CN113547949B

CN113547949B - Electricity swapping task generation method and device, terminal and storage medium

Info

Publication number: CN113547949B
Application number: CN202110748903.8A
Authority: CN
Inventors: 杨磊; 陈进清
Original assignee: Shanghai Junzheng Network Technology Co Ltd
Current assignee: Shanghai Junzheng Network Technology Co Ltd
Priority date: 2021-07-02
Filing date: 2021-07-02
Publication date: 2022-12-27
Anticipated expiration: 2041-07-02
Also published as: CN113547949A

Abstract

The application provides a power swapping task generating method, a power swapping task generating device, a power swapping terminal and a storage medium, and a site group to which a site in each preset area belongs is determined; the station is used for taking/discharging the dynamic carrier; acquiring power conversion characteristic information of each station group; processing input data containing the power conversion characteristic information to obtain circuit lines passing through a target station group; and generating a battery swapping task according to the battery swapping route. The power conversion efficiency is improved by using the station group comprising the forward station as an execution unit of the power conversion task; the evaluation of the related battery swapping characteristic information is carried out according to the related dimension information in the electric vehicle scene, and at least a battery swapping route with high battery swapping efficiency is obtained through the operation and research optimization model, so that the existing defects are overcome.

Description

Electricity swapping task generation method and device, terminal and storage medium

Technical Field

The present application relates to the field of electric vehicle technologies, and in particular, to a battery swapping task generation method, apparatus, terminal, and storage medium.

Background

With the development of the sharing trip, electric vehicles (such as moped) have been popularized in various cities.

However, unlike a shared bicycle, an electric vehicle requires battery power, which requires recharging as the user rides the vehicle and consumes more battery power.

Generally, the electric vehicle is parked at roadside sites, and if the electric vehicle is automatically charged, charging piles need to be arranged corresponding to the sites, so that more charging equipment cost is increased, and the electric vehicle is inconvenient to maintain. Moreover, the batteries in the electric vehicles of different operation platforms are often customized and do not provide a charging interface.

Thus, the replacement work needs to be performed manually. For example, each battery swapping worker in charge of an area swaps a battery of an electric vehicle having a battery swapping requirement (e.g., a battery is lower than a threshold) at each station in the area. However, there are still major problems therein. On one hand, the operation and maintenance responsible person is required to replace the battery efficiently in time, and if the timeliness of replacing the battery of the moped is not enough, the operation and maintenance responsible person can influence the riding of the user, so that the electric vehicle is in short of power and the lease order of the user is lost. The operation and maintenance responsible person often checks the electric quantity of each carrier through manual work, and the problem of untimely perception exists. Moreover, the operation and maintenance cannot be known by manually checking whether the station where the carrier is located has a user to ride at the current time or the future time. On the other hand, the operation and maintenance responsible person has many stations for swapping power, and how to plan each time of swapping power to achieve the highest swapping efficiency cannot be obtained. On the other hand, in consideration of benefits of the operation and maintenance responsible persons, the operation and maintenance responsible persons can preferentially select stations close to the operation and maintenance responsible persons to change the power, and often abandon stations far away from the operation and maintenance responsible persons, so that the stations are lost.

Disclosure of Invention

In view of the above drawbacks of the prior art, an object of the present application is to provide a battery swapping task generating method, device, terminal, and storage medium, thereby solving the problems of the prior art.

A first aspect of the present application provides a method for generating a battery swapping task, including: determining a site group to which sites in each preset area belong; each station is used for taking/discharging the power carrier; acquiring power change characteristic information of the station group; processing input data containing the electricity swapping feature information to obtain a electricity swapping route passing through a target station group; and generating a battery swapping task according to the battery swapping route.

In an embodiment of the first aspect, the method comprises: and taking the road sections formed between the adjacent intersections in the road network as the preset area.

In an embodiment of the first aspect, the determining a site group to which a site in each preset area belongs includes: all the stations on two sides of the unidirectional road section belong to the same station group; and respectively classifying the stations on two sides of the bidirectional road section into different station groups.

In an embodiment of the first aspect, the maximum number of stations of the station group is a maximum value of a first number of stations in a certain proportion to a preset trip average number of stations and a second number of stations corresponding to the power swapping time limit.

In an embodiment of the first aspect, the obtaining of the power swapping feature information of the station group includes: and evaluating the power change importance of the station group.

In an embodiment of the first aspect, the evaluating the power swapping importance of the site group includes: evaluating the power swapping importance of each station in the station group through a preset power swapping importance parameter; summing the power swapping importance degrees of all the stations in the station group to obtain the power swapping importance degree of the station group; wherein, the preset battery replacement importance parameter comprises: the number of electric vehicles in the station, the electric quantity of each electric vehicle in the station, the riding quantity of future users of the station and the probability of single-loss due to power shortage caused by power failure.

In an embodiment of the first aspect, the processing input data including the swapping feature information to obtain a swapping route passing through a target site group includes: running an operation and planning optimization model to select a target site group which meets an expected target under the constraint condition of a power swapping scene so as to form the power swapping route; the desired goals include: and the battery replacement characteristic information combination of the selected target station group meets a preset condition.

In an embodiment of the first aspect, the combination of the swapping characteristic information of the selected target site group meets a preset condition, and includes: and the sum of the power conversion importance degrees of each station in the selected target station group is maximized.

In an embodiment of the first aspect, the constraint is determined by a first parameter set by the operations optimization model: the first parameter comprises at least one of the following:

a parameter indicating that the station group needs to be completely in one power conversion route;

a parameter representing an upper/lower limit threshold of the total station number of the power switching line;

a parameter of an upper/lower threshold value representing the number of electric vehicles to be charged in the charging route;

parameters of an upper/lower limit threshold value representing the number of the carried batteries required for completing the battery replacement task;

a parameter representing an upper/lower limit threshold value of a distance between station groups in the power switching line;

a parameter indicating that the selected head and tail stations in the station group should be located in the same road section;

the parameter represents an upper limit threshold value of a geographical position distance between an executor of the battery swapping task and a head station in the station group;

a parameter representing an upper/lower limit threshold of a station spacing in the power switching line;

the parameter represents the upper/lower limit threshold value of an included angle between the positions of any two stations in the power conversion line and the geographical position of the operation and maintenance responsible person;

defining parameters of an upper limit threshold value and a lower limit threshold value of the distance between the nearest battery swapping stations;

a parameter representing an upper/lower threshold for the elapsed time of the power change line.

