Online quality evaluation method, device and equipment for unmanned logistics vehicle, and medium

Abstract

The invention discloses an online quality evaluation method, device and equipment for an unmanned logistics vehicle and a medium. The method comprises the following steps: determining at least two pieces of resistance data of a logistics vehicle in a driving state; based on the at least two pieces of resistance data, theoretical acceleration parameter information of the logistics vehicle is determined; when the first flag bit triggering condition is detected, the actual acceleration of the logistics vehicle is obtained, the target mass of the logistics vehicle within the preset time period is determined based on the actual acceleration and the at least two pieces of resistance data, and the target mass serves as the evaluation mass; according to the embodiment of the invention, the theoretical acceleration parameter information of the logistics vehicle is determined through the at least two pieces of resistance data, the actual acceleration of the logistics vehicle is obtained when the first flag bit triggering condition is detected, and the evaluation quality of the logistics vehicle within the preset time period is determined based on the actual acceleration and the at least two pieces of resistance data. The problem of large fluctuation of mass calculation can be solved, and the accuracy of vehicle mass calculation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quality assessment of unmanned logistics vehicles, and in particular to an online quality assessment method, device, equipment and medium for unmanned logistics vehicles. Background Art

[0002] One of the main characteristics of unmanned logistics vehicles is that their mass will change with the weight of the carried materials. For unmanned vehicles, changes in mass have a certain impact on the control and performance of the vehicle. Therefore, obtaining mass parameters is very meaningful for vehicle control optimization.

[0003] Mass sensors are usually affected by the installation position or installation structure, and usually cannot accurately sense changes in mass. They are difficult to install and the cost is high. In addition, since acceleration is usually highly volatile or has a lag, this will greatly interfere with mass estimation, resulting in volatility or large deviations in mass estimation values. Summary of the invention

[0004] In view of this, the present invention provides an online quality assessment method, device, equipment and medium for unmanned logistics vehicles, which can solve the problem of large volatility in quality calculation and improve the accuracy of vehicle quality calculation.

[0005] According to one aspect of the present invention, an embodiment of the present invention provides an online quality assessment method for an unmanned logistics vehicle, the method comprising:

[0006] Determine at least two resistance data of the logistics vehicle in a driving state;

[0007] Determining theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data;

[0008] When the first flag trigger condition is detected, the actual acceleration of the logistics vehicle is obtained, and the target mass of the logistics vehicle within a preset time period is determined based on the actual acceleration and the at least two resistance data, and the target mass is used as the evaluation mass;

[0009] Among them, the first flag triggering condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

[0010] According to another aspect of the present invention, an embodiment of the present invention further provides an online quality assessment device for an unmanned logistics vehicle, the device comprising:

[0011] A resistance determination module, used to determine at least two resistance data of the logistics vehicle in a driving state;

[0012] A theoretical acceleration information determination module, used to determine theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data;

[0013] A quality assessment module, configured to obtain the actual acceleration of the logistics vehicle when the first flag trigger condition is detected, determine the target quality of the logistics vehicle within a preset time period based on the actual acceleration and the at least two resistance data, and use the target quality as the assessment quality;

[0014] Among them, the first flag triggering condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

[0015] According to another aspect of the present invention, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the online quality assessment method for unmanned logistics vehicles described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the online quality assessment method for an unmanned logistics vehicle described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, characterized in that the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the online quality assessment method for the unmanned logistics vehicle described in any embodiment of the present invention.

[0021] The technical effect of the present invention is that by determining at least two resistance data of a logistics vehicle in a driving state, the theoretical acceleration parameter information of the logistics vehicle is determined based on the at least two resistance data, and when the first flag trigger condition is detected, the actual acceleration of the logistics vehicle is obtained, and the evaluation quality of the logistics vehicle within a preset time period is determined based on the actual acceleration and at least two resistance data. This can solve the problem of large volatility in quality calculation and improve the accuracy of vehicle quality calculation.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flowchart of an online quality assessment method for an unmanned logistics vehicle provided by an embodiment of the present invention;

[0025] Figure 2 A flowchart of another online quality assessment method for an unmanned logistics vehicle provided by an embodiment of the present invention;

[0026] Figure 3 A process framework diagram of power calculation and acceleration monitoring provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of a triggering process of a first flag bit triggering condition and a flow chart of triggering a second flag bit provided in an embodiment of the present invention;

[0028] Figure 5 A schematic diagram of a process for calculating the vehicle mass of a logistics vehicle provided by an embodiment of the present invention;

[0029] Figure 6 A flow chart of another online quality assessment method for an unmanned logistics vehicle provided by an embodiment of the present invention;

[0030] Figure 7 A structural block diagram of an online quality assessment device for an unmanned logistics vehicle provided by an embodiment of the present invention;

[0031] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] In one embodiment, Figure 1 A flow chart of an online quality assessment method for an unmanned logistics vehicle provided in accordance with an embodiment of the present invention. This embodiment is applicable to situations in which an online quality assessment of an unmanned logistics vehicle is performed. The method can be performed by an online quality assessment device for an unmanned logistics vehicle. The online quality assessment device for an unmanned logistics vehicle can be implemented in the form of hardware and / or software. The online quality assessment device for an unmanned logistics vehicle can be configured in an electronic device.

