CN108760595B - Distributed gas turbidity monitoring point - Google Patents
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Abstract
The invention provides distributed gas turbidity monitoring points which are communicated with a server, and each gas turbidity monitoring point responds to an instruction of the server to obtain gas turbidity and transmits the gas turbidity to the server so that the server can classify the gas turbidity monitoring points according to the obtained gas turbidity. The invention provides an automatic gas turbidity classification system consisting of distributed gas turbidity monitoring points and a server, wherein the monitored gas turbidity mainly refers to the concentration of submicron particles. The system can realize the automatic control acquisition of the concentration of submicron particles, the automatic monitoring alarm of a gas turbidity monitoring point and the automatic classification of gas turbidity data, and obviously, the automatic classification of the gas turbidity provides a reliable index for the subsequent pollution treatment of the gas turbidity.
Description
Technical Field
The invention relates to the field of environmental protection, in particular to a distributed gas turbidity monitoring point.
Background
In environmental research, soot pollution is an important part, and submicron particle pollution is the most obvious harm to human body, so that monitoring and analysis on the submicron particle pollution are necessary. However, the prior art lacks solutions for automated monitoring and automated analysis of submicron particle contamination. The pollution degree of the submicron particles is classified, so that subsequent pollution treatment can be performed according to the classification result, and the prior art does not provide a scheme for automatic classification.
Disclosure of Invention
In order to solve the technical problem, the invention provides a distributed gas turbidity monitoring point.
The invention is realized by the following technical scheme:
distributed gas turbidity monitoring points which are communicated with the server, wherein each gas turbidity monitoring point responds to the instruction of the server to acquire gas turbidity and transmit the gas turbidity to the server so that the server can classify the gas turbidity monitoring points according to the obtained gas turbidity.
Further, the device comprises a gas turbidity detection module, a gas turbidity calculation module, a memory and a comparator;
the gas turbidity detection module is formed by connecting a first gas turbidity detector, a second gas turbidity detector and a third gas turbidity detector in series, and the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector detect gas turbidity by sensing the change of light in the transmission process inside the detector.
Further, gaseous turbidity detector of first gas, gaseous turbidity detector of second and the gaseous turbidity detector of third possess the same structure, and it is inside including the light source, still is provided with photoelectric sensor on light source emergent light propagation direction, photoelectric sensor's output and high-gain fortune are put and are connected, high-gain fortune is put and is connected with the converter that is used for converting the signal of telecommunication into gaseous turbidity, the converter with gaseous turbidity calculation module connects.
Furthermore, the wavelengths of the light sources in the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector are different, and are respectively 450nm, 500nm and 550nm, and the response ranges of the photoelectric sensors to the spectrum are all 400-600 nm.
Further, the gas turbidity calculation module comprises:
a gas turbidity obtaining unit for obtaining the first concentration rho measured by the first gas turbidity detector1The first concentration rho measured by the second gas turbidity detector2The first concentration rho measured by the third gas turbidity detector3；
A cluster center calculation unit for calculating a first concentration ρ1And a second concentration rho2And a cluster center ρ of a third concentration ρ 30；
A weight calculation unit for calculating the first concentration ρ1Corresponding weight value sigma1Second concentration ρ2Corresponding weight value sigma2And a third concentration ρ3Corresponding weight value sigma3；
A calculation result output unit for outputting a gas concentration calculation result; the calculation result is obtained by using a weighted average method
If | ρ0-ρiIf is, then directly apply its corresponding rhoiAs a result of the calculation by the gas turbidity calculating unit.
Further, the memory is used for storing the gas turbidity calculation results of the past times, and the comparator is used for comparing the gas turbidity difference between two gas turbidities obtained by the latest calculation; if the absolute value of the gas turbidity difference is larger than a preset first threshold value, a classification driving request is issued to a server, and if the absolute value of the gas turbidity difference is larger than a preset second threshold value, an alarm instruction is issued to the server.
Further, in the process of interacting with the server, the gas turbidity monitoring point responds to a gas turbidity acquisition instruction issued by the server and starts to acquire gas turbidity;
the gas turbidity acquisition instruction comprises gas turbidity acquisition times and a gas turbidity acquisition time interval; responding to the gas turbidity acquisition instruction, the gas turbidity monitoring point generates a counter and a timer, the counter is used for recording the times of acquiring the gas turbidity, and the timer drives the gas turbidity monitoring point to acquire the gas turbidity; after the gas turbidity is obtained every time, the gas turbidity value is pressed into a gas turbidity queue, after the gas turbidity is collected, a gas turbidity sample vector corresponding to the gas turbidity monitoring point is generated according to gas turbidity data in the gas turbidity queue, and the gas turbidity sample vector is transmitted to a server.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings, which are merely for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be construed as limiting the invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the invention, the meaning of "a plurality" is two or more unless otherwise specified.
