Logistics vehicle for transporting finished products

Abstract

The invention discloses a logistics vehicle for finished product transportation. A vibration sensor is arranged on the logistics vehicle and used for collecting a real-time vibration signal corresponding to the logistics vehicle; the integrated circuit is used for carrying out anomaly detection on the real-time vibration signals according to the first amplitude threshold value to obtain a first result, determining the loading state of the logistics vehicle and a signal monitoring model corresponding to the loading state under the condition that the first result does not meet a first fault condition, carrying out anomaly monitoring on the real-time vibration signals through the signal monitoring model, and outputting the anomaly monitoring result. Under the condition that the second result meets a second fault condition, the fault signal is sent to the motor and the electric control cabinet; the motor is used for stopping running under the condition that the fault signal is received; and the electric control cabinet is used for sending the fault signal to the cloud server under the condition that the fault signal is received. According to the logistics vehicle, fault self-detection can be carried out based on the vibration signals, and manual real-time monitoring is not needed. According to the invention, the accuracy of logistics vehicle fault monitoring is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer application technologies, and particularly to a logistics vehicle for finished product transportation. Background Art

[0002] Product production is inseparable from transportation. For example, transporting the finished products produced at production node A to production node B for finished product packaging, etc. Commonly, a logistics vehicle that can run on a transportation track is usually used for finished product transportation. However, inevitably, the logistics vehicle or the track may malfunction, and in severe cases, the transportation of the entire transportation track may be interrupted, affecting production efficiency.

[0003] Currently, a comprehensive monitoring is usually installed in the production workshop. When it is observed manually that the logistics vehicle shows deceleration, termination, vibration or other conditions, manual maintenance is carried out in a timely manner to avoid serious transportation accidents. However, the manual monitoring method has a high labor cost and is prone to omissions. Summary of the Invention

[0004] The present invention provides a logistics vehicle for finished product transportation to solve the technical problems that the current manual monitoring method for logistics vehicle failures has a high labor cost and is prone to omissions.

[0005] According to one aspect of the present invention, a logistics vehicle for finished product transportation is provided. A vibration sensor, an integrated circuit, a motor and an electric control cabinet are deployed on the logistics vehicle; wherein,

[0006] The vibration sensor is used to collect the real-time vibration signal corresponding to the logistics vehicle;

[0007] The integrated circuit is used to perform anomaly detection on the real-time vibration signal according to a first amplitude threshold to obtain a first result. In the case that the first result does not meet the first failure condition, determine the load state of the logistics vehicle and a signal monitoring model corresponding to the load state, and perform anomaly monitoring on the real-time vibration signal through the signal monitoring model to obtain a second result. In the case that the second result meets the second failure condition, generate a failure signal and send the failure signal to the motor;

[0008] The motor is used to stop running when receiving the failure signal;

[0009] The electric control cabinet is used to send the failure signal to the cloud server when receiving the failure signal.

[0010] According to another aspect of the present invention, a logistics vehicle is provided. A position scanner is further deployed on the logistics vehicle; wherein,

[0011] The position scanner is configured to scan position identifiers on a transportation track to determine real-time position information of the logistics vehicle;

[0012] The integrated circuit is configured to generate a real-time position signal based on the real-time position information and send the real-time position signal to the electric control cabinet when the second result meets the second condition;

[0013] The electric control cabinet is configured to send the real-time position signal to a cloud server when receiving the real-time position signal.

[0014] According to another aspect of the present invention, a logistics vehicle is provided, and a vehicle distance sensor is further deployed on the logistics vehicle; wherein,

[0015] The vehicle distance sensor is configured to obtain real-time vehicle distance information between the logistics vehicle and an adjacent vehicle;

[0016] The integrated circuit is further configured to determine a vehicle distance monitoring result based on the real-time vehicle distance information, generate a vehicle distance warning signal and send the vehicle distance warning signal to the electric control cabinet when the vehicle distance monitoring result meets a vehicle distance warning condition;

[0017] The electric control cabinet is configured to send the vehicle distance warning signal to a cloud server when receiving the vehicle distance warning signal.

