CN116050664B - Garbage yield prediction method, system, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to a garbage yield prediction method, a system, an electronic device and a readable storage medium, wherein the method comprises the steps of obtaining historical garbage data and garbage data to be predicted, wherein the historical garbage data comprises a region identifier, a date identifier, garbage yield, a weather identifier and event parameters; screening first historical data with weather identification equal to a standard weather preset value; classifying the first historical data according to the area identifier and the date identifier; for the first historical data of each class, determining standard garbage yield according to standard yield calculation rules; determining event influence parameters according to preset event influence calculation rules, standard garbage yield and event parameters; determining weather influence parameters according to a preset weather influence calculation rule, standard garbage yield and weather identification; and determining the predicted garbage yield according to a preset analysis method, garbage data to be predicted, weather influence parameters and event influence parameters. The method has the effect of improving the prediction accuracy of the garbage yield.
Description
Technical Field
The present disclosure relates to the field of garbage yield prediction, and in particular, to a garbage yield prediction method, system, electronic device, and readable storage medium.
Background
With the economic development and the improvement of the living standard of people, the garbage generated by social activities becomes increasingly a problem of environmental pollution and affecting the production and living of people. To keep the environment clean, municipal departments may regularly clean and recycle the garbage. At present, the municipal department gate can confirm the output of rubbish and then dispatch motor sweeper and rubbish clearance car that corresponds with rubbish output accomplish cleaning and retrieving the transportation to rubbish according to manual experience, but can have the great problem of predicted rubbish output and actual rubbish output deviation according to manual experience, can lead to the waste of the equipment that deploys to rubbish output when predicted rubbish output is higher than actual rubbish output, can lead to the plan of deployment not comprehensive when predicted rubbish output is lower than actual rubbish output, and then the untimely condition of rubbish clearance appears.
The prior art solutions described above have the following drawbacks: the prediction accuracy of the garbage yield is low.
Disclosure of Invention
In order to improve the problem of low prediction accuracy of garbage yield, the application provides a garbage yield prediction method, a system, an electronic device and a readable storage medium.
In a first aspect of the present application, a garbage yield prediction method is provided. The method comprises the following steps:
acquiring historical garbage data, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises a region identifier, a date identifier, garbage yield, a weather identifier and event parameters;
screening first historical data of the weather identification equal to a standard weather preset value;
classifying the first historical data according to the area identifier and the date identifier;
for the first historical data of each class, determining standard garbage yield according to standard yield calculation rules;
determining event influence parameters according to a preset event influence calculation rule, the standard garbage yield and the event parameters;
determining weather influence parameters according to a preset weather influence calculation rule, the standard garbage yield and the weather identification;
obtaining garbage data to be predicted;
and determining the predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameter and the event influence parameter.
According to the technical scheme, the historical garbage data are obtained, the historical garbage data are screened and classified, and after screening and classifying are completed, weather identification, area identification and date identification are the same for the garbage yield data of each type, so that the garbage yield of each type is not influenced by weather, area and date, the corresponding standard garbage yield of each type is calculated, then the corresponding event influence parameter and weather influence parameter are calculated according to the standard garbage yield of each type and event parameter or weather identification, then the garbage data to be predicted, the corresponding weather influence parameter and event influence parameter are obtained, and the predicted garbage yield is determined. Through analysis and calculation of historical garbage data, weather influence parameters and event influence parameters corresponding to different weather identifications and event parameters can be obtained, so that the garbage yield can be predicted, and the effect of improving the prediction accuracy of the garbage yield can be achieved.
In one possible implementation, the determining, for each class of the first historical data, a standard garbage yield according to a standard yield calculation rule includes:
obtaining garbage yield of each type of first historical data;
determining yield fluctuation data according to a preset data fluctuation calculation rule and the garbage yield;
when the yield fluctuation data is larger than a fluctuation preset value;
sorting the garbage yields, and calculating yield differences of two adjacent garbage yields;
determining a first abnormal yield according to a preset difference judgment rule and the yield difference;
rejecting the first abnormal yield and determining a target garbage yield;
determining target fluctuation data according to the data fluctuation calculation rule and the target garbage yield;
when the target fluctuation data is smaller than or equal to a fluctuation preset value;
and calculating the average number of the target garbage yields, and determining the standard garbage yield.
