Congestion prediction method and device of battery swap station, terminal equipment and medium

Abstract

The invention discloses a congestion prediction method and device for a battery swap station, terminal equipment and a medium, and relates to the technical field of electric vehicles. The method comprises the following steps: responding to a battery replacement instruction, and determining a battery replacement station set according to the position of a target vehicle; the number of to-be-replaced vehicles in each battery replacement station in the battery replacement station set and the average battery replacement duration of the to-be-replaced vehicles are obtained; and predicting a congestion degree according to at least one of the number of the to-be-replaced vehicles and the average replacement time so as to determine a target replacement station according to the congestion degree. The prediction method can accurately predict the crowding condition of the battery swap station site, and guides the user to select a proper battery swap station according to the crowding condition of the battery swap station site, so that the convenience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a congestion prediction method for a battery swap station, a congestion prediction device for a battery swap station, a computer-readable storage medium and a terminal device. Background Art

[0002] Currently, battery swap stations mainly rely on manual judgment and experience, which makes it difficult to accurately predict and deal with congestion during peak periods of battery swapping. This brings inconvenience to drivers at battery swap stations, makes it impossible to rationally allocate and efficiently utilize resources, and fails to predict failures or emergencies in advance, further affecting the driver's battery swapping experience. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present invention is to propose a congestion prediction method for a battery swap station, respond to a battery swap instruction, and determine a set of battery swap stations according to the location of the target vehicle; obtain the number of vehicles to be swapped and the average battery swap time of the vehicles to be swapped in each battery swap station in the battery swap station set; predict the degree of congestion based on at least one of the number of vehicles to be swapped and the average battery swap time, so as to determine the target battery swap station according to the degree of congestion. The prediction method of the present invention can accurately predict the congestion situation at the battery swap station site, and guide users to choose a suitable battery swap station according to the congestion situation at the battery swap station site, so that the convenience of users is improved.

[0004] The second objective of the present invention is to provide a congestion prediction device for a battery swap station.

[0005] The third objective of the present invention is to provide a terminal device.

[0006] A fourth object of the present invention is to provide a vehicle.

[0007] To achieve the above-mentioned objectives, an embodiment of the first aspect of the present invention proposes a congestion prediction method for a battery swap station, the method comprising: responding to a battery swap instruction, and determining a set of battery swap stations based on the location of a target vehicle; obtaining the number of vehicles to be swapped and the average battery swap time of the vehicles to be swapped in each battery swap station in the battery swap station set; predicting the degree of congestion based on at least one of the number of vehicles to be swapped and the average battery swap time, so as to determine the target battery swap station based on the degree of congestion.

[0008] According to one embodiment of the present invention, the degree of congestion is predicted based on at least one of the number of vehicles to be battery-replaced and the average battery-replacement time, including: when the number of vehicles to be battery-replaced is greater than or equal to a first preset number threshold, the degree of congestion is a first degree; when the number of vehicles to be battery-replaced is greater than or equal to a second preset number threshold and less than the first preset number threshold, if the average battery-replacement time is greater than or equal to the first preset time threshold, the degree of congestion is a first degree; when the number of vehicles to be battery-replaced is less than the second preset number threshold, if the average battery-replacement time is greater than or equal to the second preset time threshold, the degree of congestion is a first degree, wherein the second preset time threshold is greater than the first preset time threshold.

[0009] According to one embodiment of the present invention, the above-mentioned prediction method also includes: when the number of vehicles to be replaced with batteries is greater than or equal to a second preset number threshold and less than a first preset number threshold, if the average battery replacement time is less than the first preset time threshold, then the congestion level is the second level; when the number of vehicles to be replaced with batteries is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold and less than the second preset time threshold, then the congestion level is the second level.

[0010] According to one embodiment of the present invention, the above-mentioned prediction method also includes: when the number of vehicles to be replaced is less than the second preset number threshold, if the average battery replacement time is less than the first preset time threshold, determining the congestion level to be the third level.