In an embodiment of the first aspect, the expected goal is determined by a second parameter set by the operational research optimization model; the second parameter includes at least one of:

parameters representing that the battery replacement characteristic information combination of the target station group meets preset conditions;

a parameter representing the minimization of the total distance of the power switching line;

representing a parameter for maximizing the number of replaced batteries in the battery replacement task;

representing a parameter for maximizing the station battery replacement time efficiency;

a parameter for minimizing the number of swapping task passes is indicated.

In an embodiment of the first aspect, the method comprises: extracting presentation data from site group data of the site group to participate in site group data-related calculations in place of the site group data; wherein the presentation data comprises: site data of selected head and tail sites in the site group; and the station data of the rest stations in the station group is used as the associated data of the representation data.

A second aspect of the present application provides an electricity swapping task generating device, including: the station group determining module is used for determining a station group to which the station in each preset area belongs; the station is used for taking/discharging the dynamic carrier; the station group characteristic acquisition module is used for acquiring the battery replacement characteristic information of the station group; the power conversion route planning module is used for processing input data containing the power conversion characteristic information to obtain a power conversion route passing through a target station group; and the battery swapping task generating module is used for generating a battery swapping task according to the battery swapping route.

A third aspect of the present application provides a service apparatus, including: a communicator, a memory, and a processor; the communicator is used for communicating with the outside; the memory stores program instructions; the processor is configured to execute the program instructions to execute the power swapping task generating method of any one of the first aspect.

A fourth aspect of the present application provides a user terminal, comprising: a communicator, a memory, and a processor; the communicator is configured to communicate with the serving apparatus of the third aspect; the memory stores program instructions; the processor is configured to execute the program instructions to perform operations comprising: sending the geographical position information of the operation and maintenance responsible person to the service device; and receiving the battery swapping task generated and issued according to the geographic position from the service device.

A fifth aspect of the present application provides a computer-readable storage medium, which stores program instructions that, when executed, perform the swapping task generating method according to any one of the first aspects.

In summary, an embodiment of the present application provides a method, an apparatus, a terminal, and a storage medium for generating a swapping task, where the method includes: determining a site group to which sites in each preset area belong; the station is used for taking/discharging the dynamic carrier; acquiring power change characteristic information of the station group; processing input data containing the electricity swapping feature information to obtain a power swapping route passing through a target station group; and generating a battery swapping task according to the battery swapping line. The station group comprising the forward path stations is an execution unit of the power switching task, so that the power switching efficiency is improved; and the evaluation of the related battery replacement characteristic information is carried out according to the related dimension information in the scene of the electric vehicle, and a battery replacement route with high battery replacement efficiency is obtained at least through an operation and research optimization model, so that the existing defects are overcome.

Drawings

Fig. 1 shows a schematic structural diagram of a power swapping application scenario in an embodiment of the present application.

Fig. 2 shows a schematic structural diagram of a computer device in the embodiment of the present application.

Fig. 3 shows a schematic flow chart of a power swapping task generation method in the embodiment of the present application.

Fig. 4A shows a schematic diagram of a station of an electric vehicle in an embodiment of the present application.

Fig. 4B shows a schematic diagram of the site setup in the road network block in the embodiment of the present application.

Fig. 4C shows a schematic diagram of dividing the stations on both sides of the unidirectional road segment into station groups in this embodiment.

Fig. 4D shows a schematic diagram of dividing the two-side stations of the bidirectional road segment into station groups in the embodiment of the present application.

Fig. 4E shows a schematic diagram of dividing a block into site groups according to an embodiment of the present application.

Fig. 5 shows a schematic diagram of angle limitation in a constraint condition of the operation optimization model according to an embodiment of the present application.

Fig. 6 shows a schematic block diagram of a swapping task generating device in an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present application. It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.

Embodiments of the present application will be described in detail below with reference to the accompanying drawings so that those skilled in the art to which the present application pertains can easily carry out the present application. The present application may be embodied in many different forms and is not limited to the embodiments described herein.

In order to clearly explain the present application, components that are not related to the description are omitted, and the same reference numerals are given to the same or similar components throughout the specification.

Throughout the specification, when a device is referred to as being "connected" to another device, this includes not only the case of being "directly connected" but also the case of being "indirectly connected" with another element interposed therebetween. In addition, when a device "includes" a certain component, unless otherwise stated, the device does not exclude other components, but may include other components.

When a device is said to be "on" another device, this may be directly on the other device, but may be accompanied by other devices in between. When a device is said to be "directly on" another device, there are no other devices in between.

Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first interface, a second interface, etc. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "a, B or C" or "a, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" include plural forms as long as the words do not expressly indicate a contrary meaning. The term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and/or components.

Terms representing relative spatial terms such as "lower", "upper", and the like may be used to more readily describe one element's relationship to another element as illustrated in the figures. Such terms are intended to include not only the meanings indicated in the drawings, but also other meanings or operations of the device in use. For example, if the device in the figures is turned over, elements described as "below" other elements would then be oriented "above" the other elements. Thus, the exemplary terms "under" and "beneath" all include above and below. The device may be rotated 90 or other angles and the terminology representing relative space is also to be interpreted accordingly.

Although not defined differently, including technical and scientific terms used herein, all terms have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms defined in commonly used dictionaries are to be interpreted as having meanings consistent with those of the related art documents and the present prompts, and must not be excessively interpreted as having ideal or very formulaic meanings unless defined otherwise.

Generally, the replacement of the battery of the electric vehicle is performed by an operation and maintenance responsible person. However, the manual operation is completely adopted, which causes the problems of untimely perception, low power changing efficiency, power changing by picking up a station and the like.

In view of this, the embodiments of the present application provide corresponding solutions.

As shown in fig. 1, a schematic structural diagram of a battery swapping application scenario in the embodiment of the present application is shown.

In this application scenario, the electric vehicles 101, the service device 102, and the user terminal 103 of the operation and maintenance responsible person 104 are shown.

