CN116415866B - Logistics transportation device, control method, system and medium - Google Patents
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Abstract
The embodiment of the specification provides a logistics transportation device, a control method, a system and a medium, wherein the method is used for controlling the logistics transportation device, and the logistics transportation device comprises the following steps: the system comprises a control system, a data monitoring device, a transportation component and a tray; the control system is in communication connection with the transport component, the data monitoring device and the tray; the data monitoring device is at least arranged at the tray and is used for acquiring monitoring data; the method is executed based on a control system and comprises the following steps: generating target information based on the monitoring data, the target information including at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction, and a communication status adjustment instruction; and transmitting the target information to at least one of the data monitoring device, the transport component, and the pallet.
Description
Technical Field
The present disclosure relates to the field of logistics transportation, and in particular, to a logistics transportation device, a control method, a system, and a medium.
Background
Logistics transportation is deeply popularized in various industries, and great convenience is brought to life of people. Currently, the increasing logistics demand year by year also brings great challenges for controlling the cargo loss of logistics transportation and ensuring the efficiency of logistics transportation.
Therefore, it is desirable to provide a logistics transportation device, a control method, a system and a medium, which can realize intelligent management of the logistics device, thereby reducing the goods loss and improving the logistics transportation efficiency.
Disclosure of Invention
One or more embodiments of the present specification provide a logistics transportation apparatus, the apparatus comprising: control system, data monitoring device, transportation part and tray. The control system is in communication connection with the transport component, the data monitoring device and the tray; the data monitoring device is at least arranged at the tray and is used for acquiring monitoring data; the control system is used for: generating target information based on the monitoring data, the target information including at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction, and a communication status adjustment instruction; and transmitting the target information to at least one of the data monitoring device, the transport component, and the pallet.
One or more embodiments of the present specification provide a control method of a logistics transportation apparatus, the method for controlling the logistics transportation apparatus, the logistics transportation apparatus comprising: the system comprises a control system, a data monitoring device, a transportation component and a tray; the control system is in communication connection with the transport component, the data monitoring device and the tray; the data monitoring device is at least arranged at the tray and is used for acquiring monitoring data; the method is executed based on a control system and comprises the following steps: generating target information based on the monitoring data, the target information including at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction, and a communication status adjustment instruction; and transmitting the target information to at least one of the data monitoring device, the transport component, and the pallet.
One or more embodiments of the present specification provide a system based on a logistics transportation apparatus, the system comprising a processor for performing the method of controlling a logistics transportation apparatus as described in any of the above embodiments.
One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer, perform the method of controlling a logistics transportation apparatus as set forth in any one of the embodiments above.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is an exemplary schematic illustration of a logistics transportation apparatus, as shown in accordance with some embodiments of the present specification;
FIG. 2 is an exemplary schematic diagram of a control system generating and transmitting target information according to some embodiments of the present description;
FIG. 3 is an exemplary flow chart for generating target information based on monitoring data according to some embodiments of the present description;
FIG. 4 is an exemplary schematic diagram of a risk prediction model shown in accordance with some embodiments of the present description;
fig. 5 is an exemplary schematic diagram of a shipping target tray to a smart charging device according to some embodiments of the present disclosure.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, units, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is an exemplary schematic diagram of a logistics transportation apparatus, according to some embodiments of the present disclosure.
As shown in fig. 1, the logistics transportation apparatus 100 may include a control system 110, a data monitoring apparatus 120, a transportation component 130, and a tray 140.
The control system 110 may be used to analyze and process related data/information and to receive/send related instructions to effect control of the various device components in the logistics transport apparatus 100. For example, the control system 110 may generate target information based on the monitoring data and transmit the target information to at least one of the data monitoring device, the transport component, and the pallet. For more on generating and transmitting target information see fig. 2 and its related description. In some embodiments, the control system 110 may be communicatively coupled to the data monitoring device 120, the transport component 130, and the tray 140. The control system may be implemented based on various hardware and/or software approaches.
The data monitoring device 120 may be used to collect acquisition monitoring data. The data monitoring device may be provided at each device component of the logistics transportation apparatus to obtain monitoring data of the corresponding device component. In some embodiments, the data monitoring device may be disposed at least at the tray.
In some embodiments, the data monitoring device 120 may include at least one of a positioning unit 120-1, a motion monitoring unit 120-2, and a pressure detection unit 120-3 to correspond to the collected position information, motion information, and pressure. In some embodiments, the data monitoring device 120 may further include at least one of a communication status monitoring unit 120-4, an obstacle monitoring unit 120-5, and a power monitoring unit 120-6 to correspondingly acquire a communication status, an obstacle condition, and a current power.