[0018] In the technical solution of the embodiment of the present invention, a sensor, an integrated circuit, a motor and an electric control cabinet are deployed on a logistics vehicle; wherein, the vibration sensor is configured to collect real-time vibration signals corresponding to the logistics vehicle; the integrated circuit is configured to perform anomaly detection on the real-time vibration signals according to a first amplitude threshold to obtain a first result, and when the first result does not meet a first fault condition, determine a load state of the logistics vehicle and a signal monitoring model corresponding to the load state, perform anomaly monitoring on the real-time vibration signals through the signal monitoring model to obtain a second result, generate a fault signal and send the fault signal to the motor and the electric control cabinet respectively when the second result meets a second fault condition; the motor is configured to stop operating when receiving the fault signal; the electric control cabinet is configured to send the fault signal to a cloud server when receiving the fault signal. The present invention integrates a vibration sensor, an integrated circuit, a motor and an electric control cabinet on a logistics vehicle, realizes real-time fault self-checking of the logistics vehicle based on vibration signals, and stops transportation and reports the fault situation to the cloud server in real time when a fault occurs. The logistics vehicle of the present invention can perform fault self-checking based on vibration signals without manual real-time monitoring. The present invention improves the accuracy of fault monitoring of the logistics vehicle and reduces labor costs.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood from the following description. Brief Description of the Drawings

[0020] 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 in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 is a connection relationship diagram of a deployment device on a logistics vehicle provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a connection relationship diagram of a deployment device on a logistics vehicle provided according to Embodiment 2 of the present invention;

[0023] Figure 3 is a schematic diagram of a deployment device on a logistics vehicle provided according to an embodiment of the present invention;

[0024] Figure 4 is a flowchart of a vibration signal abnormality detection method provided according to an embodiment of the present invention. Detailed Description of the Embodiments

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] 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 do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these process, method, product, or device.

[0027] Embodiment 1

[0028] Figure 1 It is a connection diagram of a deployment device on a logistics vehicle provided according to Embodiment 1 of the present invention; this embodiment is applicable to the situation where the logistics vehicle performs fault self-check during the transportation of finished products on the track. As Figure 1 shown, a vibration sensor 11, an integrated circuit 12, a motor 13, and an electric control cabinet 14 can be deployed on the logistics vehicle.

[0029] Among them, the vibration sensor 11 is used to collect the real-time vibration signal corresponding to the logistics vehicle;

[0030] The integrated circuit 12 is used to perform anomaly detection on the real-time vibration signal according to the first amplitude threshold to obtain a first result. When the first result does not meet the first fault condition, determine the load state of the logistics vehicle and the signal monitoring model corresponding to the load state, and perform anomaly monitoring on the real-time vibration signal through the signal monitoring model to obtain a second result. When the second result meets the second fault condition, generate a fault signal and send the fault signal to the motor and the electric control cabinet respectively;

[0031] The motor 13 is used to stop running when receiving the fault signal;

[0032] The electric control cabinet 14 is used to send the fault signal to the cloud server when receiving the fault signal.

[0033] Among them, the vibration sensor can be used to collect the vibration signal of the logistics vehicle in real time and send the collected vibration signal to the integrated circuit. Optionally, the real-time vibration signal can be the vibration signal within the real-time acquisition period. Among them, the real-time acquisition period can be the real signal acquisition period. In the embodiments of the present invention, the signal acquisition period can be preset according to the scenario requirements and will not be specifically limited here. Exemplarily, the signal acquisition period can be 3 seconds, 5 seconds, 10 seconds, etc.

[0034] The integrated circuit can be used to perform anomaly detection on the vibration signal.

