CN115994625A - Pavement garbage prediction method and system and intelligent terminal - Google Patents
Pavement garbage prediction method and system and intelligent terminal Download PDFInfo
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Abstract
The application relates to a pavement garbage prediction method, a pavement garbage prediction system and an intelligent terminal, which comprise the steps of obtaining historical garbage data of a target area, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises weather information, dust garbage amount and activity information; classifying the historical junk data according to the activity information to construct a first data set; classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set; based on a preset model algorithm, constructing a prediction model according to the second data set; obtaining garbage data to be predicted; and carrying the garbage data to be predicted into a prediction model to obtain the predicted dust and garbage amount. The method has the effect of improving the prediction accuracy of the pavement garbage.
Description
Technical Field
The application relates to the technical field of pavement garbage prediction, in particular to a pavement garbage prediction method, a pavement garbage prediction system and an intelligent terminal.
Background
Along with the economic development and the improvement of the living standard of people, the road surface garbage gradually becomes the problems of environmental pollution and influence on the production and the living of people, and in order to keep the road surface clean, the road surface is cleaned, recovered and transported regularly by special people.
At present, a worker dispatches a sweeper and a garbage truck to clean a road surface according to working experience, but when the worker only depends on the working experience of the worker, a large deviation exists between predicted road surface garbage and actual road surface garbage, and the situation that the prediction accuracy is low occurs.
Disclosure of Invention
In order to improve the accuracy of predicting pavement garbage, the application provides a pavement garbage prediction method, a pavement garbage prediction system and an intelligent terminal.
The purpose of the application is to provide a pavement garbage prediction method.
The first object of the present application is achieved by the following technical solutions:
a pavement refuse prediction method, comprising;
acquiring historical garbage data of a target area, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises weather information, dust garbage amount and activity information;
classifying the historical junk data according to the activity information to construct a first data set;
classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set;
based on a preset model algorithm, constructing a prediction model according to the second data set;
obtaining garbage data to be predicted;
and carrying the garbage data to be predicted into a prediction model to obtain the predicted dust and garbage amount.
By adopting the technical scheme, the historical garbage data in the target area are classified one by one according to the activity information and the weather information, the obtained data has regularity and distinguishing property, the classified data is modeled according to a preset model algorithm, a prediction model is constructed, the garbage data to be predicted is then brought into the prediction model, the predicted dust and garbage amount is obtained, the working experience of a worker is limited, and compared with the dust and garbage amount obtained by the working experience of the worker, the prediction accuracy of the predicted dust and garbage amount is high.
The present application may be further configured in a preferred example to: classifying the historical garbage data according to the activity information to construct a first data set, wherein the first data set comprises;
the activity information comprises activity and inactivity;
classifying the movable historical garbage data to obtain a first classification result;
classifying the inactive historical garbage data to obtain a second classification result;
and constructing a first data set according to the first classification result and the second classification result.
The present application may be further configured in a preferred example to: classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set, wherein the second data set comprises;
classifying the historical garbage data in the first classification result according to a preset standard weather to obtain a third classification result;
classifying the historical garbage data in the second classification result according to a preset standard weather to obtain a fourth classification result;
and constructing a second data set according to the third classifying result and the fourth classifying result.
The present application may be further configured in a preferred example to: the preset model algorithm comprises a neural network algorithm, a decision tree algorithm and a logistic regression algorithm.
The present application may be further configured in a preferred example to: said constructing a predictive model from said second dataset comprising;
deleting first abnormal data in the historical rubbish data of each class in the third classification result to obtain a first deletion result;
deleting second abnormal data in the historical garbage data of each class in the fourth classification result to obtain a second deletion result;
and constructing a prediction model according to the first deleting result and the second deleting result.
The present application may be further configured in a preferred example to: deleting first abnormal data in the historical garbage data of each class in the third classification result to obtain a first deletion result, wherein the first deletion result comprises;
comparing the dust and garbage amount in each class of the third classification result with a preset fluctuation data threshold;
if the dust and garbage amount is within a preset fluctuation data threshold, reserving the data of the dust and garbage amount;
and if the dust and garbage amount is not within the preset fluctuation data threshold, deleting the data of the dust and garbage amount.
