CN113379580A - Multi-data-source fusion traceability analysis method and device based on environmental monitoring and terminal - Google Patents
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Abstract
The invention provides a multi-data-source fusion traceability analysis method, a device and a terminal based on environmental monitoring. The method comprises the following steps: the method comprises the steps of obtaining multi-data-source data after primary cleaning, grouping the multi-data-source data after primary cleaning based on service types, and determining a plurality of service type data; performing secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning, grouping each service type data in the plurality of service type data after the secondary cleaning based on the alarm type, and determining a plurality of alarm type data; and performing source tracing analysis by using the plurality of alarm type data to determine a pollution source. The invention can perform data fusion on multiple data sources, realizes intelligent traceability analysis by taking a service scene as a driver according to data characteristics, and effectively determines a pollution source.
Description
Technical Field
The invention relates to the technical field of pollution source analysis, in particular to a multi-data source fusion traceability analysis method, a device and a terminal based on environmental monitoring.
Background
The environmental pollution is more and more serious, the environmental pollution becomes a major problem influencing the healthy life of people, and great harm is caused to the production and life of people, so the source tracing analysis of the pollution source is inevitable.
At present, the traceability analysis of the pollution source is generally performed by obtaining topographic information of the area to be detected and an emission source list of the area to be detected, obtaining emission source component spectrum data of the area to be detected, and then performing traceability analysis on the pollution source based on a source tracking model according to the topographic information, the emission source list and the emission source component spectrum data.
However, the above method for analyzing the source of the pollution source cannot solve the problem of analyzing the source of the pollution source when the monitoring data is of various and complex types.
Disclosure of Invention
The embodiment of the invention provides a multi-data-source fusion traceability analysis method, a multi-data-source fusion traceability analysis device and a multi-data-source fusion traceability analysis terminal based on environmental monitoring, and aims to solve the problem of traceability analysis of pollution sources under the condition that the monitoring data are of different types and are complex.
In a first aspect, an embodiment of the present invention provides a multi-data-source fusion traceability analysis method based on environmental monitoring, including:
acquiring multi-data-source data after primary cleaning;
grouping the multiple data sources after the data are cleaned for one time based on the service types, and determining multiple service type data;
carrying out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning;
grouping each service type data in the service type data after secondary cleaning based on the alarm type, and determining a plurality of alarm type data;
and performing source tracing analysis by using the plurality of alarm type data to determine a pollution source.
In one possible implementation, the secondary cleaning includes an abnormal data cleaning;
carrying out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after secondary cleaning, wherein the method comprises the following steps:
the method for cleaning the abnormal data of the plurality of service types to obtain the plurality of service types after cleaning the abnormal data comprises the following steps:
acquiring a plurality of service type data;
and sequentially carrying out null value cleaning, negative value cleaning and outlier data cleaning on the plurality of service type data, and determining the plurality of service type data after the abnormal data cleaning.
In a possible implementation manner, sequentially performing null value cleaning, negative value cleaning, and outlier data cleaning on a plurality of service type data, and determining a plurality of service type data after abnormal data cleaning, includes:
eliminating null values in the plurality of service type data to obtain a plurality of service type data after one elimination;
eliminating negative values in the plurality of service type data after the primary elimination to obtain a plurality of service type data after the secondary elimination;
acquiring the mean value corresponding to the plurality of service type data after secondary elimination in the preset time;
and if the service type data and the average value in the plurality of service type data after secondary elimination meet a first preset condition, eliminating the service type data to obtain a plurality of service type data after abnormal data cleaning.
In one possible implementation, the alarm type data includes over-average data, continuous high data, and abnormally active data;
grouping each service type data in the service type data after the secondary cleaning based on the alarm type, and determining the alarm type data, wherein the grouping comprises the following steps:
if the service type data in the service type data after the secondary cleaning meets a second preset condition, obtaining the over-average data;
if the service type data in the plurality of service type data after the secondary cleaning meets a third preset condition, obtaining continuous high data;
and if the service type data in the plurality of service type data after the secondary cleaning meets a fourth preset condition, obtaining abnormal active data.
In a possible implementation manner, if service type data in the service type data after the secondary cleaning meets a second preset condition, obtaining the over-average data includes:
acquiring a reference value;
and if the service type data in the service type data after the secondary cleaning is larger than the reference value and the first preset threshold value, taking the service data as the over-average data.