[0011] According to one embodiment of the present invention, the average battery replacement time of vehicles to be replaced is obtained, including: obtaining the entry and exit time of each vehicle to be replaced in the battery replacement station; and determining the average battery replacement time of the vehicles to be replaced based on the number of vehicles to be replaced and the entry and exit time of each vehicle to be replaced.

[0012] According to one embodiment of the present invention, the above-mentioned prediction method also includes: obtaining the battery swap distance between the current vehicle and each battery swap station in the battery swap station set; obtaining the number of available batteries at each battery swap station; and determining the target battery swap station based on the congestion level, battery swap distance and number of available batteries of each battery swap station in the battery swap station set.

[0013] According to one embodiment of the present invention, it also includes: obtaining historical battery swapping data of each battery swapping station, the current congestion level of the battery swapping station and the battery swapping trend to warn of the congestion level of the battery swapping station, and feeding back to the cloud platform, wherein the battery swapping trend determines the number of vehicles to be battery swapped at the current battery swapping station in the future based on the geographical location of the battery swapping station and off-peak electricity prices.

[0014] To achieve the above-mentioned purpose, the second aspect of the embodiment of the present invention proposes a congestion prediction device for a battery swap station, including: a determination module, used to respond to a battery swap instruction and determine a set of battery swap stations according to the location of a target vehicle; an acquisition module, used to obtain the number of vehicles to be swapped and the average battery swap time of the vehicles to be swapped in each battery swap station in the battery swap station set; a prediction module, used to predict the degree of congestion based on at least one of the number of vehicles to be swapped and the average battery swap time, so as to determine the target battery swap station according to the congestion degree.

[0015] To achieve the above-mentioned purpose, the third aspect of the embodiment of the present invention proposes a terminal device, including a memory, a processor, and a congestion prediction program for a battery swap station stored in the memory and executable on the processor. When the processor executes the congestion prediction program for the battery swap station, the aforementioned congestion prediction method for the battery swap station is implemented.

[0016] To achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a vehicle, including a memory, a processor, and a congestion prediction program for a battery swap station stored in the memory and executable on the processor. When the processor executes the congestion prediction program for the battery swap station, the aforementioned congestion prediction method for the battery swap station is implemented.

[0017] According to the congestion prediction method, device, terminal device and medium of the battery swap station of the embodiment of the present invention, the battery swap command is responded to, and the battery swap station set is determined according to the location of the target vehicle; the number of vehicles to be swapped and the average battery swap time of the vehicles to be swapped in each battery swap station in the battery swap station set is obtained; the congestion level is predicted according to at least one of the number of vehicles to be swapped and the average battery swap time, so as to determine the target battery swap station according to the congestion level. The prediction method of the present invention can accurately predict the congestion situation at the battery swap station site, and guide the user to select a suitable battery swap station according to the congestion situation at the battery swap station site, so that the convenience of the user is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of a congestion prediction method for a battery swap station according to some embodiments of the present invention;

[0019] Figure 2 A block diagram of a congestion prediction device for a battery swap station according to some embodiments of the present invention;

[0020] Figure 3 is a block diagram of a terminal device according to some embodiments of the present invention;

[0021] Figure 4 is a block diagram of a vehicle according to some embodiments of the present invention. DETAILED DESCRIPTION

[0022] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0023] The following describes in detail the congestion prediction method, apparatus, terminal device and medium of a battery swap station according to an embodiment of the present invention with reference to the accompanying drawings.

[0024] Figure 1 Flow chart of a method for predicting congestion at a battery swap station according to some embodiments of the present invention. Figure 1 The congestion prediction method of the battery swap station in the embodiment of the present application may include the following steps:

[0025] S110, respond to the battery swap command and determine the battery swap station set according to the location of the target vehicle.

[0026] Specifically, when a user has a need to replace a battery, he or she can use a terminal device (such as a mobile phone, a vehicle-mounted terminal device, etc.) to issue a battery replacement command. After the terminal device responds to the battery replacement command, it obtains the geographic location of the target vehicle and determines all battery replacement stations within a preset range based on the location of the target vehicle to form a battery replacement station set.