The present application may be further configured in a preferred example to: the step of constructing a prediction model according to the first deleting result and the second deleting result comprises the following steps of;
distributing the historical garbage data of each class in the first deleting result according to a preset first proportion to obtain a first distributing result, wherein the first distributing result comprises first model data and first training data;
training the first model data by the first training data to obtain a first training result;
distributing the historical garbage data of each class in the second deleting result according to a preset second proportion to obtain a second distribution result, wherein the second distribution result comprises second model data and second training data;
training the second training data on the second model data to obtain a second training result;
and determining a prediction model according to the first training result and the second training result.
The present application may be further configured in a preferred example to: the first training data is smaller than the first model data, and the second training data is smaller than the second model data.
The second purpose of the application is to provide a pavement garbage prediction system.
The second object of the present application is achieved by the following technical solutions:
a pavement refuse prediction system, comprising;
the first acquisition module is used for acquiring historical garbage data of the target area, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises weather information, dust garbage amount and activity information;
the first construction module is used for classifying the historical garbage data according to the activity information and constructing a first data set;
the second construction module is used for classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set;
the third construction module is used for constructing a prediction model according to the second data set based on a preset model algorithm;
the second acquisition module is used for acquiring the garbage data to be predicted;
and the prediction module is used for bringing the garbage data to be predicted into the prediction model to obtain the predicted dust and garbage amount.
The third purpose of the application is to provide an intelligent terminal.
The third object of the present application is achieved by the following technical solutions:
an intelligent terminal comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute the pavement garbage prediction method.
In summary, the present application includes the following beneficial technical effects:
by adopting the technical scheme, the historical garbage data in the target area are classified one by one according to the activity information and the weather information, the obtained data has regularity and distinguishing property, the classified data is modeled according to a preset model algorithm, a prediction model is constructed, the garbage data to be predicted is then brought into the prediction model, the predicted dust and garbage amount is obtained, the working experience of a worker is limited, and compared with the dust and garbage amount obtained by the working experience of the worker, the prediction accuracy of the predicted dust and garbage amount is high.
Drawings
Fig. 1 is a schematic flow chart of a pavement refuse prediction method according to an embodiment of the present application.
Fig. 2 is a system schematic diagram of a pavement refuse prediction system according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an intelligent terminal according to an embodiment of the present application.
Reference numerals illustrate: 21. a first acquisition module; 22. a first building block; 23. a second building block; 24. a third building module; 25. a second acquisition module; 26. a prediction module; 301. a CPU; 302. a ROM; 303. a RAM; 304. a bus; 305. an I/O interface; 306. an input section; 307. an output section; 308. a storage section; 309. a communication section; 310. a driver; 311. removable media.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
The present embodiment is merely illustrative of the present application and is not intended to be limiting, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as necessary, but are protected by patent laws within the scope of the claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a pavement garbage prediction method, a pavement garbage prediction system and an intelligent terminal, which can improve the prediction accuracy of pavement garbage.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
Referring to fig. 1:
step S100: and acquiring historical garbage data of the target area.
Specifically, the history garbage data includes a plurality of pieces of data, each piece of data including date information, weather information, dust garbage amount, and activity information. The related historical data materials are called from the meteorological department and the municipal department and are arranged into the data strips which are marked by date information, weather information, dust and garbage amount and activity information, and a plurality of data strips are used for facilitating the summarization and analysis of the data. In this embodiment of the present application, the target area is a street, a cell or a city, which is not described herein in detail.
Step S200: and classifying the historical junk data according to the activity information to construct a first data set.
Specifically, the activity information comprises two conditions of activity and inactivity, active data strips are screened from historical garbage data, and classification processing is carried out to obtain a first classification result; and screening the inactive data strips from the historical garbage data, and classifying to obtain a second classification result.
List one
Watch II
The first table is a first classification result, which is summarized by inactive data bars; and the second table is a second classification result, and the active data bars are summarized together. A first data set is constructed from the co-constructs of Table one and Table two.
Step S300: and classifying each type of historical junk data in the first data set according to weather information to construct a second data set.
Specifically, the above known method includes screening and classifying historical garbage data to form a first table and a second table, and further screening and classifying the first table and the second table according to preset standard weather respectively, namely screening and classifying data strips in the first classification result according to the preset standard weather to obtain a third classification result, and screening and classifying the data strips in the second classification result according to the preset standard weather to obtain a fourth classification result. It should be noted that the preset standard weather is a threshold of a range of weather information, such as 10ml of light rain, 12ml of light rain and 13ml of light rain, which are all included in the range of light rain.
