Customized omnidirectional electric carrier

Abstract

The invention is applicable to the technical field of electric carriers, and particularly relates to a customized omnidirectional electric carrier, a customized omnidirectional electric carrier control method is applied to the customized omnidirectional electric carrier, and the method comprises the following steps: acquiring target task information; initial pressure distribution information of the goods on the bearing part is detected through a pressure detection device; determining an early warning variable and a critical motion condition based on the cargo category, the driving task category and the initial pressure distribution information; when the motion detection device detects that the motion information of the electric carrier main body meets a critical motion condition, the pressure detection device detects first pressure distribution information of goods on the bearing part; on the basis of the first pressure distribution information and the early warning variable, under the condition that it is determined that the goods on the bearing part are in a falling state or a to-be-dropped state, the customized omni-directional electric carrier is controlled to take emergency measures. The technical problem that it is difficult to carry out timely early warning and control on the falling condition of carried goods in complex tasks is solved.

Description

Technical Field

[0001] The present application belongs to the technical field of electric transport vehicles, and in particular relates to a customized omnidirectional electric transport vehicle. Background Art

[0002] Omnidirectional electric trucks are widely used for cargo handling in industrial production and logistics. However, during the handling process, cargo can fall due to factors such as improper placement, vibration from the truck, acceleration or deceleration, and uneven surfaces. Falling cargo not only damages the cargo and increases financial losses, but also poses a threat to the safety of surrounding personnel and equipment. Furthermore, if dropped cargo is not promptly addressed, it can impact other cargo handling operations along the same route.

[0003] Currently, to address the potential for cargo to fall, existing electric transport vehicles rely on manual visual monitoring or surveillance image monitoring. Manual visual monitoring typically relies on observation by on-site dispatchers or remote monitoring personnel. After cargo falls, the electric transport vehicle mission along the same route is suspended, and manual processing is performed. This approach often fails to address cargo falls promptly, and can lead to even greater losses if personnel are negligent. Surveillance image monitoring typically relies on real-time image monitoring of the electric transport vehicle, continuously comparing image information to determine whether the cargo is still on the vehicle. While this method can warn of dropped cargo, it struggles to detect leaks, such as those from mobile cargo loaded in fixed containers. Furthermore, the continuous image comparison process significantly increases energy consumption and collects excessive data unrelated to cargo falls. Furthermore, it is susceptible to interference from environmental background factors, adding to the burden of image analysis.

[0004] Therefore, it is difficult for existing omnidirectional electric transport vehicles to provide timely warning and control of falling cargo during complex tasks. Summary of the Invention

[0005] The embodiment of the present application provides a customized omnidirectional electric transport vehicle, which can improve the technical problem that existing omnidirectional electric transport vehicles are difficult to provide timely warning and control of falling cargo during complex tasks.

[0006] In a first aspect, an embodiment of the present application provides a control method for a customized omnidirectional electric transport vehicle, which is applied to a customized omnidirectional electric transport vehicle, wherein the customized omnidirectional electric transport vehicle includes an electric transport vehicle body, a pressure detection device, and a motion detection device. The electric transport vehicle body includes a load-bearing portion for carrying goods, the pressure detection device is disposed on the load-bearing portion, and the motion detection device is disposed on the electric transport vehicle body. The customized omnidirectional electric transport vehicle control method includes: Obtain target task information; wherein the target task information includes cargo category and driving task category; detecting initial pressure distribution information of the cargo on the load-bearing portion by the pressure detection device; determining a warning variable and a critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information; When the motion detection device detects that the motion information of the electric transport vehicle body meets the critical motion condition, the pressure detection device detects first pressure distribution information of the cargo on the carrying portion; When it is determined based on the first pressure distribution information and the warning variable that the cargo on the carrying portion is in a falling state or is about to fall, the customized omnidirectional electric transporter is controlled to implement emergency measures.

[0007] The above technical solutions in the embodiments of the present application have at least the following technical effects: The target mission information and initial pressure distribution information are used to determine a warning variable and a critical motion condition. A motion detection device detects that the motion information of the customized omnidirectional electric transport vehicle satisfies the critical motion condition. Based on the first pressure distribution information and the warning variable, it is determined that the cargo on the load-bearing portion is in a dropped state or is about to drop, and the customized omnidirectional electric transport vehicle is controlled to implement emergency measures. This control method determines the warning variable and critical motion condition based on the target mission information and the initial pressure distribution information, thereby being able to cope with cargo drop scenarios in different complex missions and can be considered a customized control method for different cargo types, driving mission types, and initial pressure distribution information. In motion states where cargo drop is unlikely, no additional information is collected. Pressure distribution information collection is triggered only when an action that may cause cargo drop occurs, thereby avoiding excessive energy consumption and excessive data collection unrelated to cargo drop. By comparing the cargo pressure distribution information with the surveillance image monitoring in the prior art, it is possible to analyze whether the cargo has suffered quality loss during driving, reducing interference from environmental background factors during cargo drop monitoring. Therefore, this control method overcomes the technical problem of existing omnidirectional electric transport vehicles having difficulty in providing timely warnings and control for cargo drop scenarios carried during complex missions.

[0008] In a second aspect, an embodiment of the present application provides a customized omnidirectional electric transport vehicle control device, which is applied to a customized omnidirectional electric transport vehicle. The customized omnidirectional electric transport vehicle includes an electric transport vehicle body, a motion detection device, and a pressure detection device. The electric transport vehicle body includes a load-bearing portion for carrying goods, the pressure detection device is provided on the load-bearing portion, and the motion detection device is provided on the electric transport vehicle body. The customized omnidirectional electric transport vehicle control device includes: An acquisition module is used to acquire target task information; wherein the target task information includes cargo category and driving task category; a first detection module, configured to detect initial pressure distribution information of the cargo on the load-bearing portion through the pressure detection device; a determination module, configured to determine a warning variable and a critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information; a second detection module, configured to detect first pressure distribution information of the cargo on the carrying portion through the pressure detection device when the motion information of the customized omnidirectional electric transporter meets the critical motion condition through the motion detection device; An execution module is used to control the customized omnidirectional electric transporter to implement emergency measures when it is determined that the cargo on the carrying portion is in a falling state or is about to fall state based on the first pressure distribution information and the warning variable.

[0009] In a third aspect, an embodiment of the present application provides a customized omnidirectional electric transport vehicle, comprising: The electric transport vehicle body includes a carrying portion for carrying goods; a pressure detection device, provided on the load-bearing portion, for detecting the pressure distribution of the cargo on the load-bearing portion; A motion detection device, provided on the electric transport vehicle body, for detecting motion information of the electric transport vehicle body; A control device is electrically connected to the pressure detection device and the motion detection device respectively. The control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the first aspect is implemented.

[0010] It can be understood that the beneficial effects of the second to third aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. 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 any creative work.

