Shared electric bicycle overload identification method, device, equipment, medium and product

Abstract

The invention discloses a shared electric bicycle overload identification method, device and equipment, a medium and a product, and relates to the field of shared electric bicycles, and the method comprises the steps: collecting the pressure data of a shared electric bicycle and the pressure film point location data; uploading the pressure data and the pressure film point location data to a cloud in real time; the cloud stores historical load capacity and historical pressure film point location data of the current riding user; in the cloud end, the load capacity is calculated according to the pressure data; according to the load capacity, the pressure film point location data, the historical load capacity and the historical pressure film point location data, whether the current shared electric bicycle is overloaded or not is judged, the judgment error is reduced, and the judgment accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of shared electric motorcycles, and in particular to a method, device, equipment, medium and product for identifying overload of shared electric motorcycles. Background Art

[0002] As a new and convenient mode of travel, shared electric motorcycles have effectively solved the problem of short-distance travel for users. Once launched, they have been widely loved by everyone. Nowadays, shared electric motorcycles are operating in major cities across the country.

[0003] As the shared electric motorcycle market develops faster and faster and the number of vehicles put into use increases, more and more shared electric motorcycle management problems are generated, such as illegal parking, illegal riding, speeding, and overloading. Among them, the problem of vehicle overloading not only causes damage to the vehicle, but also threatens the life safety of the rider.

[0004] Most of the shared electric motorcycles currently on the market do not have weight detection, or if they have weight detection, they only judge whether the vehicle is loaded based on the vehicle's load capacity. It is not perfect to judge whether the vehicle is overloaded only by weight. When the user is heavy, misjudgment is likely to occur, which has a very bad impact on the riding experience. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for identifying overload of shared electric motorcycles, so as to solve the problem that the existing methods for identifying overload of shared electric motorcycles are prone to misjudgment.

[0006] To achieve the above objectives, this application provides the following solutions.

[0007] In a first aspect, the present application provides a method for identifying overload of a shared electric motorcycle, comprising the steps.

[0008] Collect pressure data and pressure film point data of shared electric motorcycles.

[0009] The pressure data and the pressure film point data are uploaded to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user.

[0010] In the cloud, the load capacity is calculated according to the pressure data.

[0011] It is determined whether the current shared electric motorcycle is overloaded based on the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

[0012] In a second aspect, the present application provides a shared electric motorcycle overload identification device, comprising the following modules.

[0013] Pressure sensors and pressure film sensors are used to collect pressure data and pressure film point data of shared electric motorcycles.

[0014] The uploading module is used to upload the pressure data and the pressure film point data to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user.

[0015] The cloud end is used to calculate the load weight according to the pressure data, and to determine whether the current shared electric motorcycle is overloaded according to the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for identifying overloaded shared electric motorcycles.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for identifying overloaded shared electric motorcycles.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for identifying overloaded shared electric motorcycles.

[0019] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: the present application judges from multiple aspects whether the current shared electric motorcycle is overloaded by combining pressure data and pressure film point data. Compared with the shared electric motorcycle overload identification method of single weight measurement, the judgment error is reduced and the judgment accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 Flowchart of the shared electric motorcycle overload identification method provided in this application.

[0022] Figure 2 Schematic diagram of the vehicle body shock absorber provided in this application.

[0023] Figure 3 This is a schematic diagram of the distribution of the pressure film sensors provided in this application.

[0024] Figure 4 Schematic diagram of the vehicle motor provided for this application.

[0025] Figure 5 Schematic diagram of the shared electric motorcycle overload identification process provided in this application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] This application is mainly used in the shared electric motorcycle (hereinafter referred to as vehicle) market. At present, the steps to implement the overload identification method mainly rely on the body shock absorber, seat cushion film hardware, and vehicle motor output power. The electronic control unit (Electronic Control Unit, ECU) is transmitted through these hardware. The ECU transmits the data to the cloud, and the cloud calls and judges according to the data transmitted by the ECU.

[0029] The present application embodiment provides a method for identifying overloaded shared electric motorcycles. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the present application embodiment, Figure 1 As shown, the method includes the following steps.

[0030] S1: Collect pressure data and pressure film point data of shared electric motorcycles.

[0031] S2: Upload the pressure data and the pressure film point data to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user.

[0032] S3: In the cloud, the load capacity is calculated according to the pressure data.

[0033] S4: Determine whether the current shared electric motorcycle is overloaded based on the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

[0034] In an exemplary embodiment, S1 may be replaced by the following steps.

[0035] S11: Collect pressure data using a pressure sensor installed on the shock absorber of the shared electric motorcycle body.

[0036] like Figure 2 As shown, the vehicle body shock absorber can record the actual pressure data of each first batch of riding users and transmit it to the cloud for storage. The cloud will calculate and store the actual load weight based on the actual pressure data. If the actual load weight exceeds the set load weight threshold, the vehicle will prompt an overload power-off. The next time the user rides, the previous data will be called to compare the actual load weight to see how big the error is with the previous one. If it exceeds the load weight threshold, it will be considered as overloaded.