[0035] The first amplitude threshold can be understood as a threshold related to the signal amplitude. Optionally, the first amplitude threshold can include an amplitude intensity threshold and an amplitude range threshold. Exemplarily, the amplitude intensity threshold can include the maximum amplitude and the minimum amplitude, etc. The amplitude range threshold can be the threshold corresponding to the difference between the maximum amplitude and the minimum amplitude.

[0036] The first result can characterize whether the real-time vibration signal is abnormal. Optionally, the first result includes detection anomaly or detection normal.

[0037] Optionally, the abnormal detection of the real-time vibration signal according to the first amplitude threshold to obtain the first result may include, that is, the integrated circuit may also be used for:

[0038] Extract the amplitude feature of the real-time vibration signal to obtain the real-time amplitude feature value, where the real-time amplitude feature value includes the maximum and minimum values of the amplitude intensity and / or the difference value of the amplitude range;

[0039] When the real-time amplitude feature exceeds the preset first amplitude threshold, determine the first result as the detection of abnormality, otherwise, determine the first result as the detection of normality.

[0040] Among them, the maximum and minimum values of the amplitude intensity can be understood as the maximum and minimum values of the real-time amplitude of the vibration signal. The real-time amplitude maximum and minimum values include the real-time maximum value and the real-time minimum value. The difference value of the amplitude range may be the difference between the real-time maximum value and the real-time minimum value.

[0041] The first fault condition can be used to determine whether there is a fault in the logistics vehicle under the real-time vibration signal of the first result. Optionally, the first fault condition may be that the first result is the detection of abnormality.

[0042] The load state can characterize whether the logistics vehicle is loaded. Optionally, the load state includes loaded or unloaded.

[0043] Optionally, the determination of the load state of the logistics vehicle may include, that is, the integrated circuit may also be used for:

[0044] Determine the load state of the logistics vehicle according to the comparison vibration signal and the real-time vibration signal, where the comparison vibration signal includes a preset first comparison signal or a second comparison signal corresponding to the historical adjacent period.

[0045] Among them, the first comparison signal may be a reference vibration signal of the logistics vehicle in the loaded state or a reference vibration signal of the logistics vehicle in the unloaded state.

[0046] The historical adjacent period can be understood as a historical period adjacent to the real-time acquisition period. It can be understood that before obtaining the real-time vibration signal, the second comparison signal has been obtained and the load state associated with the second comparison signal has been determined.

[0047] Optionally, the integrated circuit is further configured to perform signal amplification processing on the real-time vibration signal to obtain an amplified vibration signal, perform high-low frequency decomposition on the amplified vibration signal to obtain a low-frequency vibration signal, and determine the load state of the logistics vehicle according to the comparison vibration signal and the low-frequency vibration signal.

[0048] Among them, the amplified vibration signal can be understood as the vibration signal after amplification processing. In the embodiments of the present invention, the specific manner of performing amplification processing on the real-time vibration signal can be preset according to the scenario requirements and will not be specifically limited here.

[0049] Specifically, perform high-low frequency decomposition on the amplified vibration signal to obtain a low-frequency vibration signal and a high-frequency vibration signal. In the embodiments of the present invention, the logistics vehicle can be fault-identified according to the low-frequency vibration signal. It should be understood that the obtained high-frequency vibration signal is usually associated with noise unrelated to the logistics vehicle. The present invention decomposes the real-time vibration signal and removes the high-frequency vibration signal, and only performs fault identification on the logistics vehicle through the low-frequency vibration signal, which can effectively improve the accuracy of fault identification.

[0050] Associated with the above-mentioned second comparison signal, the second comparison signal can be the low-frequency vibration signal obtained after performing high-low frequency decomposition on the historical vibration signal. The historical vibration signal can be the vibration signal collected by the vibration sensor in historical adjacent cycles.

[0051] Optionally, the determining the load state of the logistics vehicle according to the comparison vibration signal and the low-frequency vibration signal may include, that is, the integrated circuit is further configured to determine a first eigenvalue corresponding to a signal point in the low-frequency vibration signal, and determine the load state of the logistics vehicle according to the first eigenvalue and a second eigenvalue corresponding to a signal point in the comparison vibration signal, where the eigenvalue includes a square root value and / or an average value.