[0012] Figure 1 This is a structural diagram of a customized omnidirectional electric transport vehicle provided in one embodiment of the present application; Figure 2 This is a flow chart of a method for controlling a customized omnidirectional electric transport vehicle provided in one embodiment of the present application; Figure 3 This is a schematic diagram of the implementation flow of the customized omnidirectional electric transport vehicle control method provided in an embodiment of the present application; Figure 4 Schematic diagram of the distribution of the pressure detection device on the bearing portion provided by one embodiment of the present application; Figure 5 This is a schematic structural diagram of a customized omnidirectional electric transport vehicle control device provided in one embodiment of the present application; Figure 6 Schematic diagram of the structure of the control device of the customized omnidirectional electric transport vehicle provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0015] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0019] In the related art, manual visual monitoring methods generally rely on the observation of on-site dispatchers or remote monitoring personnel. After the cargo falls, it is necessary to first suspend the driving mission of the electric transport vehicle on the same route, and then manually go to the scene to deal with it. This method often cannot deal with the cargo drop problem in a timely manner. If the staff is negligent, it may cause greater losses. The monitoring image monitoring method generally relies on real-time image monitoring of the electric transport vehicle, and continuously compares the image information to analyze whether the cargo is still on the transport vehicle. Although this method can warn of the situation that the cargo has fallen, it is difficult to detect the leakage of mobile cargo such as those loaded in fixed containers. In addition, the continuous image comparison process not only significantly increases energy consumption and collects too much data unrelated to the cargo drop, but is also easily interfered by environmental background factors, which increases the burden of image analysis. In addition, electric transport vehicles have a wide range of uses. The need to use electric transport vehicles in the same scenario is not specific to a single cargo type and a single driving mission. However, when facing different cargo categories and different driving mission requirements, the above two monitoring methods adopt a single monitoring method and a single control means, which makes it difficult to meet the needs of safe cargo handling in actual use scenarios.

[0020] To address the above-mentioned issues, an embodiment of the present application provides a control method for a customized omnidirectional electric transport vehicle. In this method, target task information is first acquired, wherein the target task information includes the cargo category and the driving task category; then, the initial pressure distribution information of the cargo on the load-bearing portion is detected by a pressure detection device; then, based on the cargo category, the driving task category, and the initial pressure distribution information, a warning variable and a critical motion condition are determined; then, when the motion detection device detects that the motion information of the electric transport vehicle body meets the critical motion condition, the pressure detection device detects the first pressure distribution information of the cargo on the load-bearing portion; finally, based on the first pressure distribution information and the warning variable, when it is determined that the cargo on the load-bearing portion is in a falling state or is about to fall, the customized omnidirectional electric transport vehicle is controlled to implement preset emergency measures.

[0021] The warning variables and critical motion conditions of this control method are determined by the target task information and the initial pressure distribution information, so that it can cope with the situation of cargo falling in different complex tasks; in the motion state where no cargo falling will occur, no additional information is collected, and only when the behavior that may cause the cargo falling occurs, the collection of pressure distribution information will be triggered, avoiding excessive energy consumption and collecting too much data that is not related to cargo falling; by comparing the pressure distribution information of the cargo with the monitoring image monitoring in the existing technology, it is possible to analyze whether the quality of the cargo has been lost during the driving process, and reduce the interference caused by environmental background factors in the cargo falling monitoring process; therefore, this control method improves the technical problem that the existing omnidirectional electric transport vehicle is difficult to provide timely warning and control for the falling of the cargo carried in complex tasks.

[0022] The control method provided in the embodiment of the present application can be applied to a customized omnidirectional electric transport vehicle. In this case, the customized omnidirectional electric transport vehicle is the executor of the control method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the customized omnidirectional electric transport vehicle.

[0023] See also Figure 1 In some embodiments, a customized omnidirectional electric transport vehicle 100 includes an electric transport vehicle body 10, a pressure detection device 20, a motion detection device 30, and a control device 40. The electric transport vehicle body 10 includes a load-bearing portion 11 for carrying goods. The pressure detection device 20 is provided on the load-bearing portion 11 for detecting the pressure distribution of the goods on the load-bearing portion 11. The motion detection device 30 is provided on the electric transport vehicle body 10 for detecting the motion information of the electric transport vehicle body. The control device 40 is electrically connected to the pressure detection device 20 and the motion detection device 30, respectively, for receiving the pressure distribution and motion information collected by the pressure detection device 20 and the motion detection device 30, and executing the above-mentioned control method.

[0024] It can be understood that the electric transport vehicle body is the main part of the customized omnidirectional electric transport vehicle, which can be various types of electric transport vehicles, such as a Mecanum wheel omnidirectional transport vehicle, or an omnidirectional transport vehicle with a combination of universal wheels and drive wheels.

[0025] The pressure detection device may include multiple pressure sensors. The pressure sensors may be various types of sensors capable of detecting pressure, such as piezoresistive pressure sensors or capacitive pressure sensors, as long as they can detect pressure information. The embodiments of the present application do not limit the specific type of pressure detection device. The distribution of the pressure sensors on the load-bearing portion may adopt a preset distribution on the forks of the electric transport vehicle body, such as uniform distribution, dense distribution on the periphery and sparse distribution in the middle. The embodiments of the present application do not limit the distribution of the pressure sensors on the load-bearing portion.

[0026] The motion detection device is a device for detecting the motion state of the electric transport vehicle body. For example, it may include a sensor for monitoring the acceleration and vibration amplitude of the electric transport vehicle body. For example, it may be a micro-electromechanical system three-axis acceleration sensor or a piezoelectric acceleration sensor, which can detect the acceleration and vibration amplitude of the omnidirectional electric transport vehicle. The motion detection device may also include a sensor for monitoring the speed of the electric transport vehicle body. For example, it may be a Hall effect sensor. The motion detection device may be provided by the electric transport vehicle body itself, or it may be additionally provided on the body of the electric transport vehicle. When additionally provided, the position that can achieve the function that the motion detection device needs to achieve shall prevail. For example, the micro-electromechanical system three-axis acceleration sensor or the piezoelectric acceleration sensor may be provided on the chassis inside the electric transport vehicle body, and the Hall effect sensor may be provided on the wheel hub. The embodiments of the present application do not limit the specific type and specific position of the sensor in the motion detection device.

[0027] The control device can be a computing device such as a microcontroller, microprocessor, or digital signal processor, and is electrically connected to the pressure detection device and the motion detection device, for example, by pin connection or DuPont wire connection. The control device can be located in a well-protected and efficient signal transmission location, such as above a chassis mounted within the main body of the electric transport vehicle, secured with a shock-absorbing base and aligned with the battery and sensor. Alternatively, the control device can be mounted above the main body of the electric transport vehicle and electrically connected to a control interface on the main body of the electric transport vehicle.

[0028] In order to better understand the control method of the customized omnidirectional electric transport vehicle provided in the embodiment of the present application, the specific implementation process of the control method of the customized omnidirectional electric transport vehicle provided in the embodiment of the present application is exemplarily introduced below.

[0029] Figure 2 and Figure 3The flowchart of the control method of the customized omnidirectional electric transport vehicle provided in the embodiment of the present application and the implementation flowchart of the control method of the customized omnidirectional electric transport vehicle are respectively shown. Specifically, the control method of the customized omnidirectional electric transport vehicle includes: S100, obtaining target task information; wherein the target task information includes cargo category and driving task category.