In one possible implementation manner, the determining the event impact parameter according to the preset event impact calculation rule, the standard garbage yield and the event parameter includes:
acquiring event parameters corresponding to the first abnormal yield;
screening second historical data of the event parameters, the event parameters of which are equal to the event parameters corresponding to the first abnormal yield;
classifying the second historical data according to event parameters corresponding to the first abnormal yield;
for the second historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier;
and determining a second abnormal yield and event influencing parameters according to the yield change calculation rule, the standard garbage yield and the garbage yield.
In one possible implementation manner, the determining weather effect parameters according to the preset weather effect calculation rule, the standard garbage yield and the weather identification includes:
acquiring weather identification corresponding to the second abnormal yield;
screening weather identification corresponding to the weather identification equal to the second abnormal yield and third historical data of which the event parameters are equal to event preset values;
classifying the third historical data according to weather identifications corresponding to the second abnormal yield;
for the third historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier;
and determining weather influence parameters according to the yield change calculation rule, the standard garbage yield and the garbage yield.
In one possible implementation, the determining the second abnormal yield and the event impact parameter according to the yield change calculation rule, the standard garbage yield, and the garbage yield includes:
determining a first yield change duty ratio according to a preset duty ratio calculation rule, the standard garbage yield and the garbage yield;
determining first variation fluctuation data according to the data fluctuation calculation rule and the first yield variation duty ratio;
when the first variation fluctuation data is larger than a fluctuation preset value;
sequencing the first yield change duty ratios, and calculating first change difference values of two adjacent first yield change duty ratios;
determining a second abnormal yield according to a preset difference judgment rule and the first variation difference;
rejecting a first yield change duty ratio corresponding to the second abnormal yield, and determining a first target change duty ratio;
determining first target fluctuation data according to the data fluctuation calculation rule and the first target change duty ratio;
when the first target fluctuation data is smaller than or equal to a fluctuation preset value;
an average of the first target change duty cycles is calculated and an event affecting parameter is determined.
In one possible implementation, the determining weather-influencing parameters according to the yield-change calculation rule, the standard garbage yield and the garbage yield includes:
determining a second yield change duty ratio according to a preset duty ratio calculation rule, the standard garbage yield and the garbage yield;
determining second variation fluctuation data according to the data fluctuation calculation rule and the second yield variation duty ratio;
when the second variation fluctuation data is larger than a fluctuation preset value;
sorting the second yield change duty ratios, and calculating second change difference values of two adjacent second yield change duty ratios;
determining a second target change duty ratio according to a preset difference value judging rule and the second change difference value;
determining second target fluctuation data according to the data fluctuation calculation rule and the second target change duty ratio;
when the second target fluctuation data is smaller than or equal to a fluctuation preset value;
and calculating the average number of the second target change duty ratio, and determining weather influence parameters.
In a possible implementation manner, the determining the predicted garbage yield according to the preset analysis method, the garbage data to be predicted, the weather influence parameter and the event influence parameter includes:
the garbage data to be predicted comprises a region identifier to be predicted, a date identifier to be predicted, a weather identifier to be predicted and event parameters to be predicted;
according to the region identifier to be detected and the date identifier to be detected, standard garbage yield, event influencing parameters and weather influencing parameters corresponding to the region identifier to be detected and the date identifier to be detected are called;
predicted garbage yield = the standard garbage yield (1+ event impact parameter) × (1+ weather impact parameter).
In a second aspect of the present application, a garbage yield prediction system is provided. The system comprises:
the first data acquisition module is used for acquiring historical garbage data, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises a region identifier, a date identifier, garbage yield, a weather identifier and event parameters;
the data screening module is used for screening first historical data of which the weather identification is equal to a standard weather preset value;
the data classification module is used for classifying the first historical data according to the area identifier and the date identifier;
the standard yield determining module is used for determining standard garbage yield according to the standard yield calculation rule for the first historical data of each type;
the first influence parameter determining module is used for determining event influence parameters according to a preset event influence calculation rule, the standard garbage yield and the event parameters;
the second influence parameter determining module is used for determining weather influence parameters according to a preset weather influence calculation rule, the standard garbage yield and the weather identification;
the second data acquisition module is used for acquiring garbage data to be predicted;
and the yield prediction module is used for determining the predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameter and the event influence parameter.