[0052] Optionally, randomly extract 50,000 signal points in the low-frequency vibration signal, determine the eigenvalues between the 50,000 signal points; and directly obtain the eigenvalues corresponding to 50,000 signal points in the comparison vibration signal that has been calculated, compare the two eigenvalues, and determine the load state of the logistics vehicle according to the comparison result. It should be understood that the second eigenvalue can represent a load state.

[0053] The signal monitoring model can be used to perform abnormal monitoring on vibration signals. Optionally, the signal monitoring model may include the first model or the second model.

[0054] Optionally, determining the signal monitoring model corresponding to the load state may include, that is, the integrated circuit may further be configured to:

[0055] When the load state is the loaded state, the first model is used to perform anomaly monitoring on the low-frequency vibration signal to obtain a second result, where the first model is obtained by training a deep learning model based on a first signal sample, and the first signal sample includes the vibration signal of the logistics vehicle in the loaded state.

[0056] Among them, the second result can characterize whether the low-frequency vibration signal is abnormal. In the embodiments of the present invention, the second result can characterize whether the logistics vehicle vibrates abnormally, and can also characterize the fault type of the logistics vehicle in the case of abnormal vibration.

[0057] The first signal sample can be understood as the vibration signal sample of the logistics vehicle in the loaded state. The signal sample can include positive samples and negative samples. The positive sample can be understood as a vibration signal without abnormality. The negative sample can be understood as an abnormal vibration signal. The label marked on the negative sample can characterize the fault type corresponding to the abnormal vibration signal. The fault type can include types such as vehicle faults or track faults. Associated with the fault type, the second result can include a first sub-result and / or a second sub-result corresponding to the first sub-result. The first sub-result characterizes whether the logistics vehicle vibrates abnormally, and the second sub-result characterizes the fault type of the logistics vehicle in the case of abnormal vibration. The fault type includes vehicle faults or track faults.

[0058] Optionally, determining the signal monitoring model corresponding to the load state may include, that is, the integrated circuit can also be used for:

[0059] When the load state is the unloaded state, the second model is used to perform anomaly monitoring on the low-frequency vibration signal to obtain a second result, where the second model is obtained by training a deep learning model based on a second signal sample, and the second signal sample includes the vibration signal of the logistics vehicle in the unloaded state.

[0060] Optionally, the deep learning model can be a classification model. Exemplarily, the classification model can include learning models such as random forest, support vector machine, and logistic regression. In the embodiments of the present invention, the selection of the deep learning model can be preset according to the scenario requirements and will not be specifically limited here. The deep learning model used to train the first model and the deep learning model used to train the second model can be the same or different.

[0061] The second signal sample can be understood as the vibration signal sample of the logistics vehicle in the unloaded state.

[0062] Based on the above embodiment solutions, the integrated circuit can first determine the load state of the logistics vehicle, and then perform fault identification on the vibration signal based on the learning model corresponding to the load state. By performing vibration signal fault identification through the learning model corresponding to the real-time load state of the logistics vehicle, the accuracy of fault identification can be effectively improved. Further, the integrated circuit can determine whether there is a fault in the logistics vehicle under the identified vibration signal, and in the case of a fault, whether the fault exists in the logistics vehicle or on the track on which the logistics vehicle runs, so that maintenance personnel can perform targeted maintenance based on the fault category, reducing the workload of maintenance personnel and effectively improving the work efficiency of maintenance personnel.

[0063] The second fault condition can be used to determine whether there is a fault in the logistics vehicle under the low-frequency vibration signal of the second result. Optionally, the second fault condition can be that the second result is the monitoring anomaly.

[0064] The motor can be used to drive the logistics vehicle.

