CN117689272A - Method for measuring value of ESG (electronic service provider) power exchange system, electronic equipment and storage medium - Google Patents
Method for measuring value of ESG (electronic service provider) power exchange system, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method for measuring the value of an ESG (electronic service provider) power exchange system, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring electricity exchange order data and battery GPS positioning data of a rider, and determining a fixed departure point of the rider according to the battery GPS positioning data; obtaining real track data of a rider according to the power change order data and the battery GPS positioning data; replacing the position of the battery-changing cabinet in the real track data with a fixed departure point to obtain assumed track data; calculating the residual electric quantity of the battery when a rider reaches a fixed departure point through a pre-constructed battery electric quantity estimation model based on the assumed track data to obtain an electric quantity estimation value, and replacing the battery electric quantity of the fixed departure point in the assumed track data with the electric quantity estimation value to obtain final assumed track data; and analyzing the value of the ESG battery exchange system according to the real track data and the final assumption track data respectively. The invention firstly excavates the value existing in the power conversion cabinet network based on the hypothesis data, so that the evaluation result is more accurate.
Description
Technical Field
The present disclosure relates to the field of ESG battery replacement systems, and in particular, to a method, an electronic device, and a storage medium for measuring a value of an ESG battery replacement system.
Background
At present, many electric cabinet networks in the market can provide electric energy for a rider to ensure the sustainability of the taking-out order by the rider, but a reasonable evaluation scheme is not provided in the prior art to measure how much electric energy the electric cabinet network can provide for the rider, how much single amount is added for the electric cabinet network, and how much electric anxiety is relieved for the electric cabinet network. From the perspective of the rider, there are two modes of measuring the value of the battery-changing cabinet network: one is shallow, i.e., the total power supplied to each rider; the other is deep, and the electric energy is supplied by a rider through the electric cabinet network, so that compared with the electric energy supply of the rider without the electric cabinet network, the electric energy is saved or the invalid mileage is reduced, and the take-out order quantity is increased.
The total power supplied by each rider is scaled for the system: the situation is related to a plurality of factors including the charging rated power of the power conversion cabinet, weather conditions and the power conversion frequency of the rider, and if each rider belongs to the dimension of the power conversion cabinet, the total electric energy provided by each power conversion cabinet for the rider every day can be roughly calculated. However, the method has a single evaluation angle, is not transversely compared with other methods, and cannot effectively explain the potential value of the battery changing cabinet for a rider.
In addition, the value provided by the power conversion cabinet network may be quite large, including power supply, power conversion batch, take-out order amount sent by a rider, etc., but the value of the power conversion cabinet network is not sufficiently measured by the direct conversion index, and the accuracy is low. The prior art is used for measuring the network value of the power conversion cabinet to be somewhat absolute, namely, the value of whether the power conversion cabinet is supplied or not can only be directly reflected, but also the necessity of the network of the power conversion cabinet cannot be objectively described. Therefore, it is necessary to provide a method for converting the value of the network of the battery-changing cabinet based on the assumption, which can indirectly explain the value of the network of the battery-changing cabinet, whether from the perspective of a rider, the perspective of a company or even the perspective of society, so that the evaluation result of the battery-changing cabinet is more accurate and has reference.
Disclosure of Invention
The embodiment of the application provides a method for measuring the value of an ESG (electronic control system), a resistor device and a storage medium, so as to at least solve the problem of inaccurate value evaluation of the ESG.
In a first aspect, an embodiment of the present application provides a method for measuring a value of an ESG battery exchange system, including:
acquiring electricity changing order data of a rider and battery GPS positioning data, determining a fixed departure point of the rider according to the battery GPS positioning data, and determining the position of an electricity changing cabinet according to the electricity changing order data;
Obtaining real track data of a rider according to the position of the battery changing cabinet and the GPS positioning data of the battery; the real track data comprise position information sequenced by time and corresponding battery power;
replacing the position of the battery changing cabinet in the real track data with the fixed departure point to obtain assumed track data;
calculating the residual electric quantity of the battery when the rider reaches the fixed departure point through a pre-constructed battery electric quantity estimation model based on the assumed track data to obtain an electric quantity estimation value, and replacing the battery electric quantity of the fixed departure point in the assumed track data with the electric quantity estimation value to obtain final assumed track data;
and respectively calculating the electricity consumption condition of the battery in the real track data and the final assumed track data, and analyzing the value of the ESG battery replacement system according to the electricity consumption condition.
In one embodiment, the GPS positioning data includes a plurality of position coordinates, and a time and a battery power corresponding to the position coordinates, and the determining the rider's stationary departure point based on the battery GPS positioning data includes:
performing cluster analysis on the position coordinates, extracting the position coordinates and corresponding time of the battery which do not move within a preset time period, and recording the position coordinates, the stay start time and the stay end time as stay positions;
Calculating the difference value between the residence starting time and the residence ending time to obtain the residence time of the residence position;
traversing all the stay time lengths of each day of the rider, comparing the sizes, and marking the stay position corresponding to the maximum stay time length as a fixed departure point of the rider.