In a third aspect of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first aspect of the present application.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of obtaining historical garbage data, screening and classifying the historical garbage data, after screening and classifying, ensuring that the historical garbage data of each type is not influenced by weather, areas and dates, calculating the corresponding standard garbage yield of each type, calculating the corresponding event influence parameters and weather influence parameters according to the garbage standard yield of each type and event parameters or weather identifications, obtaining the garbage data to be predicted, the corresponding weather influence parameters and event influence parameters, and determining the predicted garbage yield. By the calculation method, the garbage yield can be predicted, and the effect of improving the prediction accuracy of the garbage yield is achieved.
Drawings
Fig. 1 is a schematic flow chart of a garbage yield prediction method provided in the present application.
Fig. 2 is a schematic structural diagram of the garbage yield prediction system provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, a garbage yield prediction system; 201. a first data acquisition module; 202. a data screening module; 203. a data classification module; 204. a standard yield determination module; 205. a first influencing parameter determination module; 206. a second influencing parameter determination module; 207. a second data acquisition module; 208. a yield prediction module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. removable media.
Detailed Description
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 based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application provides a garbage yield prediction method, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: and acquiring historical garbage data and garbage data to be predicted, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises an area identifier, a date identifier, garbage yield, a weather identifier and event parameters.
Specifically, the historical garbage data includes a plurality of pieces of data, each piece of data includes an area identifier, a date identifier, a garbage yield, a weather identifier and an event parameter, the area identifier represents a unique identifier of a certain area, the date identifier includes a year, a month and a day, the garbage yield represents a time corresponding to the certain date identifier and a total amount of garbage generated in the area corresponding to the certain area identifier, the weather identifier represents a weather condition of a corresponding area corresponding to the garbage yield, the weather condition includes, but is not limited to, sunny, cloudy, rainy and snowy, the event parameter represents whether the area has an aggregation activity, the event parameter includes an activity number and an activity area, the activity number represents all people participating in the aggregation activity, and the activity area represents a occupation area of the aggregation activity.
Step S102: and screening the first historical data with the weather identification equal to the standard weather preset value.
Specifically, the historical garbage data is screened according to the weather identification, and when the weather identification is equal to a standard weather preset value, the historical garbage data is recorded as first historical data. In this embodiment, the standard weather preset value is fine, and in other embodiments, may be other values, where the standard weather preset value may be adjusted according to weather occurring in different areas, and the weather condition that occurs most in the area is generally selected as the standard weather preset value.
Step S103: the first history data is classified according to the area identifier and the date identifier.
Specifically, the first history data is classified according to the area identifier and the date identifier, and data with the same area identifier and the same month and day in the date identifier are used as a class. Through data screening and data classification, for each type of data, their area identification and weather identification are the same and the month and day of date identification are the same.
Step S104: for each class of first historical data, a standard garbage yield is determined according to standard yield calculation rules.
Specifically, the garbage yield of each type of first historical data is obtained, for each type of garbage yield, yield fluctuation data is determined according to a preset data fluctuation calculation rule, in this embodiment, the yield fluctuation data is a standard deviation of the type of garbage yield, when the yield fluctuation data is greater than a fluctuation preset value, it means that the yield of garbage is influenced by factors of other events besides the influence of areas, time and weather, the garbage yields of the corresponding types are ordered, yield difference values of two adjacent garbage yields are calculated, and first abnormal yields are determined according to a preset difference value judgment rule and the yield difference values. For a certain data in the sequence, two difference values are included, namely, the difference value of the data and the previous data and the difference value of the data and the next data, for the first data and the last data, only one difference value is corresponding, the sum of the corresponding difference values of each data is obtained, for the first data and the last data, the corresponding difference value is multiplied by two, the difference value data corresponding to each data is obtained through the calculation, the calculated difference value data is compared, and the corresponding garbage yield with the largest difference value data is used as the first abnormal yield. And for the sequence of garbage yield compositions of each class, eliminating the first abnormal yield, and calculating yield fluctuation data after eliminating the first abnormal yield again, wherein when the yield fluctuation data is smaller than a fluctuation preset value, the sequence of garbage yield compositions at the moment is recorded as target garbage yield. And calculating the average number of the garbage yields contained in the target garbage yield, wherein the average number is the standard garbage yield. The fluctuation preset value is set manually according to specific data conditions.