[0027] It should be noted that the preset range can be determined based on the distance that the target vehicle can travel with the remaining power of its battery, thus ensuring to a certain extent that the target vehicle can smoothly reach the battery swap station.

[0028] S120, obtaining the number of vehicles to be replaced in each battery replacement station in the battery replacement station set and the average battery replacement time of the vehicles to be replaced.

[0029] Specifically, each battery swap station in the battery swap station set is equipped with an electronic fence, which is used to obtain the number of vehicles to be swapped. At the same time, combined with IoT devices such as sensors and cameras, the time of vehicles entering and exiting the battery swap station can be collected in real time, and the average battery swap time of the vehicles to be swapped can be determined based on the entry and exit time of the vehicles to be swapped.

[0030] S130, predicting the degree of congestion based on at least one of the number of vehicles to be battery-swapped and the average battery-swapped time, so as to determine a target battery-swapped station based on the degree of congestion.

[0031] Specifically, the congestion level of the battery swap station may include the first level, the second level, and the third level, wherein the first level indicates congestion, the second level indicates normal, and the third level indicates idleness. The terminal device will determine the target battery swap station based on the congestion level of each battery swap station in the battery swap station set, for example, determine an idle battery swap station as the target battery swap station, and guide the user to go there for battery swapping.

[0032] The specific congestion level can be determined based on the number of vehicles to be replaced. For example, if the number of vehicles to be replaced in the replacement station is too large, the congestion level of the replacement station is determined to be the first level. It can also be predicted based on the total replacement time calculated based on the number of vehicles to be replaced and the average replacement time. For example, if the total replacement time calculated based on the number of vehicles to be replaced and the average replacement time is too long, the congestion level of the replacement station is also determined to be the first level. If the total replacement time calculated based on the number of vehicles to be replaced and the average replacement time is long, the congestion level of the replacement station is determined to be the second level. If the total replacement time calculated based on the number of vehicles to be replaced and the average replacement time is short, the congestion level of the replacement station is determined to be the third level.

[0033] It should be noted that the total battery replacement time is the product of the number of vehicles to be replaced and the average battery replacement time of the vehicles to be replaced.

[0034] The prediction method of the present invention can accurately predict the congestion situation at a battery swap station, and guide users to select a suitable battery swap station according to the congestion situation at the battery swap station, thereby improving user convenience.

[0035] In some embodiments, the degree of congestion is predicted based on at least one of the number of vehicles to be replaced and the average battery replacement time, including: when the number of vehicles to be replaced is greater than or equal to a first preset number threshold, the degree of congestion is a first degree; when the number of vehicles to be replaced is greater than or equal to a second preset number threshold and less than the first preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold, the degree of congestion is the first degree; when the number of vehicles to be replaced is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the second preset time threshold, the degree of congestion is the first degree, wherein the second preset time threshold is greater than the first preset time threshold.

[0036] Exemplarily, if the number of vehicles to be replaced by batteries is greater than or equal to a first preset number threshold, it means that the number of vehicles to be replaced by batteries in the battery swap station is too many, and the congestion level is determined to be the first level; if the number of vehicles to be replaced by batteries is greater than or equal to a second preset number threshold and less than the first preset number threshold, and the average battery replacement time is greater than or equal to the first preset time threshold, it means that the average battery replacement time is long, and the number of vehicles to be replaced by batteries in the battery swap station is large, resulting in a total battery replacement time that is too long, and the congestion level is also determined to be the first level; if the number of vehicles to be replaced by batteries is less than the second preset number threshold, and the average battery replacement time is greater than or equal to the second preset time threshold, it means that although the number of vehicles to be replaced by batteries is small, the average battery replacement time is too long, resulting in a total battery replacement time that is too long, and the congestion level is also determined to be the first level.

[0037] In some embodiments, the above-mentioned prediction method also includes: when the number of vehicles to be replaced with batteries is greater than or equal to a second preset number threshold and less than a first preset number threshold, if the average battery replacement time is less than the first preset time threshold, then the congestion level is the second level; when the number of vehicles to be replaced with batteries is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold and less than the second preset time threshold, then the congestion level is the second level.

