Methods and platforms for monitoring the safety of goods in transit

Abstract

The present application relates to the field of smart logistics technology, and provides a method and platform for the safety supervision of goods in transit. The method includes: receiving video surveillance data uploaded by a data terminal, and determining whether the video surveillance data carries an abnormal reminder mark; in the case where the video surveillance data carries an abnormal reminder mark, performing image recognition on the video surveillance data to determine whether there is an abnormality in the monitoring screen corresponding to the video surveillance data; in the case where there is an abnormality in the monitoring screen, determining the warning level according to the abnormality type; wherein, the video surveillance data is sent to the data terminal by the video acquisition terminal when it is determined that the communication with the supervision platform is abnormal, and forwarded to the supervision platform by the data terminal; the abnormal reminder mark is set by the data terminal when it is determined that the video surveillance data is abnormal according to the transmission characteristics of the video surveillance data, and the integrity of the monitoring data is ensured by the temporary storage of the data that cannot be uploaded in time by the data terminal.

Description

Technical Field

[0001] The present application relates to the field of smart logistics technology, and in particular to a method and platform for monitoring the safety of goods in transit. Background Art

[0002] During the logistics transportation process, in order to ensure the transportation safety of goods, various monitoring devices are usually installed on the vehicle to obtain the real-time status of the vehicle, including the driving route, monitoring video, and the driving status of the vehicle, etc., and then analyze whether there is a risk to the safety of the goods based on this status information, so as to grasp and record abnormal situations in time.

[0003] However, network interruptions often occur during the actual transportation process, resulting in the inability to synchronize the relevant data collected by the monitoring equipment to the background in a timely manner. The data can only be cached locally and uploaded when the network is restored. Due to the large amount of video surveillance data, especially when the network interruption time is long, the storage capacity of the video surveillance equipment may not be able to meet the storage requirements of all cached data, resulting in data loss, resulting in the background being unable to obtain complete monitoring data, and there is a monitoring loophole. Summary of the invention

[0004] In order to effectively store video surveillance data during network interruption so that the background can obtain complete surveillance data as much as possible, the embodiments of the present application provide a method for supervising the safety of in-transit transportation of goods and a platform for supervising the safety of in-transit transportation of goods.

[0005] On the one hand, the in-transit transportation safety supervision method provided in the embodiment of the present application is applied to a transportation safety management system, the system comprising a in-transit transportation safety supervision platform for goods, a data terminal and a video acquisition terminal, the data terminal and the video acquisition terminal being installed on the same transport vehicle, the method being executed by the supervision platform, comprising the steps of: receiving video surveillance data uploaded by the data terminal, and determining whether the video surveillance data carries an abnormal reminder mark; in the case where the video surveillance data carries an abnormal reminder mark, performing image recognition on the video surveillance data to determine whether there is an abnormality in the monitoring screen corresponding to the video surveillance data; in the case where there is an abnormality in the monitoring screen, determining the warning level according to the type of abnormality; wherein, the video surveillance data is sent to the data terminal by the video acquisition terminal when it is determined that there is an abnormality in communication with the supervision platform, and is forwarded to the supervision platform by the data terminal; the abnormal reminder mark is set by the data terminal when it is determined that there is an abnormality in the video surveillance data according to the transmission characteristics of the video surveillance data.

[0006] Based on the above technical solution, the video acquisition terminal can use the data terminal to store the video surveillance data that has not been uploaded in time and forward it to the supervision platform when the network is restored. On the one hand, the storage capacity of the data terminal can be used to expand the storage capacity of the video surveillance data. On the other hand, when the network is restored, the video acquisition terminal can focus on uploading real-time data without consuming resources to send historical data, thereby ensuring the priority synchronization of real-time data and the stable operation of the video acquisition terminal. At the same time, it also reduces the hardware requirements for the video acquisition terminal, thereby reducing equipment costs. In addition, it can ensure that the supervision platform obtains complete video surveillance data as much as possible to prevent monitoring loopholes. Furthermore, the data terminal performs preliminary anomaly identification based on the transmission characteristics, thereby preliminarily screening out video surveillance data that may be abnormal, and setting abnormal reminder marks, so that the supervision platform can prioritize the identification of video surveillance data based on the abnormal reminder marks, so that abnormal situations can be handled in a timely manner.