[0030] It can be understood that cargo categories refer to the types of cargo that the electric transport truck is intended to carry, as well as the cargo form or loading method that is appropriate for the type of cargo that the electric transport truck is intended to carry. For example, cargo categories may include individually transported precision equipment, stacked boxes of fragile ceramics, stacked bags of ore raw materials, or bulk metal castings tied with ropes.

[0031] A driving mission category is the type of driving mission that the electric transport truck must perform during operation, as well as the driving route requirements that are appropriate for the type of mission that the electric transport truck must perform during operation. For example, a driving mission category can be a pre-set route with different levels of complexity due to different speeds, number of turns, accelerations, or decelerations.

[0032] The target task information can be obtained by the customized omnidirectional electric transport vehicle receiving manually input target task information, such as an operator entering the target task information in advance. The target task information can also be obtained by the customized omnidirectional electric transport vehicle obtaining it from other terminals, such as a warehouse management system inputting the target task information and sending it to the customized omnidirectional electric transport vehicle. The target task information can also be obtained by the electric transport vehicle automatically identifying it, such as when picking up a shipment, the customized omnidirectional electric transport vehicle obtains the target task information by scanning a barcode or QR code on the outer packaging of the shipment.

[0033] S200: Initial pressure distribution information of the cargo on the load-bearing portion is detected by a pressure detection device.

[0034] It is understood that the initial pressure distribution information of the cargo on the load-bearing portion is the initial pressure distribution of the cargo at various locations on the load-bearing portion, as collected by the pressure detection device. For example, the pressure detection device may convert the detected pressure signal into an electrical signal and transmit the electrical signal to the control device. The control device may then process and analyze the electrical signal, calculate the pressure value at each pressure sensor location in the pressure detection device, and, based on the layout information of the pressure sensors, draw a pressure distribution diagram of the cargo on the load-bearing portion.

[0035] Before the goods are placed on the load-bearing portion, the pressure detection device generally performs an initialization operation. After the goods are placed on the load-bearing portion, the pressure detection device begins to detect the pressure changes at various positions in real time. The pressure distribution information generated by the initialization operation and the re-placement of the goods is the initial pressure distribution information of the goods on the load-bearing portion referred to in the embodiments of the present application.

[0036] S300 , determining warning variables and critical motion conditions based on cargo category, driving mission category, and initial pressure distribution information.

[0037] It can be understood that critical motion conditions refer to the maximum motion parameters that a customized omnidirectional electric transport vehicle can achieve while ensuring safe and stable transportation of cargo. Examples include the vehicle's maximum critical speed, acceleration, and vibration amplitude. Early warning variables are indicators used to monitor cargo safety while the vehicle is in motion. For example, this can be represented by the critical pressure exerted by the cargo on the load-bearing structure.

[0038] The cargo category reflects the physical properties of the cargo, the driving mission category characterizes the requirements for stable transportation during the driving process, and the initial pressure distribution information reflects the initial state of the cargo. Therefore, based on the cargo category, driving mission category and initial pressure distribution information, the warning variables and critical motion conditions required to control the omnidirectional electric transport vehicle can be determined.

[0039] Specifically, the method of determining warning variables and critical motion conditions based on cargo category, driving task category and initial pressure distribution information can be based on a database established with empirical data. For example, according to the cargo category, driving task category and initial pressure distribution information, the corresponding warning variables and critical motion conditions are determined by matching the conditions in a pre-established database.

[0040] A machine learning model can also be established by determining warning variables and critical motion conditions based on cargo category, driving task category and initial pressure distribution information. For example, it can be a decision tree or neural network model. By inputting the associated information of cargo falling in historical tasks, including cargo category, driving task category and initial pressure distribution information as input data, and warning variables and critical motion conditions as output data, the model is trained multiple times to finally obtain a trained model; by inputting cargo category, driving task category and initial pressure distribution information into the trained model, the corresponding warning variables and critical motion conditions are output through the model.

[0041] In the embodiment of the present application, the method of determining the warning variables and critical motion conditions based on the cargo category, driving task category and initial pressure distribution information is not specifically limited.

[0042] S400: When the motion detection device detects that the motion information of the electric transport vehicle body meets the critical motion condition, the pressure detection device detects first pressure distribution information of the cargo on the load-bearing portion.

[0043] It can be understood that when the motion information of the electric transport vehicle body does not meet the critical motion conditions, the omnidirectional electric transport vehicle does not need to continuously monitor the pressure distribution information in this motion state; only when the motion information of the electric transport vehicle body meets the critical motion conditions, the omnidirectional electric transport vehicle may not be able to guarantee the safe and stable transportation of goods in this motion state, and therefore it is necessary to monitor the pressure distribution information of the goods on the load-bearing part again through the pressure detection device.

[0044] The first pressure distribution information of the cargo on the load-bearing portion is the pressure distribution of the cargo at various positions on the load-bearing portion collected by the pressure detection device when the motion detection device detects that the motion information of the electric transport vehicle body meets the critical motion condition.

[0045] In one possible implementation, the motion information includes at least one of acceleration and vibration amplitude. The critical motion condition includes at least one of the critical acceleration and critical vibration amplitude corresponding to the motion information. The motion information of the customized omnidirectional electric transporter satisfies the critical motion condition if at least one of the acceleration and vibration amplitude of the motion information is greater than or equal to at least one of the critical acceleration and critical vibration amplitude corresponding to the critical motion condition.

[0046] It is understood that the motion factors that trigger the omnidirectional electric transport vehicle to drop cargo include acceleration, deceleration, turning, and driving on uneven roads. The vibration amplitude of the omnidirectional electric transport vehicle during driving is also a factor that affects the stability of cargo on the omnidirectional electric transport vehicle. Therefore, in this embodiment, at least one of acceleration and vibration amplitude can be used as the motion information to be monitored by the omnidirectional electric transport vehicle and the corresponding critical motion condition.

[0047] Optionally, the acceleration includes vertical acceleration and horizontal acceleration, wherein the vertical acceleration is a component of acceleration in a direction parallel to the direction of gravity of the electric transport vehicle body, and the horizontal acceleration is a component of acceleration in a direction perpendicular to the vertical acceleration. The critical acceleration includes a critical vertical acceleration and a critical horizontal acceleration corresponding to the vertical acceleration and the horizontal acceleration.

[0048] The motion information of the electric transport vehicle body meets the critical motion condition, which means that the horizontal acceleration is greater than or equal to the critical horizontal acceleration, and / or the vertical acceleration is greater than or equal to the critical vertical acceleration.

[0049] With this setting, the acceleration is decomposed into vertical acceleration in the direction parallel to the gravity direction of the electric transport vehicle body and horizontal acceleration in the direction perpendicular to the vertical acceleration, so that technicians can first understand the specific circumstances of the omnidirectional electric transport vehicle experiencing acceleration, deceleration, turning or driving on uneven roads, which is convenient for checking the impact of the on-site environment or route design on the safe transportation of goods by the omnidirectional electric transport vehicle.