In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted", "connected" and "connected" are to be construed broadly, e.g. as being fixed or detachable or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the creation of the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention has the beneficial effects that:
the invention provides an automatic gas turbidity classification system consisting of distributed gas turbidity monitoring points and a server, wherein the monitored gas turbidity mainly refers to the concentration of submicron particles. The system can realize the automatic control acquisition of the concentration of submicron particles, the automatic monitoring alarm of a gas turbidity monitoring point and the automatic classification of gas turbidity data, and obviously, the automatic classification of the gas turbidity provides a reliable index for the subsequent pollution treatment of the gas turbidity.
Drawings
FIG. 1 is a block diagram of a body turbidity monitoring point provided in the present embodiment;
FIG. 2 is a block diagram of a gas turbidity calculation module provided in the present embodiment;
FIG. 3 is a flow chart of the automatic classification of gas turbidity according to the present embodiment;
FIG. 4 is a block diagram of an automatic gas turbidity classification apparatus according to the present embodiment;
fig. 5 is a schematic diagram of a server structure provided in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below.
The embodiment of the invention provides an automatic turbidity classification system which is composed of gas turbidity monitoring points and a server, wherein the distributed gas turbidity monitoring points are communicated with the server. Each gas turbidity monitoring point responds to the instruction of the server to acquire gas turbidity and transmits the gas turbidity to the server, and the server is used for classifying the gas turbidity monitoring points according to the obtained gas turbidity.
Specifically, the gas turbidity monitoring point is shown in fig. 1 and comprises a gas turbidity detection module, a gas turbidity calculation module, a memory and a comparator.
The gas turbidity detection module is formed by connecting a first gas turbidity detector, a second gas turbidity detector and a third gas turbidity detector in series, and the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector detect gas turbidity by sensing the change of light in the transmission process inside the detector. If the turbidity of the gas increases, the absorption and refraction of light from the light source by the submicron particles in the gas increases and the light flux sensed by the light detector inside the instrument decreases.
Specifically, the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector have the same structure. The light source is arranged in the photoelectric sensor, and the photoelectric sensor is arranged in the light source in the emergent light transmission direction. The output end of the photoelectric sensor is connected with a high-gain operational amplifier, the high-gain operational amplifier is connected with a converter used for converting an electric signal into gas turbidity, and the converter is connected with the gas turbidity calculation module. The wavelengths of the light sources in the first gas turbidity detecting instrument, the second gas turbidity detecting instrument and the third gas turbidity detecting instrument are different, and in a feasible embodiment, the wavelengths are respectively 450nm, 500nm and 550nm, and the response ranges of the photoelectric sensors to the spectrum are all 400-600 nm. The principle of selecting the wavelength of the light source in the embodiment of the invention is as follows: colleagues in ascertaining the concentration of submicron particles in gases try to avoid the effects of water vapor and carbon dioxide on the attenuation of the light source.
The gas turbidity calculation module is shown in fig. 2 and comprises:
a gas turbidity obtaining unit for obtaining the first concentration rho measured by the first gas turbidity detector1The first concentration rho measured by the second gas turbidity detector2The first concentration rho measured by the third gas turbidity detector3。
A cluster center calculation unit for calculating a first concentration ρ1And a second concentration rho2And a third richDegree rho3Cluster center ρ of0. The calculation method of the cluster center can refer to the prior art.
A weight calculation unit for calculating the first concentration ρ1Corresponding weight value sigma1Second concentration ρ2Corresponding weight value sigma2And a third concentration ρ3Corresponding weight value sigma3. The weight value is obtained as the function
Where | ρ0-ρiL identifies the euclidean distance of the concentration from the center of the cluster.
And the calculation result output unit is used for outputting a gas concentration calculation result. The calculation result is obtained by using a weighted average method
If | ρ0-ρiIf is, then directly apply its corresponding rhoiAs a result of the calculation by the gas turbidity calculating unit.
Specifically, the memory is used for storing the gas turbidity calculation results of past times, and the comparator is used for comparing the gas turbidity difference between two recently calculated gas turbidities. If the absolute value of the gas turbidity difference is larger than a preset first threshold value, a classification driving request is issued to a server, and if the absolute value of the gas turbidity difference is larger than a preset second threshold value, an alarm instruction is issued to the server.
Specifically, the first threshold is smaller than the second threshold, and if the absolute value of the gas turbidity difference is larger than a preset first threshold, the server is triggered to re-collect and re-classify the gas turbidity at each gas turbidity monitoring point; and if the absolute value of the gas turbidity difference is larger than a preset second threshold value, prompting that an abnormality occurs near the gas turbidity monitoring point, and issuing an alarm instruction to a server.