The transport member 130 refers to a device for transporting the tray. For example, the transport component may be formed from one or more of a fork lift truck, a robotic arm, a conveyor belt, and the like.
The tray 140 refers to a device for loading goods. The tray may include various forms.
It should be noted that the above description of the logistics transportation apparatus is for convenience of description only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, it is possible to combine various device components arbitrarily or to construct a subsystem in connection with other device components without departing from such principles.
FIG. 2 is an exemplary schematic diagram of a control system generating and transmitting target information according to some embodiments of the present description.
As shown in fig. 2, the control system 110 may generate target information 220 based on the monitoring data 210 and transmit the target information 220 to at least one of the data monitoring device 120, the transport unit 130, and the tray 140.
The monitoring data may refer to data related to the operation of various device components (e.g., trays, transport components, etc.) of the logistics transport apparatus. For example, positional information, movement information (e.g., velocity, acceleration, etc.), received pressure, etc. of each device component.
In some embodiments, the monitoring data for each device component may be obtained by a data monitoring device disposed at the corresponding component. The position information, the motion information and the pressure information can be obtained through a positioning unit, a motion monitoring unit and a pressure detecting unit of the data monitoring device respectively.
In some embodiments, the data monitoring device may enable collection of monitoring data based on the monitoring parameters. The monitoring parameters are related working parameters of the data monitoring device. The monitoring parameters may include monitoring frequency (e.g., 1 minute 1 time), monitoring duration, etc. In some embodiments, the control system may set initial values of the monitoring parameters based on historical empirical data, system defaults, and the like.
In some embodiments, the monitoring data may include a communication status of the tray. The communication state of the tray refers to the communication connection state of the tray and the control system. If the tray is in communication with the control system, the tray is in a first communication state. If the tray is not in communication with the control system, the tray is in a second communication state. In some embodiments, the communication status may be obtained by a communication status monitoring unit of the data monitoring device.
In some embodiments, the monitoring data may include an obstacle condition. When the logistics transportation apparatus is carrying goods, an obstacle may occur in the vicinity of the logistics transportation apparatus or in the moving path. The obstacle situation may include a multidirectional obstacle existence situation, a distance of the logistics transportation apparatus from the obstacle, and the like. In some embodiments, the obstacle condition may be obtained by an obstacle monitoring unit of the data monitoring device.
In some embodiments, the monitoring data may include a current charge of the tray. The current electric quantity of the tray can be obtained through the electric quantity monitoring unit of the data monitoring device.
The target information may refer to related information for maintaining the normal operation of the logistics transportation apparatus. The target information may be a reminder information and/or an adjustment instruction, etc. For more description of reminder information, see fig. 3 and its associated description.
In some embodiments, the target information may include at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction, and a communication status adjustment instruction. The monitoring parameter adjustment instructions may be used to adjust the monitoring frequency of the data monitoring device. The transport parameter adjustment instructions may be used to adjust an operating parameter of the transport component. The communication state adjustment instruction may be used to switch the first communication state and the second communication state of the tray. For more explanation of how to determine the monitoring parameter adjustment instructions, the communication status adjustment instructions, see fig. 3 and the related description thereof. For more explanation of how to determine the transportation parameter adjustment instructions, see fig. 5 and its associated description.
In some embodiments, the control system may implement generating the target information based on the monitoring data based on a variety of ways. For example, the target information may be determined based on a look-up table including a comparison of the monitored data and the target information.
In some embodiments, the control system may generate the target information based on the unexpected risk probability. For a specific description, reference may be made to fig. 3 and the associated description.
In some embodiments, the control system may determine a transport parameter adjustment instruction based on the target tray to be charged and send to the transport component to control the transport component to transport the target tray to the smart charging device. For a specific description, reference may be made to fig. 5 and the associated description.
In some embodiments, the control system may send the target information to the corresponding device component based on the target information and a preset correspondence between different target information and different device components of the logistics transportation apparatus, for example, when the target information is a monitoring parameter adjustment instruction, the monitoring parameter adjustment instruction may be sent to the data monitoring apparatus. After receiving the target information, the device component corresponding to the logistics transportation device can adjust or remind the user based on the target information.
According to the method, the reminding information or the adjusting instruction of each device part is determined by acquiring the monitoring data of each device part of the logistics transportation device, so that unitization and intelligent management of the logistics transportation device are realized, the cargo loss is reduced, and the logistics transportation efficiency is improved.
FIG. 3 is an exemplary flow chart for generating target information based on monitoring data according to some embodiments of the present description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a control system.
Step 310, determining an operation scene of the first tray based on the first monitoring data of the first tray.
The first tray may refer to a tray in a first communication state. For further description of the first communication state, see fig. 2 and its associated description.