Watch III
Table four
And the third table is a table of one weather in the third classification result, the fourth table is a table of one weather in the fourth classification result, and the third classification result and the fourth classification result jointly construct a second data set. It should be noted that there are multiple tables in the third categorization result and the fourth categorization result.
And step 400, constructing a prediction model according to the second data set based on a preset model algorithm.
Specifically, the preset model algorithm comprises a neural network algorithm, a decision tree algorithm and a logistic regression algorithm.
Firstly, comparing the dust and garbage amount in each class of the third classification result with a preset first fluctuation data threshold value:
if the dust and garbage amount in the third classification result is within the preset first fluctuation data threshold, the data strip is reserved, and if the dust and garbage amount in the third classification result is not within the preset first fluctuation data threshold, the data strip is marked as first abnormal data, and the data strip is deleted to obtain a first deletion result, and the first deletion result is the reserved data strip.
Similarly, comparing the dust and garbage amount in each category in the fourth classification result with a preset second fluctuation data threshold value:
if the dust and garbage amount in the fourth classification result is within the preset second fluctuation data threshold, the data bar is reserved, and if the dust and garbage amount in the fourth classification result is not within the preset second fluctuation data threshold, the data bar is marked as second abnormal data, and the data bar is deleted to obtain a second deletion result, and it is noted that the second deletion result is the reserved data bar.
It is known that the preset first fluctuation data threshold value and the preset second fluctuation data threshold value are set according to actual conditions.
And then, distributing the data strips of each class in the first deleting result according to a preset first proportion to obtain a first distributing result, wherein the first distributing result comprises first model data and first training data, namely if 50 data strips of a certain class are included in the first deleting result, distributing according to the preset first proportion, wherein one part of the data strips are used as the first model data, and the other part of the data strips are used as the first training data. And training the first model data by the first training data to obtain a first training result. In the embodiment of the present application, the preset first ratio is set according to the actual situation.
And similarly, distributing the data strips of each class in the second deleting result according to a preset second proportion to obtain a second distributing result, wherein the second distributing result comprises second model data and second training data, namely if the total number of the data strips of a certain class in the second deleting result is 100, distributing according to the preset second proportion, wherein one part of the data strips are used as the second model data, and the other part of the data strips are used as the second training data. And training the second model data by the second training data to obtain a second training result. In the embodiment of the present application, the preset second ratio is set according to the actual situation.
And finally, constructing a prediction model according to a preset model algorithm, the first training result and the second training result.
Step S500: and obtaining the garbage data to be predicted.
Specifically, staff obtain to-be-predicted garbage data from the meteorological department and the municipal department, wherein the to-be-predicted garbage data comprises date, weather and activity information.
Step S600: and carrying the garbage data to be predicted into a prediction model to obtain the predicted dust and garbage amount.
Specifically, the known garbage data to be predicted is brought into the prediction model, and the predicted dust and garbage amount can be obtained through the prediction of the prediction model, so that a worker can reasonably arrange a special person to clean and recycle garbage.
Of course, only one embodiment is disclosed in the examples of the present application for reference, but the order of all the steps is not limited.
In summary, by adopting the above technical scheme, by classifying the historical garbage data in the target area one by one according to the activity information and the weather information, the obtained data has regularity and distinguishing property, and further, according to a preset model algorithm, modeling is performed on the classified data to construct a prediction model, and then the garbage data to be predicted is brought into the prediction model to obtain the predicted dust garbage amount.
Referring to fig. 2, a road surface refuse prediction system includes a first acquisition module 21, a first construction module 22, a second construction module 23, a third construction module 24, a second acquisition module 25, and a prediction module 26, wherein:
a first obtaining module 21, configured to obtain historical garbage data of a target area;
a first construction module 22, configured to classify historical garbage data according to the activity information, and construct a first data set;
a second construction module 23, configured to classify each type of historical garbage data in the first data set according to the weather information, and construct a second data set;
a third construction module 24, configured to construct a prediction model according to the second data set based on a preset model algorithm;
a second obtaining module 25, configured to obtain garbage data to be predicted;
the prediction module 26 is configured to bring the garbage data to be predicted into the prediction model to obtain a predicted dust garbage amount.
Referring to fig. 3, the smart terminal includes a Central Processing Unit (CPU) 301, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read out therefrom is installed into the storage section 308 as needed.