[0035] like Figure 1 As shown, the online quality assessment method of the unmanned logistics vehicle in this embodiment includes the following specific steps:

[0036] S110. Determine at least two resistance data of the logistics vehicle in a driving state.

[0037] The resistance data includes at least driving force, rolling resistance, wind resistance, ramp resistance and braking force.

[0038] In this embodiment, the static parameter data of the logistics vehicle and the dynamic parameter data of the logistics vehicle in the driving state are obtained, and at least two resistance data of the logistics vehicle in the driving state can be determined through the static parameter data and the dynamic parameter data of the logistics vehicle in the driving state. Among them, the static parameter data can be understood as the parameter information of the vehicle itself, and the static parameters include the vehicle's curb weight, maximum load, tire rolling radius, rolling resistance coefficient, wind resistance coefficient, gear ratio, main reduction ratio and transmission efficiency. The dynamic parameter data can be understood as the dynamic data generated by the vehicle in the driving state, and the dynamic parameter information may include but is not limited to motor execution torque, speed, vehicle braking pressure, ramp and other information.

[0039] S120. Determine theoretical acceleration parameter information of the logistics vehicle based on at least two resistance data.

[0040] The theoretical acceleration parameter information includes: theoretical acceleration, theoretical acceleration upper limit and theoretical acceleration lower limit.

[0041] In this embodiment, the driving force, wind resistance, rolling resistance, slope resistance, etc. of the current vehicle are calculated respectively in combination with static and dynamic input parameters. Based on this, the theoretical acceleration of the vehicle is calculated through the driving force, wind resistance, rolling resistance, slope resistance, etc. of the current vehicle and the initial mass of the vehicle. The upper and lower limits of the vehicle mass floating corresponding to the logistics vehicle are determined according to the initial mass and the preset mass floating value. The upper limit of the theoretical acceleration of the logistics vehicle is determined according to the first ratio of the upper limit of the vehicle mass floating and the resistance data. The lower limit of the theoretical acceleration is determined according to the second ratio of the lower limit of the vehicle mass floating and the resistance data. Of course, in this embodiment, other methods can also be used to determine the theoretical acceleration parameter information of the logistics vehicle, and this embodiment does not limit this.

[0042] S130. When the first flag trigger condition is detected, the actual acceleration of the logistics vehicle is obtained, and the target quality of the logistics vehicle within a preset time period is determined based on the actual acceleration and at least two resistance data, and the target quality is used as the evaluation quality; wherein, the first flag trigger condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

[0043] The actual acceleration may also be referred to as true acceleration, which is the actual acceleration calculated based on the current vehicle speed and time range.

[0044] In this embodiment, the real acceleration of the current vehicle can be obtained through the acceleration sensor or vehicle speed change information. Since the real acceleration of the logistics vehicle obtained may have a large jump deformation problem, that is, it has a large fluctuation over a period of time, at this time, the acceleration with large fluctuations can be converted into a smooth real acceleration through Kalman filtering. Then, it is determined whether the actual acceleration is within the range of the theoretical acceleration parameter information. Specifically, the actual acceleration and the theoretical acceleration parameter information can be compared, and the comparison result can be used to determine whether the actual acceleration is within the range of the theoretical acceleration parameter information.

[0045] In this embodiment, the first flag trigger condition can be understood as the flag condition that the logistics vehicle meets the vehicle mass calculation, and the flag can be set manually. The preset working condition requirement can be understood as the working condition when the vehicle is in an accelerating state or a decelerating state; in this embodiment, if the vehicle is in a static state, there is no acceleration, and if it is at a constant speed, there is no acceleration. This situation does not meet the working condition requirement, and only acceleration and deceleration can be used for acceleration calculation. The preset minimum acceleration threshold can be understood as the minimum acceleration threshold set by humans based on experience or demand customization. Acceleration that does not exceed this threshold cannot be used for mass calculation, and mass calculation can only be performed when it exceeds it.

[0046] In this embodiment, when it is detected that the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold, the target quality of the logistics vehicle within the preset time period is determined based on the actual acceleration and at least two resistance data, and the target quality is used as the evaluation quality. Specifically, the initial state continuously detects whether the working condition meets the calculation. If it meets, it first detects whether there is an acceleration exceeding the boundary event. If it meets, the acceleration is detected in real time. If not, the acceleration exceeding the boundary event is continuously detected. After the acceleration exceeds the boundary, the acceleration is detected in real time to determine whether the current acceleration is higher than the calculated minimum Threshold, when the preset working condition requirements are met, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold, the mass is weighted averaged according to at least two resistance data and the input real acceleration, and the start calculation flag is detected in real time to see if it is 0. The number of calculations needs to be accumulated, and the calculated mass is accumulated. The evaluation quality is the average of multiple accumulated masses. Before this, it is necessary to determine whether the accumulated number exceeds the upper limit. If it exceeds the accumulated upper limit, the accumulated number is reset to 1, and the accumulated mass is reset to the current evaluation mass. If it does not exceed the number, the average of multiple accumulated masses is the calculated evaluation quality.