[0038] Exemplarily, if the number of vehicles to be replaced with batteries is greater than or equal to the second preset number threshold and less than the first preset number threshold, and the average battery replacement time is less than the first preset time threshold, it means that although the average battery replacement time is short, the number of vehicles to be replaced with batteries is large, resulting in a longer total battery replacement time, and the congestion level is determined to be the second level; if the number of vehicles to be replaced with batteries is less than the second preset number threshold, and the average battery replacement time is greater than or equal to the first preset time threshold and less than the second preset time threshold, it means that although the number of vehicles to be replaced with batteries is small, the average battery replacement time is long, resulting in a longer total battery replacement time, and the congestion level is also determined to be the second level.

[0039] In some embodiments, the above prediction method also includes: when the number of vehicles to be replaced is less than the second preset number threshold, if the average battery replacement time is less than the first preset time threshold, determining the congestion level to be the third level.

[0040] Exemplarily, if the number of vehicles to be replaced with batteries is less than the second preset number threshold, and the average battery replacement time is less than the first preset time threshold, it means that the number of vehicles to be replaced with batteries is small and the average battery replacement time is short, resulting in a short total battery replacement time, then the congestion level is determined to be the third level.

[0041] It should be noted that the first preset number threshold, the second preset number threshold, the first preset duration threshold and the second preset duration threshold are all determined according to actual conditions and are not specifically limited here.

[0042] In some embodiments, obtaining the average battery replacement time of vehicles to be replaced includes: obtaining the entry and exit time of each vehicle to be replaced in the battery replacement station; and determining the average battery replacement time of the vehicles to be replaced based on the number of vehicles to be replaced and the entry and exit time of each vehicle to be replaced.

[0043] Specifically, the specific time of each vehicle entering and leaving the battery swap station is collected in real time according to the electronic fences, sensors, cameras and other IoT devices set up at the battery swap station. The battery swap time of each vehicle to be swapped is obtained based on the specific time of entering and leaving the battery swap station. The average battery swap time of the vehicles to be swapped is obtained by summing up the battery swap time and dividing it by the number of vehicles to be swapped.

[0044] In some embodiments, the above prediction method also includes: obtaining the battery swap distance between the current vehicle and each battery swap station in the battery swap station set; obtaining the number of available batteries at each battery swap station; and determining the target battery swap station based on the congestion level, battery swap distance and number of available batteries of each battery swap station in the battery swap station set.

[0045] Specifically, the battery swap distance between the current vehicle and each battery swap station in the battery swap station set can be calculated and obtained based on the current geographic location information of the vehicle and the geographic location information of each battery swap station in the battery swap station set. The number of available batteries at each battery swap station can be obtained based on the battery power and fault information in the battery swap station. For example, if the battery power in the battery swap station is greater than or equal to 80% and there is no battery fault, the battery is considered to be an available battery, and the available batteries in the battery swap station are counted in real time to obtain the number of available batteries in the battery swap station. After the terminal device responds to the battery swap instruction, the congestion level, battery swap distance and number of available batteries of each battery swap station will be comprehensively considered. For example, a machine learning algorithm such as a convolutional neural network is used to determine the comprehensive evaluation value of each battery swap station in the battery swap station set based on the congestion level, battery swap distance and number of available batteries and their corresponding weight coefficients, and the target battery swap station is determined based on the comprehensive evaluation value. For example, the battery swap station with the highest comprehensive evaluation value is used as the target battery swap station.

[0046] Exemplarily, the calculation formula for the comprehensive evaluation value of the battery swap station is as follows:

[0047] y=w1x1+w2x2+w3x3

[0048] Among them, y represents the comprehensive evaluation value of the battery swap station, x1 represents the degree of congestion, w1 represents the weight coefficient corresponding to the degree of congestion, x2 represents the battery swap distance, w2 represents the weight coefficient corresponding to the battery swap distance, x3 represents the number of available batteries, and w3 represents the weight coefficient corresponding to the number of available batteries.