In some embodiments, each of the electric vehicles 101 may be, for example, an electric bicycle, in which a battery (such as a lithium battery) is detachably mounted to drive the electric vehicle 101 to move through the battery. In some embodiments, the electric vehicle 101 is a shared electric vehicle. The electric vehicle 101 has therein an intelligent circuit system capable of communicating with the service device 102 by accessing a network through wireless communication to report messages related to, for example, unlocking, locking events, power, and geographic location information leased by a user. In the message, the ID of the electric vehicle 101 itself may be added to indicate the identity. The electric moped can be parked in pre-planned stations on two sides of a road. In other examples, the electric vehicle 101 may also be a user-owned electric vehicle.

The service device 102 may plan a battery swapping route for the operation and maintenance responsible person 104 according to the information reported by the electric vehicle 101, further generate a battery swapping task, and distribute the battery swapping task to the user terminal 103 of the corresponding operation and maintenance responsible person 104. The operation and maintenance responsible person 104 can log in the service device 102 by using the user ID through the user terminal 103 thereof for communication interaction. Optionally, the operation and maintenance responsible person 104 may report a message including information of a geographical location where the operation and maintenance responsible person is located, so that when the service device 102 plans the battery swapping path, each station is planned from the location where the operation and maintenance responsible person 104 is located.

The user terminal 103 may receive the issued swap task from the service device 102, and may display a swap route S therein, for example, presented through a graphical interface 105 of electronic map software in the figure, so as to guide, for example, an operation and maintenance responsible person 104 (e.g., an operation and maintenance person, an automatic driving vehicle or a swap robot loading the operation and maintenance responsible person 104, etc.) to swap batteries according to the swap route.

In some embodiments, the service device 102 may belong to an operation platform of an operator of the electric vehicle 101, which includes a server or a group of servers to provide remote services. The service type of the service device 102 may be any one of the following: saaS (software as a service), paaS (platform as a service), iaaS (information as a service).

The user terminal 103 may be implemented as, for example, a notebook computer, a personal desktop computer, a smart phone, a tablet computer, a smart bracelet, a smart watch, and the like.

The service device 102 and the user terminal 103 can perform communication interaction through a network, such as a mobile internet, a wired internet, and the like.

On one hand, the service device 102 plans the battery replacement path for the operation and maintenance responsible person 104 from the machine perspective, and information such as efficiency (factors such as on-road and power shortage degree) and business (such as priority people flow, carrier quantity, battery replacement of sites expected to have a high single probability) can be considered in the planning process, so that the battery replacement efficiency is higher, the user experience is better, and the operation profit is improved.

On the other hand, the service device 102 issues the swap task, so that the route is determined, and the operation and maintenance responsible person 104 is prevented from tending to pick up orders at the nearby site.

In the embodiment shown in fig. 1, the server of the service device 102, the user terminal 103, and the intelligent circuit system in the electric vehicle 101 may be implemented according to the architecture of a computer processing system.

Fig. 2 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The computer apparatus 200 includes a bus 201, a processor 202, a communicator 203, and a memory 204. The processor 202, memory 204, and communicator 203 may communicate over a bus 201. The communicator 203 is used for communicating with the outside. The memory 204 may store program instructions (such as system or application software), and the memory 204 may also store data to be read and written by the program instructions. The processor 202 implements the functionality of the intelligent circuitry in the service device, user terminal, or electric vehicle by executing program instructions. For example, in the user terminal, the transmission of the geographical location information of the operation and maintenance responsible person to the service device, the reception of the battery replacement task generated and distributed according to the geographical location from the service device, and the like are realized. For example, the service device determines a power exchange route for the operation and maintenance responsible person, and generates and distributes a power exchange task. As another example, in smart circuitry in an electric vehicle.

The bus 201 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. Although only one thick line is shown in fig. 2 for ease of illustration, it is not intended to represent only one bus or type of bus.

In some embodiments, the processor 202 can be implemented as a Central Processing Unit (CPU), a micro Processing Unit (MCU), a System On a Chip (System On Chip), or a field programmable logic array (FPGA). The Memory 204 may include a Volatile Memory (Volatile Memory) for temporary storage of data when the program is executed, such as a Random Access Memory (RAM). The Memory 204 may also include a non-volatile Memory (non-volatile Memory) for data storage, such as a Read-Only Memory (ROM), a flash Memory, a Hard Disk Drive (HDD) or a Solid-State Disk (SSD).

The communicator 203 is used for communicating with the outside. In particular examples, the communicator 203 may include one or more wired and/or wireless communication circuit modules. For example, the communicator 203 may include one or more of, for example, a wired network card, a USB module, a serial interface module, and the like. The wireless communication protocol followed by the wireless communication module includes: such as one or more of Near Field Communication (NFC) technology, infrared (IR) technology, global System for Mobile communications (GSM), general Packet Radio Service (GPRS), code Division Multiple Access (CDMA), wideband Code Division Multiple Access (WCDMA), time Division Multiple Access (Time-Division Code Division Multiple Access, TD-SCDMA), long Term Evolution (LTE), blueTooth (BlueTooth, BT), global Navigation Satellite System (GNSS), etc.

It is understood that the types of the communicator 201, the processor 202, and the memory 203 may be different corresponding to different service devices, user terminals, and smart circuitry in the electric vehicle. For example, in the service device, a wired network card may be used as a communicator to connect to a network to access the network, a x 86-bit single-path or multi-path server CPU may be used as a processor, a random access memory (e.g., DDR) may be used as an internal memory, and a hard disk may be used as an external memory. For another example, if the user terminal is a smart phone or a tablet pc, the user terminal may include one or more of a WiFi module, a 2G/3G/4G/5G mobile communication, NFC, bluetooth, and infrared communicator, and may use, for example, an ARM-based SoC as a processor, a low power random access memory (LPDDR) as an operating memory, and a Flash memory (e.g., NAND Flash RAM) as an external memory.

In addition, the user terminal may further have a display screen (e.g., LCD, OLED, etc.) for displaying a Graphical User Interface (GUI) to show information related to the battery swapping task, show a battery swapping route in an interface of an electronic map, and the like.