[0047] In one embodiment, the triggering process of the first flag trigger condition includes: determining whether the logistics vehicle meets the preset working condition requirements; if the preset working condition requirements are met, performing Kalman filtering on the actual acceleration to obtain an actual acceleration with smooth characteristics, comparing the actual acceleration with the theoretical acceleration upper limit and the theoretical acceleration lower limit, and continuing to determine whether the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit; if it exceeds the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit, continuing to determine whether the actual acceleration exceeds the preset acceleration minimum threshold, if it exceeds the preset acceleration minimum threshold, triggering the first flag and setting the first flag as the first identification; if the preset working condition requirements are not met, continuously monitoring the vehicle working condition.

[0048] In this embodiment, the input acceleration is calculated by a Kalman filter to reduce the interference of acceleration fluctuation on mass calculation. In this embodiment, Kalman filtering is a filtering method in the prior art, which can adjust data with large fluctuations to smooth data, and will not be described in detail in this embodiment.

[0049] The technical solution of the embodiment of the present invention determines at least two resistance data of the logistics vehicle in a driving state, determines the theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data, obtains the actual acceleration of the logistics vehicle when the first flag trigger condition is detected, and determines the assessed quality of the logistics vehicle within a preset time period based on the actual acceleration and at least two resistance data. This can solve the problem of large volatility in quality calculation and improve the accuracy of vehicle quality calculation.

[0050] In one embodiment, the method further includes:

[0051] In any of the following cases, when it is detected that the preset time period is exceeded, the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit in the theoretical acceleration parameter information, or the logistics vehicle meets the preset working condition requirements, the second flag is triggered, it is determined to stop calculating the vehicle mass, and the preset period is continuously monitored to see whether the trigger condition of the first flag is met.

[0052] The second flag can be understood as a flag that is triggered when the vehicle mass calculation stops, that is, when the second flag is triggered, it is considered that the vehicle mass calculation condition is not met. The second flag is set artificially. Of course, the setting flags of the first flag and the second flag are different, and the meanings represented are different.

[0053] In this embodiment, when it is detected that the preset time period is exceeded, the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit in the theoretical acceleration parameter information, or the logistics vehicle meets the preset working condition requirements, the second flag is triggered to determine to stop calculating the vehicle mass, and to continuously monitor whether the first flag trigger condition is met according to the preset period. It can be understood that there is an effective duration in the mass calculation stage, and when the effective duration is exceeded, the calculation of this stage is stopped, or it is determined whether the current acceleration meets the boundary or whether to exit the working condition. Any event will trigger the calculation to stop.

[0054] In one embodiment, Figure 2 A flow chart of another online quality assessment method for unmanned logistics vehicles provided for an embodiment of the present invention. Based on the above embodiments, this embodiment further refines the method by determining at least two resistance data of the logistics vehicle in a driving state, determining theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data, and determining the target quality of the logistics vehicle within a preset time period based on the actual acceleration and at least two resistance data, and taking the target quality as the assessment quality.

[0055] like Figure 2 As shown, the online quality assessment method of the unmanned logistics vehicle in this embodiment may specifically include the following steps:

[0056] S210. Obtain static parameter data of the logistics vehicle and dynamic parameter data of the logistics vehicle under driving status.

[0057] In this embodiment, static parameter data of the logistics vehicle and dynamic parameter data of the logistics vehicle under driving status can be collected. Specifically, in this embodiment, the vehicle uses the unmanned vehicle chassis domain controller CDCU as the main control unit and the motor controller as the execution unit to obtain real-time information such as the motor execution torque and speed under driving status, such as the motor execution torque, speed, vehicle braking pressure, ramp, etc.

[0058] S220. Determine the resistance data of the logistics vehicle in the driving state based on the dynamic parameter data and the static parameter data.

[0059] In this embodiment, the resistance data of the logistics vehicle in the driving state can be determined by dynamic parameter data and static parameter data. In this embodiment, the method of determining the resistance data of the logistics vehicle in the driving state based on dynamic parameter data and static parameter data is the method of determining resistance data in the prior art, and this embodiment will not be specifically introduced here. Exemplarily, based on multiple wheel-end torques and multiple rolling radii, multiple driving forces of the vehicle are determined; wherein the resistance data at least includes driving force, rolling resistance, wind resistance, ramp resistance, and braking force.

[0060] S230: Determine the theoretical acceleration of the vehicle according to the resistance data and the initial mass of the logistics vehicle.

[0061] In this embodiment, the theoretical acceleration of the vehicle is determined by the resistance data and the initial mass of the logistics vehicle. Specifically, the resistance data such as driving force, rolling resistance, wind resistance, ramp resistance and braking force are substituted into the dynamics formula to calculate the theoretical acceleration of the vehicle, where the dynamics formula is expressed as: ma = Z + B + F air +F st +F p , where a represents the theoretical acceleration; Z represents the driving force; B represents the braking force; F air Expressed as air resistance; F st Expressed as rolling resistance; F p Expressed as ramp resistance.

[0062] S240. Determine the vehicle mass floating upper limit and the vehicle mass floating lower limit corresponding to the logistics vehicle based on the initial mass and the preset mass floating value.