In a possible implementation manner, if service type data in the plurality of service type data after the secondary cleaning satisfies a third preset condition, obtaining continuous high data includes:
acquiring a reference value and continuous service type data of a preset period;
and if the service type data of each preset period in the continuous preset periods are both larger than the reference value, and the increase amplitude of the service type data of the next preset period in the two adjacent preset periods relative to the service type data of the previous preset period meets a second preset threshold value, taking the service type data as continuous high data.
In a possible implementation manner, if service type data in the plurality of service type data after the secondary cleaning meets a fourth preset condition, obtaining abnormally active data includes:
acquiring service type data in a plurality of service type data after secondary cleaning within preset time;
and if the variation coefficient of the service type data in the plurality of service type data subjected to secondary cleaning within the preset time is larger than the change multiple, taking the service type data as the abnormal active data.
In a possible implementation manner, before acquiring the multiple data source data after one cleaning, the method further includes:
acquiring data of multiple data sources;
and cleaning invalid data in the multi-data-source data to obtain the multi-data-source data after one-time cleaning.
In a second aspect, an embodiment of the present invention provides an environment monitoring-based multi-data source fusion traceability analysis apparatus, including:
the data acquisition module is used for acquiring the multi-data source data after the primary cleaning;
the service type data determining module is used for grouping the multi-data source data after the primary cleaning based on the service type and determining a plurality of service type data;
the secondary cleaning module is used for carrying out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning;
the alarm type data determining module is used for grouping each service type data in the plurality of service type data after secondary cleaning based on the alarm type to determine a plurality of alarm type data;
and the pollution source determining module is used for performing traceability analysis by utilizing the plurality of alarm type data to determine a pollution source.
In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect.
The embodiment of the invention provides a multi-data-source fusion traceability analysis method, a device and a terminal based on environmental monitoring. The invention performs data fusion on multiple data sources, takes the service scene as the drive according to the data characteristics, realizes intelligent traceability analysis and effectively determines the pollution source.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an implementation of a multi-data-source fusion traceability analysis method based on environmental monitoring according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an environment monitoring-based multi-data source fusion traceability analysis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, it shows a flowchart of an implementation of the method for analyzing the source-tracing fusion based on multiple data sources for environmental monitoring according to the embodiment of the present invention, which is detailed as follows:
step S101: acquiring multi-data-source data after primary cleaning;
step S102: grouping the multiple data sources after the data are cleaned for one time based on the service types, and determining multiple service type data;
step S103: carrying out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning;
step S104: grouping each service type data in the service type data after secondary cleaning based on the alarm type, and determining a plurality of alarm type data;
step S105: and performing source tracing analysis by using the plurality of alarm type data to determine a pollution source.
In one embodiment, the data of multiple data sources is data of multiple data sources, such as air quality monitoring data, pollution source and quality control data, sub-meter electricity metering data, enterprise carbon monoxide data, enterprise intelligent access control data, enterprise VOCs monitoring data, alarm data and the like. The invention carries out data fusion aiming at multiple data sources, takes a service scene as a driver according to data characteristics, realizes intelligent traceability analysis and realizes intelligent data application.
The embodiment of the invention determines a plurality of service type data by obtaining the multi-data source data after primary cleaning and grouping the multi-data source data after primary cleaning based on the service types, then carries out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after secondary cleaning, then groups each service type data in the plurality of service type data after secondary cleaning based on the alarm type to determine a plurality of alarm type data, and finally utilizes the plurality of alarm type data to carry out traceability analysis and determine the pollution source. The invention performs data fusion on multiple data sources, takes the service scene as the drive according to the data characteristics, realizes intelligent traceability analysis and effectively determines the pollution source.
In one possible implementation, step S103 includes:
the method for cleaning the abnormal data of the plurality of service types to obtain the plurality of service types after cleaning the abnormal data comprises the following steps:
step S201: acquiring a plurality of service type data;
step S202: and sequentially carrying out null value cleaning, negative value cleaning and outlier data cleaning on the plurality of service type data, and determining the plurality of service type data after the abnormal data cleaning.
In one embodiment, the second cleaning includes an outlier cleaning, wherein the outlier includes a null, a negative, and an outlier. For example, the plurality of service type data are respectively a service type data a, a service type data B and a service type data C, each service data in the service type data a is first subjected to null value cleaning, negative value cleaning and outlier data cleaning, and the operations of the service type data B and the service type data C according to the service type data a are similar, and are not described herein again.