[0065] The electric control cabinet can be used to upload the signals of the logistics vehicle to the cloud server so that the monitoring terminal can obtain the fault situation of the logistics vehicle in real time. In the embodiments of the present invention, the monitoring terminal can simultaneously obtain the signals of the logistics vehicle and perform real-time fault monitoring on multiple logistics vehicles.

[0066] Associated with the above second result, the monitoring terminal can receive the signal and determine whether there is a fault in the logistics vehicle based on the received signal, and can also determine the fault type of the logistics vehicle based on the received signal.

[0067] Optionally, the integrated circuit is further configured to generate the fault signal and send the fault signal to the motor and the electric control cabinet respectively when the first result meets the first condition.

[0068] In the embodiments of the present invention, when the real-time amplitude feature corresponding to the real-time vibration signal exceeds the preset first amplitude threshold, the first result is determined as the detection anomaly, the fault signal is directly generated, and the fault signal is sent to the motor and the electric control cabinet respectively.

[0069] The technical solution of the embodiment of the present invention is to deploy sensors, integrated circuits, motors, and electric control cabinets on the logistics vehicle; wherein, the vibration sensor is used to collect the real-time vibration signal corresponding to the logistics vehicle; the integrated circuit is used to perform anomaly detection on the real-time vibration signal according to the first amplitude threshold to obtain a first result, and when the first result does not meet the first fault condition, determine the load state of the logistics vehicle and the signal monitoring model corresponding to the load state, and perform anomaly monitoring on the real-time vibration signal through the signal monitoring model to obtain a second result. When the second result meets the second fault condition, generate a fault signal and send the fault signal to the motor and the electric control cabinet respectively; the motor is used to stop running when receiving the fault signal; the electric control cabinet is used to send the fault signal to the cloud server when receiving the fault signal. The present invention integrates vibration sensors, integrated circuits, motors, and electric control cabinets on the logistics vehicle, realizes real-time fault self-checking of the logistics vehicle based on vibration signals, and stops transportation and reports the fault situation to the cloud server when a fault occurs. The logistics vehicle of the present invention can perform fault self-checking based on vibration signals without manual real-time monitoring. The present invention improves the accuracy of fault monitoring of the logistics vehicle and reduces the labor cost.

[0070] Embodiment 2

[0071] Figure 2 FIG. is a connection diagram of a deployment device on a logistics vehicle provided according to Embodiment 2 of the present invention. This embodiment is a further addition to the deployment device for the logistics vehicle in the above embodiment. As Figure 2 shown, a position scanner 15 may also be deployed on the logistics vehicle.

[0072] Among them, the position scanner 15 is used to scan the position identifier on the transportation track to determine the real-time position information of the logistics vehicle;

[0073] The integrated circuit 12, when the second result meets the second condition, generates a real-time position signal according to the real-time position information and sends the real-time position signal to the electric control cabinet;

[0074] The electric control cabinet 14 is used to send the real-time position signal to the cloud server when receiving the real-time position signal.

[0075] Among them, the position scanner can be used to scan the position identifiers on the transportation track where the logistics vehicle travels to determine the real-time position information of the logistics vehicle. The real-time position information can be the real-time position of the logistics vehicle on the transportation track. The position identifier can represent the position information. Optionally, the position identifier can be a barcode or a QR code pasted on the transportation track. Specifically, during the process of the logistics vehicle traveling on the transportation track, the position scanner scans the position identifiers on the transportation track in real time to determine the real-time position of the logistics vehicle on the transportation track.

[0076] The real-time position signal can be understood as a transmission signal carrying the real-time position information.

[0077] Based on the above embodiment solutions, when the integrated circuit detects that the logistics vehicle has a fault, the electric control cabinet can feedback the location of the logistics vehicle to the cloud server, so that the maintenance personnel can determine the location of the faulty logistics vehicle and perform fault repair in a timely manner. The present invention can effectively assist the maintenance personnel in the fault repair work of the logistics vehicle.