As shown in fig. 3, a schematic flow chart of a power swapping task generating method in the embodiment of the present application is shown. The battery swapping task generation method can be executed on the service device side in the above embodiment, so as to plan a battery swapping route with high battery swapping efficiency for an operation and maintenance responsible person, and obtain a corresponding battery swapping task.

The method specifically comprises the following steps:

step S301: determining a site group to which sites in each preset area belong; the station is used for taking/placing the electric vehicle.

Wherein, each station is used for fetching/discharging the electric vehicle, and the electric vehicle can be an electric moped for leasing by 'sharing' and the like. For example, as shown in FIG. 4A, each broken out area is treated as a site 401A along the area on both sides of the street that is opened up by warning lines (e.g., white). Alternatively, in other examples, multiple discrete but adjacent zones may be divided into one site, etc. It should be noted that the number of electric vehicles in a station depicted in the station is only illustrative and does not limit the actual number of electric vehicles.

In some embodiments, the preset area may be set according to a natural extending and dividing manner of a road, so that each station in the station groups divided in this way is "in-line", and each station group in the battery swapping line may also be in-line, thereby improving the battery swapping efficiency of the battery swapping line. For example, from the road level, two adjacent stations on one side of a road are divided into a group of stations, and so on. From the aspect of city planning, there are roads staggered horizontally and vertically on a map, like a net, and therefore, the map is called a "road network", and an area surrounded by the staggered roads can be called a "block". For example, as shown in FIG. 4B, roads A1, A2, A3, A4 enclose a block B forming a square. In a block may be a residential home, commercial square, office building, school, or other facility. Typically, the station 401B of the electric vehicle is set on a road around the neighborhood.

In consideration of the principle of following roads in geographic locations, in an alternative example, a road segment formed between adjacent intersections in a road network may be used as a preset region to divide a station group. The method is simple, and the station groups are divided for all stations on the road sections naturally divided from each intersection to each intersection.

In some embodiments, different station groups may be divided according to road segments of different road types. For example, stations on both sides of a unidirectional road section are assigned to the same station group. For example, as shown in fig. 4C, the sites C1 and C2 are divided into the same site group D; and respectively classifying the stations on two sides of the bidirectional road section into different station groups. For example, as shown in fig. 4D, in a bidirectional road 2 (for example, there is a single yellow line or a double yellow line in the middle for dividing the bidirectional road, and there may be no line but allowing bidirectional passage), the stations C3 and C4 are divided into different station groups E and F.

As further shown in fig. 4E, for example, a diagram illustrating the division of the station groups into several road segments is shown. The station group Y and the station group W belong to different road sections of the same road, and may be divided into different station groups. The station groups X and Y are respectively on both sides of the bidirectional road segment, and may be divided into different station groups. The station group Z and the station group W are divided into different station groups in two road segments.

The number of sites per site group is set to be considered. The smaller the site group (i.e. the smaller the number of sites involved), the more flexible the optimization and the larger the optimization tuning space. For example, when the number of sites in a site group is small, the schemes that the available batteries carried by the operation and maintenance responsible persons are respectively allocated to each site group are more and more flexible; however, there is a drawback that the smaller the site group is, the larger the number of site groups is inevitably, and when the calculation is performed in units of site groups, the amount of calculation corresponding to data for each calling of each site group is also increased, and the number of Bad cases (Bad cases) is also increased. However, if the division of the site group is too large, the distribution scheme of the available battery number among a plurality of site groups is limited, for example, a problem that the same number of batteries is enough to be distributed to one site group but not enough to be distributed to two site groups is encountered, and the problem that the site group is small has less influence.

For this reason, it is necessary to set a reasonable maximum number of stations per station group to avoid the above-described problems. In some embodiments, the maximum number of stations of each station group is a maximum value of a first number of stations in a certain ratio to a preset number of average stations and a second number of stations corresponding to the power swapping time limit. For example, the certain ratio may be 1/3, i.e., the maximum number of sites per site group may be 1/3 of the number of sites in a preset trip. Further, the maximum number of sites of the site group is set as: max (N, 1/3 preset number of lap average stations); the 1/3 setting ratio is for convenience to have an optimization space, and N is the number of the second stations, and since the positions and the electric quantity of the electric vehicles parked in the stations are changed along with the riding of the user, appropriate limitation is also required; and the N stations may be adapted to the time required for appropriate swapping, for example, when N =5 is estimated according to historical data, the corresponding average swapping is more appropriate in about 30 minutes, and then N =5 is set to take a larger value from 1/3 × preset lap average station number as the maximum station number of each station group.

Since the relevant information of each site includes: geographical location information of the site; ID of each electric vehicle in the station, current remaining power amount, and the like. Generally, if a site group is to be taken as a unit, the corresponding data set (e.g., data structure) will contain the data of each site therein. Such as the geographical location of each site, the amount of battery charge remaining for each electric vehicle involved, and other data. Thus, each time a site group is used, the entire data set needs to be read, which may affect the computational efficiency.

In fact, it is not necessary to use all the data in the data set associated with a site group for each calculation, for example, when only the calculation associated with the geographical position is required, the data of all the sites are not required, but only the representative representation data is required to represent the site group. In some embodiments, presentation data is extracted from site group data of the site group to participate in site group data-related calculations in place of the site group data. Wherein the presentation data comprises: site data of the selected head site and tail site in the site group; and the station data of the rest stations in the station group is used as the associated data of the representation data.

This may be further explained in conjunction with fig. 4E. Road sections naturally divided from road network blocks are used as boundaries of different station groups, and the head station and the tail station are respectively the stations closest to the intersection at two ends of the road sections. The head and tail stations are shown in fig. 4E as slanted bottom stripes to distinguish them from other stations, and the uppermost station in station groups Y and X is the head station, and the lowermost station is the tail station, or vice versa.

A site group is a collection of off-road sites for a road segment. Since the head and tail stations selected from the station group are used as the representative stations of the station group, when the calculation related to the geographical position of the station group is mainly related, the calculation can be performed by using the data of the head and tail stations (such as the geographical position data of the head and tail stations) as the representation data, and the data of other stations such as the number of interchangeable carriers can be called as the association data when necessary by using the association with the head and tail stations. Specifically, in order to reduce unnecessary calculation in the operation process of the operational optimization model, data of other stations are affiliated to the representation data of the head and tail representative stations during calculation, such as the number of electric vehicles of other stations, and after the calculation is finished, the associated station data are refilled in the planned swapping line. Therefore, the calculation time can be greatly reduced, and the optimization calculation effect in the limited time is better. Meanwhile, the experience problem of operation and maintenance accountants caused by the fact that the station is neglected in the road and the station is placed in the battery swapping task in the road which is not passable is also reduced.