The first monitoring data refers to monitoring data of the tray in the first communication state, for example, pressure, speed, acceleration, and the like, to which the tray is subjected. For more description of monitoring data, see fig. 2 and its associated description.
The operation scene may refer to the usage of the tray in operation. Such as, for example, cargo handling, cargo container stacking, etc.
In some embodiments, the control system may determine the operational scenario of the first tray based on the first monitoring data of the first tray in a variety of ways. For example, the control system may set preset thresholds corresponding to the pressure, speed, and acceleration to which the tray is subjected in advance based on historical empirical data, system default values, and the like. When the pressure applied to the display tray in the first monitoring data exceeds the corresponding preset threshold value, but the speed and the acceleration do not exceed the corresponding preset threshold value, the control system can determine that the operation scene is cargo container stacking. When the pressure, speed and acceleration of the tray exceed the corresponding preset thresholds, the control system can determine that the operation scene is cargo handling.
In some embodiments, the control system may determine an operational scenario of the tray in the first communication state based on the change profile of the first monitored data.
The change curve may characterize a change in the value of the first monitored data over time. The control system may generate a variation curve based on the values of the first monitoring data acquired at several points in time.
The control system can determine the duration that the first monitoring data exceeds the preset threshold according to the change curve, so as to judge the operation scene. Specifically, when the change curve shows that the duration that the pressure exerted on the tray exceeds the corresponding preset threshold value reaches the preset duration, but the duration that the speed and the acceleration exceed the corresponding preset threshold value does not reach the preset duration, the control system can determine that the operation scene is cargo container stacking. When the duration that the pressure, the speed and the acceleration of the tray exceed the corresponding preset thresholds reaches the preset duration, the control system can determine that the operation scene is cargo carrying. The preset time period may be set based on historical experience data, system defaults, and the like.
It will be appreciated that, as the idle pallets are being collated, moved, there may be situations where the speed and/or acceleration briefly exceeds the corresponding preset threshold, and situations where the pressure experienced while the pallets are being stacked may also be where the preset threshold is being briefly exceeded. Therefore, the control system needs to determine that the duration of the first monitoring data exceeding the preset threshold reaches the preset duration, so as to avoid misjudgment on the operation scene.
In some embodiments, the control system may switch the communication state of the second tray.
Specifically, the control system may obtain second monitoring data for the second tray. The second tray refers to a tray in a second communication state. The second monitoring data refers to monitoring data of the second tray.
Based on the second monitoring data, the control system may determine whether the second tray satisfies an activation condition. The activation condition refers to a condition that the tray is switched from the second communication state to the first communication state. For example, the activation condition may be that the second monitoring data indicates that the pressure to which the second tray is subjected exceeds a set threshold, that the second monitoring data indicates that the acceleration of the second tray exceeds a set threshold, and so on. In some embodiments, the control system may determine whether the second tray satisfies the activation condition by comparing whether the second monitoring data is within a value or range of values corresponding to the activation condition.
In response to the activation condition being met, the control system generates a communication state adjustment instruction and sends the communication state adjustment instruction to the second tray, thereby causing the second tray to switch to the first communication state. Accordingly, the second tray may be switched to the first tray.
It can be understood that the second tray which does not enter the first communication state can close the communication function, so that the electricity consumption is reduced, and energy conservation and environmental protection are realized. When the second tray is determined to be used or other conditions requiring information exchange with the control system, the communication state of the second tray can be switched to be connected with the control system, so that the control system can monitor the related information of the tray in time, and intelligent control of the tray is realized.
Step 320, determining an unexpected risk probability of the first tray based on the first monitoring data and the operational scenario.
Unexpected risks may refer to conditions that may occur that affect logistics transportation, e.g., overload risks, collision risks, etc. The unexpected risk probability may refer to a probability of occurrence of an unexpected risk.
In some embodiments, the control system may enable determining the unexpected risk probability of the first tray based on the first monitoring data and the operational scenario in a variety of ways. For example, the control system may generate a first mapping relation table of the first reference data and the corresponding historical unexpected risk probability in advance based on the historical first monitoring data and the corresponding historical operation scene of the first tray at the historical time as the first reference data. The control system can use the corresponding historical unexpected risk probability as the current unexpected risk probability by inquiring first reference data which is the same as or similar to the current first monitoring data and the running scene in the first mapping relation table.
In some embodiments, the control system may determine the unexpected risk probability based on the first monitoring data and the scenario-related data. For a specific description, reference may be made to fig. 4 and the associated description.
In step 330, target information is generated based on the unexpected risk probability.
In some embodiments, the control system may generate the alert information based on an unexpected risk that the unexpected risk probability exceeds a preset threshold. The preset threshold may be set based on an empirical value or a system default value.