Through the calculation, the first abnormal yield can be determined, and the first abnormal yield indicates that when a certain data has a large difference from the similar data under the condition that the weather identification, the area identification and the date identification are the same, the data is greatly influenced by event parameters, namely, the event parameters corresponding to the data can greatly influence the garbage yield. For similar data, if the event parameters of certain data are different from those of other similar data, but the event parameters do not show great difference in garbage yield, it is indicated that the event parameters corresponding to the data do not have great influence on garbage yield. By determining the first abnormal yield, it is possible to determine event parameters that have an impact on the garbage yield, and in subsequent analysis of the event parameters, it is possible to reduce the amount of computation, i.e. without the need to analyze all the different event parameters.
Step S105: and determining event influence parameters according to preset event influence calculation rules, standard garbage yield and event parameters.
Specifically, the event parameters corresponding to the first abnormal yield are obtained, one or more first abnormal yields may exist according to the determination of the first abnormal yield, event parameters corresponding to all the first abnormal yields are obtained, second historical data, in which the event parameters are equal to the event parameters corresponding to the first abnormal yield, are screened according to the historical garbage data and the event parameters corresponding to the first abnormal yield, and the second historical data are classified according to the event parameters corresponding to the first abnormal yield. For each class of second historical data, determining the garbage yield and the standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier. According to the calculation of the standard garbage yield, it can be known that each group of area identifier and date identifier corresponds to one standard garbage yield, and according to the yield change calculation rule, the standard garbage yield and the garbage yield, the second abnormal yield and the event influence parameters are determined. According to a preset duty ratio calculation rule, the standard garbage yield and the garbage yield, a first yield change duty ratio is determined, the first yield change duty ratio= (garbage yield-standard garbage yield)/standard garbage yield is calculated according to the first yield change duty ratio, each garbage yield corresponds to a first yield change duty ratio can be known, first change fluctuation data is determined according to a data fluctuation calculation rule and the first yield change duty ratio calculated by each type of garbage yield, in this embodiment, the first change fluctuation data is a standard deviation of each type of first yield change duty ratio, when the first change fluctuation data is larger than a fluctuation preset value, the first yield change duty ratios are ordered, first change difference values of two adjacent first yield change duty ratios are calculated, a second abnormal yield is determined according to a preset difference value judgment rule and the first change difference values, and a determination method of the second abnormal yield is the same as a determination method of the first abnormal yield, and is not repeated. And eliminating the first yield change duty ratio corresponding to the second abnormal yield, calculating the first change fluctuation data after eliminating the first yield change duty ratio corresponding to the second abnormal yield again to be recorded as first target fluctuation data, and when the first target fluctuation data is smaller than a fluctuation preset value, at the moment, recording a sequence formed by the first yield change duty ratio after eliminating the first yield change duty ratio corresponding to the second abnormal yield as the first target change duty ratio, and calculating the average number of the first target change duty ratio, wherein the average number is an event influence parameter.
Because the event parameters include the number of people and the activity area, in this embodiment, when the data is classified according to the event parameters, when the number of people and the activity area are within a certain range, the corresponding data can be regarded as a class, for example, the number of people in the range of 300 to 350, the activity area in the range of 1000 to 1200 square meters belongs to a class, and the number of people and the activity area in the range of the activity are regarded as a class.
Through the calculation, the second abnormal yield can be determined, and the second abnormal yield indicates that when certain data has a large difference from the similar data under the condition that the event parameters, the area identifiers and the date identifiers are the same, the data is greatly influenced by the weather identifiers, namely, the weather identifiers corresponding to the data can greatly influence the garbage yield. For similar data, if the weather identification of one data is different from the weather identifications of other similar data, but the weather identifications do not show great difference in garbage yield, the weather identification corresponding to the data is indicated not to have great influence on garbage yield. By determining the second abnormal yield, the weather identification that has an impact on the garbage yield can be determined, and in the subsequent analysis of the weather identification, a certain amount of computation can be reduced, i.e. without the need to analyze all the different weather identifications.
Step S106: and determining weather influence parameters according to preset weather influence calculation rules, standard garbage yield and weather identification.