[0007] In one implementation, the method by which the data terminal determines whether the video surveillance data is abnormal based on the transmission characteristics of the video surveillance data includes: in the process of receiving the video surveillance data, monitoring the transmission characteristics of the video surveillance data in real time; obtaining the changing trend of the transmission characteristics; and determining that the video surveillance data is abnormal when it is detected that the changing trend is inconsistent with the picture characteristics corresponding to the video acquisition terminal.

[0008] Based on the above technical solution, the data terminal can simultaneously acquire the transmission characteristics during the process of receiving data, and then complete the preliminary judgment based on the transmission characteristics. There is no need to parse and process the specific content of the video surveillance data, and the processing efficiency is high.

[0009] In one implementation, when it is monitored that the change trend is inconsistent with the picture feature corresponding to the video acquisition terminal, determining that the video surveillance data is abnormal includes: if the picture feature corresponding to the video acquisition terminal is a first feature, when it is monitored that the change trend meets a first preset condition, determining that the video surveillance data is abnormal.

[0010] In one implementation, when it is monitored that the change trend is inconsistent with the picture feature corresponding to the video acquisition terminal, determining that the video surveillance data is abnormal includes: if the picture feature corresponding to the video acquisition terminal is a second feature, when it is monitored that the change trend meets a second preset condition, obtaining the vehicle driving status, and if the vehicle driving status is abnormal, determining that the video surveillance data is abnormal.

[0011] Based on the above technical solution, different judgment conditions are used according to the picture features to improve the recognition accuracy.

[0012] In one implementation, the transmission characteristic is determined based on the bit rate control mode of the video acquisition terminal. When the bit rate control mode is a constant bit rate, the transmission characteristic is determined as the amount of video surveillance data corresponding to a unit acquisition time length; when the bit rate control mode is a non-constant bit rate, the transmission characteristic is determined as the transmission rate.

[0013] In one implementation, the method further includes: when it is determined that the video surveillance data is the remaining data after being deleted by the data terminal, pre-marking the storage space of the data terminal to be expanded; when the number of pre-marking times corresponding to the data terminal exceeds a preset threshold, generating a capacity expansion recommendation for the data terminal.

[0014] In one implementation, the method for determining whether the video surveillance data is the remaining data after being deleted by the data terminal includes: analyzing the continuity of the acquisition time corresponding to the video surveillance data; if the acquisition time is discontinuous, determining that the video surveillance data has been deleted.

[0015] In one implementation, the method for deleting video surveillance data by the data terminal includes: determining the device priority of the target video acquisition terminal corresponding to the video surveillance data in storage; determining the data priority of each video surveillance data segment based on the changing trend of the transmission characteristics; determining the data to be deleted from each of the video surveillance data segments according to the device priority and the data priority, and deleting the data to be deleted; wherein, when the number of the target video acquisition terminals is multiple, the video surveillance data segment with the lowest data priority is selected from the video surveillance data segments corresponding to the target video acquisition terminal with the lowest device priority as the candidate data segment, and determining whether there is a video surveillance data segment that has an intersection with the candidate data segment in the video acquisition period and carries the abnormal reminder mark, and if so, raising the data priority of the candidate data segment to the second priority; if not, determining the candidate data segment to be deleted; judging whether the remaining storage space is greater than a preset storage space threshold, if not, repeating the above operation until the remaining storage space is greater than the preset storage space threshold.

[0016] Based on the above technical solution, hierarchical management of stored data can be achieved, and when the storage capacity is insufficient, some data can be deleted and abnormal data can be retained first.

[0017] On the other hand, the goods in-transit transportation safety supervision platform provided in the embodiment of the present application is respectively communicated with the video acquisition terminal and the data terminal installed on the same transport vehicle to implement the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of the structure of a transportation safety management system provided in an embodiment of the present application is shown.

[0021] Figure 2 A flow chart of a method for identifying anomalies in video surveillance data by a data terminal provided in an embodiment of the present application is shown.

[0022] Figure 3 A flow chart of a method for monitoring the safety of in-transit transportation of goods provided in an embodiment of the present application is shown.