In some embodiments, the alert information may include an unexpected risk type and a corresponding alert level. Wherein the alert level may be related to an unexpected risk probability. For example, the control system may determine preset comparison tables of different numerical ranges of the unexpected risk probabilities and different alert levels in advance, and determine the corresponding alert level by looking up a table based on the numerical range in which the unexpected risk probability corresponding to the current unexpected risk is located.
In some embodiments, the control system may issue a reminder message to the logistics transportation apparatus and/or the target terminal in one or more combinations including, but not limited to, data instructions, alarms, text pushes, images, voice, etc., to prompt the relevant personnel to exclude the unintended risk.
In some embodiments, the control system may also generate monitoring parameter adjustment instructions. For more description of monitoring parameter adjustment instructions, see fig. 2 and its associated description.
In some embodiments, the monitoring parameter adjustment instructions may include an adjustment amplitude of the monitoring frequency of the target unit of the data monitoring device. The target unit refers to a unit (such as an obstacle monitoring unit) of the data monitoring device, which needs to adjust the monitoring parameters, and can be determined based on the current unexpected risk type and the preset corresponding relation between different unexpected risk types and different target units. For more explanation of the monitoring frequency, see fig. 2 and its associated description.
In some embodiments, the control system may determine the magnitude of adjustment of the monitoring frequency based on the type of risk that is not expected. For example, the control system may set the adjustment amplitude of the corresponding monitoring frequency based on the unexpected risk type. For example, when the unexpected risk type is collision risk or cargo slip risk, the monitoring frequency may be increased, and the current adjustment amplitude is the adjustment amplitude set for the corresponding type. For another example, the type of unexpected risk is a route deviation risk and the target unit is a positioning unit. If the positioning unit is not started before, the monitoring parameter adjusting instruction can also comprise the on-off state of the adjusting target unit besides the adjusting amplitude set by the corresponding type, and the positioning unit can be switched from the off state to the on state.
In some embodiments, the control system may also determine an adjustment magnitude of the monitoring frequency based on the alert level and the type of unexpected risk. For example, the control system may previously establish a second mapping relationship table of the second reference data and the history adjustment amplitude based on the history unexpected risk type and the history reminding level of the first tray at the history time as the second reference data. The control system can use the corresponding historical adjustment amplitude as the current adjustment amplitude by inquiring second reference data which is the same as or similar to the current unexpected risk type and the reminding level in the second mapping relation table.
According to the method disclosed by some embodiments of the specification, the adjustment amplitude of the monitoring frequency is determined based on the unexpected risk type and the reminding level, so that the collection of the monitoring data can be adapted to different unexpected risk types, and the error of the tray monitoring is reduced.
According to the method, the unexpected risk type with high occurrence probability can be determined, and the reminding information is generated to prompt the system or related staff in time so as to take corresponding protective measures subsequently, thereby avoiding the loss of goods and the failure of the transportation device.
According to the method, corresponding target information is determined by combining the use scenes of the first tray in communication connection with the control system, and the method can be suitable for various use scenes of the tray, so that accurate intelligent management of the tray is achieved.
In some embodiments, in response to the operational scenario being a cargo handling, the unexpected risk includes at least one of a collision risk and a route deviation risk, the control system may obtain scenario-related data for the operational scenario based on the operational scenario; and determining an unexpected risk probability based on the first monitoring data and the scene correlation data.
The risk of collision refers to the situation in which the pallet and/or the transport element may collide with other objects when in operation.
In some embodiments, the collision risk comprises a probability of collision risk of the tray in a plurality of directions. The collision risk probability for each direction may be determined based on the obstacle condition for the corresponding direction in the first monitoring data. For more explanation about the obstacle situation, see fig. 2 and its associated description.
Specifically, the control system obtains the collision risk probability of each direction through a plurality of evaluation sublayers for evaluating the collision risk based on the obstacle condition of each direction. In some embodiments, each of several assessment sublayers for assessing collision risk may correspond to a direction. Each evaluation sub-layer can process the obstacle situation in the corresponding direction and determine the collision risk probability in the corresponding direction. For more description of the evaluation sub-layer, see fig. 4 and its associated description.
According to the method disclosed by the embodiments of the specification, the collision risk situation of the tray can be more comprehensively and accurately determined by determining the multi-directional collision risk probability, so that the corresponding target information can be further and accurately determined later.
The off-course risk refers to the off-course condition that may exist for the pallet and/or transport component in the course of reaching the designated location.
The scene association data refers to data related to a current operation scene of the first tray. In some embodiments, the scenario-related data may include pallet type, cargo type, environmental data, historical data, and the like.
The type of tray may be determined according to the number of the tray. Each tray may have a unique code that includes a tray type code and a tray serial number. Different tray type codes may be preset to represent different tray types. The code may be provided on the tray in the form of a readable information carrier (e.g. a two-dimensional code, a bar code, etc.).