[0050] S500 , when it is determined based on the first pressure distribution information and the warning variable that the cargo on the carrying portion is in a falling state or is about to fall, controlling the customized omnidirectional electric transporter to implement emergency measures.

[0051] When the truck's motion information meets critical motion conditions, the first pressure distribution information obtained reflects the pressure distribution exerted by the cargo on the load-bearing portion, in a possible motion state where the omnidirectional electric truck cannot guarantee safe and stable transportation of the cargo. If the cargo is placed stably and securely, the pressure distribution will not change significantly. However, if the cargo tilts, slides, or is about to fall, the pressure distribution will change significantly, such as a sudden increase or decrease in pressure in certain areas. Therefore, the first pressure distribution information and the warning variable can serve as an important basis for determining whether the cargo is falling or about to fall.

[0052] When it is determined based on the first pressure distribution information and the warning variables that the cargo is not in a falling state or a state to be dropped, the omnidirectional electric transport vehicle does not need to be controlled and continues to move along the set driving route; and when it is determined that the cargo is in a falling state or a state to be dropped, it is necessary to promptly control the omnidirectional electric transport vehicle to implement preset emergency measures, which can avoid damage caused by the dropping of cargo, prevent the dropped cargo from causing harm to the surrounding environment, personnel and other equipment, and ensure the safety of the entire work scene.

[0053] In one possible implementation, S300, determining warning variables and critical motion conditions based on cargo type, driving mission type, and initial pressure distribution information, includes: S310, determining a corresponding initial load pressure total value based on the initial pressure distribution information; S311 : Determine a critical load pressure total value of a warning variable based on the cargo category, the driving mission category, and the initial load pressure total value.

[0054] Determining that the cargo on the load-bearing portion is in a falling state based on the first pressure distribution information and the warning variable includes: S511, determining a corresponding first load pressure total value based on the first pressure distribution information; S512: When it is detected that the first load pressure total value is less than or equal to the critical load pressure total value, it is determined that the cargo on the carrying portion is in a falling state.

[0055] It can be understood that the total initial load pressure value is the total initial pressure value of the cargo on the omnidirectional electric transport vehicle; correspondingly, the total first load pressure value is the total pressure value of the cargo on the load-bearing part of the omnidirectional electric transport vehicle when the motion information of the electric transport vehicle body is detected by the motion detection device to meet the critical motion condition. The critical load pressure total value is a part of the early warning variable and is a judgment value for determining whether the cargo is in a falling state. The total initial load pressure value and the total first load pressure value can be obtained by simply summing the pressure values ​​monitored by each pressure sensor in the pressure detection device; for example, there are 9 pressure sensors distributed on the load-bearing part, and the detected pressure values ​​are They are , ,……, , then the total initial load pressure Similarly, the total value of the first load pressure can be obtained .

[0056] The critical load pressure total value of the warning variable is determined based on the cargo category, the driving task category and the initial load pressure total value. The first proportional coefficient can be determined based on the cargo category and the driving task category. , total critical load pressure It can be obtained by multiplying the proportional coefficient and the total value of the initial load pressure, that is: .

[0057] The first proportional coefficient is determined based on the cargo category and driving mission category. The method can be to divide the cargo categories and travel task categories into multiple levels based on experience, with each level corresponding to a weighted value. The first proportional coefficient corresponding to each situation is obtained by multiplying the weighted values ​​corresponding to the cargo category and the travel task category, thereby establishing a first database for determining the first proportional coefficient based on the cargo category and the travel task category. When determining the first proportional coefficient, the first proportional coefficient can be determined by matching the first database established based on the level of the cargo category and the travel task category. For example, cargo categories can be divided into three levels based on the shape of the cargo and the way it is loaded, with weighted values ​​of 1, 0.9, and 0.8 respectively; driving task categories can be divided into three levels based on the complex gradient of the driving path, with weighted values ​​of 1, 0.95, and 0.90 respectively; and the product of the two weighted values ​​can be used to obtain a database of first proportional coefficients ranging from 1 to 0.72.

[0058] The first proportional coefficient is determined based on the cargo category and driving mission category. Alternatively, a machine learning model may be established, such as a neural network model, by taking the cargo category and driving task category as input data and the first proportional coefficient as output data. , input model for training, specifically, it can be pre-trained by combining historical cargo drop correlation data and the first proportional coefficient evaluated by the operator to obtain a basic model, and use the basic model to further learn the correlation data after the model is deployed to continuously strengthen the trained model.

[0059] With this configuration, the first total load pressure value is not directly compared with the initial total load pressure value, but rather with the critical total load pressure value. This allows for consideration of the impact of different cargo types and driving mission types on load pressure. If the first total load pressure value is greater than the critical total load pressure value, the cargo has not suffered significant weight loss and is not in a dropped state or in a state of imminent drop. If the first total load pressure value is less than or equal to the critical total load pressure value, the cargo on the load-bearing portion is determined to be in a dropped state, such as partially dropped or leaking.

[0060] In one possible implementation, S300, determining warning variables and critical motion conditions based on cargo type, driving mission type, and initial pressure distribution information, includes: S310, determining a corresponding initial load pressure total value based on the initial pressure distribution information; S320, determining a local critical pressure value of the warning variable based on the cargo category, the driving mission category, and the total initial load pressure value; S500, determining that the cargo on the load-bearing portion is in a state of about to fall based on the first pressure distribution information and the warning variable, includes: S521 determines a local pressure value of the cargo on the load-bearing portion at any local position of the load-bearing portion based on the first pressure distribution information; S522: When the local pressure value is greater than or equal to the local critical pressure value, it is determined that the cargo is in a first type of ready-to-drop state.

[0061] It can be understood that the process of determining the total value of the initial load pressure is the same as that of the previous embodiment; the local pressure value is measured by the pressure sensor at any local position of the load-bearing part, for example, the pressure value detected by the pressure sensor j , which is understandable It is the local pressure value at the local position where the sensor is located on the load-bearing part.

[0062] For example, the local critical pressure value can be a second proportional coefficient determined based on the cargo type and the driving task type. , local critical pressure value The second proportionality factor and the total initial load pressure The product is: .

[0063] Among them, the local position is not limited to the position where a pressure sensor is located. The local pressure value can also be the sum of the pressure values ​​measured by pressure sensors at multiple adjacent positions. For example, the local pressure value can be obtained by multiple adjacent pressure sensors at the tail position of the carrying part. Correspondingly, the local critical pressure value is the sum of the local critical pressure values ​​corresponding to multiple positions.

[0064] The second proportional coefficient is determined based on the cargo category and driving mission category. The method can be determined by matching a second database established based on experience. For example, each cargo category is classified according to the form of the cargo and the loading method of the cargo. According to the classification of the cargo category and the complexity gradient of the driving task category, a second database is established. When determining the second proportional coefficient, the corresponding conditions can be matched in the established second database to determine the second proportional coefficient. The process and method of establishing the second database are similar to those of establishing the first database in the previous embodiment, and will not be described in detail here.