[0023] Figure 4 A flow chart of an execution method of a storage data deletion task provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, and "first", "second" and various digital numbers are only distinguished for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0026] The features, structures or characteristics in this application may be combined in one or more embodiments in any suitable manner. In various embodiments of this application, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0027] Some optional features in the embodiments of the present application may be implemented independently in some scenarios without relying on other features to solve corresponding technical problems and achieve corresponding effects. They may also be combined with other features in some scenarios as needed.

[0028] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships. The implementation methods of this application do not constitute a limitation on the scope of protection of this application.

[0029] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0030] Please refer to Figure 1 The embodiment of the present application provides a transportation safety management system, including a data terminal 101 and a video acquisition terminal 102 installed on the same transport vehicle, and a goods in-transit transportation safety supervision platform 103 (hereinafter referred to as the supervision platform), wherein the supervision platform 103 is deployed in the cloud, and the data terminal 101 and the video acquisition terminal 102 are pre-registered on the supervision platform 103, so that the supervision platform 103 can establish an association relationship between the data terminal 101, the video acquisition terminal 102 and the corresponding transport vehicle.

[0031] The data terminal 101 has data processing and storage capabilities, and can communicate with the video acquisition terminal 102 based on a local area network, and can also communicate with the supervision platform 103 through a wireless network.

[0032] During the transportation process, the video acquisition terminal 102 communicates with the supervision platform 103 and uploads video monitoring data in real time, so that the supervision platform 103 can obtain the real-time safety status of the transported goods based on the recognition of the video monitoring data.

[0033] The process of uploading video surveillance data from the video acquisition terminal 102 to the supervision platform 103 includes first encoding and compressing the original video surveillance data, writing it into the cache, and reading the compressed video surveillance data from the cache and uploading it to the supervision platform 103. When the network is normal, the data upload rate can be basically consistent with the cache write rate to ensure the real-time data transmission, while also avoiding data loss caused by the data in the cache being cleared.

[0034] When an abnormality occurs in the network, the video acquisition terminal 102 detects that the upload rate is significantly lower than the cache write rate, and then chooses to send the data to be uploaded to the data terminal 101 for temporary storage. After receiving the video surveillance data sent by the video acquisition terminal 102, the data terminal 101 first saves it, and starts to detect the communication status with the supervision platform 103. When the communication status is normal, the received video surveillance data is forwarded to the supervision platform 103.

[0035] It can be understood that the video acquisition terminal 102 determines whether the upload rate is significantly lower than the cache write rate based on the growth of the current cache data volume. If the cache data volume continues to increase within the preset time period, it is determined that the upload rate is significantly lower than the cache write rate. The preset time period can be set according to the actual cache capacity. The larger the capacity, the longer the preset time period.

[0036] In order to enable the supervision platform to quickly capture abnormal information in the video surveillance data when receiving delayed video surveillance data, the data terminal preliminarily identifies the abnormalities of the video surveillance data, sets abnormal reminder marks for the video surveillance data that are initially identified as abnormal, and sends them to the supervision platform together, so that the supervision platform can directly locate the video surveillance data that may have abnormalities based on the abnormal reminder marks, and then process the video surveillance data first. Among them, the method of setting the abnormal reminder mark includes setting specific label information in the data message.

[0037] Please refer to Figure 2 ,The method for a data terminal to identify abnormalities in video surveillance data includes the following steps.

[0038] S201, in the process of receiving video surveillance data, monitoring the transmission characteristics of the video surveillance data in real time.

[0039] Specifically, the transmission characteristics include the transmission rate and the amount of video surveillance data corresponding to a unit acquisition time, which can be used to assist in determining whether the amount of information in the video surveillance image has changed.

[0040] The data terminal determines the type of transmission feature to be monitored based on the bit rate control mode adopted by the video acquisition terminal. When the video acquisition terminal adopts a constant bit rate, the transmission feature is determined to be the amount of video surveillance data corresponding to the unit acquisition time. When the bit rate control mode adopted by the video acquisition terminal is a non-constant bit rate, the transmission feature is determined to be the transmission rate, that is, the rate at which the video acquisition terminal sends data to the data terminal.