[0037] S12: Collect pressure film point data using a pressure film sensor provided on the seat of the shared electric motorcycle.

[0038] like Figure 3 As shown, four contacts (i.e., pressure film sensors) are installed on the inner film of the seat cushion. When a normal person sits on the seat, two or three contacts will generally light up. When four contacts light up, the data is transmitted to the cloud through the ECU, and the user is judged to be overloaded.

[0039] Furthermore, when riding, the vehicle will collect the vehicle's pressure data and pressure film point data every 5 seconds and upload them to the cloud.

[0040] In an exemplary embodiment, S3 may be replaced by the following steps.

[0041] S31: Calculate the load using F=G×9.8, where G is the load and F is the pressure data.

[0042] In an exemplary embodiment, S4 may be replaced by the following steps.

[0043] S41: Determine whether the current riding user has performed any riding behavior. If so, execute S42; if not, execute S46.

[0044] S42: Obtain the historical average value of the load weight and the historical average value of the pressure film point data corresponding to the current riding user.

[0045] S43: Determine whether the difference between the load weight and the historical average load weight and the difference between the pressure film point data and the historical average pressure film point data are both greater than a first threshold. If so, execute S44; if not, execute S45; the first threshold includes a load weight threshold and a pressure film point data threshold.

[0046] S44: Determine whether the current riding user has overloaded the vehicle, and determine whether the current shared electric motorcycle is overloaded.

[0047] S45: Determine that the current riding user has not performed any overloading behavior, and record the load weight and pressure film point data of the current riding user.

[0048] S46: Determine whether the current riding user is overloaded based on the historical average load weight of users with the same gender and age group as the current riding user and the historical average pressure film point data.

[0049] Furthermore, when the user has ridden, the historical average load value and the average pressure film point data of the user's historical riding are obtained and compared with the data reported by the current vehicle.

[0050] When the difference between the load weight calculated by the pressure data reported by the vehicle and the average load weight of historical riding, and the pressure film point data reported by the vehicle and the average pressure film point data of historical riding both exceed the first threshold, it is determined that the current riding user has overloaded, and an overload power-off voice reminder is issued, and the vehicle issues a power-off operation at the same time.

[0051] Since users need to complete real-name authentication registration and provide ID card information before riding, when the user has not ridden before, the historical riding load and pressure film point data of users with the same gender and age group as the current riding user are compared with the load and pressure film point data reported by the current vehicle. If the difference exceeds the second threshold, it is determined that the current riding user is overloaded, and an overload power-off voice reminder is issued, and the vehicle simultaneously issues a power-off operation.

[0052] When no overloading occurs, the load weight and pressure film point data of the current riding user are recorded to provide data support for subsequent overloading judgments.

[0053] In an exemplary embodiment, after S4, it also includes: judging whether the current shared electric motorcycle is overloaded based on the voltage data and current data of the shared electric motorcycle motor.

[0054] Further, such as Figure 4 As shown, the motor voltage data and current data are transmitted to the ECU, and the ECU transmits the data to the cloud through the Internet of Things, and calculates through the cloud to determine whether the user is overloaded.

[0055] In actual applications, ECU mainly processes data sent from various hardware and transmits the data to the cloud through the internal 4G Internet of Things card.

[0056] In an exemplary embodiment, based on the voltage data and current data of the shared electric motorcycle's motor, it is determined whether the current shared electric motorcycle is overloaded, specifically including: determining whether the voltage data and the current data are both greater than a second threshold, to obtain a third judgment result; the second threshold includes a voltage threshold and a current threshold; if the third judgment result is yes, it is determined that the current riding user has overloaded, and it is determined that the current shared electric motorcycle is overloaded; if the third judgment result is no, combined with the second judgment result, it is determined whether the current shared electric motorcycle is overloaded.

[0057] like Figure 5 As shown, the cloud-based judgment logic, that is, the shared electric motorcycle overload identification method provided in the present application, is more flexible and convenient than the traditional vehicle ECU-based judgment. At the same time, the range of the first threshold and the second threshold can be dynamically adjusted. For example, the user's historical average load is 50kg, and exceeding n% is overloaded. n can be dynamically configured, and the pressure film point threshold can also be configured and adjusted in the same way. The overload judgment conditions (that is, the load weight and pressure film point data) can also be dynamically adjusted to make the judgment logic more accurate and effective.

[0058] At the same time, this application can also use big data models to intelligently analyze the user's historical riding data to obtain the user's load change curve and predict the user's weight information, providing data basis for the system to determine whether the vehicle is overloaded.