[0063] Among them, the preset mass floating value can be adjusted according to the actual vehicle mass. It is set manually according to the actual situation and may be related to the mass of the vehicle itself. If the mass itself is heavier, the upper and lower limits of the mass floating will fluctuate a little more. If the vehicle command itself is not very large, then the setting may be relatively small.

[0064] In this embodiment, the initial mass and the preset mass floating value can be used to calculate the vehicle mass floating upper limit and the vehicle mass floating lower limit corresponding to the logistics vehicle. Specifically, the sum of the initial mass and the preset mass floating value is used as the vehicle mass floating upper limit corresponding to the logistics vehicle, and the difference between the initial mass and the preset mass floating value is used as the vehicle mass floating lower limit corresponding to the logistics vehicle. More specifically, m high =m+m off , m low =mm off , where m high is the upper limit of mass fluctuation, m low is the lower limit of mass fluctuation, m off It is the floating value of mass, which can be generally adjusted according to the actual mass of the vehicle. For example, the value is 50kg.

[0065] S250. Determine the theoretical upper limit of acceleration of the logistics vehicle according to the first ratio of the resistance data to the lower limit of vehicle mass fluctuation.

[0066] The first ratio is the ratio of the resistance data to the lower limit of the vehicle mass fluctuation.

[0067] In this embodiment, the theoretical acceleration upper limit of the logistics vehicle is determined according to the first ratio of the resistance data and the lower limit of the vehicle mass floating. Specifically, the sum of the driving force, rolling resistance, wind resistance, ramp resistance and braking force is calculated, and the ratio between the sum of the driving force, rolling resistance, wind resistance, ramp resistance and braking force and the upper limit of the vehicle mass floating is used as the theoretical acceleration upper limit of the logistics vehicle, which can be expressed by the formula: Among them, m low It is expressed as the theoretical lower limit of acceleration, Z is the driving force, B is the braking force, and F is the air Expressed as air resistance; F st Expressed as rolling resistance; F p Expressed as ramp resistance.

[0068] S260: Determine a theoretical acceleration lower limit according to a second ratio of the resistance data to the vehicle mass floating upper limit.

[0069] The second ratio is the ratio of the resistance data to the upper limit of the vehicle mass fluctuation.

[0070] In this embodiment, the theoretical acceleration lower limit is determined according to the second ratio of the resistance data and the vehicle mass floating upper limit. Specifically, the sum of the driving force, rolling resistance, wind resistance, ramp resistance and braking force is calculated, and the ratio of the sum of the driving force, rolling resistance, wind resistance, ramp resistance and braking force to the vehicle mass floating upper limit is used as the theoretical acceleration lower limit of the logistics vehicle, which can be expressed by the formula: Among them, m high It is expressed as the theoretical lower limit of acceleration, Z is the driving force, B is the braking force, and F is the air Expressed as air resistance; F st Expressed as rolling resistance; F p Expressed as ramp resistance.

[0071] In one embodiment, in order to better understand the monitoring process of whether the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit, Figure 3 A process framework diagram of power calculation and acceleration monitoring provided by an embodiment of the present invention, Figure 3 The dynamic calculation module in is the calculation steps S230-S260 in the above embodiment, so as to obtain the theoretical acceleration, the theoretical acceleration upper limit and the theoretical acceleration lower limit; Figure 3As shown, according to the input of static parameters and dynamic parameters, the vehicle driving force, rolling resistance, wind resistance, slope resistance, braking force, etc. are calculated respectively. The theoretical acceleration under the current mass can be calculated by combining the dynamic model and the current mass. The theoretical acceleration corresponding to the upper limit of the mass and the theoretical acceleration corresponding to the lower limit of the mass can be calculated according to the floating mass. Then the actual acceleration is compared with the upper limit of the theoretical acceleration and the lower limit of the theoretical acceleration. It can be understood that, to judge whether the real acceleration exceeds the theoretical acceleration boundary, firstly, the real acceleration of the current vehicle can be obtained according to the acceleration sensor or the vehicle speed change information, but generally the acceleration fluctuates greatly. The real acceleration is obtained after the input acceleration is Kalman filtered. The acceleration is compared with the upper and lower limits of the theoretical acceleration in real time. If it exceeds the boundary, it is considered that the current mass needs to be re-evaluated.

[0072] S270. When the first flag trigger condition is detected, the actual acceleration of the logistics vehicle is obtained, the current time is selected within the preset time period, and the current actual acceleration and current resistance data at the current time are obtained; wherein the current resistance data includes the current driving force, the current rolling resistance, the current wind resistance, the current ramp resistance and the current braking force.

[0073] In this embodiment, when it is detected that the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset minimum acceleration threshold, the current time is selected within the preset time period, and the current actual acceleration and current resistance data at the current time are obtained; wherein the current resistance data includes the current driving force, the current rolling resistance, the current wind resistance, the current ramp resistance, and the current braking force

[0074] S280: Determine the ratio of the current resistance data to the current actual acceleration, and use the ratio as the current vehicle mass corresponding to the current time.

[0075] In this embodiment, the ratio of the current resistance data to the current actual acceleration is determined, and the ratio is used as the current vehicle mass corresponding to the current time.