And in the process of interacting with the server, the gas turbidity monitoring point starts to collect the gas turbidity in response to a gas turbidity collecting instruction issued by the server.
Specifically, the gas turbidity acquisition instruction comprises the gas turbidity acquisition times and the gas turbidity acquisition time interval. Responding to the gas turbidity acquisition instruction, the gas turbidity monitoring point generates a counter and a timer, the counter is used for recording the times of acquiring the gas turbidity, and the timer drives the gas turbidity monitoring point to acquire the gas turbidity. After the gas turbidity is obtained every time, the gas turbidity value is pressed into a gas turbidity queue, after the gas turbidity is collected, a gas turbidity sample vector corresponding to the gas turbidity monitoring point is generated according to gas turbidity data in the gas turbidity queue, and the gas turbidity sample vector is transmitted to a server.
The server performs automatic classification of gas turbidity, as shown in fig. 3, and includes:
s201, obtaining n gas turbidity monitoring points PiTransmitted gas turbidity sample vector xiA set of samples is constructed.
S202, obtaining automatic classification parameters and a target function.
S203, acquiring a classification number c.
In the embodiment of the present invention, the classification number c is 3.
And S204, obtaining a classification result according to the classification number, the automatic classification parameters and the target function.
In particular, the objective function comprises two sub-functions,
a first sub-function:
wherein A isjIs classified asijIs a sample xiFor class AjDegree of membership of; dijRepresenting a sample vector xiAnd prototype vjThe distance between them; δ is a first tunable parameter; α is the second adjustable parameter and β is the third adjustable parameter.
The second sub-function of the first sub-function,
wherein v iskIs a class prototype, v ', representing classification'kClass prototypes, ω, representing new classeskRepresents class AjThe category (1); i.e. the second sub-function represents an iterative process.
In the embodiment of the present invention, the following definitions are made for the class, the category and the class prototype:
there may be multiple classes in a class, where a class corresponds to a class prototype, and the class prototype characterizes the classification result.
Specifically, the values of δ, α, and β may be optimally adjusted according to the classification result, but δ, α, and β all belong to fixed parameters in the iterative process of automatic classification.
The obtaining of the classification result according to the classification number, the automatic classification parameter and the objective function specifically comprises:
s2041, randomly selecting c sample points from a sample set as an initial class prototype;
s2042, calculating a membership matrix according to the first sub-function;
s2043, calculating a class prototype according to the second sub-function;
and S2044, iteratively executing steps S2042 and S2043 until the class prototype does not change any more.
The class prototype is the classification result.
The embodiment of the invention also discloses an automatic gas turbidity classification device, which comprises the following components as shown in fig. 4:
a sample set acquisition module for acquiring n gas turbidity monitoring points PiTransmitted gas turbidity sample vector xiA set of samples is constructed.
And the first parameter acquisition module is used for acquiring the automatic classification parameters and the target function.
And the second parameter acquisition module is used for acquiring the classification number c.
And the classification module is used for obtaining a classification result according to the classification number, the automatic classification parameters and the target function.
The inventive device embodiment and the inventive method embodiment are based on the same inventive concept.
Embodiments of the present invention also provide a storage medium, which can be used to store program codes used in implementing the embodiments. Optionally, in this embodiment, the storage medium may be located in at least one network device of a plurality of network devices of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Specifically, fig. 5 is a schematic diagram of a server structure provided by an embodiment of the present invention, where the server structure may be used to operate an automatic gas turbidity classification apparatus. The server 800, which may vary significantly depending on configuration or performance, may include one or more Central Processing Units (CPUs) 822 (e.g., one or more processors) and memory 832, one or more storage media 830 (e.g., one or more mass storage devices) storing applications 842 or data 844. Memory 832 and storage medium 830 may be, among other things, transient or persistent storage. The program stored in the storage medium 830 may include one or more modules (not shown), each of which may include a series of instruction operations for the server. Still further, a central processor 822 may be provided in communication with the storage medium 830 for executing a series of instruction operations in the storage medium 830 on the server 800. The server 800 may also include one or more power supplies 826, one or more wired or wireless network interfaces 850, one or more input-output interfaces 858, and/or one or more operating systems 841, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth. The steps performed by the above-described method embodiment may be based on the server structure shown in fig. 5.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal can be implemented in other manners. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. The automatic gas turbidity classification system is characterized by comprising distributed gas turbidity monitoring points and a server, wherein the distributed gas turbidity monitoring points are communicated with the server, and each gas turbidity monitoring point responds to an instruction of the server to acquire gas turbidity and transmits the gas turbidity to the server so that the server can classify the gas turbidity monitoring points according to the acquired gas turbidity;
the server is used for carrying out automatic classification of gas turbidity based on the following method, comprising the following steps:
obtaining n gas turbidity monitoring points PiTransmitted gas turbidity sample vector xiForming a sample set;
acquiring automatic classification parameters and a target function;
obtaining a classification number c;
obtaining a classification result according to the classification number, the automatic classification parameters and the target function; the objective function comprises two sub-functions,
a first sub-function:
wherein A isjIs classified asijIs a sample xiFor class AjDegree of membership of; dijRepresenting a sample vector xiAnd prototype vjThe distance between them; δ is a first tunable parameter; α is a second tunable parameter, β is a third tunable parameter;
the second sub-function:
wherein v iskIs a class prototype, v ', representing classification'kClass prototypes, ω, representing new classeskRepresents class AjThe category (1); i.e. the second sub-function represents an iterative process;
wherein, the class has a plurality of classes, one class corresponds to one class prototype, and the class prototype represents the classification result
The obtaining of the classification result according to the classification number, the automatic classification parameter and the objective function specifically comprises:
randomly selecting c sample points from a sample set as initial class prototypes;
calculating a membership matrix according to the first sub-function;
calculating a class prototype according to the second sub-function;
the iteration execution step calculates a membership matrix according to the first sub-function and calculates a class prototype according to the second sub-function until the class prototype does not change any more; the class prototype is the classification result.