[0049] In some embodiments, it also includes: obtaining historical battery swapping data of each battery swapping station, the current congestion level of the battery swapping station and the battery swapping trend to warn of the congestion level of the battery swapping station, and feeding back to the cloud platform, wherein the battery swapping trend determines the number of vehicles to be swapped at the current battery swapping station in the future based on the geographical location of the battery swapping station and peak-off-peak electricity prices.

[0050] Specifically, in addition to predicting the target battery swap station, it is also necessary to predict the congestion level of the battery swap station in the future based on the historical battery swap data of each battery swap station, the current congestion level of the battery swap station, and the battery swap trend. Among them, the historical battery swap data of each battery swap station includes the number of vehicles to be swapped, the average battery swap time, and the congestion level in each time period. The battery swap trend can be determined based on the geographical location of the battery swap station and the peak and valley electricity prices. For example, if the geographical location of the battery swap station is good and the electricity price is low, there will be more vehicles to be swapped.

[0051] When it is predicted that the battery swap station will be congested in the future, an early warning will be issued so that users and battery swap station operation and maintenance personnel can take corresponding measures. For example, it can guide users to choose the appropriate battery swap station, enable users to reasonably plan the battery swap time, and enable the battery swap station to optimize the number of equipment, personnel configuration and operation plan of the battery swap station, so as to achieve reasonable allocation of resources and improve operation efficiency and service quality.

[0052] In some embodiments, the above prediction method also includes: obtaining the battery replacement time of each vehicle to be replaced in the battery replacement station; and determining the cause of congestion based on the battery replacement time.

[0053] Specifically, the IoT devices such as electronic fences, sensors and cameras installed at the battery swap station can also collect the battery replacement time of each vehicle to be replaced in the power station in real time. Generally speaking, the battery replacement time of each vehicle to be replaced in the power station is the same. If it is found that the battery replacement time of the vehicle to be replaced increases, causing congestion in the battery swap station, the reason for the increase in battery replacement time (such as battery failure or battery swap station failure, etc.) will be pushed to the battery swap station operation and maintenance personnel in real time. The operation and maintenance personnel will take corresponding measures based on this information, such as repairing the battery or battery swap station, etc., to promptly relieve congestion and improve service quality.

[0054] To sum up, the prediction method of the present invention can accurately predict the congestion situation at the battery swap station site, and guide users to choose a suitable battery swap station according to the congestion situation at the battery swap station site, so that the convenience of users is improved; and the congestion level of the battery swap station in the future period is predicted. When it is predicted that congestion will occur at the battery swap station in the future, an early warning is issued in advance so that users and battery swap station operation and maintenance personnel can take corresponding measures. In this way, users can be guided to choose a suitable battery swap station, and through data analysis and report analysis, the number of equipment, personnel configuration and operation plans of the battery swap station can be optimized to achieve reasonable allocation of resources, improve operational efficiency and service quality, and reduce operating costs; in addition, the cause of the congestion can also be pushed to the battery swap station operation and maintenance personnel in real time, so that the operation and maintenance personnel can take corresponding measures to relieve the congestion in time and improve service quality.

[0055] Corresponding to the above-mentioned embodiment, the present application also proposes a congestion prediction device for a battery swap station.

[0056] Reference Figure 2 The congestion prediction device 200 of the battery swap station includes: a determination module 210, an acquisition module 220, and a prediction module 230.

[0057] Among them, the determination module 210 is used to respond to the battery swap command and determine the battery swap station set according to the location of the target vehicle. The acquisition module 220 is used to obtain the number of vehicles to be swapped and the average battery swap time of the vehicles to be swapped in each battery swap station in the battery swap station set. The prediction module 230 is used to predict the congestion level according to at least one of the number of vehicles to be swapped and the average battery swap time, so as to determine the target battery swap station according to the congestion level.