Step S302: and acquiring the battery replacement characteristic information of the station group.

In some embodiments, the battery swapping feature information may include a battery swapping importance degree. The swapping importance is positively correlated with the swapping priority, for example, the higher the swapping importance of a certain station group is, the higher the possibility of being selected in one swapping task is. In addition, the swapping importance of each site group may be represented by a sum of the swapping importance of each site included in the swapping importance.

In some embodiments, the swapping importance of each site in each site group may be evaluated by the swapping importance parameter, and then summed (for example, a weighted sum, where weights of each site may be equal or different) to obtain the swapping importance of the site group. The battery replacement importance parameter may be related to user experience income and operation income in an electric vehicle operation scene. Illustratively, the swapping importance parameter includes: the number of electric vehicles in the station, the electric quantity of each electric vehicle in the station, the riding quantity of future users of the station and the probability of single-loss due to power shortage caused by power failure. For example, the greater the number of electric vehicles at a station, the greater the number of possible vehicles in the neighborhood, and the greater the number of electric vehicles rented, the greater the number of units and the greater the possibility, the greater the importance. For example, the electric power of the electric vehicle at the station is more or less related to the battery replacement behavior, and the more vehicles in a "power shortage" state (for example, the remaining battery power is lower than a certain threshold), the higher the importance of battery replacement is. Illustratively, the station future user ride volume is (i.e., the volume of the electric vehicle's ride unit) may be predicted from historical data of the station's user ride volume, such as historical N-day ride volume, historical contemporaneous day (e.g., historical data of past monday, data used to predict future monday), real-time vehicle distribution, weather, holidays, etc., to predict the volume of the N-hour future ride unit. Data models used for prediction, such as mean regression, moving average (ARIMA, for example), exponential smoothing prediction (Holt-Winters, for example), and the like, and machine learning models, such as support vector machines, tree models (GBM, QRF, for example), neural network models (CNN, RNN, LSTM, GRU, for example), and the like, are used, and when the predicted user riding amount is higher, the corresponding battery swapping importance degree is higher.

In a similar principle, the probability of losing the list due to power shortage caused by power failure can also be predicted according to the relevant data of losing the list due to power failure caused by power failure (for example, the situation that the user gives up renting electric vehicles due to power shortage after scanning codes), and the relevant data can include: the above-mentioned historical riding single amount still includes: historical power shortage data, operation and maintenance power conversion data and the like so as to predict the future power shortage and single loss probability. The adopted prediction model can be any one or combination of a plurality of algorithms listed above, and the importance of battery replacement is higher when the probability that a single battery is lost due to power shortage caused by battery replacement is higher.

In some embodiments, the swapping importance may be quantified by a score, which may be between 0 and 1, or between 0 and 100, or other values, as long as the swapping importance can be presented by the value being high or low. In addition, when a plurality of battery replacement importance parameters such as the number of the station carriers, the electric quantity of the station carriers, the riding quantity of future users of the station and the like are integrally considered, different weights can be respectively given, and the integral battery replacement importance is obtained through weighting and/or other calculation methods.

After step S302 in fig. 3, step S303 is also shown: and processing input data containing the electricity changing characteristic information to obtain an electricity changing route passing through the target site group.

Specifically, each target station group is selected through input data related to dimension information such as the power conversion importance degree, and the target station groups are connected in series to obtain the power conversion route.

In some embodiments, the processing input data including swapping feature information of each station group to obtain a swapping route passing through a target station group includes: running an operation and planning optimization model to select a target site group which accords with an expected target under the constraint condition of a power swapping scene so as to form a power swapping route; the desired goals include: and the battery replacement characteristic information combination of the selected target station group meets a preset condition. It should be noted that the swapping routes generated according to the operation and maintenance optimization model are not limited to one time, and may also be multiple times, that is, an operation and maintenance responsible person may run multiple times of different swapping routes to cover all the site groups, for example, the swapping route in the first time covers the site group a- > B- > C, and the swapping route in the second time covers the site group D- > E.

It can be understood that, on one hand, the determination of the power swapping route takes into account the above power swapping importance degree related to user experience and commercial profit, and the higher the total power swapping importance degree of the station group through which the power swapping route passes, the greater the corresponding profit. On the other hand, in the electric vehicle scenario, some constraint conditions (may also include conditions related to commercial revenue) in the actual scenario are also generated according to the characteristics of the station and the station group, so the operation and planning optimization model needs to solve each reasonable battery replacement route satisfying the optimization goal (i.e., reaching the maximum revenue) under the constraint condition.

In some embodiments, the constraint is determined by a first parameter set by the operational research optimization model, the first parameter including at least one of:

1) And the parameter indicates that the station group needs to be completely in one power switching route. Specifically, each station in each station group cannot be allocated to a power swapping route of different trips, which may cause a back-and-forth turn-back between two power swapping routes, and therefore, a limitation needs to be imposed.

2) An upper/lower threshold parameter representing the total number of stations per battery route swap. For example, considering the displayability of the operation and maintenance responsibility person map (avoiding too many stations possibly causing too complex displayed routes), and simultaneously preventing each time of the power conversion task from consuming too long, the upper limit value of the station can be set; or may correspond to a lower threshold requiring a minimum amount of power change. In addition, a lower limit threshold value of the minimum station number requiring power change may be set correspondingly.

3) And a parameter of an upper/lower threshold value representing the number of electric vehicles to be charged of the charging route. For example, since the carrying space of the transportation vehicle (such as a bicycle, a moped, a three-way vehicle, etc.) of the operation and maintenance responsible person is limited, and the available batteries to be taken out in one trip are limited, the upper limit value of the number of the electric vehicles to be exchanged, which can be exchanged, can be set. In addition, a lower limit threshold value requiring the lowest power exchange amount may be set correspondingly.