[0076] S290, select the next time and use the next time as the current time, return to the step of obtaining the current actual acceleration and current resistance data at the current time, until it exceeds the preset time period, determine to trigger the second flag, and obtain the current vehicle mass corresponding to at least two current times.

[0077] The next time includes the first next time corresponding to the current time, the second next time, and all next times until the last next time within the preset time.

[0078] In this embodiment, the next time is selected and used as the current time, and the step of obtaining the current actual acceleration and current resistance data at the current time is returned until the preset time period is exceeded, and the second flag is determined to be triggered, so as to obtain the current vehicle mass corresponding to at least two current times.

[0079] S2100. Count the cumulative number of calculations of the current mass within a preset time period, as well as the average value of each current mass, and determine the target mass of the logistics vehicle within the preset time period based on the cumulative number of calculations and a preset calculation number threshold.

[0080] The cumulative number of calculations can be understood as the number of times the mass of the logistics vehicle is calculated cumulatively within a preset time period. The preset calculation number threshold is a calculation number threshold that is customized based on experience or demand.

[0081] In this embodiment, the cumulative number of calculations of the current mass within a preset time period and the average value of the accumulation of each current mass are counted, and the target mass of the logistics vehicle within the preset time period is determined based on the cumulative number of calculations and the preset number of calculations threshold. Specifically, if the cumulative number of calculations exceeds the preset number of calculations threshold, the cumulative number of calculations is cleared and reset, and the last vehicle mass calculated within the preset time period is used as the target mass of the logistics vehicle. If it does not exceed the preset number of calculations threshold, the average value of the accumulation of each current mass within the preset time period is used as the target mass of the logistics vehicle. In this embodiment, no large jump will occur through the accumulation method, but after reaching a certain number of times, the cumulative number of times must be cleared, and the last mass calculated in sequence before clearing is used as the final mass. The advantage is that the larger the number of cumulative times, the weaker the effect may be. Therefore, a preset number of calculations threshold is set so that when the cumulative number exceeds the threshold, it is cleared, and then the cumulative count is restarted after clearing, so that no large jump will occur.

[0082] In one embodiment, determining the target quality of the logistics vehicle within a preset time period according to the cumulative number of calculations and a preset number of calculations threshold includes:

[0083] Determine whether the cumulative number of calculations exceeds a preset calculation number threshold;

[0084] If the preset calculation times threshold is exceeded, the accumulated calculation times will be cleared and reset, and the last vehicle mass calculated within the preset time period will be used as the target mass of the logistics vehicle;

[0085] If the preset calculation times threshold is not exceeded, the average value of the current masses accumulated within the preset time period is used as the target mass of the logistics vehicle.

[0086] In this embodiment, it is determined whether the cumulative number of calculations exceeds the preset calculation number threshold. If it exceeds the preset calculation number threshold, the cumulative number of calculations is cleared and reset to 1, and the last vehicle mass calculated within the preset time period is used as the target mass of the logistics vehicle; if it does not exceed the preset calculation number threshold, the average value of the current masses accumulated within the preset time period is used as the target mass of the logistics vehicle. In this embodiment, the calculation time from the mass calculation to the start flag being 0 is a calculation time. When the calculation flag is 0, the calculation is stopped and a current calculated mass is output. Since the current calculated mass may have a large jump deformation according to actual engineering experience and debugging experience, the actual vehicle mass cannot jump particularly large, so it is optimized through multiple mass calculations. All calculations are accumulated once, and the calculated mass is also accumulated.

[0087] In one embodiment, in order to better understand the calculation timing of logistics vehicles and the statistics of vehicle mass calculation within a preset time period, Figure 4 A triggering process of a first flag triggering condition and a flow chart of triggering a second flag provided in an embodiment of the present invention are flow charts of the timing for starting the mass calculation of a logistics vehicle and the timing for stopping the calculation of the vehicle mass. Figure 5 A schematic diagram of a flow chart for calculating the mass of a logistics vehicle provided in accordance with an embodiment of the present invention.

[0088] like Figure 4 As shown in the figure, after the acceleration exceeds the boundary, it is used to manage the timing of quality calculation. The main steps are shown in the figure: the initial state continuously detects whether the working condition meets the calculation. If it meets the requirements, it enters the quality calculation timing management module, otherwise it continues to detect whether the working condition meets the requirements; after entering the quality calculation timing management module, first detect whether there is an acceleration exceeding the boundary event. If it meets the requirements, the acceleration is detected in real time. If it does not meet the requirements, the acceleration exceeding the boundary event is continuously detected; after the acceleration exceeds the boundary, the acceleration is detected in real time to determine whether the current acceleration is higher than the minimum threshold of the calculation; if the acceleration is higher than the minimum threshold of the detection, the start calculation flag is set to 1; enter the quality calculation stage, which has an effective duration. When the effective duration is exceeded, the calculation of this stage is stopped, or it is determined whether the current acceleration meets the boundary or whether to exit the working condition. Any event will trigger the calculation stop, and the quality is calculated on a weighted average during this period; enter the quality calculation stop, the stop calculation flag is 1, the quality calculation stops, and the above steps are repeated when returning to the initial state, and the start calculation flag and the stop calculation flag are reset to 0.