[0058] According to one embodiment of the present invention, the prediction module 230 is specifically used to, when the number of vehicles to be replaced with batteries is greater than or equal to a first preset number threshold, the congestion level is the first level; when the number of vehicles to be replaced with batteries is greater than or equal to a second preset number threshold and less than the first preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold, the congestion level is the first level; when the number of vehicles to be replaced with batteries is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the second preset time threshold, the congestion level is the first level, wherein the second preset time threshold is greater than the first preset time threshold.

[0059] According to one embodiment of the present invention, the prediction module 230 is specifically used to, when the number of vehicles to be replaced with batteries is greater than or equal to the second preset number threshold and less than the first preset number threshold, if the average battery replacement time is less than the first preset time threshold, then the congestion level is the second level; when the number of vehicles to be replaced with batteries is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold and less than the second preset time threshold, then the congestion level is the second level.

[0060] According to one embodiment of the present invention, the prediction module 230 is specifically used to determine the congestion level as the third level when the number of vehicles to be replaced is less than a second preset number threshold and the average battery replacement time is less than a first preset time threshold.

[0061] According to one embodiment of the present invention, the acquisition module 220 is specifically used to obtain the entry and exit time of each vehicle to be replaced in the battery swap station; and determine the average battery swap time of the vehicles to be replaced based on the number of vehicles to be replaced and the entry and exit time of each vehicle to be replaced.

[0062] According to one embodiment of the present invention, the prediction module 230 is also used to obtain the battery swap distance between the current vehicle and each battery swap station in the battery swap station set; obtain the number of available batteries at each battery swap station; and determine the target battery swap station based on the congestion level, battery swap distance and number of available batteries of each battery swap station in the battery swap station set.

[0063] According to one embodiment of the present invention, the prediction module 230 is also used to obtain the historical battery swapping data of each battery swapping station, the current congestion level of the battery swapping station and the battery swapping trend to provide early warning for the congestion level of the battery swapping station, and provide feedback to the cloud platform, wherein the battery swapping trend determines the number of vehicles to be swapped at the current battery swapping station in the future based on the geographical location of the battery swapping station and the off-peak electricity prices.

[0064] It should be pointed out that the above-mentioned explanation of the embodiment and beneficial effects of the congestion prediction method for the battery swap station is also applicable to the congestion prediction device for the battery swap station according to the embodiment of the present invention, and will not be elaborated here in order to avoid redundancy.

[0065] Corresponding to the above embodiments, the present application also proposes a computer-readable storage medium.

[0066] The computer-readable storage medium of the present application stores a congestion prediction program for a battery swap station, and when the congestion prediction program for the battery swap station is executed by a processor, the congestion prediction method for the battery swap station described above is implemented.

[0067] It should be pointed out that the above-mentioned explanation of the embodiment and beneficial effects of the congestion prediction method for the battery swap station is also applicable to the computer-readable storage medium of the embodiment of the present invention, and will not be elaborated here in order to avoid redundancy.

[0068] Corresponding to the above embodiment, the present application also proposes a terminal device.

[0069] See also Figure 3 As shown, the terminal device 300 of the present application includes a memory 310, a processor 320, and a congestion prediction program for a battery swap station stored on the memory 310 and executable on the processor 320. When the processor executes the congestion prediction program for the battery swap station, the aforementioned congestion prediction method for the battery swap station is implemented.

[0070] It should be pointed out that the above-mentioned explanation of the embodiment and beneficial effects of the congestion prediction method for the battery swap station is also applicable to the terminal device of the embodiment of the present invention. To avoid redundancy, it will not be elaborated here.

[0071] Corresponding to the above embodiments, the present application also proposes a vehicle.

[0072] See also Figure 4 As shown, the vehicle 400 of the present application includes a memory 410, a processor 420, and a congestion prediction program for a battery swap station stored on the memory 410 and executable on the processor 420. When the processor executes the congestion prediction program for the battery swap station, the aforementioned congestion prediction method for the battery swap station is implemented.

[0073] It should be pointed out that the above-mentioned explanation of the embodiment and beneficial effects of the congestion prediction method for the battery swap station is also applicable to the vehicle of the embodiment of the present invention. In order to avoid redundancy, it will not be elaborated here.