The type of goods may be determined based on the positioning information of the pallet and the query database information. The positioning information may include the warehouse where the pallet is located and the specific area of the warehouse. The control system may determine the current cargo type of the pallet based on the positioning information by querying a database comprising the warehouse and the cargo types corresponding to each region of the warehouse.
The environment data may reflect the current environment of the tray. Such as the width of the aisle of the warehouse, the goods placement uniformity, etc. The control system can acquire the current position of the tray through the positioning unit, and determine the channel width corresponding to the current position through inquiring the database. The control system can also acquire cargo images through an image acquisition device (such as a camera), and acquire cargo placement uniformity through image recognition.
The historical data may include historical monitoring data corresponding to a tray in the historical record having the same tray type, cargo type, and environmental data as the current first tray.
In some embodiments, the control system may employ various data analysis algorithms to analyze the first monitored data and the scene correlation data to determine the probability of unexpected risk.
For example, the control system may determine corresponding historical data vectors in advance based on the historical first monitoring data of the plurality of trays, the historical scene association data, and determine a correspondence between the historical data vectors and the unexpected risk probability based on the actual unexpected risk probability corresponding to each of the historical data vectors. The control system may determine a corresponding data vector to be measured based on the first monitoring data of the current first tray and the scene association data. Further, the control system may determine a target historical data vector having a smallest vector distance from the data vector to be measured among the plurality of historical data vectors based on a vector distance (e.g., euclidean distance) of the data vector to be measured from the historical data vector. The control system can take the unexpected risk probability corresponding to the target historical data vector as the unexpected risk probability according to the corresponding relation.
In some embodiments, the control system may determine the unexpected risk probability by a risk prediction model based on the first monitoring data and the scenario-related data.
The risk prediction model may be used to analyze the first monitored data and the scenario-related data to determine an unexpected risk probability. In some embodiments, the risk prediction model may be a machine learning model of the custom structure below. The risk prediction model may also be a machine learning model of other structures, such as a neural network model or the like. The inputs of the risk prediction model may be the first monitoring data and the scene correlation data, and the outputs may be various types of unexpected risk probabilities.
FIG. 4 is an exemplary schematic diagram of a risk prediction model shown in accordance with some embodiments of the present description. As shown in fig. 4, risk prediction model 420 may include a feature processing layer 421 and an evaluation layer 422.
The feature processing layer may process the first monitored data to determine an evaluation feature. The evaluation feature is a feature obtained after feature extraction of the first monitoring data. In some embodiments, the first monitoring data may be represented in a sequence, with different elements of the sequence reflecting different parameters of the first monitoring data (e.g., location information, motion information, pressure information, etc.). In some embodiments, the feature processing embedding layer may be a convolutional neural network. As shown in fig. 4, the input of the feature processing layer 421 may be the first monitoring data 410 and the output may be the evaluation feature 420.
The evaluation layer may process the evaluation features and the scene correlation data to determine an unexpected risk probability. In some embodiments, the evaluation layer may be a decision tree, a support vector machine, or the like, or any combination thereof. As shown in FIG. 4, the evaluation layer may include a plurality of evaluation sublayers (422-1, 422-2 … … 422-n). In some embodiments, each evaluation sub-layer may correspond to an unexpected risk type. In some embodiments, one or several of the plurality of evaluation sublayers may be used to evaluate collision risk in different directions. Each evaluation sub-layer can process all evaluation features and scene association data to determine an unexpected risk probability of a corresponding type. As shown in fig. 4, the inputs to the evaluation layer 422 may be evaluation features 430 and scene association data 440, and the outputs may be unexpected risk probabilities 450.
In some embodiments, the feature processing layer and the evaluation layer may be derived by joint training. The control system may jointly train the initial risk prediction model based on the sets of labeled first training samples. Each set of first training samples may include sample first monitoring data of a sample tray, sample scene association data. The label of each set of first training samples may be an actual occurrence of an unexpected risk corresponding to the set of first training samples, for example, the label may include all unexpected risk types that have occurred historically, where the unexpected risk that has occurred actually may be labeled 1 and the unexpected risk that has not occurred actually may be 0. It should be appreciated that the sample tray may be of the same tray type as the current tray to be tested.
In joint training, the control system may input sample first monitoring data in a first training sample into the initial feature processing layer. The output of the initial feature processing layer and sample scene correlation data in the first training sample are then input to each of a plurality of initial evaluation sublayers, and a loss function is constructed based on the outputs and labels of the plurality of initial evaluation sublayers. And iteratively updating parameters of each layer in the initial risk prediction model based on the loss function so that the loss function of the model meets preset conditions to obtain a trained risk prediction model. The preset conditions may include, but are not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold.