In an embodiment, the obtaining real track data of the rider according to the battery-powered cabinet position and the battery GPS positioning data includes:
marking all position coordinates in the battery GPS positioning data according to a preset rule to obtain initial track data;
and carrying out interpolation processing on the initial track data according to the position of the battery changing cabinet to obtain the real track data of the rider.
In an embodiment, the preset rule includes:
if the current position coordinate coincides with the fixed departure point and the corresponding stay time is not equal to zero, marking the current position coordinate as the fixed departure point; if the current position coordinate is not coincident with the fixed departure point and the corresponding stay time is not equal to zero, marking the current position coordinate as a take-out order position; and if the stay time corresponding to the current position coordinate is equal to zero, marking the current position coordinate as a riding path.
In an embodiment, the obtaining real track data of the rider according to the battery-powered cabinet position and the battery GPS positioning data includes:
the power change order data comprise power change time and power change cabinet positions, and track time is obtained from the battery GPS positioning data;
if the track time is equal to the power changing time and the position coordinate corresponding to the track time is marked as a riding path, replacing the current position mark with a power changing cabinet position;
if the power change time is between the two track times, inserting the power change time and the power change cabinet position into the initial track data, and marking and replacing the power change time and the power change cabinet position to generate the real track data.
In one embodiment, the construction of the battery power estimation model includes:
constructing a transducer network according to the requirement of a user to obtain a transducer network to be trained;
acquiring real track data of a plurality of riders, acquiring time and corresponding battery electric quantity from the real track data, and forming sampling data;
and acquiring a preset loss function, and training the to-be-trained converter network according to the preset loss function and the sampling data to obtain a battery electric quantity estimation model.
In an embodiment, the training the to-be-trained converter network according to the preset loss function and the sampling data to obtain a battery power estimation model includes:
extracting battery electric quantity of a rider at a plurality of moments before the rider reaches a fixed departure point from the sampling data to obtain an electric quantity sequence;
extracting the residual electric quantity of the battery from the sampling data when the rider arrives at a fixed departure point, and taking the residual electric quantity as a label;
respectively inputting the electric quantity sequence and the label of each rider into a to-be-trained transducer network to learn network parameters;
and calculating the preset loss function, and obtaining a battery electric quantity estimation model when the preset loss function is converged.
In an embodiment, the calculating the power consumption of the battery according to the real track data and the final assumed track data, and analyzing the value of the ESG battery replacement system according to the power consumption includes:
acquiring a first total electric quantity of a new battery, calculating the electric quantity consumed by a rider when the rider reaches the fixed departure point from the current position and reaches the next distribution position from the fixed departure point according to the first total electric quantity and the final assumed track data, and recording the electric quantity as a first consumed electric quantity;
Acquiring a second total electric quantity of a new battery, calculating the electric quantity consumed when a rider arrives at the power conversion cabinet position from the current position and arrives at the next distribution position from the power conversion cabinet position according to the total electric quantity and the real track data, and recording the electric quantity as the second consumed electric quantity;
and calculating a difference value between the first consumed electric quantity and the second consumed electric quantity, and if the difference value is larger than zero, judging that the ESG power exchange system reduces the electricity consumption cost of a rider.
In a second aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the method for measuring the value of the ESG power exchanging system according to the first aspect when the processor executes the computer program.
In a third aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method for measuring the value of an ESG battery system as described in the first aspect above.
The method, the electronic device and the storage medium for measuring the value of the ESG battery exchange system have at least the following technical effects:
The method for measuring the value of the ESG power conversion system is based on the value existing in the hypothetical data mining power conversion cabinet network for the first time, and in the power conversion field, the method not only shows that the power conversion cabinet network provides effective electric energy for a rider, but also makes a certain contribution to social energy conservation and emission reduction. According to the invention, big data information is fully utilized, hypothesis is scientifically provided for the track data of the rider, the hypothesis is constructed by utilizing the hypothesis idea, and the electric quantity difference value of the rider for carrying out electric conversion through the electric conversion cabinet network compared with the electric conversion by taking full batteries from a fixed starting point, and further, the reduced invalid mileage, the increased take-out order number and the reduced carbon emission can be converted; compared with the direct calculation of the network value scheme of the power conversion cabinet, the evaluation result is more accurate. In addition, the invention also provides a method for accurately estimating the electric quantity of the rider at a fixed departure point by using the time sequence network transducer, which provides theoretical support for the follow-up excavation of the rider power-changing habitual data.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a method of measuring the value of an ESG battery replacement system according to one embodiment of the present application;
FIG. 2 is a flow chart of a method of measuring the value of an ESG battery replacement system according to another embodiment of the present application;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein refers to two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
Noun interpretation: ESG: efficient And Sustainable Graph (high efficiency sustainability network).