[0074] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0075] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0076] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0077] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0078] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0079] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A congestion prediction method for a battery swap station, characterized in that: The method comprises: Respond to the battery swap command and determine the battery swap station set based on the location of the target vehicle; Obtain the number of vehicles to be replaced in each of the battery replacement stations in the battery replacement station set and the average battery replacement time of the vehicles to be replaced; The congestion level is predicted based on at least one of the number of vehicles to be battery-swapped and the average battery-swapped time, so as to determine a target battery-swapped station based on the congestion level.

2. The congestion prediction method for a battery swap station according to claim 1, characterized in that: Predicting the congestion level according to at least one of the number of vehicles to be replaced with batteries and the average battery replacement time includes: When the number of vehicles to be replaced is greater than or equal to a first preset number threshold, the congestion level is a first level; When the number of vehicles to be replaced is greater than or equal to the second preset number threshold and less than the first preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold, the congestion level is the first level; When the number of vehicles to be replaced is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the second preset time threshold, the congestion level is the first level, wherein the second preset time threshold is greater than the first preset time threshold.

3. The congestion prediction method for a battery swap station according to claim 2, characterized in that: The method further comprises: When the number of vehicles to be replaced is greater than or equal to the second preset number threshold and less than the first preset number threshold, if the average battery replacement time is less than the first preset time threshold, the congestion level is the second level; When the number of vehicles to be replaced is less than the second preset number threshold, if the average battery replacement time is greater than or equal to the first preset time threshold and less than the second preset time threshold, the congestion level is the second level.

4. The congestion prediction method for a battery swap station according to claim 2 or 3, characterized in that: The method further comprises: When the number of vehicles to be replaced with batteries is less than the second preset number threshold and if the average battery replacement time is less than the first preset time threshold, the congestion level is determined to be the third level.

5. The congestion prediction method for a battery swap station according to claim 1, characterized in that: Obtaining the average battery replacement time of the vehicle to be replaced, including: Obtaining the entry and exit time of each vehicle to be replaced at the battery replacement station; The average battery replacement time of the vehicles to be replaced is determined based on the number of vehicles to be replaced and the entry and exit time of each vehicle to be replaced.

6. The congestion prediction method for a battery swap station according to claim 1, characterized in that: The method further comprises: Obtaining a battery swap distance between the current vehicle and each battery swap station in the battery swap station set; Obtaining the number of available batteries at each of the battery swap stations; The target battery swap station is determined according to the congestion level of each battery swap station in the battery swap station set, the battery swap distance and the number of available batteries.

7. The congestion prediction method for a battery swap station according to claim 1, characterized in that: Also includes: The historical battery swapping data of each battery swapping station, the current congestion level of the battery swapping station and the battery swapping trend are obtained to provide early warning for the congestion level of the battery swapping station and feedback to the cloud platform, wherein the battery swapping trend determines the number of vehicles to be swapped at the current battery swapping station in the future based on the geographical location of the battery swapping station and off-peak electricity prices.

8. A congestion prediction device for a battery swap station, characterized in that: include: A determination module, used to respond to a battery swap command and determine a battery swap station set according to the location of the target vehicle; An acquisition module, used to acquire the number of vehicles to be replaced in each of the battery replacement stations in the battery replacement station set and the average battery replacement time of the vehicles to be replaced; A prediction module is used to predict the congestion level based on at least one of the number of vehicles to be battery-swapped and the average battery-swapped time, so as to determine a target battery-swapped station based on the congestion level.

9. A terminal device, characterized in that: The invention comprises a memory, a processor and a congestion prediction program for a battery swap station which is stored in the memory and can be run on the processor. When the processor executes the congestion prediction program for the battery swap station, the congestion prediction method for the battery swap station according to any one of claims 1 to 7 is implemented.

10. A vehicle, characterized in that: The invention comprises a memory, a processor and a congestion prediction program for a battery swap station which is stored in the memory and can be run on the processor. When the processor executes the congestion prediction program for the battery swap station, the congestion prediction method for the battery swap station according to any one of claims 1 to 7 is implemented.

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