[0089] like Figure 5As shown, wait for the calculation of mass, check whether the start calculation flag is 1; start the mass calculation, in this state, the mass is weighted averaged according to the driving mechanical equation and the input real acceleration, and check in real time whether the start calculation flag is 0; when the mass calculation of this stage is completed, a mass calculated in this period will be obtained, which is the current calculated mass, and this mass will be used as the input of the mass statistics module. The mass statistics module is carried out in the following steps:

[0090] Check whether the stop calculation flag is 1. When the calculation flag is set to 1, it means that the current quality period calculation module has ended, and the calculation quality can be statistically managed; when the quality period calculation module stops, it is necessary to accumulate the number of calculations and the calculation quality. The evaluation quality is the average of multiple cumulative qualities. Before this, it is necessary to determine whether the cumulative number exceeds the upper limit. If it exceeds the cumulative upper limit, the cumulative number is reset to 1, and the cumulative quality is reset to the current evaluation quality; the quality is accumulated and calculated, and the result is synchronously updated to the current evaluation quality.

[0091] The technical solution of the embodiment of the present invention determines the resistance data of the logistics vehicle in the driving state through dynamic parameter data and static parameter data, determines the theoretical acceleration of the vehicle according to the resistance data and the initial mass of the logistics vehicle, determines the vehicle mass floating upper limit and the vehicle mass floating lower limit corresponding to the logistics vehicle according to the initial mass and the preset mass floating value, determines the theoretical acceleration upper limit of the logistics vehicle according to a first ratio of the vehicle mass floating upper limit and the resistance data, determines the theoretical acceleration lower limit according to a second ratio of the vehicle mass floating lower limit and the resistance data, selects the current time within the preset time period, obtains the current actual acceleration and the current resistance data at the current time, determines the current resistance data and the current actual acceleration, and determines the relationship between the current resistance data and the current actual acceleration. The acceleration ratio is selected, and the ratio is used as the current vehicle mass corresponding to the current time, the next time is selected, and the next time is used as the current time, and the step of obtaining the current actual acceleration and current resistance data at the current time is returned until the preset time period is exceeded, and the second flag is determined to be triggered, and the current vehicle mass corresponding to at least two current times is obtained. The cumulative number of calculations of the current vehicle mass within the preset time period and the accumulated average value of each current vehicle mass are counted, and the target mass of the logistics vehicle within the preset time period is determined based on the cumulative number of calculations and the preset number of calculations threshold, which can further solve the problem of large volatility in mass calculation and improve the accuracy of vehicle mass calculation.

[0092] In one embodiment, in order to better understand the online quality assessment method of unmanned logistics vehicles, Figure 6 A flowchart of an online quality assessment method for an unmanned logistics vehicle provided by an embodiment of the present invention is shown as follows: Figure 6 As shown in FIG. 1 , the online quality assessment method of the unmanned logistics vehicle specifically includes the following steps:

[0093] a1. Static parameter data of the logistics vehicle, and dynamic parameter data of the logistics vehicle under driving status.

[0094] a2. Combine static and dynamic input parameters to calculate the current vehicle's driving force, wind resistance, rolling resistance, slope resistance, etc., and use the dynamics formula to calculate the vehicle's theoretical acceleration.

[0095] a3. Based on the current vehicle mass and the upper and lower limits of mass fluctuation, calculate the upper and lower limits of theoretical acceleration currently allowed and compare them with the actual acceleration. If they exceed the boundaries, it means that the current mass needs to be re-evaluated.

[0096] a4. Identify the current working conditions and boundary conditions in real time. When it is detected that the current situation is suitable for quality calculation, the quality calculation will be started, the quality will be calculated continuously within the specified time, and the quality will be output to the quality assessment module for cumulative quality assessment.

[0097] The embodiment of the present invention combines the static parameters of the vehicle and the vehicle dynamics formula, identifies the mass calculation conditions, monitors and screens the acceleration in real time, evaluates the mass through multiple calculations, solves the mass calculation fluctuations and improves the accuracy of the mass calculation.

[0098] In one embodiment, Figure 7 This is a structural block diagram of an online quality assessment device for unmanned logistics vehicles provided in one embodiment of the present invention. The device is suitable for performing online quality assessment on unmanned logistics vehicles. The device can be implemented by hardware / software. It can be configured in an electronic device to implement an online quality assessment method for unmanned logistics vehicles in an embodiment of the present invention.

[0099] like Figure 7 As shown, the device includes: a resistance determination module 710, a theoretical acceleration information determination module 720, and a quality assessment module 730;

[0100] Wherein, the resistance determination module 710 is used to determine at least two resistance data of the logistics vehicle in a driving state;

[0101] Theoretical acceleration information determination module 720, used to determine theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data;

[0102] The quality assessment module 730 is used to obtain the actual acceleration of the logistics vehicle when the first flag trigger condition is detected, determine the target quality of the logistics vehicle within a preset time period based on the actual acceleration and the at least two resistance data, and use the target quality as the assessment quality;

[0103] Among them, the first flag triggering condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

[0104] In an embodiment of the present invention, a theoretical acceleration information determination module determines theoretical acceleration parameter information of a logistics vehicle through at least two resistance data; a quality assessment module obtains actual acceleration of the logistics vehicle when a first flag trigger condition is detected, and determines the assessment quality of the logistics vehicle within a preset time period based on the actual acceleration and at least two resistance data. This can solve the problem of large volatility in quality calculation and improve the accuracy of vehicle quality calculation.