In the market, the power conversion cabinet networks are numerous, electric energy can be provided for a rider, and the sustainability of the take-out order sent by the rider is guaranteed, however, a reasonable evaluation index is not provided in the market to measure how much electric energy can be provided for the rider by the power conversion cabinet network, how much single amount is added for the rider, and how much electric anxiety can be relieved for the rider, so the invention aims to provide a method for measuring the value of the power conversion cabinet network, and further to explain how much income can be brought for the rider by the power conversion network.
In the process of value evaluation, we propose an assumption that a rider does not change electricity through a network of electricity changing cabinets when sending takeaway, but stores full-charge batteries in advance in a fixed departure point (such as a home of the rider, a distribution starting point of the fixed departure point and the like) and uses the full-charge batteries to change electricity. Compared with the assumption, the electric cabinet network can save more electric energy for a rider, reduce more invalid mileage, and convert the electric cabinet network into more single-file delivery amount? The scheme utilizes a transverse comparison method to laterally excavate the potential value of the individual and the whole network of the power exchange cabinet.
In an embodiment of the present invention, a method for measuring the value of an ESG battery exchange system is provided, and referring to fig. 1, the method is implemented by the following steps.
Step S1, acquiring electricity changing order data and battery GPS positioning data of a rider from a cloud platform, determining a fixed starting point of the rider according to the battery GPS positioning data, and determining the position of an electricity changing cabinet according to the electricity changing order data. The data of the power exchange order of the embodiment comprises user ID, ordering time, power exchange cabinet position and the like of a rider; the battery GPS positioning data comprise a battery ID, position coordinates, time and battery electric quantity corresponding to the position coordinates, and the battery GPS positioning data are log data of the battery, so that the moving path of the battery and the real-time electric quantity change of the battery can be recorded in real time, and the position of the battery at each moment and the current battery electric quantity can be determined from the battery GPS positioning data.
Because the data size of the rider in the cloud platform is too large, in order to improve the efficiency, the method for acquiring the power conversion order data and the battery GPS positioning data of the rider further comprises the following operations:
step S11, sampling the related data of the rider using the ESG battery exchange system by a uniform sampling method to obtain sampling data;
Step S12, equally dividing the sampled data into N groups, and calculating the missing rate of each group of sampled data by a k-fold-missing rate sampling method;
and step S13, selecting the sampling data with the loss rate meeting a preset rule as sampling result data, for example, the loss rate is larger than a preset threshold value, or the sampling data of the first groups are extracted as sampling result data according to the sequence from the large to the small of the loss rate, wherein the sampling result data in the embodiment comprises the power conversion order data and the battery GPS positioning data.
After sampling is completed, the GPS positioning data of the embodiment comprises a plurality of position coordinates, time and battery power corresponding to the position coordinates, and then a fixed departure point of a rider is determined according to the battery GPS positioning data. Specifically, firstly performing cluster analysis on the position coordinates, extracting the position coordinates and corresponding time of the battery which do not move within a preset time period, and recording the position coordinates, the stay start time and the stay end time as stay positions; and then, calculating the difference value between the residence start time and the residence end time to obtain the residence time. After the GPS positioning data of each rider are subjected to clustering analysis, traversing all the stay time lengths of each day of the rider, comparing the stay time lengths, and recording the stay position corresponding to the maximum stay time length as a fixed departure point of the rider.
S2, obtaining real track data of a rider according to the position of the battery changing cabinet and the GPS positioning data of the battery; the real track data includes time-ordered position information and corresponding battery power, i.e. the real track data in this embodiment mainly includes time, corresponding position coordinates and battery power.
Specifically, marking all position coordinates in the battery GPS positioning data according to a preset rule to obtain initial track data, namely marking all positions as fixed departure points, take-out order positions or riding paths, so that the initial track data is the action path of a characterization rider; then carrying out interpolation processing on the initial track data according to the position of the power exchange cabinet to obtain real track data of a rider, namely judging which position coordinate is the position of the power exchange cabinet according to the power exchange order data, marking the position as the position of the power exchange cabinet, if the position of the battery change cabinet is near the position where the take-out is dispatched by the rider, and the battery is changed by the rider during the take-out dispatch, it is necessary to insert a position coordinate as the battery change cabinet position in accordance with the battery change time in the initial trajectory data.
