue, the method further comprises:
creating a data queue;
inputting the flow data uploaded by the ultrasonic water meter into the data queue according to a time sequence to obtain a data queue to be sequenced;
and sequencing the data queues to be sequenced according to the size of the forward and reverse time difference data of the flow data, so as to obtain the first data queue.
6. The method for processing noise of an ultrasonic water meter based on the internet of things according to claim 1, wherein before the adjusting weights are obtained according to a probability density function based on gaussian distribution, the method further comprises:
generating random numbers meeting Gaussian distribution;
and calculating a probability density function corresponding to the random number to obtain the probability density function based on Gaussian distribution.
7. The method for processing noise of an ultrasonic water meter based on the internet of things according to claim 1, wherein adding the first traffic data into a first data queue and sequencing to obtain a second data queue comprises:
adding the first flow data into a first data queue to obtain the first data queue to be ordered;
and sequencing the first data queue to be sequenced according to the bubbling sequencing method to obtain a second data queue.
8. Ultrasonic wave water gauge noise processing system based on thing networking, its characterized in that is applied to thing networking system, thing networking system includes: the system comprises an object platform, a sensing network platform and a management platform which are interacted in sequence, wherein the object platform is used for accessing an ultrasonic water meter, and the ultrasonic water meter noise processing system based on the Internet of things comprises:
the eliminating module is used for eliminating discrete data of the flow data uploaded by the ultrasonic water meter according to the discrete distance and the standard flow data to obtain first flow data;
the sequencing module is used for adding the first flow data into a first data queue and sequencing the first flow data to obtain a second data queue; the first data queue is obtained according to the sequence of the flow data uploaded by the ultrasonic water meter;
the adjusting module is used for obtaining adjusting weights according to probability density functions based on Gaussian distribution;
and the product module is used for multiplying the adjusting weight with each flow data in the second data queue respectively to obtain target flow data.
9. A computer readable storage medium storing a computer program, wherein the computer program when loaded and executed by a processor implements the method for processing noise of an ultrasonic water meter based on the internet of things according to any one of claims 1 to 7.
10. An electronic device comprising a processor and a memory, wherein,
the memory is used for storing a computer program;
the processor is used for loading and executing the computer program to enable the electronic equipment to execute the ultrasonic water meter noise processing method based on the internet of things according to any one of claims 1-7.
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