[0065] The second proportional coefficient is determined based on the cargo category and driving mission category. Alternatively, a machine learning model can be established, such as a neural network model, by taking the cargo category and driving task category as input data and the second proportional coefficient as output data. , input model for training, for example, it can be pre-trained by combining historical cargo drop correlation data and the first proportional coefficient evaluated by the operator to obtain a basic model, and use the basic model to further learn the correlation data after the model is deployed to continuously strengthen the trained model.

[0066] Optionally, in the case where the possibility of cargo falling at different positions of the load-bearing part is inconsistent, the second proportional coefficient corresponding to different local positions is determined based on the cargo category and the driving task category. They may not be consistent. Specifically, the second proportional coefficient obtained in the above solution may be multiplied by a correction coefficient β less than or equal to 1 for different positions. For example, the correction coefficients for the edge positions on both sides of the direction of movement of the supporting part are: , the correction coefficient at the edge position of the tail of the load-bearing part is , the correction coefficient for the inner position of the load-bearing part is ,So > > , the corrected second proportional coefficient It should also be adjusted to .

[0067] Through the above steps S310 and S320, and steps S500, S521, and S522, on the basis of the previous embodiment, even if the cargo is not a whole, for example, the cargo is metal castings placed side by side, it is possible to further set a suitable second proportional coefficient and a local critical pressure value based on the pressure change of part of the cargo on the load-bearing part. In the case where the pressure of part of the cargo on the local position is too high, it can also be determined that the pressure distribution of the part of the cargo at the local position is abnormal and may start to fall from this position, and thus determine that the cargo is in a state of being about to fall.

[0068] In one possible implementation, S300, determining warning variables and critical motion conditions based on cargo type, driving mission type, and initial pressure distribution information, includes: S330, determining a first critical value of the warning variable based on the cargo type and the driving task type; wherein the first critical value refers to a critical value of the distance between the pressure center of the cargo on the load-bearing portion and a preset edge; In step S500, determining that the cargo on the load-bearing portion is in a state of about to fall based on the first pressure distribution information and the warning variable includes: S530, determining the distance from the pressure center corresponding to the goods on the load-bearing portion to the preset edge based on the first pressure distribution information; S531: When the distance from the pressure center to the preset edge is less than or equal to a first critical value, determine that the cargo on the carrying portion is in a second type of ready-to-fall state.

[0069] See also Figure 4 It can be understood that the preset edge is a predefined boundary on the contact plane between the load-bearing portion and the cargo. Its shape and length are determined based on factors such as the structure of the load-bearing portion and the motion characteristics of the omnidirectional electric transport vehicle. For example, when the load-bearing portion is a fork, the preset edge can be the four sides of a rectangle enclosed by the four endpoints of the outer edges of the two forks parallel to the direction of travel within the plane where the upper surfaces of the two forks are located. In particular, considering the actual situation during travel, the preset edge can also be the other three sides of the rectangle excluding the side connecting the load-bearing portion to the main body of the omnidirectional electric transport vehicle. Of course, the preset edge can also have other shapes based on work needs and the actual situation of the customized omnidirectional electric transport vehicle, and is not particularly limited in the embodiments of the present application.

[0070] The pressure center is used to analyze the placement of goods on the load-bearing part. It is the point on the contact surface between the load-bearing part and the goods. For the method of determining the pressure center, please refer to Figure 4, a plane rectangular coordinate system can be first established based on a first direction parallel to the driving direction and a second direction perpendicular to the driving direction, and the position information of each pressure sensor is determined in the plane rectangular coordinate system. The position information of the pressure center can be obtained by multiplying the weighted position components of each pressure sensor by the pressure value obtained by the pressure sensor. For example, the coordinates of a pressure sensor 1 containing the pressure value and position information are ( ), correspondingly, the coordinates of pressure sensor i are ( ), the pressure center position coordinates obtained by n pressure sensors ( ) are: The first critical value refers to the critical value of the distance between the pressure center of the cargo on the load-bearing portion and the preset edge. When the distance between the pressure center of the cargo on the load-bearing portion and the preset edge is less than the first critical value, the cargo is at risk of falling, so it can be determined that the cargo on the load-bearing portion is in a state of about to fall. The method of determining the first critical value of the warning variable based on the cargo category and the driving task category can be a method that relies on empirical data. For example, first determine a basic distance, which can be 5cm or 10cm, and then assign different weighting coefficients according to the classification of the cargo category and the complex gradient of the driving task category. The corresponding first critical value is obtained by multiplying the weighting coefficient with the basic distance, so that a third database for determining the first critical value of the cargo category and the driving task category can be established. When determining the first critical value, the corresponding first critical value can be determined by matching the conditions in the pre-established database.

[0071] The method of determining the first critical value of the warning variable based on the cargo category and the driving task category can also be to establish a machine learning model, such as a decision tree or neural network model, and assign different weighted values ​​to different cargo categories and driving task categories. The weighted value as input data and the first critical value as output data are input into the machine learning model for training, and the historical data in the work are continued to be input into the basic model obtained after training to further strengthen the model.

[0072] Optionally, S300, determining the warning variables and critical motion conditions based on the cargo category, the driving mission category, and the initial pressure distribution information, further includes: S340, determining a second critical value of the warning variable and a pressure center offset critical value based on the cargo type and the driving mission type, wherein the second critical value refers to a critical value of the distance between the pressure center corresponding to the cargo on the load-bearing portion and a preset edge of the load-bearing portion, and the second critical value is greater than the first critical value; In step S500, determining that the cargo on the load-bearing portion is in a state of about to fall based on the first pressure distribution information and the warning variable further includes: S541: When it is detected that the distance between the pressure center corresponding to the goods on the load-bearing portion and the preset edge is greater than a first threshold value and less than or equal to a second threshold value, the pressure detection device continuously detects second pressure distribution information of the goods on the load-bearing portion within a preset time period; S542, determining a pressure center offset value within a preset time period based on the second pressure distribution information; S543: When it is detected that the pressure center offset value is greater than or equal to the pressure center offset critical value, it is determined that the goods on the carrying portion are in a third type of ready-to-fall state.

[0073] It can be understood that the second critical value, like the first critical value, is used to characterize the critical value of the distance between the pressure center of the goods on the load-bearing portion and the preset edge. The method for determining the second critical value is similar to the method for determining the first critical value of the aforementioned solution.

[0074] The preset time period is a predetermined time period, which may be determined manually based on experience and practice in actual operation, such as 1, 2, or 3 seconds. During the preset time period, the pressure detection device continuously detects the second pressure distribution information of the cargo on the load-bearing portion, similar to the method for obtaining the initial and first pressure distribution information in the aforementioned embodiment, with the difference that the second pressure distribution information generated in this embodiment is continuously acquired by the pressure sensor.

[0075] The pressure center offset value refers to the displacement of the pressure center corresponding to the cargo on the load-bearing portion within a preset time period. Because it only needs to reflect, to a certain extent, whether the cargo is securely fixed, the embodiment of the present application does not limit the direction of the pressure center offset. The pressure center offset value can be determined by weighted summing the pressure sensor's coordinate information and pressure information to obtain the pressure center's coordinate information. By comparing the pressure center coordinate information at each time point within the preset time period, the maximum distance between the pressure center at each time point and the pressure center at the beginning of the preset time period is taken.