[0078] Optionally, a distance sensor is also deployed on the logistics vehicle; among them,

[0079] The distance sensor is used to obtain the real-time distance information between the logistics vehicle and the adjacent vehicle;

[0080] The integrated circuit is further configured to determine a distance monitoring result according to the real-time distance information, and generate a distance warning signal and send the distance warning signal to the electric control cabinet when the distance monitoring result meets the distance warning condition;

[0081] The electric control cabinet is configured to send the distance warning signal to the cloud server when receiving the distance warning signal.

[0082] Among them, the distance sensor can be used to obtain the distance information between the deployed logistics vehicle and the adjacent vehicle in real time. The adjacent vehicle can be understood as the logistics vehicle adjacent to the target logistics vehicle. It should be understood that multiple logistics vehicles can travel on the same transportation track at the same time. The real-time distance information can be understood as real-time distance information. The distance information can represent the distance between the logistics vehicle and the adjacent vehicle. This distance is related to the actual driving situation of the logistics vehicle and is not specifically limited here. Exemplarily, the distance between the logistics vehicle and the adjacent vehicle can be 1 meter, 2 meters or 3 meters, etc.

[0083] The distance monitoring result can represent whether the real-time distance information is abnormal. Optionally, the distance monitoring result can include normal distance or abnormal distance.

[0084] Optionally, the determination of the vehicle distance monitoring result based on the real-time vehicle distance information, that is, the integrated circuit, is further configured to: determine the vehicle distance monitoring result according to the real-time vehicle distance information and a preset vehicle distance threshold. Specifically, when the distance value corresponding to the real-time vehicle distance information exceeds the vehicle distance threshold, the vehicle distance monitoring result is determined to be abnormal vehicle distance; otherwise, the vehicle distance monitoring result is determined to be normal vehicle distance. Herein, the vehicle distance threshold can be understood as a threshold related to the vehicle distance. The vehicle distance threshold can be preset according to scenario requirements and will not be specifically limited herein. Exemplarily, the vehicle distance threshold can be 2 meters, 3 meters, 4 meters, etc.

[0085] The vehicle distance warning condition can be used to determine whether a warning is required for the vehicle distance monitoring result. Optionally, the vehicle distance warning condition can be that the vehicle distance monitoring result is abnormal vehicle distance.

[0086] The vehicle distance warning signal can be understood as a signal indicating abnormal vehicle distance.

[0087] Based on the solution of the above embodiment, when the integrated circuit monitors that the distance between the logistics vehicles is too close and there is a collision risk, the electric control cabinet feeds back the abnormal vehicle distance signal to the cloud server so that the maintenance personnel can timely adjust the vehicle distance between multiple logistics vehicles and avoid vehicle collisions.

[0088] The technical solution of the embodiment of the present invention is to deploy a position scanner on the logistics vehicle; wherein, the position scanner is configured to scan the position identifier on the transportation track to determine the real-time position information of the logistics vehicle; the integrated circuit, when the second result meets the second condition, generates a real-time position signal according to the real-time position information and sends the real-time position signal to the electric control cabinet; the electric control cabinet is configured to send the real-time position signal to the cloud server when receiving the real-time position signal. In the case that the integrated circuit monitors that the logistics vehicle has a fault, the electric control cabinet feeds back the location of the logistics vehicle to the cloud server so that the maintenance personnel can determine the location of the faulty logistics vehicle and perform fault maintenance in a timely manner. The present invention can effectively assist the maintenance personnel in the fault maintenance work of the logistics vehicle.

[0089] Figure 3 is a schematic diagram of a deployment device on a logistics vehicle provided according to an embodiment of the present invention. The following is combined with Figure 3, an optional logistics vehicle in an embodiment of the present invention and devices deployed on the logistics vehicle are introduced. Among them, an optional one of the devices that can be deployed on the logistics vehicle includes 1-11 and 13. 1 represents an electric control cabinet; 2 represents a general assembly cover; 3 and 9 represent two pallets, commonly known as the left pallet and the right pallet; 4 represents a power supply; 5 represents an integrated circuit; 6 represents a vibration sensor; 7 represents a vehicle distance sensor installed at the front of the vehicle; 8 represents a motor; 10 represents a wheel; 11 represents a vehicle distance sensor installed at the rear of the vehicle, and 13 represents a position scanner. 12 represents the transportation track where the logistics vehicle is located.

