CN115114983A - Electric quantity data acquisition and analysis method based on big data equipment and computer system - Google Patents

Electric quantity data acquisition and analysis method based on big data equipment and computer system Download PDF

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CN115114983A
CN115114983A CN202210756711.6A CN202210756711A CN115114983A CN 115114983 A CN115114983 A CN 115114983A CN 202210756711 A CN202210756711 A CN 202210756711A CN 115114983 A CN115114983 A CN 115114983A
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CN115114983B (en
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张桂杰
刘春玲
周明
李淑梅
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Anhui Rongzhao Intelligent Co ltd
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Jilin Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G06F16/278—Data partitioning, e.g. horizontal or vertical partitioning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention relates to the technical field of big data, in particular to an electric quantity data acquisition and analysis method and a computer system based on big data equipment.

Description

Electric quantity data acquisition and analysis method based on big data equipment and computer system
Technical Field
The invention relates to the technical field of big data, in particular to an electric quantity data acquisition and analysis method and a computer system based on big data equipment.
Background
The existing outdoor power storage equipment is difficult to collect and analyze the electric quantity in real time during operation, as the number of people is small, the equipment (such as an outdoor rented electric bicycle without a signal feedback function to a main server) can move frequently, the residual electric quantity data of the outdoor power storage equipment can be collected for a limited time, the possible residual electric quantity of the equipment can be estimated manually and roughly according to the service condition of the equipment, but the accurate estimation of the possible residual electric quantity of the equipment is difficult to achieve because the electric quantity of a storage battery of the equipment is influenced by a plurality of factors, such as temperature, wind speed and humidity, and also such as historical service conditions (the historical service conditions can influence the electric quantity of the storage battery), and the like, and no related technology is available in the prior art to realize more accurate collection and analysis.
Disclosure of Invention
The invention aims to provide a method for acquiring and analyzing electric quantity data based on big data equipment and a computer system, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a method for collecting and analyzing electric quantity data based on big data equipment comprises the following steps:
acquiring latest electric quantity residual data, working energy consumption power data and expected power data of the equipment through a hardware port of the equipment, and storing and dividing the latest electric quantity residual data, the working energy consumption power data and the expected power data into first type data;
acquiring temperature data, wind speed data and humidity data of a current area of the equipment through a big data port, and storing and dividing the temperature data, the wind speed data and the humidity data into second type data;
acquiring historical working time data and historical working energy consumption power change data of the equipment through a hardware port or a historical data storage port of the equipment, and storing and dividing the historical working time data and the historical working energy consumption power change data into third-class data;
establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first class of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second class of data and the third class of data;
respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model;
then learning the influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 based on the comprehensive discriminant model, and feedback-modifying the weight values P1-P5 by the learned influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 until a mature comprehensive discriminant model is obtained;
inputting the latest electric quantity residual data, working energy consumption power data and expected power data of the equipment collected at a hardware port of the equipment by a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with time.
Further, the establishing of the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictor function f1 through the second type data and the third type data specifically includes establishing an influence function T1 of the influence of the temperature on the magnitude of the effect of the change of the main predictor function f 1.
Further, the establishing of the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictor function f1 through the second type data and the third type data specifically includes establishing an influence function T2 of the influence of the wind speed on the magnitude of the influence of the change of the main predictor function f 1.
Further, the establishing of the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictor function f1 through the second type data and the third type data specifically includes establishing an influence function T3 of the influence of the humidity on the magnitude of the effect of the change of the main predictor function f 1.
Further, the "establishing an influence function T1-T5 of influence on the magnitude of the effect of the external factor on the change amount of the main prediction function f1 through the second type data and the third type data" specifically includes establishing an influence function T4 of influence on the magnitude of the effect of the historical operating time on the change amount of the main prediction function f 1.
Further, the "establishing an influence function T1-T5 of the influence of the external factors on the change amount of the main prediction function f1 through the second type data and the third type data" specifically includes establishing an influence function T5 of the influence of the historical operating energy consumption power change on the change amount of the main prediction function f 1.
Further, the comprehensive discriminant model specifically includes:
Figure BDA0003722771460000021
Figure BDA0003722771460000022
t1 is an independent variable representation temperature value of an influence function T1, T2 is an independent variable representation wind speed value of the influence function T2, T3 is an independent variable representation humidity value of the influence function T3, T4 is an independent variable representation historical working time value of the influence function T4, T5 is an independent variable representation historical working energy consumption power change value of the influence function T5, T1-T5 are all larger than 0, Q1-Q5 are all constants, Q1-Q5 are all natural numbers and are used for representing the calculation accuracy, P1-P5 are respectively weight values of influence effects of the influence functions T1-T5 on a main estimation function f1, e is a natural constant, b is a control parameter between 0 and 1, and f1 is a main estimation function; the comprehensive discrimination model can input the first class data, the second class data and the third class data, can output quantity with the same dimension as the main pre-estimation function, and can output accurate pre-estimation data of the residual electric quantity of the equipment along with the time change.