The preset rule for marking the position in this embodiment is as follows:
if the current position coordinate coincides with the fixed departure point and the corresponding stay time is not equal to zero, the rider places the battery car in the fixed departure point at the moment, and the current position coordinate is marked as the fixed departure point;
if the current position coordinate is not coincident with the fixed departure point and the corresponding stay time is not equal to zero, the condition that the battery car is placed near a take-out delivery place by a rider at the moment is indicated, then the take-out is dispatched to a client by walking, and other conditions such as meal rest and the like of the rider are possible, and the take-out is dispatched to the client by the rider, so that the current position coordinate is marked as a take-out order position;
and if the stay time corresponding to the current position coordinate is equal to zero, indicating that the position of the battery is always changing, marking the current position coordinate as a riding path.
Interpolation processing is carried out on the initial track data according to the position of the battery changing cabinet to obtain real track data of a rider, and the method comprises the following steps:
the power change order data comprise power change time and power change cabinet position, track time corresponding to the power change cabinet position is obtained from the battery GPS positioning data,
If the position coordinates corresponding to the track time are marked as take-out order positions and the track time is equal to the electricity changing time, replacing the current position mark with an electricity changing cabinet position;
if the power change time is between the stay start time and the stay end time of any stay position, the power change time and the power change cabinet position are inserted into the initial track data, so that complete real track data is obtained, wherein the real track data is a complete riding route in working and comprises a position coordinate of a rider living, a position coordinate of a delivery takeaway, a position coordinate of the power change cabinet and the riding route.
And S3, replacing the position of the battery changing cabinet in the real track data with the fixed departure point to obtain the assumed track data. The embodiment assumes another rider trajectory, that is, if the rider does not utilize the battery-changing cabinet network to change the power, he stores full-power batteries at a fixed starting point and uses the full-power batteries to change the power. According to the embodiment, the position of the battery changing cabinet in the real track data is replaced by a fixed starting point, so that a rider can replace a battery at the fixed starting point, and hypothetical rider track data, namely hypothetical track data, is formed.
Step S4, calculating the residual quantity of the battery when the rider reaches the fixed departure point through a pre-constructed battery quantity estimation model based on the assumed track data to obtain a quantity estimation value, and replacing the battery quantity of the fixed departure point in the assumed track data with the quantity estimation value to obtain final assumed track data;
specifically, the construction of the battery power estimation model includes:
in step S41, a transform network is built according to the user' S requirement, so as to obtain a transform network to be trained, for example, the transform network to be trained in this embodiment includes four layers, the first three layers are encoder network layers, and the last layer is an MLP network.
Step S42, acquiring real track data of a plurality of riders, and acquiring time and corresponding battery electric quantity from the real track data to form sampling data. In the real track data, the remaining battery power of the battery at any time is known, and the embodiment acquires a sequence (such as data of ten minutes before the rider arrives at the fixed departure point) of the remaining battery power at a plurality of times before the rider arrives at the fixed departure point, and takes the remaining battery power of the rider arriving at the fixed departure point as a label, and inputs the sequence and the label as sampling data into a Transformer network to learn network parameters.
Step S43, a preset loss function is obtained, and training is carried out on the to-be-trained transducer network according to the preset loss function and the sampling data, so that a battery electric quantity estimation model is obtained. Specifically, in this embodiment, the battery power of the rider at a plurality of moments before reaching the fixed departure point is extracted from the sampling data to obtain a power sequence; extracting the residual electric quantity of the battery from the sampling data when the rider arrives at a fixed departure point, and taking the residual electric quantity as a label; and finally, respectively inputting the electric quantity sequence and the label of each rider into the converter network model to learn network parameters, calculating a preset loss function, and when the preset loss function converges, indicating that the built converter network achieves the optimal estimation effect, thereby obtaining a battery electric quantity estimation model.
And S5, respectively calculating the electricity consumption conditions of the batteries in the real track data and the final assumed track data, and analyzing the value of the ESG electricity conversion system according to the electricity consumption conditions. The real trajectory data and the final assumed trajectory number each include the position coordinates of the battery at each time and the battery remaining power, and then the amount of power consumed by the rider from one position to another can be calculated from the position coordinates and the battery remaining power.
Specifically, a first total electric quantity of a new battery is obtained, and the electric quantity consumed when a rider reaches the fixed departure point from the current position and then reaches the next distribution position from the fixed departure point is calculated according to the first total electric quantity and the final assumed track data and is recorded as a first consumed electric quantity; acquiring a second total electric quantity of a new battery, calculating the electric quantity consumed when a rider arrives at the power conversion cabinet position from the current position and arrives at the next distribution position from the power conversion cabinet position according to the total electric quantity and the real track data, and recording the electric quantity as the second consumed electric quantity; and calculating a difference value between the first consumed electric quantity and the second consumed electric quantity, and if the difference value is larger than zero, judging that the ESG power exchange system reduces the electricity consumption cost of a rider. That is, the value of the ESG battery replacement system can be estimated according to the difference, for example, the battery consumption can be saved by replacing the battery through the battery replacement cabinet, the time cost of the rider can be reduced, and the difference of the consumed electric quantity can enable the rider to increase the order of distribution.