In some embodiments, the input to the evaluation layer may include an amount of power for the target time in addition to the previously entered evaluation features and scene correlation data. For more explanation about the amount of power at the target time, see fig. 5 and its associated description.
As shown in fig. 4, the evaluation layer 422 may process the evaluation feature 430, the scene correlation data 440, and the power 460 for the target time, outputting the unexpected risk probability 450. Accordingly, each set of first training samples may further include an amount of power of the sample tray at the second sample time. It will be appreciated that the sample first monitoring data, sample scene association data, of each set of first training samples is then the corresponding data at a first sample time earlier than a second sample time.
It can be appreciated that if the electrical quantity of the tray at the future time is insufficient, the monitoring data may be inaccurate, and thus the prediction accuracy of the unexpected risk probability is affected, so that the electrical quantity at the target time may be introduced as an analysis basis.
According to the method disclosed by the embodiments of the specification, the first monitoring data and the scene association data are comprehensively analyzed through the model, the electric quantity of the target time can be introduced, and the future electric quantity condition of the tray is further combined, so that the unexpected risk probability is obtained more accurately and rapidly.
According to the method, the first monitoring data and the scene association data are analyzed, so that the accurate unexpected risk probability is determined, more accurate target information is determined later, and unexpected risks are prevented.
Fig. 5 is an exemplary schematic diagram of a shipping target tray to a smart charging device according to some embodiments of the present disclosure.
As shown in fig. 5, the control system may determine a target tray 510 that needs to be charged based on the current power of the tray; and based on the target tray 510, a transportation parameter adjustment instruction is determined and sent to the transportation section 130 to control the transportation section 130 to transport the target tray to the intelligent charging apparatus 520.
The target tray refers to a tray needing to be charged, such as a tray with lower current electric quantity and a tray with faster power consumption.
In some embodiments, the control system may determine the target tray in a variety of ways. For example, the control system may directly take a tray with a power below a power threshold as the target tray. The power threshold is the lowest power for maintaining the normal operation of the tray, and can be set based on historical experience values and default values of the system.
In some embodiments, the control system may predict the amount of power of the tray at the target time based on the current amount of power of the tray and the monitored parameters; and determining a target tray based on the current electric quantity and the electric quantity of the target time.
In some embodiments, the tray may transmit the current power to the control system based on a set point in time or interval while in the first communication state.
For more explanation of the monitored parameters, see fig. 2 and its associated description.
The target time may refer to a future time, such as a future day, at which the tray power needs to be predicted.
In some embodiments, the target time may be set based on historical empirical values, system defaults, or actual demand.
In some embodiments, the target time may be related to a fluctuation of a current power of the tray or a change curve of the first monitoring data in response to the tray being the first tray. For further description of the first tray, see fig. 3 and its associated description. For example, the higher the current charge of the tray, the more in the future the charge will not run out, and the further from the current time the target time may be. For another example, the larger the fluctuation of the variation curve of the first monitoring data is, the more complex the working condition of the tray may be, the data monitoring device is required to monitor frequently and communicate with the control system, the faster the electricity consumption speed of the tray may be, and at this time, the closer the target time may be to the current time.
According to the method disclosed by some embodiments of the specification, the distance between the target time and the current time is determined by estimating the electricity consumption condition of the tray, so that the more suitable electricity of the target time can be determined, and the more suitable target tray is determined.
In some embodiments, the control system may implement predicting the power of the tray at the target time based on the current power of the tray and the monitored parameters in a variety of ways. For example, the control system may generate, in advance, a correspondence between a first historical power amount and a historical monitoring parameter of the current tray at a plurality of first historical times and a second historical power amount of the corresponding plurality of second historical times, where the first historical time may be earlier than the second historical time. Accordingly, the control system can determine the first historical electric quantity which is the same as or similar to the current electric quantity and the monitoring parameter and the second historical electric quantity corresponding to the historical monitoring parameter as the electric quantity of the target time based on the current electric quantity, the monitoring parameter and the corresponding relation of the tray to be detected.
In some embodiments, the control system may predict the power at the target time through a power prediction model.
In some embodiments, the power prediction model is a machine learning model. The power prediction model may be a combination of one or more of a convolutional neural network model, a deep neural network model, and the like. The electric quantity prediction model can be used for analyzing the current electric quantity, the monitoring frequency and the monitoring duration in the monitoring parameters, the communication condition and the target time and predicting the electric quantity of the target time. For more description of the monitoring frequency and the monitoring duration, see fig. 2 and its associated description. The communication condition can reflect the communication condition (such as communication frequency and exchange data volume) from the last full charge of the tray to the current moment, and can be directly obtained from the system record.