A computer system based on big data device electric quantity data acquisition and analysis comprises a connected controller, a processor, a memory, an input device and an output device;
the input device is used for acquiring the latest power remaining data, the latest working energy consumption power data and the latest expected power data of the device through a hardware port of the device, storing the latest power remaining data, the latest working energy consumption power data and the latest expected power data in a memory and dividing the latest power remaining data, the latest working energy consumption power data and the latest expected power data into first type data,
the device is also used for acquiring the temperature data, the wind speed data and the humidity data of the current area of the equipment through the big data port and storing the data in the memory to be divided into second data,
the device is also used for acquiring historical working time data and historical working energy consumption power change data of the device through a hardware port of the device or a storage port of the historical data and storing the data in a memory to divide the data into third-class data;
the memory is further configured to store target instructions, and the processor is configured to execute the target instructions;
the target instruction comprises the following steps: establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first type of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second type of data and the third type of data; respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model; learning a change quantity influence action characteristic value of source data in an influence function T1-T5 on a main prediction function f1 based on a comprehensive discriminant model, and feedback-modifying weights P1-P5 by the learned change quantity influence action characteristic value of the source data in the influence function T1-T5 on a main prediction function f1 until a mature comprehensive discriminant model is obtained;
the processor is also used for inputting the latest electric quantity residual data, the working energy consumption power data and the expected power data of the equipment by using a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with the time;
the output equipment is used for outputting the calculated accurate estimated data of the change of the residual electric quantity of the equipment along with the time;
the controller is used for coordinating the work among the processor, the memory, the input device and the output device.
Compared with the prior art, the invention has the beneficial effects that:
the method and the device have the advantages that the weight which influences the change quantity of the main estimation function is distributed for the influence function, the comprehensive judgment model is formed, then the corresponding weight is adjusted through learning feedback, the comprehensive judgment model can be continuously improved, the method and the device can accurately estimate the change quantity of the main estimation function based on the comprehensive judgment model and factors in external big data, the latest electric quantity residual data, working energy consumption power data and expected power data of the device collected at a hardware port of the device can be input based on the comprehensive judgment model, the accurate estimation data of the change quantity of the device along with the time can be obtained, and the problems in the background technology are solved.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The application discloses a method for acquiring and analyzing electric quantity data based on big data equipment, which comprises the following steps of acquiring latest electric quantity residual data, working energy consumption power data and expected power data of the equipment through a hardware port of the equipment, and storing and dividing the latest electric quantity residual data, the working energy consumption power data and the expected power data into first-class data;
acquiring temperature data, wind speed data and humidity data of a current area of the equipment through a big data port, and storing and dividing the temperature data, the wind speed data and the humidity data into second type data;
acquiring historical working time data and historical working energy consumption power change data of the equipment through a hardware port or a historical data storage port of the equipment, and storing and dividing the historical working time data and the historical working energy consumption power change data into third-class data;
establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first type of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second type of data and the third type of data;
respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model;
then learning the influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 based on the comprehensive discriminant model, and feedback-modifying the weight values P1-P5 by the learned influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 until a mature comprehensive discriminant model is obtained;
inputting the latest electric quantity residual data, working energy consumption power data and expected power data of the equipment collected at a hardware port of the equipment by a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with time.
In a specific implementation, the learning of the influence characteristic value of the source data on the change amount of the main prediction function f1 in the influence function T1-T5 based on the comprehensive discriminant model is specifically completed by training and learning of a multi-layer neural network model, and in a more preferable implementation, "establishing the influence function T1-T5 of the influence of the external factors on the change amount of the main prediction function f1 through the second type of data and the third type of data" specifically includes establishing the influence function T1 of the influence of the temperature on the change amount of the main prediction function f 1.
The establishing of the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main prediction function f1 through the second type data and the third type data specifically includes establishing an influence function T2 of the influence function of the wind speed on the magnitude of the influence of the change of the main prediction function f 1.
The establishing of the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main prediction function f1 through the second type data and the third type data specifically includes establishing an influence function T3 of the influence of the humidity on the magnitude of the effect of the change of the main prediction function f 1.
The "establishing an influence function T1-T5 of the influence of the external factors on the change amount of the main prediction function f1 through the second type data and the third type data specifically includes establishing an influence function T4 of the influence of the historical working time on the change amount of the main prediction function f 1.