4) And the parameter represents the upper/lower limit threshold value of the number of the carried batteries required for completing the battery replacement task. In one embodiment, the number of available batteries carried by the operation and maintenance responsible person is limited by the vehicle, and cannot be unlimited, so a reasonable number limit needs to be set. In addition, a lower threshold requiring the minimum number of batteries may be set correspondingly.

5) And the parameter represents the upper/lower limit threshold value of the distance between the station groups in the power switching line. For example, to achieve the purpose of "forward", the distance between the adjacent station groups in the power switching line should not be too large, and an upper limit value of the distance is correspondingly set. In addition, if the distance between adjacent station groups in the power switching line is not too small, a distance lower limit threshold may be correspondingly set.

6) And the parameter indicates that the selected head station and the tail station in the station group are located in the same road section. For example, since the stations in the station group belong to a road segment (see the road segment between the adjacent intersections in the previous example), and the head and tail stations are representative stations of the station group, the head and tail stations are limited to be adjacent to each other, that is, not to cross the road segment, and other stations in the station group can be filled between the head and tail stations without omission.

7) And the parameter represents an upper limit threshold value of the geographic position distance between the executor of the battery swapping task and the head site in the site group. Specifically, when planning the power switching route for the operation and maintenance responsible person, the current location of the operation and maintenance responsible person may need to be considered, and then the route from the operation and maintenance responsible person to the head site may need to be planned, so that the distance between the geographic locations of the operation and maintenance responsible person is limited to be not too large (passing through an upper threshold) when the head site is determined, so that the operation and maintenance responsible person can realize efficient power switching from the very beginning of the power switching route.

8) And the parameter represents an upper/lower limit threshold value of the station distance in the power switching line. Specifically, by setting an upper limit threshold of the distance between adjacent stations, i.e., a maximum distance between two adjacent stations, in the power switching line, the distance from one station to the next station in the power switching line is prevented from being too far, and the user experience of an operation and maintenance responsible person is improved. In addition, a lower threshold value can be set to avoid the situation that adjacent stations are too close to each other and the same station is overlapped.

9) And the parameter represents an upper/lower limit threshold value of an included angle between the positions of any two stations in the power conversion line and the geographic position of the operation and maintenance responsible person. For example, the included angle between the geographical positions of any two stations of the one-time battery swapping task and the geographical position connecting line where the operation and maintenance accountant is located can be limited, so that the problem that the operation and maintenance accountant needs to turn back is prevented, and the experience of the operation and maintenance accountant is improved. For example, as shown in fig. 5, the included angle α (i.e., < JM K) between JM and KM is an obtuse angle between J and K sites relative to the position of the operation and maintenance responsible person M, which indicates that M has a turn back (no matter M- > J- > K or M- > K- > J) in the route passing through J and K, and is unfavorable for efficiency. For another example, an included angle β (i.e. angle JNK) between the operation and maintenance responsible person N, JN and KN is an acute angle, and the operation and maintenance responsible person N can pass through K and J in a straight-way manner, i.e. a straight-way path of N- > K- > J. It will be appreciated that when this angle is smaller, the return of the operation and maintenance responsible between the two stations may be relatively lower. So that an upper threshold angle can be set to limit the route turnaround. In addition, a lower threshold value can be set to avoid the situation of station distribution which is not actually possible.

It should be noted that the way of limiting the turn-back by the included angle in fig. 5 is only an example, and in an actual scene, the change may be made, for example, the turn-back of the route is determined according to comparison between two inner angles, three inner angles, or combination of inner angles and outer angles in a triangle formed by JKM, so the above example is not limited.

10 Parameters defining upper/lower threshold values for the spacing of the nearest swapping sites. For example, an upper threshold of the distance between the nearest power conversion stations is set to avoid selecting a station which is not the most favorable path, and the like. In addition, a lower limit threshold of the distance between the nearest power exchange stations is set, so that the phenomenon that the stations are too close and the same station is overlapped, which is not actually possible, is avoided.

11 A parameter representing an upper/lower threshold for the elapsed time of the power change route. In a specific example, if the time consumed for replacing the power line in a single time is too long, which may affect the efficiency of the overall power replacing task, an upper limit of the time consumed for replacing the power line may be set. In addition, a lower limit value may be set to avoid time consumption for switching power lines, which may not actually occur, for example, 1 second.

In some embodiments, more dimensional optimization objectives may also be introduced. For example, the expected goal is determined by setting a second parameter to the operational research optimization model. The second parameter includes at least one of: a parameter indicating that the combination of the power conversion feature information of the target station groups meets a preset condition (i.e., the sum of the power conversion importance degrees of the target station groups is maximized, for example); a parameter representing the minimization of the total distance of the power switching line; representing a parameter for maximizing the number of replaced batteries in the battery replacement task; representing a parameter for maximizing the power conversion time efficiency of the station; a parameter for minimizing the number of swapping task passes is indicated.

Specifically, the sum of the swapping importance of the target site group in each swapping task is maximized, the optimization goal is to maximize the sum of the swapping importance of each site passed by the swapping task of the operation and maintenance responsible person under the limitation of the constraint conditions, and the total swapping importance of the sites needing swapping in unit time is maximized because the swapping time of the operation and maintenance responsible person is limited. Illustratively, the total distance of the power exchange line is minimized, that is, the total distance of the power exchange line is the shortest possible, and on the basis of the maximum benefit of the total power exchange importance degree, the movement distance of the power exchange line of the operation and maintenance responsible person is expected to be as short as possible, so that the experience of the operation and maintenance responsible person is improved. Illustratively, the maximization of the number of the replaced batteries in the battery replacement task refers to allocating more batteries as much as possible in each battery replacement task, and the higher the number of the replaced batteries is, the higher the income of the operation and maintenance responsible person is. The number of laps is minimized, the running time of an operation and maintenance responsible person is reduced, and the battery replacement efficiency is improved. The station swapping time efficiency is maximized, that is, the station swapping time efficiency of each station in the station group is further refined to be evaluated, for example, the benefit brought by selecting a certain station for swapping at a certain time point, and the station is selected according to the maximization to plan a swapping route. Illustratively, the number of battery swapping task runs is minimized, namely, the number of times that the operation and maintenance responsible person runs through the battery swapping route is reduced as much as possible, and the efficiency and the experience of the operation and maintenance responsible person are improved.