[0105] In one embodiment, the device further comprises:

[0106] A calculation stop module is used to trigger a second flag to stop calculating the vehicle mass and continuously monitor whether the first flag trigger condition is met according to a preset period when it is detected that the preset time period is exceeded, the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit in the theoretical acceleration parameter information, or the logistics vehicle meets the preset working condition requirements.

[0107] In one embodiment, the resistance determination module 710 includes:

[0108] A data acquisition unit, used to acquire static parameter data of the logistics vehicle and dynamic parameter data of the logistics vehicle under driving state;

[0109] A resistance determination unit is used to determine the resistance data of the logistics vehicle in a driving state based on the dynamic parameter data and the static parameter data; wherein the resistance data at least includes driving force, rolling resistance, wind resistance, slope resistance and braking force.

[0110] In one embodiment, the theoretical acceleration information determination module 720 includes:

[0111] A theoretical acceleration determination unit, used to determine a theoretical acceleration of the vehicle according to the resistance data and the initial mass of the logistics vehicle;

[0112] A mass floating upper limit and lower limit determination module, used to determine the vehicle mass floating upper limit and vehicle mass floating lower limit corresponding to the logistics vehicle according to the initial mass and the preset mass floating value;

[0113] An acceleration upper limit determination module, used to determine a theoretical acceleration upper limit of the logistics vehicle according to a first ratio of the resistance data to the vehicle mass floating lower limit;

[0114] The acceleration lower limit determination module is used to determine the theoretical acceleration lower limit according to the second ratio of the resistance data to the vehicle mass floating upper limit.

[0115] In one embodiment, the triggering process of the first flag triggering condition includes:

[0116] Determine whether the logistics vehicle meets the preset working condition requirements;

[0117] If the preset working condition requirement is met, Kalman filtering is performed on the actual acceleration to obtain an actual acceleration with a smoothing characteristic, the actual acceleration with a smoothing characteristic is compared with the theoretical acceleration upper limit and the theoretical acceleration lower limit respectively, and it is further determined whether the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit;

[0118] If the actual acceleration exceeds the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit, then continue to determine whether the actual acceleration exceeds the preset acceleration minimum threshold; if it exceeds the preset acceleration minimum threshold, then trigger the first flag bit, and set the first flag bit to the first identification;

[0119] If the preset operating condition requirement is not met, the vehicle operating condition is continuously monitored.

[0120] In one embodiment, the quality assessment module 730 includes:

[0121] A current resistance data determination unit, used to select the current time within a preset time period and obtain the current actual acceleration and current resistance data at the current time; wherein the current resistance data includes the current driving force, the current rolling resistance, the current wind resistance, the current ramp resistance and the current braking force;

[0122] a current vehicle mass determination unit, configured to determine a ratio of the current resistance data to the current actual acceleration, and use the ratio as the current vehicle mass corresponding to the current time;

[0123] A loop unit, configured to select a next time, and use the next time as the current time, and return to the step of obtaining the current actual acceleration and current resistance data at the current time, until the preset time period is exceeded, and determine that the second flag is triggered, and obtain the current vehicle mass corresponding to at least two current times respectively;

[0124] The quality assessment unit is used to count the cumulative number of calculations of the current vehicle mass within the preset time period, as well as the accumulated average of the current vehicle masses, and determine the target mass of the logistics vehicle within the preset time period based on the cumulative number of calculations and a preset calculation number threshold.

[0125] In one embodiment, the quality assessment unit comprises:

[0126] The quality number judgment subunit is used to judge whether the cumulative number of calculations exceeds a preset calculation number threshold;

[0127] A first mass determination subunit is used to clear and reset the accumulated number of calculations if it exceeds a preset calculation number threshold, and to use the last vehicle mass calculated within a preset time period as the target mass of the logistics vehicle;

[0128] The second quality determination subunit is used to take the average value of the current masses accumulated within a preset time period as the target quality of the logistics vehicle if the preset calculation times threshold is not exceeded.

[0129] The online quality assessment device for unmanned logistics vehicles provided in an embodiment of the present invention can execute the online quality assessment method for unmanned logistics vehicles provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0130] In one embodiment, Figure 8 A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0131] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the online quality assessment method of an unmanned logistics vehicle.

[0134] In some embodiments, the online quality assessment method for unmanned logistics vehicles may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the online quality assessment method for unmanned logistics vehicles described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the online quality assessment method for unmanned logistics vehicles in any other appropriate manner (e.g., by means of firmware).