[0076] The pressure center offset critical value is the critical value of the pressure center offset value. When the pressure center offset value exceeds this value, it can be determined that the goods are in a state of being about to fall. In some application scenarios, the possibility of goods falling may need to be judged more strictly. When the distance between the pressure center corresponding to the goods on the load-bearing part and the preset edge is less than the second critical value, although it still does not exceed the first critical value, if the pressure center is allowed to continue to deviate, it may still cause the goods to fall and cause serious economic losses or other adverse consequences. Therefore, the risk of goods falling can be further judged by analyzing the pressure center offset value. When it is detected that the pressure center offset value is greater than or equal to the pressure center offset critical value, further measures need to be taken to pay attention to the status of the goods. At this time, it can be determined that the goods on the load-bearing part are in the third type of state of being about to fall.

[0077] The method for comprehensively determining the critical value of the center of pressure offset based on cargo type and driving mission type can rely on a database established with empirical data. The corresponding critical value of the center of pressure offset can be determined by matching conditions within the pre-established database. For example, a basic offset distance reference value is first determined based on a preset time period and operating conditions. For example, a basic offset distance value of 5 cm is used for a preset time period of 1 second. Different weighting values ​​are then assigned to different cargo types and driving mission types. The weighting values ​​are multiplied by the offset distance reference value to obtain the corresponding critical value of the center of pressure offset. This is then used to establish the fourth database for comprehensively determining the critical value of the center of pressure offset based on cargo type and driving mission type.

[0078] Alternatively, a machine learning model can be established, such as a decision tree or neural network model, which assigns different weighted values ​​to different cargo categories and driving task categories. The weighted value as input data and the first critical value as output data are input into the machine learning model for training, and the historical data in the work are continued to be input into the basic model obtained after training to further strengthen the model.

[0079] In one possible implementation, S500, when it is determined based on the first pressure distribution information and the warning variable that the cargo on the load-bearing portion is in a fallen state or about to fall state, controlling the customized omnidirectional electric transport vehicle to implement emergency measures, including at least one of the following: sound an audible alarm; sounding a light alarm; Control the omnidirectional electric transport vehicle to slow down to a stop, and control the omnidirectional electric transport vehicle to be in a suspended driving task state; Outgoing motion information and first pressure distribution information.

[0080] It can be understood that the sound alarm and the light alarm are respectively issued by calling the sound alarm 52 and the light alarm 51 set on the omnidirectional electric transport vehicle to issue an alarm, which are used to remind the on-site dispatch personnel; wherein, the sound alarm 52 is not blocked, the sound emitted should be clear, and the light alarm 51 should be eye-catching, for example, a flashing reminder can be adopted; the specific setting positions of the sound alarm 52 and the light alarm 51 can be referred to Figure 1 As long as it does not affect the use scenarios of the omnidirectional electric transporter, such as driving tasks and handling tasks, and can meet the requirements of reminding on-site dispatchers, for example, on both sides of the main body of the omnidirectional electric transporter, the embodiments of this application are not particularly limited.

[0081] Outgoing motion information and first pressure distribution information refers to the electric truck transmitting the detected motion information and first pressure distribution information to an external system or device for remote monitoring, early warning, or data analysis. For example, the truck can send the motion information and first pressure distribution information to the on-site dispatch center through a wireless communication module to notify the operator to intervene in a timely manner; the truck can send the current motion parameters and pressure distribution map to the remote monitoring interface of the warehouse management system through the 4G network, and a pop-up window will appear on the interface to prompt: "Cargo triggers drop warning, please deal with it urgently"; or in a network-free environment, the truck can forward the data to a nearby handheld terminal via Bluetooth, and the handheld terminal will display the "slow down to stop command" and record the abnormal event and upload it to the local database.

[0082] Optionally, in S500, when it is determined based on the first pressure distribution information and the warning variable that the cargo on the load-bearing portion is in a falling state or about to fall state, controlling the customized omnidirectional electric transport vehicle to implement emergency measures includes: S551, when it is determined that the cargo on the carrying portion is in a third type of ready-to-drop state, determining an emergency driving speed based on the cargo type and the driving mission; S552, determining whether the omnidirectional electric transport vehicle can proceed to the preset emergency treatment area for treatment based on the second pressure distribution information, the warning variable, the emergency driving speed, the motion information of the electric transport vehicle body, and the position information of the customized omnidirectional electric transport vehicle; S553, when it is determined that the omnidirectional electric transport vehicle is able to go to the preset emergency treatment area for treatment, the customized omnidirectional electric transport vehicle is controlled to implement special emergency measures, including: controlling the customized omnidirectional electric transport vehicle to decelerate to the emergency driving speed, controlling the customized omnidirectional electric transport vehicle to travel to the preset emergency treatment area at the emergency driving speed, and continuing to decelerate in the preset emergency treatment area until it stops.

[0083] It can be understood that the emergency driving speed is a preset speed lower than the driving speed. Specifically, the setting method of the emergency driving speed can be to select the corresponding emergency driving speed according to the current load classification. , can also be adjusted dynamically based on the current load, for example, setting the emergency handling speed when empty to When the load is greater than or equal to 50% of the rated load and less than 70% of the rated load, the emergency handling speed is When the emergency driving speed is greater than or equal to 70% of the rated load and less than 90% of the rated load, the emergency handling speed is ; When the load is greater than or equal to 90% of the rated load, the emergency handling speed is ;in, > > > .

[0084] The preset emergency treatment area is an artificially designated area set up in advance in actual work to deal with the problem of falling goods. The way to go to the preset emergency treatment area can be to pre-mark the coordinates of the preset emergency treatment area on the electronic map of the warehouse. When the omnidirectional electric transport vehicle encounters a problem of falling goods, the path planning algorithm, such as the A* algorithm or the Dijkstra algorithm, can be used to dynamically select the nearest reachable preset emergency treatment area. If obstacles appear again on the road and prevent the vehicle from going to the nearest reachable preset emergency treatment area, the vehicle can be replanned to go to another next closest preset emergency treatment area and the required time can be recalculated.

[0085] Optionally, determining whether the omnidirectional electric transport vehicle can proceed to a preset emergency treatment area for treatment based on the second pressure distribution information, the warning variable, the motion information of the electric transport vehicle body, and the position information of the customized omnidirectional electric transport vehicle includes: S554 : Determine an estimated emergency travel time based on the motion information of the electric transport vehicle body and the position information of the customized omnidirectional electric transport vehicle, wherein the motion information of the electric transport vehicle body includes the speed of the electric transport vehicle body.

[0086] S555: Determine an estimated cargo drop time based on the second pressure distribution information and the first critical value.

[0087] S556: When the estimated cargo drop time is greater than the estimated emergency travel time, determine that the omnidirectional electric transport vehicle can go to the preset emergency processing area for processing.

[0088] S557: When the estimated cargo drop time is less than or equal to the estimated emergency travel time, determine that the omnidirectional electric transport vehicle cannot go to the preset emergency processing area for processing.