[0059] This application makes an enhanced judgment based on the vehicle load weight and the pressure film points on the vehicle seat, and uses the cloud to comprehensively judge from multiple dimensions whether the vehicle is overloaded. When the user is riding, the pressure data and pressure film point data are collected and transmitted to the cloud in real time. The cloud will make a comprehensive calculation based on the current riding user's historical load weight and the average value of the historical film point data to determine whether the user is overloaded.

[0060] Compared with the single weight detection solution, this application can more accurately determine whether the user's riding is overloaded.

[0061] Based on the same inventive concept, the embodiment of the present application also provides a shared electric motorcycle overload identification device for implementing the shared electric motorcycle overload identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more shared electric motorcycle overload identification device embodiments provided below can refer to the limitations of the shared electric motorcycle overload identification method above, and will not be repeated here.

[0062] In an exemplary embodiment, a shared electric motorcycle overload identification device is provided, which includes the following modules.

[0063] Pressure sensors and pressure film sensors are used to collect pressure data and pressure film point data of shared electric motorcycles.

[0064] The uploading module is used to upload the pressure data and the pressure film point data to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user.

[0065] The cloud end is used to calculate the load weight according to the pressure data, and to determine whether the current shared electric motorcycle is overloaded according to the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

[0066] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store shared electric motorcycle overload identification data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for identifying overloaded shared electric motorcycles is implemented.

[0067] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0068] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0069] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0070] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0072] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for identifying overloaded shared electric motorcycles, characterized in that: The shared electric motorcycle overload identification method comprises: Collect pressure data and pressure film point data of shared electric motorcycles; The pressure data and the pressure film point data are uploaded to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user; In the cloud, the load capacity is calculated according to the pressure data; It is determined whether the current shared electric motorcycle is overloaded based on the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

2. The method for identifying overloaded shared electric motorcycles according to claim 1, characterized in that: Collect pressure data and pressure film point data of shared electric motorcycles, including: The pressure data is collected by using a pressure sensor provided on the shock absorber of the shared electric motorcycle body; The pressure film point data is collected using a pressure film sensor installed on the seat of a shared electric motorcycle.

3. The method for identifying overloaded shared electric motorcycles according to claim 1, characterized in that: Calculating the load capacity according to the pressure data specifically includes: Use F=G×9.8 to calculate the load capacity; where G is the load capacity and F is the pressure data.

4. The method for identifying overloaded shared electric motorcycles according to claim 1, characterized in that: Judging whether the current shared electric motorcycle is overloaded according to the load weight, the pressure film point data, the historical load weight and the historical pressure film point data specifically includes: Determine whether the current riding user has performed a riding behavior, and obtain a first determination result; If the first judgment result is yes, obtaining the historical average value of the load weight and the historical average value of the pressure film point data corresponding to the current riding user; Determine whether the difference between the load and the historical load average value and the difference between the pressure film point data and the historical pressure film point data average value are both greater than a first threshold, and obtain a second judgment result; the first threshold includes a load threshold and a pressure film point data threshold; If the second judgment result is yes, it is determined that the current riding user has overloaded, and it is determined that the current shared electric motorcycle is overloaded; If the first judgment result is no, judging whether the current riding user has overloaded according to the historical average load weight of users with the same gender and age group as the current riding user and the historical average value of pressure film point data; If the second judgment result is no, it is determined that the current riding user has not committed overloading behavior, and the load weight and pressure film point data of the current riding user are recorded.

5. The method for identifying overloaded shared electric motorcycles according to claim 4, characterized in that: According to the load weight, the pressure film point data, the historical load weight and the historical pressure film point data, it is determined whether the current shared electric motorcycle is overloaded, and then it also includes: Based on the voltage and current data of the shared electric motorcycle's motor, determine whether the shared electric motorcycle is currently overloaded.

6. The method for identifying overloaded shared electric motorcycles according to claim 5, characterized in that: According to the voltage and current data of the shared electric motorcycle motor, it is judged whether the shared electric motorcycle is overloaded, including: Determine whether the voltage data and the current data are both greater than a second threshold, and obtain a third determination result; the second threshold includes a voltage threshold and a current threshold; If the third judgment result is yes, it is determined that the current riding user has overloaded, and it is determined that the current shared electric motorcycle is overloaded; If the third judgment result is no, combined with the second judgment result, determine whether the current shared electric motorcycle is overloaded.

7. A shared electric motorcycle overload identification device, characterized in that: The shared electric motorcycle overload identification device comprises: Pressure sensors and pressure film sensors are used to collect pressure data and pressure film point data of shared electric motorcycles; An uploading module, used to upload the pressure data and the pressure film point data to the cloud in real time; the cloud stores the historical load weight and historical pressure film point data of the current riding user; The cloud end is used to calculate the load weight according to the pressure data, and to determine whether the current shared electric motorcycle is overloaded according to the load weight, the pressure film point data, the historical load weight and the historical pressure film point data.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for identifying overloaded shared electric motorcycles as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the shared electric motorcycle overload identification method described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the shared electric motorcycle overload identification method described in any one of claims 1-6.

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