The method for measuring the value of the ESG power conversion system is based on the value existing in the hypothetical data mining power conversion cabinet network for the first time, and in the power conversion field, the method not only shows that the power conversion cabinet network provides effective electric energy for a rider, but also makes a certain contribution to social energy conservation and emission reduction. According to the invention, big data information is fully utilized, hypothesis is scientifically provided for the track data of the rider, the hypothesis is constructed by utilizing the hypothesis idea, and the electric quantity difference value of the rider for carrying out electric conversion through the electric conversion cabinet network compared with the electric conversion by taking full batteries from a fixed starting point, and further, the reduced invalid mileage, the increased take-out order number and the reduced carbon emission can be converted; compared with the direct calculation of the network value scheme of the power conversion cabinet, the evaluation result is more accurate. In addition, the invention also provides a method for accurately estimating the electric quantity of the rider at a fixed departure point by using the time sequence network transducer, which provides theoretical support for the follow-up excavation of the rider power-changing habitual data.
In another embodiment of the present invention, referring to fig. 2, the method for measuring the value of the ESG power conversion system in this embodiment obtains data from the lithium cloud platform, then performs preprocessing such as sampling, aggregation, difference value, point location replacement, and the like on the data, then performs construction of a transducer network, and trains the constructed transducer network, in the training process, 80% of the collected data is used for training, 20% of the collected data is used for verification, after the training is completed, a model for estimating the battery power is obtained, the model is stored, and the remaining power of the battery when the rider reaches a fixed departure point in the assumed track data (action= "assume_home") is output, and then the potential value of the power conversion cabinet network is estimated according to the remaining power.
Firstly, obtaining electricity exchange order data and battery GPS positioning data of a rider, wherein the data are derived from the lithium cloud network platform and comprise two parts: one part is the power change order data, and the other part is the battery GPS positioning data. In this embodiment, the power conversion order data includes a user id, a power conversion time, a power conversion cabinet position, and the like; the battery GPS positioning data includes a battery id, longitude (lng), time at which latitude (lat) has been corresponded, battery power, and the like. In this embodiment, the power conversion order data with the time domain of 2022, 3 months, and 2022, 9 months may be acquired first, and the reasons for selecting the data in this time period are two points:
1. The method has the advantages of high timeliness, high data reliability, high data density and the like in the aspect of power conversion service, and the data in the period is more suitable for data analysis and data mining on the power conversion service compared with the data before 3 months in 2022;
2. this period of data has a lower loss rate than data 3 months before 2022, and therefore it is not necessary to excessively fill the data with a loss value.
Then determining a fixed departure point of the rider according to the GPS positioning data of the battery; and obtaining the real track data of the rider according to the fixed departure point, the power change order data and the battery GPS positioning data. Specifically, since the amount of data acquired from the lithium cloud platform is large, in order to reduce the complexity of storing and cleaning the data, the embodiment uses a uniform sampling method to sample the rider, and the sampling distribution is subject to [0,1] uniform distribution, as shown in formula (1):
where a < = x < = b, a = 0, b = 1.
In addition, the embodiment also adopts a k-fold-deletion rate sampling method, divides the data of the riders into M equal parts, evaluates the integrity of each part of data by utilizing the deletion rate, finally takes top-5 parts of data as sampling result data, takes 5 parts of riders in total, limits the time to 08 to 22 points, and each part of data comprises 1 ten thousand riders of data. In other embodiments, N data with a deletion rate not lower than a preset threshold may be selected as the sampling result data, where N < M.
Because the scheme of the invention can not directly acquire the fixed departure point information of the rider and the position information of the specific take-out order of the rider, the embodiment needs to utilize the acquired data sources to carry out data mining to explore the approximate position of the residence of the rider and the position information of the take-out order of the rider.
For example, the daily battery GPS positioning data is subjected to user number customer_id, that is, is associated with a user, and then clustered according to longitude and latitude space positions, so as to obtain the time when the position of the battery is unchanged, at this time, the rider can be considered to stop the walking tool (such as an electric car) at the dispatch site accessory, and then dispatch the article to the customer through walking, so it can be assumed that the starting time when the battery is not moving is the single take-out time begin to be dispatched by the rider, the ending time when the battery is not moving is the take-out time end_time end (stay ending time) at the end of the dispatch by the rider, that is, the calculation formula of the stay time td_delta of the battery at a certain position is:
td_delta = end_time - begin_time （2）
for the mode of judging the unchanged battery position, the embodiment of the invention can be realized by the following contents: the battery will store battery GPS positioning data as part of the battery log data, including time, location coordinates (e.g., latitude and longitude), battery status, battery power, etc., at predetermined periods (e.g., 2 seconds). If the position coordinates of the plurality of data stored in the battery are the same in a certain period (for example, one minute), the battery is considered to be not moved at the moment, and the begin_time and the end_time can be obtained through data analysis.