[0135] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable unmanned logistics vehicle online quality assessment device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0137] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0140] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0142] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An online quality assessment method for unmanned logistics vehicles, It is characterized in that The method comprises: Determine at least two resistance data of the logistics vehicle in a driving state; Determining theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data; When the first flag trigger condition is detected, the actual acceleration of the logistics vehicle is obtained, and the target mass of the logistics vehicle within a preset time period is determined based on the actual acceleration and the at least two resistance data, and the target mass is used as the evaluation mass; Among them, the first flag triggering condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

2. The method according to claim 1, It is characterized in that The method further comprises: In any of the following cases, when it is detected that the preset time period is exceeded, the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit in the theoretical acceleration parameter information, or the logistics vehicle meets the preset operating conditions, the second flag is triggered, it is determined to stop calculating the vehicle mass, and whether the first flag trigger condition is met is continuously monitored according to the preset period.

3. The method according to claim 1, It is characterized in that The determining of at least two resistance data of the logistics vehicle in a driving state includes: Obtaining static parameter data of the logistics vehicle and dynamic parameter data of the logistics vehicle in a driving state; Determining the resistance data of the logistics vehicle in a driving state according to the dynamic parameter data and the static parameter data; The resistance data at least includes driving force, rolling resistance, wind resistance, ramp resistance and braking force.

4. The method according to claim 1, It is characterized in that The theoretical acceleration parameter information includes: theoretical acceleration, theoretical acceleration upper limit and theoretical acceleration lower limit; Accordingly, determining the theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data includes: Determining a theoretical acceleration of the vehicle according to the resistance data and the initial mass of the logistics vehicle; Determine the vehicle mass floating upper limit and the vehicle mass floating lower limit corresponding to the logistics vehicle according to the initial mass and the preset mass floating value; Determining a theoretical acceleration upper limit of the logistics vehicle according to a first ratio of the resistance data to the vehicle mass floating lower limit; The theoretical acceleration lower limit is determined according to a second ratio of the resistance data to the vehicle mass floating upper limit.

5. The method according to claim 1, It is characterized in that The triggering process of the first flag triggering condition includes: Determine whether the logistics vehicle meets the preset working condition requirements; If the preset working condition requirement is met, Kalman filtering is performed on the actual acceleration to obtain an actual acceleration with a smoothing characteristic, the actual acceleration with a smoothing characteristic is compared with the theoretical acceleration upper limit and the theoretical acceleration lower limit respectively, and it is further determined whether the actual acceleration is within the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit; If the actual acceleration exceeds the range of the theoretical acceleration upper limit and the theoretical acceleration lower limit, then continue to determine whether the actual acceleration exceeds the preset acceleration minimum threshold; if it exceeds the preset acceleration minimum threshold, then trigger the first flag bit, and set the first flag bit to the first identification; If the preset operating condition requirement is not met, the vehicle operating condition is continuously monitored.

6. The method according to claim 1, It is characterized in that The step of determining the target mass of the logistics vehicle within a preset time period based on the actual acceleration and the at least two resistance data, and using the target mass as the evaluation mass, comprises: Select the current time within the preset time period, and obtain the current actual acceleration and current resistance data at the current time; wherein the current resistance data includes the current driving force, the current rolling resistance, the current wind resistance, the current ramp resistance and the current braking force; Determine a ratio of the current resistance data to the current actual acceleration, and use the ratio as the current vehicle mass corresponding to the current time; Selecting a next time and taking the next time as the current time, returning to the step of obtaining the current actual acceleration and current resistance data at the current time, until exceeding the preset time period, determining to trigger the second flag, and obtaining the current vehicle mass corresponding to at least two current times respectively; The cumulative number of calculations of the current vehicle mass within the preset time period and the accumulated average value of each current vehicle mass are counted, and the target mass of the logistics vehicle within the preset time period is determined based on the cumulative number of calculations and a preset number of calculations threshold.

7. The method according to claim 6, It is characterized in that The step of determining the target quality of the logistics vehicle within a preset time period based on the cumulative number of calculations and a preset number of calculations threshold comprises: Determine whether the cumulative number of calculations exceeds a preset calculation number threshold; If the preset calculation times threshold is exceeded, the accumulated calculation times are cleared and reset, and the last vehicle mass calculated within the preset time period is used as the target mass of the logistics vehicle; If the preset calculation times threshold is not exceeded, the average value of the current masses accumulated within the preset time period is used as the target mass of the logistics vehicle.

8. An online quality assessment device for unmanned logistics vehicles, It is characterized in that The device comprises: A resistance determination module, used to determine at least two resistance data of the logistics vehicle in a driving state; A theoretical acceleration information determination module, used to determine theoretical acceleration parameter information of the logistics vehicle based on the at least two resistance data; A quality assessment module, configured to obtain the actual acceleration of the logistics vehicle when the first flag trigger condition is detected, determine the target quality of the logistics vehicle within a preset time period based on the actual acceleration and the at least two resistance data, and use the target quality as the assessment quality; Among them, the first flag triggering condition includes: the logistics vehicle meets the preset working condition requirements, the actual acceleration is not within the theoretical acceleration parameter information, and the actual acceleration exceeds the preset acceleration minimum threshold.

9. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the online quality assessment method for an unmanned logistics vehicle according to any one of claims 1-7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the online quality assessment method for an unmanned logistics vehicle according to any one of claims 1 to 7 when executed.

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