In some embodiments, parameters of the power prediction model may be derived by training. The control system may train the initial charge prediction model based on sets of labeled second training samples, each set of which may include a sample charge, a sample monitoring frequency, a sample monitoring duration, a sample communication condition, and a second sample time of the sample tray at a first sample time. The label of each set of second training samples may be the actual amount of power of the sample tray at the second sample time. It should be appreciated that the sample tray may be of the same tray type as the current tray to be tested. The first sample time should be earlier than the second sample time.
According to the method disclosed by some embodiments of the specification, the electric quantity of the target time is predicted through a model, and factors (such as monitoring parameters, communication conditions and the like) affecting the electric quantity consumption of the tray are comprehensively analyzed, so that the electric quantity of the target time is rapidly and accurately predicted.
According to the method for predicting the electric quantity of the target time, the target tray to be charged is accurately predicted by predicting the electric quantity of the target time, so that the tray with the electric quantity possibly consumed is charged in time, and normal operation of the tray is ensured.
In some embodiments, the control system may determine the target tray in a variety of ways based on the current power and the power at the target time. For example, the control system may directly use a tray, where the difference between the current power and the power at the target time is greater than a preset threshold, as the target tray. For another example, the control system may directly take a tray with an electric quantity of the target time lower than the electric quantity threshold value as the target tray.
The intelligent charging device can be connected with the remote end of the control system and used for charging the tray. For example, the smart charging device may be a smart charging rack. In some embodiments, the smart charging device may have multiple charging bits. The control system may determine a charging bit that the smart charging device is currently idle. The intelligent charging device can update the currently idle charging bit information based on the set time point or time interval and send the information to the control system.
In some embodiments, the control system may determine an idle charge level closest to the current location based on the current location of the target tray and the current idle charge level. The control system can generate a corresponding transportation parameter adjustment instruction based on the current position of the target tray, the number of the target tray and the nearest idle charging level and send the corresponding transportation parameter adjustment instruction to the transportation component, and the transportation component can firstly move to the current position of the target tray to pick up the target tray based on the transportation parameter adjustment instruction and then transport the target tray to the nearest idle charging level.
According to the method, the target tray to be charged is determined, so that the target tray is charged timely, monitoring errors caused by reduction of electric quantity of the tray or electric quantity exhaustion are avoided, meanwhile, low transportation efficiency is avoided, and intelligent management of the tray and transportation components is further achieved.
One or more embodiments of the present disclosure provide a system based on a logistics transportation apparatus, including a processor for executing any one of the control methods of the logistics transportation apparatus as provided in the embodiments of the present disclosure.
The embodiment of the present disclosure also provides a computer readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes a control method of any one of the logistics transportation apparatuses provided in the embodiment of the present disclosure.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (6)
1. A logistic transportation device, comprising: the system comprises a control system, a data monitoring device, a transportation component and a tray;
The control system is in communication connection with the transport component, the data monitoring device and the pallet;
the data monitoring device is at least arranged at the tray and is used for acquiring monitoring data;
the control system is used for:
generating target information based on the monitoring data, wherein the target information comprises at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction and a communication state adjustment instruction; and
transmitting the target information to at least one of the data monitoring device, the transport component, and the pallet;
the data monitoring device comprises a communication state monitoring unit, wherein the communication state monitoring unit is used for acquiring the communication state of the tray, and the communication state comprises a first communication state and a second communication state;
the monitoring data includes the communication status;
the control system is further configured to:
determining an operation scene of a first tray based on first monitoring data of the first tray, wherein the first tray is in the first communication state, and the first monitoring data is monitoring data of the tray in the first communication state;
determining an unexpected risk probability of the first tray based on the first monitoring data and the operational scenario; and
Generating the target information based on the unexpected risk probability, and sending the target information to the logistics transportation device;
the data monitoring device further comprises an obstacle monitoring unit;
the monitoring data comprise obstacle conditions, wherein the obstacle conditions comprise multidirectional obstacle existence conditions and distances between the logistics transportation device and the obstacle;
in response to the operational scenario being a cargo handling, the unexpected risk includes at least one of a collision risk and a route deviation risk, the control system is further to:
acquiring scene association data of the first tray based on the operation scene; and
determining the unexpected risk probability based on the first monitoring data and the scenario-related data;
the determining the unexpected risk probability based on the first monitoring data and the scenario-related data includes: determining the unexpected risk based on a risk prediction model; the risk prediction model comprises a feature processing layer and an evaluation layer; the characteristic processing layer is used for processing the first monitoring data sequence and determining evaluation characteristics; the evaluation layer comprises a plurality of evaluation sublayers, each of the plurality of evaluation sublayers corresponding to an unexpected risk probability; each of the plurality of evaluation sublayers is used for processing the evaluation characteristics, the scene association data and the electric quantity at a future time and determining the unexpected risk probability of a corresponding type;
The collision risk among the unexpected risks includes collision risks of a plurality of directions; the plurality of evaluation sublayers comprise a plurality of evaluation sublayers for evaluating the collision risk, and each evaluation sublayer for evaluating the collision risk corresponds to the collision risk in one direction.