After analyzing all stay time of the rider, scanning the battery GPS positioning data of each user every day, and marking the current longitude and latitude as the position of the fixed departure point of the rider when td_delta reaches the maximum, namely:
then, taking home (lng, lat) as a reference, the initial track data of the rider is obtained by dotting the user track data, and the dotting rule in the embodiment is as follows:
if td_delta is not equal to zero and the corresponding point location information and home (lng, lat) coincide, dotting is "home";
if td_delta is not equal to zero and the corresponding point location information and home (lng, lat) are not coincident, dotting is "order";
if td_delta is equal to zero, then the dotting is "head";
where "home" represents a fixed departure point of the rider, and "order" represents a situation where the rider is walking to send a certain take-out, or walking to a fixed departure point of the rider to take the take-out, or dining, or changing electricity, and "road" represents a situation where the position of the battery is continuously changed, and indicates the road on which the rider is riding, i.e., the riding path.
After the initial track data of the rider is obtained through the dotting rule, the positions of the rider need to be judged to be the positions where the rider changes electricity, so that interpolation operation is needed to be carried out on the initial track data according to the electricity changing order data of the rider, and the premise of the operation is that the electricity changing order data and the battery GPS positioning data are derived from the same day. Assuming that a rider uses an order_time mark at the time of power change beside a power change cabinet (the power change cabinet system can acquire the power change time of the rider), the time mark of the battery GPS positioning data is a battery_time (namely track time), and the action mark of the user behavior track action is action, the track of the rider is formed by a plurality of discrete longitudes and latitudes of the battery GPS data by using the following rule, and interpolation is carried out on the track:
If any of the pattern_times is equal to the order_time and the action= "head" at this time, then the correction action is "cabinet";
if order_time is between the track times of two battery returns, i.ebattery_time t <order_time<battery_time t+1 Then the interpolation action is "bin", i.e. a flag is added; wherein the label "cabinet" represents that the rider is carrying out a battery change alongside the lithium battery change cabinet.
The method of the invention also needs to assume another rider track, namely, if the rider does not utilize the battery-changing cabinet network to change the power, the rider stores full-power batteries at a fixed starting point and uses the full-power batteries to change the power. In this embodiment, when the battery-changing cabinet "cabin" is replaced with "assume_home", the "assume_home" indicates that the rider changes the battery at a fixed departure point, and hypothetical rider trajectory data, i.e., hypothetical trajectory data, is formed.
Based on the assumed track data, the invention provides a converter network for estimating the electric quantity at the moment because the residual electric quantity of the battery in the rider cannot be directly obtained when the rider arrives at the fixed departure point. The transform network in this embodiment includes four layers, the first three layers are the encoder network layers, the last layer is the MLP network, where the sequence length of the encoder network layers is 20, and the number of neurons in the hidden layer (MLP) is 50; the MLP network consists of three layers of network, an input layer, a Relu function (middle layer) and an output layer, respectively. The present embodiment sets that the input layer is composed of 50 neurons, and the output layer is composed of 1 neuron (representing the amount of electricity when the action= "assume_home" is currently predicted).
In the real track data, the battery residual capacity and the moment when the action= "home" of the rider are known, a sequence (such as data ten minutes before reaching a fixed departure point) before the moment is formed by the battery residual capacities at a plurality of moments, the battery residual capacity when the action= "home" is taken as a label, and the sequence and the label are input into a transform network to learn network parameters.
For the training process of the transducer network, in the deep reinforcement learning network training stage, besides inputting the sequence and the label of each rider sampled before into the transducer network for training and verification, a model Loss function and an Optimizer are also constructed, the Loss function adopts a mean square error Loss function, and the expression is as follows:
wherein, v_prediction represents the predicted value of the electric quantity when action= "assume_home", vi represents the actual value of the electric quantity when action= "home". N represents the number of training samples, and the final goal of the network training is to minimize the MAE_loss function; the Optimizer employs an Adam Optimizer with a learning rate learning_rate of 0.0001. The algorithm Model is trained for 500 rounds until the MAE_loss value is reduced to the minimum, a battery power estimation Model is obtained, and the Model is stored.
According to the Model saved in the network training stage, the electric quantity predicted value (also the electric quantity estimated value) of any rider in the action= "assume_home" is estimated, and the electric quantity of the action= "assume_home" in the assumed track data is replaced by the electric quantity predicted value to form final assumed track data, so that the condition that the rider replaces a battery after returning to a fixed starting point can be represented.