[0041] In implementation, the data terminal can extract the acquisition time information corresponding to the received video surveillance data, and then determine the data volume corresponding to the unit acquisition time, that is, the data volume encoded by the video acquisition terminal based on the original data collected within the unit acquisition time. The unit acquisition time is a preset constant, which can be set according to actual needs.

[0042] The data terminal can determine the transmission rate between the video acquisition terminal and the data terminal according to the amount of data received within a unit transmission time.

[0043] S202, obtaining a change trend of a transmission characteristic.

[0044] Among them, the changing trends include increasing and decreasing.

[0045] In one example, the transmission characteristics obtained from the current monitoring may be compared with the transmission characteristics obtained from the previous monitoring to determine the change trend.

[0046] In another example, the transmission characteristics currently monitored may be compared with the average values ​​of historical transmission characteristics to determine the change trend.

[0047] It is worth noting that changes in transmission characteristics indicate changes in the amount of information in the video surveillance image. Therefore, based on the changing trend of the transmission characteristics, the changing trend of the amount of information in the video surveillance image can be preliminarily determined. For example, when the video surveillance image is an image inside the cab, the main dynamic feature comes from the driver. During vehicle driving, the driver's activity state is relatively stable, so the amount of monitoring information is relatively similar. Correspondingly, the amount of video data compression is also similar, so the transmission characteristics are also at a relatively stable level. When the driver leaves the cab or engages in non-driving activities, the video surveillance image information will change significantly. For example, when the driver leaves the cab and the vehicle stops, the video surveillance image is relatively static, the data volume is small, the compression volume is large, and the transmission characteristics will show a clear downward trend. In this way, the video surveillance data that may be abnormal can be preliminarily screened out by obtaining the changing trend of the transmission characteristics.

[0048] S203: When it is detected that the monitored change trend is inconsistent with the picture feature corresponding to the video acquisition terminal, it is determined that the video monitoring data is abnormal.

[0049] Specifically, at least one video acquisition terminal is installed on the transport vehicle, and the installation positions and shooting angles of the video acquisition terminals are different. Therefore, the picture features corresponding to the monitoring pictures taken by each video acquisition terminal can be divided into a first feature and a second feature.

[0050] The first feature indicates that the monitoring screen is relatively static during the transportation process. For example, a video acquisition terminal installed in a carriage, whose shooting object is goods, normally shows stacked goods in the monitoring screen. During the transportation process, the goods are relatively static, so the corresponding monitoring screen is also relatively static. Another example is a video acquisition terminal installed in a truck bed for monitoring carriage doors. The shooting object is the carriage doors. During the transportation, the carriage doors should be in a closed state, so the corresponding monitoring screen is also relatively static.

[0051] The second feature indicates that the monitoring screen is relatively dynamic during the transportation process. For example, a video acquisition terminal installed in the cab for monitoring the situation in the cab includes the driver in the shooting object. During transportation, the driver is in an active state, so the corresponding monitoring screen is relatively dynamic. Another example is a video acquisition terminal installed in the vehicle body for monitoring the intrusion of people around the vehicle body. The shooting object corresponds to the environment around the vehicle body. During transportation, the environmental content changes dynamically at any time, so the corresponding monitoring screen has more dynamic elements.

[0052] In implementation, if the picture feature corresponding to the video acquisition terminal is the first feature, when the change trend of the transmission feature is monitored to meet the first preset condition, the video surveillance data is determined to be abnormal. Among them, the first preset condition includes that the change trend is that the growth amount exceeds the change amount threshold, that is, the video surveillance data increment within the unit acquisition time is greater than the change amount threshold, indicating that the change trend of the transmission feature does not match the first feature, and the video surveillance data is directly determined to be abnormal.

[0053] Specifically, under normal circumstances, the amount of data change in the monitoring screen of goods will remain within a small range. Therefore, when the data growth is greater than the change threshold, it means that larger objects may be moving in the monitoring screen, such as people entering, goods being moved, etc. In this way, it is preliminarily determined that the video surveillance data is abnormal.