Specifically, obtaining weather identifiers corresponding to the second abnormal yield, determining that one or more second abnormal yields may exist according to the determination of the second abnormal yield, obtaining weather identifiers corresponding to all second abnormal yields, screening third historical data, wherein the weather identifiers are equal to the weather identifiers corresponding to the second abnormal yield and event parameters are equal to event preset values, according to the historical garbage data and the weather identifiers corresponding to the second abnormal yield, and classifying the third historical data; and for the third historical data of each type, determining the garbage yield and the standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier, and determining weather influence parameters according to the yield change calculation rule, the standard garbage yield and the garbage yield. And determining a second yield change duty ratio according to a preset duty ratio calculation rule, a standard garbage yield and a garbage yield, wherein the calculation mode of the second yield change duty ratio is the same as that of the first yield change duty ratio, and the details are not repeated here. Determining second variation fluctuation data of each type of third historical data according to a data fluctuation calculation rule and the second variation duty ratio, when the second variation fluctuation data is larger than a fluctuation preset value, sequencing the second variation duty ratios, calculating second variation difference values of two adjacent second variation duty ratios, according to a preset difference value judgment rule and the second variation duty ratio, determining a second target variation duty ratio, namely, for a certain data in a sequence, comprising two difference values, namely, the difference value of the data and the previous data and the difference value of the next data, for the first data and the last data, only one corresponding difference value, obtaining the sum of the corresponding difference values of each data, multiplying the corresponding difference value of the first data and the last data by two, obtaining corresponding difference value data of each data through the calculation, comparing the calculated difference value data, eliminating the second variation duty ratio corresponding to the largest difference value, recording the eliminated second variation duty ratio as the second target variation duty ratio, and calculating the second fluctuation duty ratio again to be equal to the second target variation duty ratio when the second fluctuation duty ratio is smaller than the second fluctuation preset target variation duty ratio; an average of the second target change ratios is calculated, the average being a weather-affecting parameter.
Step S107: and determining the predicted garbage yield according to a preset analysis method, garbage data to be predicted, weather influence parameters and event influence parameters.
Specifically, the garbage data to be predicted includes a region identifier to be predicted, a date identifier to be predicted, a weather identifier to be predicted, and an event parameter to be predicted, where the region identifier to be predicted indicates a region identifier to be predicted for garbage yield, the date identifier to be predicted indicates a generation time to be predicted for garbage yield including year, month and day, and the event parameter to be predicted indicates whether a time corresponding to the date identifier to be predicted has aggregation activity in a region corresponding to the region identifier to be predicted. According to the region identifier to be detected and the date identifier to be detected, standard garbage yield, event influence parameters and weather influence parameters corresponding to the region identifier to be detected and the date identifier to be detected are called, and when the region identifier to be detected and the date identifier to be detected have no corresponding event influence parameters and/or weather influence parameters, the event influence parameters and/or weather influence parameters are zero; predicted garbage yield = standard garbage yield (1+ event impact parameters) × (1+ weather impact parameters).
An embodiment of the present application provides a garbage yield prediction system 200, referring to fig. 2, the garbage yield prediction system 200 includes:
a first data obtaining module 201, configured to obtain historical garbage data, where the historical garbage data includes a plurality of pieces of data, and each piece of data includes a region identifier, a date identifier, a garbage yield, a weather identifier, and an event parameter;
the data screening module 202 is configured to screen the first historical data with the weather identifier equal to a standard weather preset value;
a data classification module 203, configured to classify the first historical data according to the area identifier and the date identifier;
a standard yield determination module 204, configured to determine, for each type of first historical data, a standard garbage yield according to a standard yield calculation rule;
a first influence parameter determining module 205, configured to determine an event influence parameter according to a preset event influence calculation rule, the standard garbage yield, and the event parameter;
a second influence parameter determining module 206, configured to determine weather influence parameters according to a preset weather influence calculation rule, the standard garbage yield, and the weather identifier;
a second data obtaining module 207, configured to obtain garbage data to be predicted;
the yield prediction module 208 is configured to determine a predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather-affecting parameter, and the event-affecting parameter.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the described module, which is not described herein again.