2. The logistics transportation apparatus of claim 1, wherein,
the data monitoring device also comprises an electric quantity monitoring unit;
the monitoring data comprises the current electric quantity of the tray;
the logistics transportation device is also in communication connection with the intelligent charging device;
the control system is further configured to:
determining a target tray to be charged based on the current electric quantity; and
and determining a transportation parameter adjustment instruction based on the target tray and sending the transportation parameter adjustment instruction to the transportation component so as to control the transportation component to transport the target tray to the intelligent charging device.
3. A method of controlling a logistics transportation apparatus, the method being for controlling a logistics transportation apparatus, the logistics transportation apparatus comprising: the system comprises a control system, a data monitoring device, a transportation component and a tray; the control system is in communication connection with the transport component, the data monitoring device and the pallet; the data monitoring device is at least arranged at the tray and is used for acquiring monitoring data;
The method is executed based on the control system and comprises the following steps:
generating target information based on the monitoring data, wherein the target information comprises at least one of a monitoring parameter adjustment instruction, a transportation parameter adjustment instruction and a communication state adjustment instruction; and
transmitting the target information to at least one of the data monitoring device, the transport component, and the pallet;
the data monitoring device comprises a communication state monitoring unit, wherein the communication state monitoring unit is used for acquiring the communication state of the tray, and the communication state comprises a first communication state and a second communication state; the monitoring data further includes the communication status;
the generating target information based on the monitoring data comprises:
determining an operation scene of a first tray based on first monitoring data of the first tray, wherein the first tray is in the first communication state;
determining an unexpected risk probability of the first tray based on the first monitoring data and the operational scenario; and
generating the target information based on the unexpected risk probability;
the data monitoring device further comprises an obstacle monitoring unit; the monitoring data also includes obstacle conditions;
In response to the operational scenario being a cargo handling, the unexpected risk includes at least one of a collision risk and a lane departure risk, the determining, based on the first monitored data and the operational scenario, an unexpected risk probability for the first tray includes:
acquiring scene association data of the first tray based on the operation scene; and determining the unexpected risk probability based on the first monitoring data and the scenario-related data;
the determining the unexpected risk probability based on the first monitoring data and the scenario-related data includes: determining the unexpected risk based on a risk prediction model; the risk prediction model comprises a feature processing layer and an evaluation layer; the characteristic processing layer is used for processing the first monitoring data sequence and determining evaluation characteristics; the evaluation layer comprises a plurality of evaluation sublayers, each of the plurality of evaluation sublayers corresponding to an unexpected risk probability; each of the plurality of evaluation sublayers is used for processing the evaluation characteristics, the scene association data and the electric quantity at a future time and determining the unexpected risk probability of a corresponding type;
The collision risk among the unexpected risks includes collision risks of a plurality of directions; the plurality of evaluation sublayers comprise a plurality of evaluation sublayers for evaluating the collision risk, and each evaluation sublayer for evaluating the collision risk corresponds to the collision risk in one direction.
4. The method of claim 3, wherein the step of,
the data monitoring device also comprises an electric quantity monitoring unit;
the monitoring data also comprises the current electric quantity;
the logistics transportation device is also in communication connection with the intelligent charging device;
the method further comprises the steps of:
determining a target tray to be charged based on the current electric quantity; and
and determining a transportation parameter adjustment instruction based on the target tray and sending the transportation parameter adjustment instruction to the transportation component so as to control the transportation component to transport the target tray to the intelligent charging device.
5. A system based on a logistics transportation apparatus, characterized in that the system comprises a processor for executing the control method of the logistics transportation apparatus of any one of claims 3-4.
6. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the control method of the logistics transportation apparatus of any one of claims 3 to 4.
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| CN117707077B (en) * | 2023-12-15 | 2024-08-16 | 苏州奥特兰恩自动化设备有限公司 | AGV (automatic guided vehicle) maneuvering scheduling system, method, device and medium |
| CN118534901B (en) * | 2024-05-14 | 2025-02-21 | 果栗智造(上海)技术股份有限公司 | Control method and control device for moving object, transportation system and electronic equipment |
| CN119330098A (en) * | 2024-12-16 | 2025-01-21 | 中国电子科技集团公司第三十研究所 | A flexible manufacturing unit pull-type logistics control method and system |
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