[0054] If the picture feature corresponding to the video acquisition terminal is the second feature, when the change trend of the transmission feature is monitored to meet the second preset condition, it is necessary to further obtain the vehicle driving state. If the vehicle driving state is abnormal, the video monitoring data is determined to be abnormal. The second preset condition includes that the change trend is that the decrease exceeds the change threshold, that is, the decrease in the video monitoring data per unit acquisition time is greater than the change threshold, indicating that the change trend of the transmission feature does not meet the second feature.

[0055] Specifically, the monitoring screen of the video acquisition terminal with the second feature is relatively dynamic, so the data volume is large. When the amount of video monitoring data suddenly decreases and the decrease is greater than the change threshold, it indicates that the vehicle's driving state or the driver's activity state may have changed significantly. Therefore, it can be further combined with the vehicle's operating data to determine whether the vehicle's driving state is abnormal. If the vehicle's operating data indicates that the vehicle has stopped abnormally, the video monitoring data is determined to be abnormal.

[0056] In one example, the data terminal can obtain operating data by requesting the vehicle control system, and determine whether the vehicle's driving status is normal in combination with the transportation planning route. For example, it can determine whether the driving status is abnormal based on whether the vehicle stops at a normal parking point or deviates from the planned path.

[0057] When the data terminal detects that the communication with the supervision platform is normal, the video surveillance data is sent to the supervision platform.

[0058] In implementation, the change threshold is set based on the monitoring screen corresponding to the video acquisition terminal. The change thresholds corresponding to different video acquisition terminals are set separately based on their respective historical monitoring data. In one example, the change of historical monitoring data under abnormal and normal conditions can be statistically analyzed, and data fitting can be performed separately to obtain two curves of the change over time. The highest point of the change curve under normal conditions and the lowest point of the change curve under abnormal conditions are taken respectively, and the average value is calculated as the change threshold.

[0059] In another example, based on the above example, different change thresholds can be further set according to time periods and weather conditions to improve the accuracy of preliminary identification. For example, with cargo type and transportation route as selection dimensions, video surveillance data of all vehicles on the platform in the process of transporting the same type of cargo based on the same transportation route is obtained, and statistical analysis is performed based on these data to determine the normal change amount and abnormal change amount in different time periods and different weather conditions. When it is found that the change range in a certain time period or a certain weather condition is obviously different from other time periods, a change threshold can be set for that time period or weather condition alone.

[0060] It is worth noting that normal and abnormal situations are determined by the platform, that is, after image recognition or manual review, to ensure the accuracy of the analysis data. In addition, as historical data continues to accumulate, the change threshold may also change, thereby continuously iterating and optimizing to improve the accuracy of the initial judgment.

[0061] Please refer to Figure 3 The embodiment of the present application provides a method for supervising the safety of goods in transit, which is applied to a platform for supervising the safety of goods in transit, and specifically includes the following steps.

[0062] S301, receiving video surveillance data uploaded by a data terminal, and determining whether the video surveillance data carries an abnormal warning mark.

[0063] As mentioned above, the video surveillance data uploaded by the data terminal is sent to the data terminal when the video acquisition terminal communicates abnormally with the supervision platform.

[0064] It is worth noting that after the video acquisition terminal determines that the communication status with the supervision platform has returned to normal, it will stop sending video surveillance data to the data terminal and directly send new video surveillance data to the supervision platform.

[0065] The supervision platform saves the video surveillance data sent directly by the video acquisition terminal and performs image recognition on the video surveillance data in real time. When the image recognition result indicates that there is an abnormality in the monitoring screen, the video surveillance data of the abnormal monitoring screen is extracted and stored separately. Then, when the user requests to view it, it is displayed to the user in the form of a video clip.

[0066] In addition, users can also request to view real-time monitoring images, and the supervision platform can display real-time monitoring images to users based on the latest collected video monitoring data. It is understandable that the latest video monitoring data may come from the data terminal or the video acquisition terminal. The supervision platform will give priority to displaying real-time monitoring images to users based on the video monitoring data uploaded by the video acquisition terminal.

[0067] When the supervision platform receives video surveillance data from the data terminal, it indicates that the video surveillance data is not real-time data, but delayed historical data. In order to confirm abnormal situations in a timely manner, the supervision platform first determines whether the video surveillance data carries an abnormal reminder mark.