In one possible implementation, step S202 includes:
step S301: eliminating null values in the plurality of service type data to obtain a plurality of service type data after one elimination;
step S302: eliminating negative values in the plurality of service type data after the primary elimination to obtain a plurality of service type data after the secondary elimination;
step S303: acquiring the mean value corresponding to the plurality of service type data after secondary elimination in the preset time;
step S304: and if the service type data and the average value in the plurality of service type data after secondary elimination meet a first preset condition, eliminating the service type data to obtain a plurality of service type data after abnormal data cleaning.
In one embodiment, the previous preset time refers to a period of time before the current time period, for example, the current time is one hour before the current time period, and the previous preset time refers to one or more hours before the current hour. Comparing the current service type data with the mean value corresponding to the service type data of the last hours, if the difference between the current service type data and the mean value corresponding to the service type data of the last hours is more than 3 times, considering the service type data as outlier data, and judging the formula as follows: (absolute value ((traffic type data a-mean b)/mean b) > 3).
In one possible implementation, step S104 includes:
step S401: if the service type data in the service type data after the secondary cleaning meets a second preset condition, obtaining the over-average data;
step S402: if the service type data in the plurality of service type data after the secondary cleaning meets a third preset condition, obtaining continuous high data;
step S403: and if the service type data in the plurality of service type data after the secondary cleaning meets a fourth preset condition, obtaining abnormal active data.
In one embodiment, the alarm type data includes over-average data, continuous high data, and abnormally active data.
In one possible implementation, step S401 includes:
step S501: acquiring a reference value;
step S502: and if the service type data in the service type data after the secondary cleaning is larger than the reference value and the first preset threshold value, taking the service data as the over-average data.
In one embodiment, the first predetermined threshold is a value that exceeds the predetermined reference point by 150%. And if the service type data in the service type data after the secondary cleaning is larger than the reference value and exceeds the defined assessment point mean value by 150%, the service type data at the moment is the data exceeding the mean value, and the judgment formula is as follows: ((business type data a-comparison data mean b)/comparison data mean b) > super multiple d).
In one possible implementation, step S402 includes:
step S601: acquiring a reference value and continuous service type data of a preset period;
step S602: and if the service type data of each preset period in the continuous preset periods are both larger than the reference value, and the increase amplitude of the service type data of the next preset period in the two adjacent preset periods relative to the service type data of the previous preset period meets a second preset threshold value, taking the service type data as continuous high data.
In an embodiment, if the continuous preset period is 4 hours, if the service type data of each hour is greater than the reference value, it is determined whether the service type data is continuously high, specifically: the increase amplitude of each hour compared with the previous hour is between the proportion 1 and the proportion 2, wherein the judgment formula is as follows: ((traffic type data a-mean b/mean b) >).
In one possible implementation, step S403 includes:
step S701: acquiring service type data in a plurality of service type data after secondary cleaning within preset time;
step S702: and if the variation coefficient of the service type data in the plurality of service type data subjected to secondary cleaning within the preset time is larger than the change multiple, taking the service type data as the abnormal active data.
In an embodiment, if the preset time is three days, the data with the variation coefficient larger than the variation multiple of the service type data in the three days is the abnormal active data. Wherein, the coefficient of variation C · V is (standard deviation SD/Mean) × 100%, and can be calculated once a day for the service type data, and the calculation is performed in a rolling manner for 96 hours; 96 are configurable.
The method fuses the grouped data into a unified project, performs source tracing analysis according to the data condition, and analyzes the root cause of pollution. Firstly, tracing and analyzing a site according to data which is over-average, continuous, high and abnormally active; the second step analyzes the cause of contamination: 1. local normal contamination causes; 2. a device anomaly causes; 3. caused by external contamination; and thirdly, judging the location of the pollution source according to the actual conditions of the project, and providing accurate data support for the final tracing.
In a possible implementation manner, before step S101, the method further includes:
step S801: acquiring data of multiple data sources;
step S802: and cleaning invalid data in the multi-data-source data to obtain the multi-data-source data after one-time cleaning.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 2 shows a schematic structural diagram of an environment monitoring-based multi-data-source fusion traceability analysis apparatus provided in an embodiment of the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown, which are detailed as follows:
as shown in fig. 2, an environment monitoring-based multi-data source fusion traceability analysis apparatus 2 includes: .