2. The automatic gas turbidity classification system according to claim 1, characterized by:
the gas turbidity monitoring point comprises a gas turbidity detection module, a gas turbidity calculation module, a memory and a comparator;
the gas turbidity detection module is formed by connecting a first gas turbidity detector, a second gas turbidity detector and a third gas turbidity detector in series, and the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector detect gas turbidity by sensing the change of light in the transmission process inside the detector.
3. The automatic gas turbidity classification system according to claim 2, characterized in that:
first gaseous turbidity detector, the gaseous turbidity detector of second and the gaseous turbidity detector of third possess the same structure, and it is inside including the light source, still be provided with photoelectric sensor on light source emergent light propagation direction, photoelectric sensor's output and high-gain fortune are put and are connected, high-gain fortune is put and is connected with the converter that is used for converting the signal of telecommunication into gaseous turbidity, the converter with gaseous turbidity calculation module connects.
4. The automatic gas turbidity classification system according to claim 2, characterized in that:
the light source wavelengths in the first gas turbidity detector, the second gas turbidity detector and the third gas turbidity detector are different and are respectively 450nm, 500nm and 550nm, and the response ranges of the photoelectric sensors to the spectrums are all 400-600 nm.
5. The automatic gas turbidity classification system according to claim 2,
the gas turbidity calculation module comprises:
a gas turbidity obtaining unit for obtaining the first concentration rho measured by the first gas turbidity detector1The first concentration rho measured by the second gas turbidity detector2The first concentration rho measured by the third gas turbidity detector3；
A cluster center calculation unit for calculating a first concentration ρ1And a second concentration rho2And a third concentration ρ3Cluster center ρ of0；
A weight calculation unit for calculating the first concentration ρ1Corresponding weight value sigma1Second concentration ρ2Corresponding weight value sigma2And a third concentration ρ3Corresponding weight value sigma3；
A calculation result output unit for outputting a gas concentration calculation result; the calculation result is obtained by using a weighted average method
If | ρ0-ρiIf is, then directly apply its corresponding rhoiAs a result of the calculation by the gas turbidity calculating unit.
6. The automatic gas turbidity classification system according to claim 2,
the memory is used for storing the calculation results of the gas turbidities of the past times, and the comparator is used for comparing the gas turbidity difference between two recently calculated gas turbidities; if the absolute value of the gas turbidity difference is larger than a preset first threshold value, a classification driving request is issued to a server, and if the absolute value of the gas turbidity difference is larger than a preset second threshold value, an alarm instruction is issued to the server.
7. The automatic gas turbidity classification system according to claim 2,
in the process of interacting with the server, the gas turbidity monitoring point starts to collect the gas turbidity in response to a gas turbidity collecting instruction issued by the server; the gas turbidity acquisition instruction comprises gas turbidity acquisition times and a gas turbidity acquisition time interval; responding to the gas turbidity acquisition instruction, the gas turbidity monitoring point generates a counter and a timer, the counter is used for recording the times of acquiring the gas turbidity, and the timer drives the gas turbidity monitoring point to acquire the gas turbidity; after the gas turbidity is obtained every time, the gas turbidity value is pressed into a gas turbidity queue, after the gas turbidity is collected, a gas turbidity sample vector corresponding to the gas turbidity monitoring point is generated according to gas turbidity data in the gas turbidity queue, and the gas turbidity sample vector is transmitted to a server.
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