[0068] When the video surveillance data carries an abnormal warning mark, step S302 is executed immediately; when the video surveillance data does not carry an abnormal warning mark, the video surveillance data is stored in sequence according to the acquisition time, and after processing the video surveillance data carrying the abnormal warning mark, step S302 is executed.

[0069] S302: Perform image recognition on the video surveillance data to determine whether there is any abnormality in the surveillance picture corresponding to the video surveillance data.

[0070] Among them, based on the intelligent image recognition model, image recognition of video surveillance data can be realized to determine whether there are any abnormalities in the surveillance picture corresponding to the video surveillance data, and the type of abnormality. In one example, the abnormality of the safety of goods includes abnormal state of goods, abnormal quantity of goods, illegal entry of people into the carriage, abnormal opening of the carriage door, and people entering near the vehicle body.

[0071] If there is an abnormality in the monitoring screen, execute step S303; if there is no abnormality in the monitoring screen, execute step S304.

[0072] S303: Determine the warning level according to the abnormality type.

[0073] In one example, the supervision platform is configured with different warning levels, the abnormality types for determining each warning level, and the warning processing plans corresponding to each warning level. Therefore, when the abnormality type is determined, the warning level can be determined based on the configuration information.

[0074] S304, recording the image recognition results corresponding to the video surveillance data, and sending the video surveillance data to the management personnel for review if the driver's credit rating is at risk.

[0075] Specifically, in order to minimize the risks caused by misidentification, the platform can determine the driver's credit rating based on the driver's historical transportation records. When the image recognition results are abnormal, if the driver's credit rating is low, indicating that there is a greater potential risk, further manual review can be conducted to improve recognition accuracy and fully ensure the transportation safety of the goods.

[0076] S305: Execute a corresponding warning processing plan based on the warning level.

[0077] In one example, the early warning processing plan includes sending a confirmation message of the abnormal type to the driver, and obtaining the monitoring screen of the cab to determine how the driver handles the confirmation message, and determines the risk level of the goods based on the handling situation, and then determines the safety of the goods. When the risk level is high, further processing measures can be taken, including but not limited to notifying the consignee of the risk, refusing warehousing, etc.

[0078] Based on the above technical solution, by transferring the video surveillance data that cannot be uploaded to the supervision platform in time to a data terminal with larger storage space, data loss caused by insufficient cache of the video acquisition terminal can be prevented. At the same time, the data terminal preliminarily identifies the video surveillance data that may be abnormal based on the transmission characteristics and sets an abnormal reminder mark, so that when the supervision platform receives the video surveillance data uploaded by the data terminal, it can give priority to image recognition of the data carrying the abnormal reminder mark to determine whether there is an abnormality in the monitoring screen, and then handle it in time, which improves the efficiency of abnormal identification.

[0079] It can be seen that the method provided by the embodiment of the present application can effectively deal with the problem of abnormal communication between the video acquisition terminal and the supervision platform during transportation, ensure the data integrity of the monitoring process, and improve the abnormal recognition efficiency of the supervision platform for delayed data. At the same time, it can effectively monitor the safety status of goods in transportation, so as to timely discover abnormal situations, such as goods collapsing, damage, theft, and drivers stealing and replacing goods.

[0080] In some application scenarios, there may be a problem that the communication interruption time is long, the amount of video surveillance data received by the data terminal is too large, and the storage space is insufficient. Based on this, the data terminal can start a process to perform the storage data deletion task when the remaining storage space is less than the preset storage space threshold.

[0081] Please refer to Figure 4 ,The execution method of the storage data deletion task includes the following steps.

[0082] S401, determining the device priority of the target video acquisition terminal corresponding to the stored video surveillance data.

[0083] In implementation, the target video acquisition terminal corresponding to each video surveillance data in storage can be determined first, and the device priority corresponding to each target video acquisition terminal can be determined by obtaining configuration information. Among them, the device priority can be set according to the shooting object of the target video acquisition terminal, and can also be set according to the picture characteristics of the target video acquisition terminal.

[0084] In one example, the priority order set according to the shooting objects is from high to low: goods, driver, compartment door, and the surrounding environment of the vehicle body.