In the above example, under the restriction of the constraint condition, the operation and planning optimization model is operated to solve the optimized battery replacement route by taking the overall income of the user, the operation platform and the operation and maintenance responsible person using the electric vehicle as an optimization target.

In some embodiments, the operational optimization model may be an algorithm that solves according to a Vehicle Routing Problems (VRP). In general, the VRP algorithm specifically includes two types: exact algorithms (Exact Algorithm) and heuristic solutions (Heuristics). The precise algorithm comprises a branch boundary method, a branch cutting method, a set covering method and the like. Heuristic solutions include conservation methods, simulated annealing methods, deterministic annealing methods, tabu search methods, genetic algorithms, neural networks, ant colonizing algorithms, and the like. The operation and research optimization model can be used for establishing a corresponding mathematical model according to the constraint conditions and the optimization target, and solving by adopting an algorithm in the mathematical model to obtain the finally preferred power conversion route.

In a specific example, the operation optimization model may destroy the current solution by a random destruction method according to a heuristic solution, such as a newer Adaptive large-scale domain neighbor search algorithm (aln), and construct a new solution by a repair method, so as to perform heuristic search on the space to find the optimal solution.

After step S303 in fig. 3, step S304 is also shown: and generating a battery swapping task according to the battery swapping route.

In some embodiments, each swapping task may be limited to contain one swap lane, then if multiple swap lanes are to be run, i.e. presented by the issuance of the same number of swapping tasks. Alternatively, in another embodiment, one swapping task may include multiple swapping paths, and the multiple swapping paths are sent to the operation and maintenance responsible person through issuance of the swapping task.

As shown in fig. 1, the swapping task may be issued to a user terminal of an operation and maintenance responsible person after being generated, and may be an active pushing swapping task or a passive swapping task requested by the user terminal to be obtained, so as to instruct the user terminal to complete swapping work on stations in each target station group according to at least one swapping route.

As shown in fig. 6, a schematic block diagram of a swapping task generating device in the embodiment of the present application is shown. The specific implementation of the swapping task generating device may refer to the execution steps in the swapping task generating method in fig. 3, and the details of the same technology are not repeated here.

The battery swapping task generating device 600 includes:

a site group determining module 601, configured to determine a site group to which a site in each preset area belongs; each station is used for taking/discharging the power carrier;

a site group feature obtaining module 602, configured to obtain power swapping feature information of the site group;

a battery swapping route planning module 603 configured to process input data including the battery swapping feature information to obtain a battery swapping route passing through a target site group;

and a power swapping task generating module 604, configured to generate a power swapping task according to the power swapping route. Furthermore, the power swapping task can be issued to the operation and maintenance responsible person through a task issuing module.

In some embodiments, a road segment formed between adjacent intersections in the road network may be used as the preset region.

In some embodiments, the determining a site group to which a site in each preset area belongs includes: the stations on two sides of the unidirectional road section belong to the same station group; and respectively classifying the stations on two sides of the bidirectional road section into different station groups.

In some embodiments, the maximum number of stations in the station group is a maximum value of a first number of stations in a certain proportion to a preset trip average number of stations and a second number of stations corresponding to the power swapping time limit.

In some embodiments, the acquiring the power swapping feature information of the station group includes: and evaluating the power conversion importance of the station group.

In some embodiments, the evaluating the swapping importance of the site group includes: evaluating the power swapping importance of each station in the station group through a preset power swapping importance parameter; summing the power swapping importance degrees of all the stations in the station group to obtain the power swapping importance degree of the station group; wherein, the preset battery swapping importance parameter includes: the number of electric vehicles in the station, the electric quantity of each electric vehicle in the station, the riding quantity of future users of the station and the probability of single-loss due to power shortage caused by power failure.

In some embodiments, the processing the input data including the swapping feature information to obtain a swapping route passing through a target site group includes: running an operation and planning optimization model to select a target site group which meets an expected target under the constraint condition of a power swapping scene so as to form the power swapping route; the desired goals include: and the battery replacement characteristic information combination of the selected target station group meets a preset condition.

In some embodiments, the combination of the charging feature information of the selected target site group meets a preset condition, which includes: and the sum of the power conversion importance degrees of each station in the selected target station group is maximized.

In some embodiments, the constraint is determined by a first parameter set by the operations optimization model: the first parameter comprises at least one of the following:

a parameter representing the upper/lower limit threshold of the total station number of the power switching line;

a parameter representing an upper/lower threshold value of the number of electric vehicles to be charged of the charging route;

a parameter representing an upper limit threshold value of a geographical position distance between an executor of the battery swapping task and a head station in the station group;

defining parameters of upper/lower limit thresholds of the distance between the nearest battery swapping stations;

In some embodiments, the expected goal is determined by a second parameter set by the operational research optimization model; the second parameter includes at least one of:

a parameter for minimizing the number of swapping task passes is indicated.

In some embodiments, presentation data may be extracted from site group data of the site group to participate in site group data-related calculations in lieu of the site group data; wherein the presentation data comprises: site data of selected head and tail sites in the site group; and the station data of the rest stations in the station group is used as the associated data of the representation data.

In a possible scene, a computer system intelligent dispatching (battery replacement task order) of a service device can replace an artificial map of a battery replacement worker to find a vehicle, meanwhile, a mode of using a station group is considered to replace single station optimization, and a globally optimal at least one battery replacement route is planned according to global considerations (such as battery replacement characteristic information, constraint and optimization targets in an electric vehicle scene) to form a battery replacement task, so that the problems that an operation and maintenance responsible person cannot sense timely and can only consider local route decision and 'pick up' are solved, the battery replacement efficiency and battery replacement experience of the operation and maintenance responsible person can be improved, and a good solution is provided for electric vehicle battery replacement.

The embodiment of the present application may further provide a computer-readable storage medium, which stores program instructions, and when the program instructions are executed, the method for generating a swapping task in the foregoing embodiments is executed.

That is, the method steps in the above-described embodiments are implemented as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the method represented herein can be stored in such software processes on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA.