[0089] It is understood that the estimated emergency travel time refers to the estimated travel time required for the electric transport vehicle to reach the preset emergency handling area. Specifically, the estimated emergency travel time can be divided into a first stage of decelerating to the emergency travel speed and a second stage of traveling to the preset emergency handling area at the emergency travel speed.

[0090] The method for determining the estimated emergency travel time by using the motion information of the electric transport vehicle body and the position information of the customized omnidirectional electric transport vehicle can be to determine the speed v and average braking acceleration of the electric transport vehicle body based on the motion information of the electric transport vehicle body. ; Using the location information of the customized omnidirectional electric transport vehicle and the location information of the preset emergency treatment area, the distance x and the path complexity k of the feasible path between the transport vehicle and the nearest emergency treatment area are calculated; wherein the path complexity can be calculated in steps. For example, in one embodiment, the path complexity of the straight path is determined as =1, the path complexity when there is one turn is determined to be a value between 1.1 and 1.2 , the path complexity when there are 2 turns is a value between 1.2-1.5 ; Based on the speed v of the electric transport vehicle body and the emergency driving speed Determine the estimated emergency travel time for the first phase The estimated emergency travel time for the second phase is determined based on the emergency travel speed, the distance of the feasible path between the transport vehicle and the nearest emergency treatment area, and the path complexity. Based on the estimated emergency travel time of the first phase, the estimated emergency travel time of the second phase and the emergency redundancy time of 1-2 seconds , determine the estimated emergency travel time , you can refer to the following formula for analysis: Determining the expected cargo drop time based on the second pressure distribution information and the first critical value can be done by determining the motion trajectory of the pressure center within a preset time period through the second pressure distribution information, determining a pressure center offset velocity set based on the preset time period and the motion trajectory, wherein the pressure center offset velocity includes a velocity component set perpendicular to the directions of the various parts of the preset edge, and determining the time required for the distance from the pressure center to the preset edge to equal the first critical value through the velocity component set and the position information of the pressure center. The method for determining the expected cargo drop time based on the second pressure distribution information and the first critical value can also be based on a large amount of work data and experience summary to establish a database between the pressure center offset velocity and the possibility of cargo drop, and using machine learning training to obtain an association model based on the database between the pressure center offset velocity and the possibility of cargo drop. The association model is inputted based on the remaining distance of the current pressure center from the first critical value and the offset velocity of the pressure center to estimate the expected cargo drop time. The embodiments of the present application do not particularly limit the specific process of determining the expected cargo drop time based on the second pressure distribution information and the first critical value.

[0091] Optionally, when it is determined based on the first pressure distribution information and the warning variable that the cargo on the load-bearing portion is in a falling state or is about to fall, controlling the customized omnidirectional electric transport vehicle to implement emergency measures further includes: If it is determined that the omnidirectional electric transport vehicle cannot reach the preset emergency treatment area for treatment, control the customized omnidirectional electric transport vehicle to implement at least one of the following emergency measures: sound an audible alarm; sounding a light alarm; Control the omnidirectional electric transport vehicle to slow down to a stop, and control the omnidirectional electric transport vehicle to be in a suspended driving task state; Outgoing motion information and first pressure distribution information.

[0092] It can be understood that the process of emergency measures taken under the condition that it is determined that the omnidirectional electric transport vehicle cannot go to the preset emergency treatment area for treatment does not involve going to the preset emergency treatment area, and therefore the same measures as those in the previously discussed embodiments are taken, including sound alarms, light alarms, and controlling the omnidirectional electric transport vehicle to slow down to a stop. The process of controlling the omnidirectional electric transport vehicle to be in a suspended driving task state can refer to the previously discussed embodiments; in particular, the emergency measures include externally sending the first pressure distribution information, which is more convenient for technical personnel to achieve remote monitoring, early warning or data analysis, and the specific process of external sending can also refer to the previously discussed embodiments.

[0093] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Corresponding to the customized omnidirectional electric transport vehicle control method described in the above embodiment, the embodiment of the present application further provides a customized omnidirectional electric transport vehicle control device, and each module of the device can implement each step of the customized omnidirectional electric transport vehicle control method. Figure 5 A structural schematic diagram of a customized omnidirectional electric transporter control device provided in one embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0095] Reference Figure 5 , the device comprises: An acquisition module is used to acquire target task information; wherein the target task information includes cargo category and driving task category; A first detection module is configured to detect initial pressure distribution information of the cargo on the load-bearing portion through a pressure detection device; a determination module for determining warning variables and critical motion conditions based on cargo category, driving mission category, and initial pressure distribution information; The second detection module is configured to detect first pressure distribution information of the cargo on the load-bearing portion through a pressure detection device when the motion information of the customized omnidirectional electric transporter meets a critical motion condition through a motion detection device; The execution module is used to control the customized omnidirectional electric transport vehicle to implement emergency measures when it is determined that the goods on the carrying part are in a falling state or a state to be dropped based on the first pressure distribution information and the warning variable.

[0096] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The functional modules in the embodiment can be integrated into one processing unit, or each module can exist physically alone, or two or more modules can be integrated into one unit. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. In addition, the specific names of the functional modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of this application. The specific working process of the modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0098] The embodiment of the present application further provides a customized omnidirectional electric transport vehicle, which includes a control device, Figure 6 This is a schematic diagram of the structure of the control device of the customized omnidirectional electric transport vehicle provided in one embodiment of the present application. Figure 6 As shown, the control device 40 of this embodiment includes: at least one processor 42 ( Figure 6 Only one is shown), at least one memory 41 ( Figure 6 Only one is shown in the figure) and a computer program 43 stored in the at least one memory 41 and executable on the at least one processor 42. When the processor 42 executes the computer program 43, the control device 40 implements the steps of any of the above-mentioned embodiments of the customized omnidirectional electric transporter control method, or implements the functions of the modules in the above-mentioned device embodiments.

[0099] For example, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 42 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 43 in the control device 40.

[0100] The processor 42 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0101] In some embodiments, the memory may be an internal storage unit of the control device, such as a hard disk or memory of the control device. In other embodiments, the memory may also be an external storage device of the control device, such as a plug-in hard disk equipped on the control device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory may include both the internal storage unit of the control device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0102] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0103] An embodiment of the present application provides a computer program product. When the computer program product is run on a control device, the control device is enabled to implement the steps of any of the above method embodiments.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the customized omnidirectional electric transporter, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk.

[0105] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0106] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A customized omnidirectional electric transport vehicle control method, characterized in that: Applied to a customized omnidirectional electric transport vehicle, the customized omnidirectional electric transport vehicle includes an electric transport vehicle body, a pressure detection device, and a motion detection device. The electric transport vehicle body includes a load-bearing portion for carrying goods, the pressure detection device is provided on the load-bearing portion, and the motion detection device is provided on the electric transport vehicle body. The method includes: Obtain target task information; wherein the target task information includes cargo category and driving task category; detecting initial pressure distribution information of the cargo on the load-bearing portion by the pressure detection device; determining a warning variable and a critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information; When the motion detection device detects that the motion information of the electric transport vehicle body meets the critical motion condition, the pressure detection device detects first pressure distribution information of the cargo on the carrying portion; When it is determined based on the first pressure distribution information and the warning variable that the cargo on the carrying portion is in a falling state or is about to fall, the customized omnidirectional electric transporter is controlled to implement emergency measures.