[0085] In another example, the priority order set according to the picture features is that the first feature is greater than the second feature.

[0086] S402: Determine the data priority of each video surveillance data segment based on the change trend of the transmission characteristics.

[0087] During implementation, when the data terminal determines that an abnormality has occurred in the current video surveillance data based on the transmission characteristics, the video surveillance data at the current moment and within the preset time period before and after is determined as a video surveillance data segment, and an abnormal reminder mark is set for it. Based on this, the data terminal can first determine that the video surveillance data segment carrying the abnormal reminder mark has the highest priority, and then determine the priority based on the difference between the transmission characteristic change amount corresponding to other video surveillance data segments and the corresponding change amount threshold. The smaller the difference, the higher the priority.

[0088] S403: Determine the data to be deleted according to the device priority and the data priority.

[0089] In implementation, when the number of target video acquisition terminals is 1, the data with the lowest data priority can be selected from each video surveillance data segment as the data to be deleted.

[0090] When there are multiple target video acquisition terminals, the target video acquisition terminal with the lowest device priority can be selected first, and then the video surveillance data segment with the lowest data priority can be selected from the video surveillance data segments corresponding to the terminal as the candidate data segment, and it is determined whether there is a video surveillance data segment whose video acquisition period intersects with the candidate data segment and carries an abnormal warning mark, that is, the time when the videos collected by the two overlap. If so, the priority of the candidate data segment is increased to the second priority, that is, second only to the data segment carrying the abnormal warning mark; if not, the candidate data segment is determined to be data to be deleted.

[0091] Based on this, by giving priority to retaining data fragments carrying abnormal warning marks, and secondly retaining data fragments that have intersections with the collection time of these abnormal data fragments, the supervision platform can obtain data fragments carrying abnormal warning marks to the greatest extent. At the same time, when identifying abnormal data, it can synchronously refer to relevant data fragments of other video acquisition terminals, thereby realizing joint identification of multiple screens and ensuring the integrity of abnormal related data as much as possible.

[0092] S404, deleting the data to be deleted.

[0093] S405: Determine whether the remaining storage space is greater than a preset storage space threshold.

[0094] If so, the task is completed and the process is released; otherwise, return to step S401 and repeat the above operation until the task is completed.

[0095] Based on the above method, the data terminal can achieve effective management of storage space to retain important data as much as possible when the storage space is insufficient.

[0096] In some implementations, the supervision platform may analyze the received video surveillance data to determine whether deletions have occurred. When it is determined that deletions have occurred in the video surveillance data, the storage space of the pre-marked data terminal is to be expanded. When the number of pre-marking times corresponding to the data terminal exceeds a preset threshold, an expansion reminder is generated for the data terminal.

[0097] In one example, a method for determining whether video surveillance data has been deleted includes: analyzing the continuity of acquisition time corresponding to the video surveillance data; if the acquisition time is discontinuous, determining that the video surveillance data has been deleted.

[0098] Based on the above method, the supervision platform can provide effective and personalized equipment upgrade suggestions for data terminals, so that managers can optimize system configuration in a timely manner to prevent the loss of valid data.