After determining the true track data and the final assumed track data of any rider, the potential value of the battery-changing cabinet network can be evaluated. Specifically, in this embodiment, according to the authenticity track data and the supposition track data, a first track electricity consumption when the action= "assume_home" returns to the fixed departure point and a second track electricity consumption when the action= "assume_home" returns to the takeout order form the fixed departure point are calculated, and after the first track electricity consumption and the second track electricity consumption are added, a difference value calculation is performed between the first track electricity consumption and the second track electricity consumption and the track electricity consumption when the action= "carbide", that is, the saved electricity value is obtained. Finally, the invention estimates the data of partial riders with the time domain of 2022, 3 months and 2022, 9 months, and can be known that: the existence of the electricity changing cabinet network averagely saves electricity by 1.5 degrees per day for a rider, reduces invalid mileage by 6km, converts the electricity into take-out orders, and can send 10 take-out orders per day for each rider; further, according to the carbon emission amount of about 0.785kg per degree of electricity, the existence of the electricity changing cabinet network can reduce the carbon emission of 471 thousands kg each year.
In summary, the value of the ESG power conversion system provided by the invention can measure the electric energy and time cost saved by the intelligent power conversion network for a rider. Through data verification, the electric power conversion network system can save electric energy, increase take-out order quantity for a rider, relieve electric power anxiety when the rider takes out order, provide effective guarantee for taking out order for the rider, save time cost and electricity cost and increase income compared with the case that the rider takes a battery from a fixed starting point in the take-out and take-out process.
In a second aspect, embodiments of the present application provide an electronic device, and fig. 3 is a block diagram of the electronic device, which is shown according to an exemplary embodiment. As shown in fig. 3, the electronic device may comprise a processor 11 and a memory 12 storing computer program instructions.
In particular, the processor 11 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 12 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 12 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, solid state Drive (Solid State Drive, SSD), flash memory, optical Disk, magneto-optical Disk, tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory 12 may include removable or non-removable (or fixed) media, where appropriate. The memory 12 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 12 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, memory 12 includes Read-Only Memory (ROM) and random access Memory (Random Access Memory, RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, abbreviated PROM), an erasable PROM (Erasable Programmable Read-Only Memory, abbreviated EPROM), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, abbreviated EEPROM), an electrically rewritable ROM (Electrically Alterable Read-Only Memory, abbreviated EAROM), or a FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be Static Random-Access Memory (SRAM) or dynamic Random-Access Memory (Dynamic Random Access Memory DRAM), where the DRAM may be a fast page mode dynamic Random-Access Memory (Fast Page Mode Dynamic Random Access Memory FPMDRAM), extended data output dynamic Random-Access Memory (Extended Date Out Dynamic Random Access Memory EDODRAM), synchronous dynamic Random-Access Memory (Synchronous Dynamic Random-Access Memory SDRAM), or the like, as appropriate.
Memory 12 may be used to store or cache various data files that need to be processed and/or communicated, as well as possible computer program instructions for execution by processor 11.
The processor 11 implements any of the methods of measuring the value of the ESG battery system in the above embodiments by reading and executing computer program instructions stored in the memory 12.
In an embodiment, the electronic device may further comprise a communication interface 13 and a bus 10. As shown in fig. 3, the processor 11, the memory 12, and the communication interface 13 are connected via the bus 10 and perform communication with each other.
The communication interface 13 is used to implement communications between various modules, devices, units and/or units in the embodiments of the present application. The communication port 13 may also enable communication with other components such as: and the external equipment, the image/data acquisition equipment, the database, the external storage, the image/data processing workstation and the like are used for data communication.
Bus 10 includes hardware, software, or both, that couple components of an electronic device to each other. Bus 10 includes, but is not limited to, at least one of: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), local Bus (Local Bus). By way of example, and not limitation, bus 10 may include a graphics acceleration interface (Accelerated Graphics Port), abbreviated AGP, or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, a wireless bandwidth (InfiniBand) interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, abbreviated MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, abbreviated PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, abbreviated SATA) Bus, a video electronics standards association local (Video Electronics Standards Association Local Bus, abbreviated VLB) Bus, or other suitable Bus, or a combination of two or more of the foregoing. Bus 10 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In a third aspect, embodiments of the present application provide a computer readable storage medium having a program stored thereon, which when executed by a processor, implements the method for measuring the value of an ESG battery exchange system provided in the first aspect.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method for measuring the value of an ESG battery system as provided in the first aspect, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of measuring the value of an ESG battery exchange system comprising:
acquiring electricity changing order data of a rider and battery GPS positioning data, determining a fixed departure point of the rider according to the battery GPS positioning data, and determining the position of an electricity changing cabinet according to the electricity changing order data;
obtaining real track data of a rider according to the position of the battery changing cabinet and the GPS positioning data of the battery; the real track data comprise position information sequenced by time and corresponding battery power;
Replacing the position of the battery changing cabinet in the real track data with the fixed departure point to obtain assumed track data;
calculating the residual electric quantity of the battery when the rider reaches the fixed departure point through a pre-constructed battery electric quantity estimation model based on the assumed track data to obtain an electric quantity estimation value, and replacing the battery electric quantity of the fixed departure point in the assumed track data with the electric quantity estimation value to obtain final assumed track data;
and respectively calculating the electricity consumption condition of the battery in the real track data and the final assumed track data, and analyzing the value of the ESG battery replacement system according to the electricity consumption condition.