2. The control method of the customized omnidirectional electric transport vehicle according to claim 1, characterized in that: The motion information includes at least one of acceleration and vibration amplitude; the critical motion condition includes at least one of the critical acceleration and critical vibration amplitude corresponding to the motion information; the motion information of the customized omnidirectional electric transporter satisfies the critical motion condition if at least one of the acceleration and vibration amplitude of the motion information is greater than or equal to at least one of the critical acceleration and critical vibration amplitude corresponding to the critical motion condition.

3. The control method of the customized omnidirectional electric transport vehicle according to claim 2, characterized in that: The acceleration includes a vertical acceleration and a horizontal acceleration, wherein the vertical acceleration is a component of the acceleration in a direction parallel to the gravity direction of the electric transport vehicle body, and the horizontal acceleration is a component of the acceleration in a direction perpendicular to the vertical acceleration; the critical acceleration includes a critical vertical acceleration and a critical horizontal acceleration corresponding to the vertical acceleration and the horizontal acceleration; The motion information of the electric transport vehicle body meeting the critical motion condition means that the horizontal acceleration is greater than or equal to the critical horizontal acceleration, and / or the vertical acceleration is greater than or equal to the critical vertical acceleration.

4. The control method of the customized omnidirectional electric transport vehicle according to claim 1, wherein: The determining of the warning variable and the critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information includes: determining a corresponding initial load pressure total value based on the initial pressure distribution information; determining a critical total load pressure value of the warning variable based on the cargo category, the travel task category, and the initial total load pressure value; The determining that the cargo on the carrying portion is in a falling state based on the first pressure distribution information and the warning variable includes: determining a corresponding first load pressure total value based on the first pressure distribution information; When it is detected that the first load pressure total value is less than or equal to the critical load pressure total value, it is determined that the cargo on the carrying portion is in a falling state.

5. The control method of the customized omnidirectional electric transport vehicle according to claim 1, wherein: The determining of the warning variable and the critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information includes: determining a corresponding initial load pressure total value based on the initial pressure distribution information; determining a local critical pressure value of the warning variable based on the cargo category, the driving task category, and the initial load pressure total value; Determining that the cargo on the carrying portion is in a state of being about to fall based on the first pressure distribution information and the warning variable includes: determining a local pressure value of the cargo on the carrying portion at any local position of the carrying portion based on the first pressure distribution information; When the local pressure value is greater than or equal to the local critical pressure value, it is determined that the cargo is in a first type of ready-to-fall state.

6. The control method of the customized omnidirectional electric transport vehicle according to claim 1, wherein: The determining of the warning variable and the critical motion condition based on the cargo category, the driving task category, and the initial pressure distribution information includes: Determining a first critical value of the warning variable based on the cargo category and the driving task category; wherein the first critical value refers to a critical value of the distance from the pressure center corresponding to the cargo on the load-bearing portion to a preset edge; Determining that the cargo on the carrying portion is in a state of being about to fall based on the first pressure distribution information and the warning variable includes: determining a distance from a pressure center corresponding to the goods on the carrying portion to a preset edge based on the first pressure distribution information; When the distance from the pressure center corresponding to the goods on the carrying portion to the preset edge is less than or equal to the first critical value, it is determined that the goods on the carrying portion are in the second type of ready-to-fall state.

7. The control method of the customized omnidirectional electric transport vehicle according to claim 6, characterized in that: The determining of the warning variables and the critical motion conditions based on the cargo category, the driving task category, and the initial pressure distribution information further includes: Determining a second critical value of the warning variable and a pressure center offset critical value based on the cargo category and the driving task category, wherein the second critical value refers to a critical value of the distance from the pressure center corresponding to the cargo on the load-bearing portion to a preset edge of the load-bearing portion, and the second critical value is greater than the first critical value; The method further includes: determining that the cargo on the carrying portion is in a state of about to fall based on the first pressure distribution information and the warning variable; When it is detected that the distance from the pressure center corresponding to the goods on the carrying portion to the preset edge is greater than a first critical value and less than or equal to a second critical value, the pressure detection device continuously detects second pressure distribution information of the goods on the carrying portion within a preset time period; determining a pressure center offset value within the preset time period based on the second pressure distribution information; When it is detected that the pressure center offset value is greater than or equal to the pressure center offset critical value, it is determined that the goods on the carrying portion are in a third type of ready-to-fall state.

8. The control method of the customized omnidirectional electric transport vehicle according to claim 7, characterized in that: When it is determined based on the first pressure distribution information and the warning variable that the cargo on the carrying portion is in a falling state or is about to fall, controlling the customized omnidirectional electric transporter to implement emergency measures, including: When it is determined that the cargo on the carrying portion is in a third type of ready-to-drop state, determining an emergency driving speed based on the cargo category and the driving mission; determining whether the omnidirectional electric transport vehicle can proceed to a preset emergency treatment area for treatment based on the second pressure distribution information, the warning variable, the emergency driving speed, the motion information of the electric transport vehicle body, and the position information of the customized omnidirectional electric transport vehicle; When it is determined that the omnidirectional electric transport vehicle is able to go to the preset emergency treatment area for processing, the customized omnidirectional electric transport vehicle is controlled to implement special emergency measures, including: controlling the customized omnidirectional electric transport vehicle to decelerate to the emergency driving speed, controlling the customized omnidirectional electric transport vehicle to travel to the preset emergency treatment area at the emergency driving speed, and continuing to decelerate in the preset emergency treatment area until it stops.

9. The control method of the customized omnidirectional electric transport vehicle according to claim 8, characterized in that: Determining whether the omnidirectional electric transport vehicle can go to a preset emergency treatment area for treatment based on the second pressure distribution information, the warning variable, the emergency driving speed, the motion information of the electric transport vehicle body, and the position information of the customized omnidirectional electric transport vehicle includes: Determining an estimated emergency travel time based on the emergency travel speed, the motion information of the electric transport vehicle body, and the position information of the customized omnidirectional electric transport vehicle; wherein the motion information of the electric transport vehicle body includes the speed of the electric transport vehicle body; determining an estimated cargo drop time based on the second pressure distribution information and the first critical value; When the estimated cargo drop time is greater than the estimated emergency travel time, determining that the omnidirectional electric transport vehicle can go to a preset emergency processing area for processing; When the estimated cargo drop time is less than or equal to the estimated emergency travel time, it is determined that the omnidirectional electric transport vehicle cannot go to the preset emergency processing area for processing.

10. A customized omnidirectional electric transport vehicle, characterized in that: include: The electric transport vehicle body includes a carrying portion for carrying goods; a pressure detection device, provided on the load-bearing portion, for detecting the pressure distribution of the cargo on the load-bearing portion; A motion detection device, provided on the electric transport vehicle body, for detecting motion information of the electric transport vehicle body; A control device is electrically connected to the pressure detection device and the motion detection device, respectively. The control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of claims 1 to 9 is implemented.

Images