The "establishing an influence function T1-T5 of the influence of the external factors on the change amount of the main prediction function f1 through the second type data and the third type data" specifically includes establishing an influence function T5 of the influence of historical operating energy consumption power changes on the change amount of the main prediction function f 1.
It can be understood that, in the present application, the weight affecting the change amount of the main pre-estimation function is distributed to the influence function, and a comprehensive discrimination model is formed, and then the corresponding weight is adjusted through learning feedback, so that the comprehensive discrimination model can be continuously perfected.
In a preferred implementation, the comprehensive discriminant model is specifically:
Figure BDA0003722771460000051
Figure BDA0003722771460000052
wherein T1 is an independent variable characterizing temperature value of an influence function T1, T2 is an independent variable characterizing wind speed value of the influence function T2, T3 is an independent variable characterizing humidity value of the influence function T3, T4 is an independent variable characterizing historical operating time value of the influence function T4, T5 is an independent variable characterizing historical operating energy consumption power change value of the influence function T5, T1-T5 are all larger than 0, Q1-Q5 are all constants, Q1-Q5 are all natural numbers for characterizing the accuracy of calculation, P1-P5 are weights of the influence functions T1-T5 on the influence of the main prediction function f1, e is a natural constant, and when x tends to be positive infinity or negative infinity, the function expression of 'x power of 1 plus x' is the power (1+1/x) is equal to the limit e of the function expression, expressed by the formula, namely: lim (1+1/x) ^ x ═ e (x tends to be ± ∞), b is a control parameter between 0 and 1, and f1 is a main prediction function; the comprehensive discrimination model can input first-class data, second-class data and third-class data, can output quantity with the same dimension as the main prediction function, and namely can output accurate prediction data of the change of the residual electric quantity of the equipment along with time.
The application also discloses a computer system, which comprises a controller, a processor, a memory, an input device and an output device, the input device is used for acquiring the latest electric quantity residual data, working energy consumption power data and expected power data of the equipment through a hardware port of the equipment, storing the latest electric quantity residual data, the working energy consumption power data and the expected power data in the memory and dividing the latest electric quantity residual data, the working energy consumption power data and the expected power data into first type data, acquiring the temperature data, the wind speed data and the humidity data of the current area of the equipment through a big data port, storing the temperature data, the wind speed data and the humidity data in the memory and dividing the temperature data, the wind speed data and the humidity data into second type data, acquiring historical working time data and historical working energy consumption power change data of the equipment through a hardware port or a historical data storage port of the equipment and storing the historical data in the memory and dividing the historical working time data and the historical working energy consumption power change data into third type data, the memory is further configured to store target instructions, and the processor is configured to execute the target instructions, wherein the target instructions include instructions to: establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first class of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second class of data and the third class of data; respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model; learning a change quantity influence action characteristic value of source data in an influence function T1-T5 on a main prediction function f1 based on a comprehensive discriminant model, and feedback-modifying weights P1-P5 by the learned change quantity influence action characteristic value of the source data in the influence function T1-T5 on a main prediction function f1 until a mature comprehensive discriminant model is obtained; the processor is further used for inputting latest primary electric quantity residual data, working energy consumption power data and expected power data of the equipment by using a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with time, and the output equipment is used for outputting the calculated accurate estimation data of the change of the residual electric quantity of the equipment along with time.

Claims (8)

1. The method for acquiring and analyzing the electric quantity data based on the big data equipment is characterized by comprising the following steps of:
acquiring latest electric quantity residual data, working energy consumption power data and expected power data of the equipment through a hardware port of the equipment, and storing and dividing the latest electric quantity residual data, the working energy consumption power data and the expected power data into first type data;
acquiring temperature data, wind speed data and humidity data of a current area of the equipment through a big data port, and storing and dividing the temperature data, the wind speed data and the humidity data into second type data;
acquiring historical working time data and historical working energy consumption power change data of the equipment through a hardware port or a historical data storage port of the equipment, and storing and dividing the historical working time data and the historical working energy consumption power change data into third-class data;
establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first class of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second class of data and the third class of data;
respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model;
then learning the influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 based on the comprehensive discriminant model, and feedback-modifying the weight values P1-P5 by the learned influence characteristic value of the source data in the influence function T1-T5 on the change quantity of the main prediction function f1 until a mature comprehensive discriminant model is obtained;
inputting the latest electric quantity residual data, working energy consumption power data and expected power data of the equipment collected at a hardware port of the equipment by a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with time.