The data acquisition module 21 is configured to acquire multiple data source data after one-time cleaning;
the service type data determining module 22 is configured to group the multiple data sources data after the primary cleaning based on the service type, and determine multiple service type data;
the secondary cleaning module 23 is configured to perform secondary cleaning on the multiple service type data to obtain multiple service type data after the secondary cleaning;
the alarm type data determining module 24 is configured to group each of the plurality of service type data after the secondary cleaning based on the alarm type, and determine a plurality of alarm type data;
and the pollution source determining module 25 is configured to perform traceability analysis by using the plurality of alarm type data to determine a pollution source.
In one possible implementation, the secondary cleaning includes an abnormal data cleaning;
the secondary cleaning module 23 includes:
the method for cleaning the abnormal data of the plurality of service types to obtain the plurality of service types after cleaning the abnormal data comprises the following steps:
the service type data acquisition submodule is used for acquiring a plurality of service type data;
and the data secondary cleaning submodule is used for sequentially carrying out null value cleaning, negative value cleaning and outlier data cleaning on the plurality of service type data and determining the plurality of service type data after the abnormal data cleaning.
In one possible implementation, the data secondary washing submodule includes:
the first eliminating unit is used for eliminating null values in the plurality of service type data to obtain the plurality of service type data after one elimination;
the second eliminating unit is used for eliminating negative values in the multiple service type data after the first eliminating to obtain multiple service type data after the second eliminating;
the average value obtaining unit is used for obtaining an average value corresponding to the plurality of service type data after the secondary elimination in the preset time;
and the third eliminating unit is used for eliminating the service type data to obtain a plurality of service type data after the abnormal data is cleaned if the service type data and the average value in the plurality of service type data after the secondary elimination meet the first preset condition.
In one possible implementation, the alarm type data includes over-average data, continuous high data, and abnormally active data;
the alarm type data determination module 24 includes:
the first judgment submodule is used for obtaining the over-average value data if the service type data in the plurality of service type data after the secondary cleaning meets a second preset condition;
the second judgment submodule is used for obtaining continuous high data if the service type data in the service type data after the secondary cleaning meets a third preset condition;
and the third judging submodule is used for obtaining the abnormal active data if the service type data in the plurality of service type data after the secondary cleaning meets a fourth preset condition.
In one possible implementation manner, the first determining sub-module includes:
a first data acquisition unit configured to acquire a reference value;
and the over-average data determining unit is used for taking the service data as over-average data if the service type data in the service type data after the secondary cleaning is larger than a reference value and a first preset threshold, wherein the first preset threshold is a numerical value which exceeds a preset check point by 150%.
In one possible implementation, the second determining sub-module includes:
the second data acquisition unit is used for acquiring the reference value and the continuous service type data of the preset period;
and the continuous high data determining unit is used for taking the service type data as continuous high data if the service type data of each preset period in the continuous preset periods is larger than a reference value and the increase amplitude of the service type data of the next preset period in the two adjacent preset periods relative to the service type data of the previous preset period meets a second preset threshold value.
In a possible implementation manner, the third determining sub-module includes:
a third data obtaining unit, configured to obtain service type data in the multiple service type data after the secondary cleaning within a preset time;
and the abnormal activity data determining unit is used for taking the service type data as the abnormal activity data if the variation coefficient of the service type data in the plurality of service type data subjected to secondary cleaning in the preset time is greater than the change multiple.
In a possible implementation manner, before the data obtaining module 21, the method further includes:
the multi-data source data acquisition submodule is used for acquiring multi-data source data;
and the primary cleaning submodule is used for cleaning invalid data in the multi-data-source data to obtain the multi-data-source data after primary cleaning.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. The processor 30 executes the computer program 32 to implement the steps in each of the above-mentioned embodiments of the environment monitoring-based multi-data-source fusion traceability analysis method, such as the steps 101 to 105 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of the various modules/units in the various device embodiments described above, such as the functions of the modules/units 21 to 25 shown in fig. 2.
Illustratively, the computer program 32 may be divided into one or more modules/units, which are stored in the memory 31 and executed by the processor 30 to carry out the invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into the modules/units 21 to 25 shown in fig. 2.
The terminal 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is only an example of a terminal 3 and does not constitute a limitation of the terminal 3 and may comprise more or less components than those shown, or some components may be combined, or different components, e.g. the terminal may further comprise input output devices, network access devices, buses, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk provided on the terminal 3, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 31 may also include both an internal storage unit of the terminal 3 and an external storage device. The memory 31 is used for storing computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, 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, devices or units, and may be in an electrical, mechanical or other form.