[0090] Among them, 1 can be installed at the rear of the logistics vehicle for starting and stopping 8. When 8 receives a fault signal, it controls the logistics vehicle to stop. 3 and 9 are installed on the top of the logistics vehicle through bolts for carrying 2. The power supply 4 is installed above 2 to provide electrical energy for 5 and 6. 5 is installed above 2 for abnormal monitoring of vibration signals, and in the case of abnormal vibration signals, generates a fault signal and transmits it to 8 and 1 respectively. 6 is used to collect vibration signals and conduct them to 5. 13 is installed on 5 for scanning the position two-dimensional code on 12. 7 and 11 are installed on the front side and the rear side of the logistics vehicle respectively for detecting the distance between the logistics vehicle and the adjacent logistics vehicles in front and behind. 10 is installed below 3 and 9. The logistics vehicle travels on 12.

[0091] The logistics vehicle of the present invention can be used for transporting finished products on the track, and during the transportation of finished products, it performs self-check for faults based on the acquired vibration signals, and in the case of a fault, sends the fault type and the position of the logistics vehicle to the cloud server so that the repairman at the corresponding monitoring terminal of the cloud server can perform fault repair in a timely manner.

[0092] Figure 4 is a flowchart of a method for detecting abnormal vibration signals according to an embodiment of the present invention. Combining Figure 3 and Figure 4 , the overall process of abnormal identification of vibration signals by the integrated circuit is specifically described as follows.

[0093] 6 collects the real-time vibration signal of the logistics vehicle; transmits the real-time vibration signal to 5;

[0094] 5 extracts the features of the real-time vibration signal, determines the maximum amplitude, the minimum amplitude, and the difference between the maximum and minimum amplitudes; compares the determined amplitude-related values with a preset threshold. In the case where the amplitude-related value exceeds the threshold (that is, the first result satisfies the first fault condition), a fault signal is generated and the fault signal is sent to the motor and the electric control cabinet respectively.

[0095] When the amplitude-related value does not exceed the threshold (i.e., the first result does not meet the first fault condition), the vibration signal is amplified; the amplified vibration signal is denoised through a preprocessing algorithm, where the preprocessing algorithm may include algorithms such as wavelet decomposition, wavelet transform, and wavelet denoising; the denoised vibration signal is decomposed into low-frequency and high-frequency vibration signals; 50,000 signal points are extracted from the low-frequency vibration signal, and the square root value and the average value of the amplitudes of these 50,000 signal points are calculated; the signal point-related values calculated above are compared with the signal point-related values of a preset comparison signal to determine the load state (empty vehicle or loaded) of the logistics vehicle, and a learning model corresponding to the load state is determined; the low-frequency vibration signal obtained by decomposition is monitored for anomalies through the learning model corresponding to the load state to obtain a monitoring result (i.e., the second result). In the case of abnormal monitoring, a fault signal is generated and sent to the motor and the electric control cabinet respectively; otherwise, it returns to continue acquiring the vibration signal of the next signal acquisition cycle collected by the vibration sensor.

[0096] The logistics vehicle of the present invention can complete abnormal judgment and issue a stop command within 2 seconds after abnormal vibration occurs. It improves the safety of the logistics vehicle operation and reduces the risks of cargo loss and equipment damage.