2. The method of claim 1, wherein the GPS positioning data includes a plurality of location coordinates, and a time and a battery level corresponding to the location coordinates, the determining a rider's stationary departure point from the battery GPS positioning data comprising:
performing cluster analysis on the position coordinates, extracting the position coordinates and corresponding time of the battery which do not move within a preset time period, and recording the position coordinates, the stay start time and the stay end time as stay positions;
calculating the difference value between the residence starting time and the residence ending time to obtain the residence time of the residence position;
Traversing all the stay time lengths of each day of the rider, comparing the sizes, and marking the stay position corresponding to the maximum stay time length as a fixed departure point of the rider.
3. The method of claim 1, wherein the obtaining real trajectory data of the rider from the battery pack position and the battery GPS positioning data comprises:
marking all position coordinates in the battery GPS positioning data according to a preset rule to obtain initial track data;
and carrying out interpolation processing on the initial track data according to the position of the battery changing cabinet to obtain the real track data of the rider.
4. A method according to claim 3, wherein the preset rules comprise:
if the current position coordinate coincides with the fixed departure point and the corresponding stay time is not equal to zero, marking the current position coordinate as the fixed departure point; if the current position coordinate is not coincident with the fixed departure point and the corresponding stay time is not equal to zero, marking the current position coordinate as a take-out order position; and if the stay time corresponding to the current position coordinate is equal to zero, marking the current position coordinate as a riding path.
5. A method according to claim 3, wherein said deriving true trajectory data of the rider from said battery pack position and said battery GPS positioning data comprises:
the power change order data comprise power change time and power change cabinet positions, and track time is obtained from the battery GPS positioning data;
if the track time is equal to the power changing time and the position coordinate corresponding to the track time is marked as a riding path, replacing the current position mark with a power changing cabinet position;
if the power change time is between the two track times, inserting the power change time and the power change cabinet position into the initial track data, and marking and replacing the power change time and the power change cabinet position to generate the real track data.
6. The method of claim 1, wherein the constructing of the battery charge estimation model comprises:
constructing a transducer network according to the requirement of a user to obtain a transducer network to be trained;
acquiring real track data of a plurality of riders, acquiring time and corresponding battery electric quantity from the real track data, and forming sampling data;
and acquiring a preset loss function, and training the to-be-trained converter network according to the preset loss function and the sampling data to obtain a battery electric quantity estimation model.
7. The method of claim 6, wherein training the to-be-trained converter network according to the preset loss function and the sampling data to obtain a battery power estimation model comprises:
extracting battery electric quantity of a rider at a plurality of moments before the rider reaches a fixed departure point from the sampling data to obtain an electric quantity sequence;
extracting the residual electric quantity of the battery from the sampling data when the rider arrives at a fixed departure point, and taking the residual electric quantity as a label;
respectively inputting the electric quantity sequence and the label of each rider into a to-be-trained transducer network to learn network parameters;
and calculating the preset loss function, and obtaining a battery electric quantity estimation model when the preset loss function is converged.
8. The method of claim 1, wherein the calculating the power consumption of the battery in the real trajectory data and the final assumed trajectory data, respectively, and the analyzing the value of the ESG battery replacement system according to the power consumption comprises:
acquiring a first total electric quantity of a new battery, calculating the electric quantity consumed by a rider when the rider reaches the fixed departure point from the current position and reaches the next distribution position from the fixed departure point according to the first total electric quantity and the final assumed track data, and recording the electric quantity as a first consumed electric quantity;
Acquiring a second total electric quantity of a new battery, calculating the electric quantity consumed when a rider arrives at the power conversion cabinet position from the current position and arrives at the next distribution position from the power conversion cabinet position according to the total electric quantity and the real track data, and recording the electric quantity as the second consumed electric quantity;
and calculating a difference value between the first consumed electric quantity and the second consumed electric quantity, and if the difference value is larger than zero, judging that the ESG power exchange system reduces the electricity consumption cost of a rider.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of measuring the value of an ESG battery system according to any one of claims 1 to 8 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of measuring the value of an ESG battery system according to any one of claims 1 to 8.
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