The embodiment of the application discloses electronic equipment. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can 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 portion 307 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 by a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output portion 306 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. A driver 309 is also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 309 as needed, so that a computer program read out therefrom is installed into the storage section 307 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 embodiments, the computer program may be downloaded and installed from a network via the communication portion 308, and/or installed from the removable media 310. The above-described functions defined in the apparatus 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 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 (6)
1. A method for predicting garbage yield, comprising:
acquiring historical garbage data, wherein the historical garbage data comprises a plurality of pieces of data, and each piece of data comprises a region identifier, a date identifier, garbage yield, a weather identifier and event parameters;
screening first historical data of the weather identification equal to a standard weather preset value;
classifying the first historical data according to the area identifier and the date identifier;
for the first historical data of each class, determining standard garbage yield according to standard yield calculation rules;
said determining, for each class of said first historical data, a standard garbage yield according to standard yield calculation rules, comprising:
obtaining garbage yield of each type of first historical data;
determining yield fluctuation data according to a preset data fluctuation calculation rule and the garbage yield;
when the yield fluctuation data is larger than a fluctuation preset value;
sorting the garbage yields, and calculating yield differences of two adjacent garbage yields;
determining a first abnormal yield according to a preset difference judgment rule and the yield difference;
rejecting the first abnormal yield and determining a target garbage yield;
determining target fluctuation data according to the data fluctuation calculation rule and the target garbage yield;
when the target fluctuation data is smaller than or equal to a fluctuation preset value;
calculating the average number of the target garbage yields, and determining a standard garbage yield;
determining event influence parameters according to a preset event influence calculation rule, the standard garbage yield and the event parameters;
the determining the event influence parameter according to the preset event influence calculation rule, the standard garbage yield and the event parameter comprises the following steps:
acquiring event parameters corresponding to the first abnormal yield;
screening second historical data of the event parameters, the event parameters of which are equal to the event parameters corresponding to the first abnormal yield;
classifying the second historical data according to event parameters corresponding to the first abnormal yield;
for the second historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier;
determining a second abnormal yield and event impact parameters according to yield change calculation rules, the standard garbage yield and the garbage yield;
determining weather influence parameters according to a preset weather influence calculation rule, the standard garbage yield and the weather identification;
the determining weather effect parameters according to the preset weather effect calculation rule, the standard garbage yield and the weather identification comprises the following steps:
acquiring weather identification corresponding to the second abnormal yield;
screening weather identification corresponding to the weather identification equal to the second abnormal yield and third historical data of which the event parameters are equal to event preset values;
classifying the third historical data according to weather identifications corresponding to the second abnormal yield;
for the third historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier;
determining weather-influencing parameters according to yield change calculation rules, the standard garbage yield and the garbage yield;
obtaining garbage data to be predicted;
determining predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameters and the event influence parameters;
the determining the predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameter and the event influence parameter comprises the following steps:
the garbage data to be predicted comprises a region identifier to be predicted, a date identifier to be predicted, a weather identifier to be predicted and event parameters to be predicted;
according to the region identifier to be detected and the date identifier to be detected, standard garbage yield, event influencing parameters and weather influencing parameters corresponding to the region identifier to be detected and the date identifier to be detected are called;
predicted garbage yield = the standard garbage yield (1+ event impact parameter) × (1+ weather impact parameter).
2. The garbage yield prediction method according to claim 1, wherein the determining of the second abnormal yield and event influencing parameters based on the yield change calculation rule, the standard garbage yield and the garbage yield comprises:
determining a first yield change duty ratio according to a preset duty ratio calculation rule, the standard garbage yield and the garbage yield;
determining first variation fluctuation data according to the data fluctuation calculation rule and the first yield variation duty ratio;
when the first variation fluctuation data is larger than a fluctuation preset value;
sequencing the first yield change duty ratios, and calculating first change difference values of two adjacent first yield change duty ratios;
determining a second abnormal yield according to a preset difference judgment rule and the first variation difference;
rejecting a first yield change duty ratio corresponding to the second abnormal yield, and determining a first target change duty ratio;
determining first target fluctuation data according to the data fluctuation calculation rule and the first target change duty ratio;
when the first target fluctuation data is smaller than or equal to a fluctuation preset value;
an average of the first target change duty cycles is calculated and an event affecting parameter is determined.