2. The method for acquiring and analyzing the electric quantity data of the large data equipment according to claim 1, wherein the step of establishing the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictive function f1 through the second type data and the third type data specifically comprises establishing an influence function T1 of the influence of the temperature on the magnitude of the influence of the change of the main predictive function f 1.
3. The method for acquiring and analyzing the electric quantity data of the big data equipment according to claim 1, wherein the step of establishing the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictive function f1 through the second type data and the third type data specifically comprises establishing the influence function T2 of the wind speed on the magnitude of the influence of the change of the main predictive function f 1.
4. The method for acquiring and analyzing the electric quantity data of the big data equipment according to claim 1, wherein the step of establishing the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change of the main predictive function f1 through the second type data and the third type data specifically comprises establishing the influence function T3 of the influence of the humidity on the magnitude of the influence of the change of the main predictive function f 1.
5. The method for acquiring and analyzing the electric quantity data of the large data equipment according to claim 1, wherein the step of establishing the influence function T1-T5 of the influence of the external factors on the magnitude of the effect of the change quantity of the main predictive function f1 through the second type data and the third type data specifically comprises establishing the influence function T4 of the influence function of the historical working time on the magnitude of the influence of the change quantity of the main predictive function f 1.
6. The method for acquiring and analyzing the electric quantity data of the big data equipment according to claim 1, wherein the step of establishing the influence function T1-T5 of the influence of the external factors on the magnitude of the change of the main predictive function f1 through the second type data and the third type data specifically comprises establishing a history influence function T5 of the influence function of the change of the working energy consumption power change on the magnitude of the change of the main predictive function f 1.
7. The big data device electricity quantity data collection and analysis method according to claim 1,
the comprehensive discrimination model specifically comprises:
Figure FDA0003722771450000021
Figure FDA0003722771450000022
t1 is an independent variable representation temperature value of an influence function T1, T2 is an independent variable representation wind speed value of the influence function T2, T3 is an independent variable representation humidity value of the influence function T3, T4 is an independent variable representation historical working time value of the influence function T4, T5 is an independent variable representation historical working energy consumption power change value of the influence function T5, T1-T5 are all larger than 0, Q1-Q5 are all constants, Q1-Q5 are all natural numbers and are used for representing the calculation accuracy, P1-P5 are weight values of influence of the influence functions T1-T5 on the change quantity of a main estimation function f1, e is a natural constant, b is a control parameter between 0 and 1, and f1 is a main estimation function; the comprehensive discrimination model can input the first class data, the second class data and the third class data, can output quantity with the same dimension as the main pre-estimation function, and can output accurate pre-estimation data of the residual electric quantity of the equipment along with the time change.
8. A computer system based on the electric quantity data acquisition and analysis of big data equipment is characterized in that,
the system comprises a connection controller, a processor, a memory, an input device and an output device;
the input device is used for acquiring the latest power remaining data, the latest working energy consumption power data and the latest expected power data of the device through a hardware port of the device, storing the latest power remaining data, the latest working energy consumption power data and the latest expected power data in a memory and dividing the latest power remaining data, the latest working energy consumption power data and the latest expected power data into first type data,
the device is also used for acquiring the temperature data, the wind speed data and the humidity data of the current area of the equipment through the big data port and storing the data in the memory to be divided into second data,
the device is also used for acquiring historical working time data and historical working energy consumption power change data of the device through a hardware port of the device or a storage port of the historical data and storing the data in a memory to divide the data into third-class data;
the memory is further configured to store target instructions, and the processor is configured to execute the target instructions;
the target instruction comprises the following steps: establishing a main pre-estimation function f1 of the change of the residual quantity of the equipment along with the time through the first type of data, and establishing an influence function T1-T5 of the influence of external factors on the change quantity of the main pre-estimation function f1 through the second type of data and the third type of data; respectively distributing weights P1-P5 which have influence on the change quantity of the main prediction function f1 to the influence functions T1-T5; and forming a comprehensive discrimination model; learning a change quantity influence action characteristic value of source data in an influence function T1-T5 on a main prediction function f1 based on a comprehensive discriminant model, and feedback-modifying weights P1-P5 by the learned change quantity influence action characteristic value of the source data in the influence function T1-T5 on a main prediction function f1 until a mature comprehensive discriminant model is obtained;
the processor is also used for inputting the latest primary electric quantity residual data, the working energy consumption power data and the expected power data of the equipment by a mature comprehensive discrimination model and obtaining accurate estimation data of the change of the residual electric quantity of the equipment along with time;
the output equipment is used for outputting the calculated accurate estimated data of the change of the residual electric quantity of the equipment along with the time;
the controller is used for coordinating the work among the processor, the memory, the input device and the output device.
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