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.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above-mentioned embodiments of the method for fusion and traceability analysis based on environment monitoring and multiple data sources. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A multi-data-source fusion traceability analysis method based on environmental monitoring is characterized by comprising the following steps:
acquiring multi-data-source data after primary cleaning;
grouping the multiple data sources after the primary cleaning based on the service types, and determining multiple service type data;
performing secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning;
grouping each service type data in the service type data after the secondary cleaning based on the alarm type, and determining a plurality of alarm type data;
and performing source tracing analysis by using the plurality of alarm type data to determine a pollution source.
2. The method of claim 1, wherein the secondary cleaning comprises an abnormal data cleaning;
the performing the secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning includes:
performing abnormal data cleaning on the plurality of service type data to obtain a plurality of service type data after the abnormal data cleaning, including:
acquiring the plurality of service type data;
and sequentially carrying out null value cleaning, negative value cleaning and outlier data cleaning on the plurality of service type data, and determining the plurality of service type data after the abnormal data cleaning.
3. The method according to claim 2, wherein the sequentially performing null value cleaning, negative value cleaning and outlier data cleaning on the plurality of service type data to determine the plurality of service type data after the abnormal data cleaning comprises:
eliminating null values in the plurality of service type data to obtain a plurality of service type data after one elimination;
eliminating negative values in the plurality of service type data after the primary elimination to obtain a plurality of service type data after the secondary elimination;
acquiring the mean value corresponding to the plurality of service type data after secondary elimination in the preset time;
and if the service type data in the plurality of service type data after secondary elimination and the average value meet a first preset condition, eliminating the service type data to obtain a plurality of service type data after abnormal data cleaning.
4. The method of claim 1, wherein the alarm type data includes hyper-average data, continuous high data, and abnormally active data;
the grouping of each service type data in the service type data subjected to the secondary cleaning based on the alarm type to determine the alarm type data comprises the following steps:
if the service type data in the service type data after the secondary cleaning meets a second preset condition, obtaining over-average data;
if the service type data in the service type data after the secondary cleaning meets a third preset condition, obtaining continuous high data;
and if the service type data in the plurality of service type data after the secondary cleaning meets a fourth preset condition, obtaining abnormal active data.
5. The method according to claim 4, wherein obtaining the over-average value data if the service type data in the service type data after the secondary cleaning satisfies a second preset condition comprises:
acquiring a reference value;
and if the service type data in the service type data after the secondary cleaning is larger than the reference value and a first preset threshold value, taking the service type data as the over-average data.
6. The method according to claim 4, wherein obtaining the continuous high data if the service type data in the plurality of service type data after the secondary cleaning satisfies a third preset condition comprises:
acquiring a reference value and continuous service type data of a preset period;
and if the service type data of each preset period in the continuous preset periods is larger than the reference value, and the increase amplitude of the service type data of the next preset period in the two adjacent preset periods relative to the service type data of the previous preset period meets a second preset threshold value, taking the service type data as the continuous high data.
7. The method according to claim 4, wherein obtaining the abnormal activity data if the service type data in the service type data after the secondary cleaning satisfies a fourth preset condition comprises:
acquiring service type data in a plurality of service type data after secondary cleaning within preset time;
and if the variation coefficient of the service type data in the plurality of service type data subjected to secondary cleaning in the preset time is larger than the change multiple, taking the service type data as the abnormal active data.
8. The method according to any of claims 1-7, wherein before obtaining the multiple data source data after one cleaning, further comprising:
acquiring data of multiple data sources;
and cleaning invalid data in the multi-data-source data to obtain the multi-data-source data after one-time cleaning.
9. The utility model provides a merge traceability analytical equipment based on environmental monitoring multidata source which characterized in that includes:
the data acquisition module is used for acquiring the multi-data source data after the primary cleaning;
the service type data determining module is used for grouping the multi-data source data subjected to the primary cleaning based on service types and determining a plurality of service type data;
the secondary cleaning module is used for carrying out secondary cleaning on the plurality of service type data to obtain a plurality of service type data after the secondary cleaning;
the alarm type data determining module is used for grouping each service type data in the plurality of service type data after the secondary cleaning based on the alarm type to determine a plurality of alarm type data;
and the pollution source determining module is used for performing traceability analysis by using the plurality of alarm type data to determine a pollution source.
10. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 8 when executing the computer program.
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