In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of program instruction products. The program instruction product includes one or more program instructions. The processes or functions according to the present application occur in whole or in part when program instruction instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The program instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

For example, the functional modules in the embodiment of fig. 6 may be software implementations; or may be implemented by a combination of hardware and software, for example, by a processor in an embodiment of a computer device executing program instructions in a memory; alternatively, the present invention may be implemented by a hardware circuit.

In addition, each functional module in the embodiments of the present application may be dynamically in one processing unit, or each module may exist alone physically, or two or more modules may be dynamically in one unit. The dynamic component can be realized in a form of hardware or a form of a software functional module. The dynamic components described above, if implemented in the form of software functional modules and sold or used as a stand-alone product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.

For example, each functional module in the embodiment in fig. 6 may be implemented by a single independent program or by different program segments in a program, and in some implementation scenarios, these functional modules may be located in one physical device or may be located in different physical devices but communicatively coupled to each other.

Reference throughout this specification to the expression "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics shown may be combined in any suitable manner in any one or more embodiments or examples. Moreover, the various embodiments or examples and features of the various embodiments or examples presented in this specification may be combined and combined by those skilled in the art without being mutually inconsistent.

Furthermore, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the expressions of the present application, "plurality" means two or more unless specifically defined otherwise.

Any process or method representation in a flowchart or otherwise represented herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing the specified logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

For example, the order of the steps in the method of the embodiment of fig. 3 may be changed in a specific scenario, and is not limited to the above representation.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules described is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or may be dynamic to another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some interfaces, and may be in an electrical or other form.

The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.

Claims

1. A power swapping task generation method is characterized by comprising the following steps:

determining a site group to which sites in each preset area belong; the station is used for taking/placing the electric vehicle, and a road section formed between adjacent intersections in the road network is used as the preset area;

acquiring battery swapping feature information of the station group, including: evaluating the power change importance of the station group;

processing input data containing the electricity swapping feature information to obtain an electricity swapping route passing through a target station group, wherein the conditions selected by the target station group comprise: the sum of the battery swapping importance degrees of the selected target station group is maximized;

generating a battery swapping task according to the battery swapping line;

wherein, the determining the site group to which the site in each preset area belongs includes: the stations on two sides of the unidirectional road section belong to the same station group; respectively classifying all the sites on two sides of the bidirectional road section into different site groups;

the power swapping task generation method further comprises the following steps: extracting presentation data from site group data of the site group to participate in site group data-related calculations in place of the site group data; wherein the presentation data comprises: site data of selected head and tail sites in the site group; and taking the station data of the rest stations in the station group as the associated data of the representation data.

2. The power swapping task generating method of claim 1, wherein the maximum number of stations in the station group is a maximum value of a first station number which is a certain proportion of a preset trip average station number and a second station number which corresponds to a power swapping time limit.

3. The battery swapping task generating method of claim 1, wherein the evaluating the battery swapping importance of the station group comprises:

evaluating the power swapping importance of each station in the station group through a preset power swapping importance parameter;

summing the power swapping importance of each station in the station group to obtain the power swapping importance of the station group;

wherein, the preset battery replacement importance parameter comprises: the number of electric vehicles in the station, the electric quantity of each electric vehicle in the station, the riding quantity of future users of the station, and the probability of losing the single unit due to power shortage caused by power failure.

4. The method for generating a swapping task according to claim 1, wherein the processing input data including the swapping feature information to obtain a swapping route passing through a target site group comprises:

running an operation and planning optimization model to select a target site group which accords with an expected target under the constraint condition of a power swapping scene so as to form a power swapping route; the desired goals include: and the battery replacement characteristic information combination of the selected target station group meets a preset condition.

5. The power swapping task generating method of claim 4, wherein the power swapping feature information combination of the selected target site group meets a preset condition, and the method comprises the following steps: and the sum of the power conversion importance degrees of each station in the selected target station group is maximized.

6. The swapping task generation method of claim 4, wherein the constraint is determined by a first parameter set by the operational research optimization model:

the first parameter comprises at least one of the following:

a parameter representing an upper/lower limit threshold value of the number of the carried batteries required for completing the battery replacement task;

the parameter represents that the selected head station and the tail station in the station group are positioned in the same road section;

7. The swapping task generation method of claim 4, wherein the expected goal is determined by a second parameter set by the operational research optimization model;

the second parameter includes at least one of:

a parameter for minimizing the number of swapping task passes is shown.

8. A battery swapping task generation device, comprising:

the station group determining module is used for determining a station group to which the station in each preset area belongs; the station is used for taking/placing the electric vehicle, and a road section formed between adjacent intersections in the road network is used as the preset area;

a site group feature obtaining module, configured to obtain battery replacement feature information of the site group, where the obtaining includes: evaluating the power change importance of the station group;

the power conversion route planning module is used for processing input data containing the power conversion characteristic information to obtain a power conversion route passing through a target station group, wherein the conditions selected by the target station group comprise: the sum of the battery swapping importance degrees of the selected target site group is maximized;

the battery swapping task generating module is used for generating a battery swapping task according to the battery swapping route;

wherein, the determining the site group to which the site in each preset area belongs includes:

the stations on two sides of the unidirectional road section belong to the same station group; and the number of the first and second groups,

respectively classifying all the stations on two sides of the bidirectional road section into different station groups;

the power conversion task generating device is further used for extracting presentation data from the site group data of the site group so as to replace the site group data to participate in calculation related to the site group data; wherein the presentation data comprises: site data of selected head and tail sites in the site group; and the station data of the rest stations in the station group is used as the associated data of the representation data.

9. A service device, comprising: a communicator, a memory, and a processor; the communicator is used for communicating with the outside; the memory stores program instructions; the processor is used for executing the program instructions to execute the swapping task generating method as defined in any one of claims 1 to 7.

10. A user terminal, comprising: a communicator, a memory, and a processor; the communicator for communicating with a serving device as claimed in claim 9; the memory stores program instructions; the processor is configured to execute the program instructions to perform operations comprising:

sending the geographical position of the operation and maintenance responsible person to the service device;

and receiving the battery swapping task generated and issued according to the geographic position from the service device.

11. A computer-readable storage medium, characterized in that program instructions are stored which, when executed, perform a swapping task generation method as claimed in any of claims 1 to 7.