Claims

1. A logistics vehicle for transporting finished products, characterized in that, A vibration sensor, an integrated circuit, a motor, and an electric control cabinet are deployed on the logistics vehicle; among them, the vibration sensor is used to collect the real-time vibration signal corresponding to the logistics vehicle; the integrated circuit is used to perform anomaly detection on the real-time vibration signal according to a first amplitude threshold to obtain a first result. When the first result does not meet the first fault condition, determine the load state of the logistics vehicle and a signal monitoring model corresponding to the load state, and perform anomaly monitoring on the real-time vibration signal through the signal monitoring model to obtain a second result. When the second result meets the second fault condition, generate a fault signal and send the fault signal to the motor and the electric control cabinet respectively; the motor is used to stop running when receiving the fault signal; the electric control cabinet is used to send the fault signal to the cloud server when receiving the fault signal.

2. The logistics vehicle according to claim 1, characterized in that: The integrated circuit is further used to determine the load state of the logistics vehicle according to a comparison vibration signal and the real-time vibration signal, where the comparison vibration signal includes a preset first comparison signal or a second comparison signal corresponding to a historical adjacent period, and the load state includes loaded or unloaded.

3. The logistics vehicle according to claim 2, wherein: The integrated circuit is further used to perform signal amplification processing on the real-time vibration signal to obtain an amplified vibration signal, perform high-low frequency decomposition on the amplified vibration signal to obtain a low-frequency vibration signal, and determine the load state of the logistics vehicle according to the comparison vibration signal and the low-frequency vibration signal.

4. The logistics vehicle according to claim 3, characterized in that: The integrated circuit is further used to determine a first eigenvalue corresponding to a signal point in the low-frequency vibration signal, and determine the load state of the logistics vehicle according to the first eigenvalue and a second eigenvalue corresponding to a signal point in the comparison vibration signal, where the eigenvalue includes a square root value and / or an average value.

5. The logistics vehicle according to claim 3, characterized in that: The signal monitoring model includes a first model; the integrated circuit is further used to perform anomaly monitoring on the low-frequency vibration signal through the first model to obtain a second result when the load state is the loaded state, where the first model is obtained by training a deep learning model based on a first signal sample, and the first signal sample includes the vibration signal of the logistics vehicle in the loaded state.

6. The logistics vehicle according to claim 3, wherein: The signal monitoring model includes a second model; the integrated circuit is further used to perform anomaly monitoring on the low-frequency vibration signal through the second model to obtain a second result when the load state is the unloaded state, where the second model is obtained by training a deep learning model based on a second signal sample, and the second signal sample includes the vibration signal of the logistics vehicle in the unloaded state.

7. The logistics vehicle according to claim 5 or 6, characterized in that: The signal samples include positive samples and negative samples. The negative samples include sample labels, and the sample labels characterize the fault types of the negative samples. The second result includes a first sub-result and / or a second sub-result corresponding to the first sub-result. The first sub-result characterizes whether the logistics vehicle vibrates abnormally, and the second sub-result characterizes the fault type of the logistics vehicle in the case of abnormal vibration. The fault types include vehicle faults or track faults.

8. The logistics vehicle according to claim 1, wherein It further includes a position scanner; wherein, The position scanner is configured to scan the position identifiers on the transportation track to determine the real-time position information of the logistics vehicle; The integrated circuit, when the second result meets the second condition, generates a real-time position signal according to the real-time position information and sends the real-time position signal to the electric control cabinet; The electric control cabinet is configured to send the real-time position signal to the cloud server when receiving the real-time position signal.

9. The logistics vehicle according to claim 1, wherein, It further includes a vehicle distance sensor; wherein, The vehicle distance sensor is configured to obtain the real-time vehicle distance information between the logistics vehicle and the adjacent vehicle; The integrated circuit is further configured to determine a vehicle distance monitoring result according to the real-time vehicle distance information, generate a vehicle distance warning signal when the vehicle distance monitoring result meets the vehicle distance warning condition, and send the vehicle distance warning signal to the electric control cabinet; The electric control cabinet is configured to send the vehicle distance warning signal to the cloud server when receiving the vehicle distance warning signal.

10. The logistics vehicle according to claim 1, characterized in that, The integrated circuit is further configured to generate the fault signal when the first result meets the first condition and send the fault signal to the motor and the electric control cabinet respectively.

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