In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor, for example, as: the processor is connected to a first acquisition module 21, a first construction module 22, a second construction module 23, a third construction module 24, a second acquisition module 25 and a prediction module 26. The names of these units or modules do not constitute a limitation on the unit or module itself in some cases, and for example, the first acquisition module 21 may also be described as "a module for acquiring historical garbage data of a target area".
As another aspect, the present application also provides a computer-readable storage medium that may be included in the electronic device described in the above embodiments; or may be present alone without being incorporated into the electronic device. The computer-readable storage medium stores one or more programs that when executed by one or more processors perform the data encryption transmission method described herein.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (10)
1. A pavement garbage prediction method is characterized in that: comprises the following steps of;
acquiring historical garbage data of a target area, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises weather information, dust garbage amount and activity information;
classifying the historical junk data according to the activity information to construct a first data set;
classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set;
based on a preset model algorithm, constructing a prediction model according to the second data set;
obtaining garbage data to be predicted;
and carrying the garbage data to be predicted into a prediction model to obtain the predicted dust and garbage amount.
2. The pavement refuse prediction method according to claim 1, characterized in that: classifying the historical garbage data according to the activity information to construct a first data set, wherein the first data set comprises;
the activity information comprises activity and inactivity;
classifying the movable historical garbage data to obtain a first classification result;
classifying the inactive historical garbage data to obtain a second classification result;
and constructing a first data set according to the first classification result and the second classification result.
3. The pavement refuse prediction method according to claim 2, characterized in that: classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set, wherein the second data set comprises;
classifying the historical garbage data in the first classification result according to a preset standard weather to obtain a third classification result;
classifying the historical garbage data in the second classification result according to a preset standard weather to obtain a fourth classification result;
and constructing a second data set according to the third classifying result and the fourth classifying result.
4. The pavement refuse prediction method according to claim 1, characterized in that: the preset model algorithm comprises a neural network algorithm, a decision tree algorithm and a logistic regression algorithm.
5. A pavement refuse prediction method according to claim 3, characterized in that: said constructing a predictive model from said second dataset comprising;
deleting first abnormal data in the historical rubbish data of each class in the third classification result to obtain a first deletion result;
deleting second abnormal data in the historical garbage data of each class in the fourth classification result to obtain a second deletion result;
and constructing a prediction model according to the first deleting result and the second deleting result.
6. The pavement refuse prediction method according to claim 5, characterized in that: deleting first abnormal data in the historical garbage data of each class in the third classification result to obtain a first deletion result, wherein the first deletion result comprises;
comparing the dust and garbage amount in each class of the third classification result with a preset first fluctuation data threshold;
if the dust and garbage amount is within a preset first fluctuation data threshold, reserving the data of the dust and garbage amount;
and if the dust and garbage amount is not within the preset first fluctuation data threshold, deleting the data of the dust and garbage amount.
7. The pavement refuse prediction method according to claim 5, characterized in that: the step of constructing a prediction model according to the first deleting result and the second deleting result comprises the following steps of;
distributing the historical garbage data of each class in the first deleting result according to a preset first proportion to obtain a first distributing result, wherein the first distributing result comprises first model data and first training data;
training the first model data by the first training data to obtain a first training result;
distributing the historical garbage data of each class in the second deleting result according to a preset second proportion to obtain a second distribution result, wherein the second distribution result comprises second model data and second training data;
training the second training data on the second model data to obtain a second training result;
and determining a prediction model according to the first training result and the second training result.
8. The pavement refuse prediction method according to claim 7, characterized in that: the first training data is smaller than the first model data, and the second training data is smaller than the second model data.
9. A pavement rubbish prediction system, characterized in that: comprises the following steps of;
a first acquisition module (21) for acquiring historical trash data of a target area, the historical trash data comprising a plurality of pieces of data, each piece of data comprising weather information, dust trash amount and activity information;
a first construction module (22) for classifying historical garbage data according to the activity information to construct a first data set;
a second construction module (23) for classifying each type of historical garbage data in the first data set according to the weather information to construct a second data set;
a third construction module (24) for constructing a predictive model from the second dataset based on a pre-set model algorithm;
a second acquisition module (25) for acquiring garbage data to be predicted;
and the prediction module (26) is used for bringing the garbage data to be predicted into the prediction model to obtain the predicted dust and garbage amount.
10. An intelligent terminal, its characterized in that: comprising a memory and a processor, said memory having stored thereon a computer program capable of being loaded by the processor and performing the method according to any of claims 1 to 8.
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