3. The garbage yield prediction method according to claim 1, wherein the determining weather-influencing parameters based on yield change calculation rules, the standard garbage yield and the garbage yield comprises:
determining a second yield change duty ratio according to a preset duty ratio calculation rule, the standard garbage yield and the garbage yield;
determining second variation fluctuation data according to the data fluctuation calculation rule and the second yield variation duty ratio;
when the second variation fluctuation data is larger than a fluctuation preset value;
sorting the second yield change duty ratios, and calculating second change difference values of two adjacent second yield change duty ratios;
determining a second target change duty ratio according to a preset difference value judging rule and the second change difference value;
determining second target fluctuation data according to the data fluctuation calculation rule and the second target change duty ratio;
when the second target fluctuation data is smaller than or equal to a fluctuation preset value;
and calculating the average number of the second target change duty ratio, and determining weather influence parameters.
4. A garbage yield prediction system, comprising:
a first data acquisition module (201) for acquiring historical garbage data, the historical garbage data comprising a plurality of pieces of data, each piece of data comprising a region identifier, a date identifier, a garbage yield, a weather identifier and an event parameter;
the data screening module (202) is used for screening first historical data of which the weather identification is equal to a standard weather preset value;
a data classification module (203) configured to classify the first historical data according to the area identifier and the date identifier;
a standard yield determination module (204) for determining a standard waste yield according to standard yield calculation rules for each class of first historical data; said determining, for each class of said first historical data, a standard garbage yield according to standard yield calculation rules, comprising: obtaining garbage yield of each type of first historical data; determining yield fluctuation data according to a preset data fluctuation calculation rule and the garbage yield; when the yield fluctuation data is larger than a fluctuation preset value; sorting the garbage yields, and calculating yield differences of two adjacent garbage yields; determining a first abnormal yield according to a preset difference judgment rule and the yield difference; rejecting the first abnormal yield and determining a target garbage yield; determining target fluctuation data according to the data fluctuation calculation rule and the target garbage yield; when the target fluctuation data is smaller than or equal to a fluctuation preset value; calculating the average number of the target garbage yields, and determining a standard garbage yield;
a first influence parameter determining module (205) configured to determine an event influence parameter according to a preset event influence calculation rule, the standard garbage yield, and the event parameter; the determining the event influence parameter according to the preset event influence calculation rule, the standard garbage yield and the event parameter comprises the following steps: acquiring event parameters corresponding to the first abnormal yield; screening second historical data of the event parameters, the event parameters of which are equal to the event parameters corresponding to the first abnormal yield; classifying the second historical data according to event parameters corresponding to the first abnormal yield; for the second historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier; determining a second abnormal yield and event impact parameters according to yield change calculation rules, the standard garbage yield and the garbage yield;
a second influence parameter determining module (206) for determining weather influence parameters according to a preset weather influence calculation rule, the standard garbage yield and the weather identification; the determining weather effect parameters according to the preset weather effect calculation rule, the standard garbage yield and the weather identification comprises the following steps: acquiring weather identification corresponding to the second abnormal yield; screening weather identification corresponding to the weather identification equal to the second abnormal yield and third historical data of which the event parameters are equal to event preset values; classifying the third historical data according to weather identifications corresponding to the second abnormal yield; for the third historical data of each class, determining garbage yield and standard garbage yield corresponding to the garbage yield according to the area identifier and the date identifier; determining weather-influencing parameters according to yield change calculation rules, the standard garbage yield and the garbage yield;
a second data acquisition module (207) for acquiring garbage data to be predicted;
the yield prediction module (208) is used for determining predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameters and the event influence parameters; the determining the predicted garbage yield according to a preset analysis method, the garbage data to be predicted, the weather influence parameter and the event influence parameter comprises the following steps: the garbage data to be predicted comprises a region identifier to be predicted, a date identifier to be predicted, a weather identifier to be predicted and event parameters to be predicted; according to the region identifier to be detected and the date identifier to be detected, standard garbage yield, event influencing parameters and weather influencing parameters corresponding to the region identifier to be detected and the date identifier to be detected are called; predicted garbage yield = the standard garbage yield (1+ event impact parameter) × (1+ weather impact parameter).
5. An electronic device comprising a memory and a processor, wherein the memory has a computer program stored thereon, and wherein the processor, when executing the program, implements the method of any of claims 1-3.
6. A computer readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, implements the method according to any of claims 1-3.
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