[0099] Those skilled in the art can understand that all or part of the steps in the above-mentioned implementation method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of each implementation method of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0100] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring the safety of goods in transit, characterized in that: The method is applied to a transportation safety management system, which includes a goods in-transit transportation safety supervision platform, a data terminal, and a video acquisition terminal. The data terminal and the video acquisition terminal are installed on the same transportation vehicle. The method is executed by the supervision platform and includes the following steps: Receiving the video surveillance data uploaded by the data terminal, and determining whether the video surveillance data carries an abnormal warning mark; In the case where the video surveillance data carries an abnormality reminder mark, image recognition is preferentially performed on the video surveillance data to determine whether a surveillance picture corresponding to the video surveillance data has an abnormality; In the case where there is an abnormality in the monitoring screen, the warning level is determined according to the abnormality type; wherein, the video monitoring data is sent to the data terminal by the video acquisition terminal when it is determined that the communication with the supervision platform is abnormal, and is forwarded to the supervision platform by the data terminal; the abnormal reminder mark is set by the data terminal when it is determined that the video monitoring data is abnormal according to the transmission characteristics of the video monitoring data, wherein the video monitoring data is obtained by the video acquisition device after encoding and compressing the original video monitoring data, and the data terminal determines the type of transmission characteristics to be monitored according to the bit rate control mode adopted by the video acquisition terminal. When the video acquisition terminal adopts a constant bit rate, the transmission characteristic is determined to be the amount of video monitoring data corresponding to the unit acquisition time; when the bit rate control mode adopted by the video acquisition terminal is a non-constant bit rate, the transmission characteristic is determined to be the rate at which the video acquisition terminal sends data to the data terminal; the method for the data terminal to determine whether the video monitoring data is abnormal according to the transmission characteristics of the video monitoring data includes: In the process of receiving the video surveillance data, monitoring the transmission characteristics of the video surveillance data in real time; Obtaining a change trend of the transmission characteristic; When it is detected that the change trend does not match the picture feature corresponding to the video acquisition terminal, determining that the video monitoring data is abnormal; The method further comprises: The video surveillance data uploaded in real time by the video acquisition terminal when the communication with the supervision platform is normal is received, and the real-time safety status of the transported goods is obtained based on the recognition of the video surveillance data sent by the video acquisition terminal.

2. The method according to claim 1, characterized in that The method for the video acquisition terminal to determine whether communication with the supervision platform is normal includes: The video acquisition terminal determines that communication is abnormal when monitoring that the upload rate is significantly lower than the cache write rate; wherein, if the amount of cache data continues to increase within a preset time period, it is determined that the upload rate is significantly lower than the cache write rate.

3. The method according to claim 1, characterized in that When it is detected that the change trend does not match the picture feature corresponding to the video acquisition terminal, determining that the video monitoring data is abnormal includes: If the picture feature corresponding to the video acquisition terminal is the first feature, when it is monitored that the change trend meets the first preset condition, it is determined that the video monitoring data is abnormal.

4. The method according to claim 1 or 3, characterized in that: When it is monitored that the change trend of the transmission feature does not match the picture feature corresponding to the video acquisition terminal, determining that the video monitoring data is abnormal includes: If the picture feature corresponding to the video acquisition terminal is the second feature, when it is monitored that the change trend of the transmission feature meets the second preset condition, the vehicle driving status is obtained. If the vehicle driving status is abnormal, the video surveillance data is determined to be abnormal.

5. The method according to claim 1, characterized in that The data terminal communicates with the video acquisition terminal based on a local area network.

6. The method according to claim 1, characterized in that The method further comprises: When it is determined that the video surveillance data is the remaining data after being deleted by the data terminal, pre-marking the storage space of the data terminal to be expanded; When the number of pre-markings corresponding to the data terminal exceeds a preset threshold, a capacity expansion suggestion for the data terminal is generated.

7. The method according to claim 6, characterized in that The method for determining whether the video surveillance data is the remaining data after being deleted by the data terminal includes: Analyzing the continuity of the acquisition time corresponding to the video surveillance data; If the acquisition time is discontinuous, it is determined that the video surveillance data has been deleted.

8. The method according to claim 6, characterized in that The method for deleting video surveillance data by the data terminal includes: Determine the device priority of the target video acquisition terminal corresponding to the video surveillance data in storage; Determine the data priority of each video surveillance data segment based on the changing trend of the transmission characteristics; Determine the data to be deleted according to the device priority and data priority, and delete the data to be deleted; Determine whether the remaining storage space is greater than a preset storage space threshold; if not, repeat the above operation until the remaining storage space is greater than the preset storage space threshold.

9. The method according to claim 1, characterized in that: The method further comprises: Based on the warning level, a corresponding warning processing plan is executed, and the warning processing plan includes sending confirmation information of the abnormal type to the driver, and obtaining the monitoring screen of the cab to determine the driver's processing of the confirmation information, and determining the risk level of the goods based on the processing situation.

10. The cargo transportation safety supervision platform is characterized by: The supervision platform is respectively connected to the video acquisition terminal and the data terminal installed on the same transport vehicle for implementing the method according to any one of claims 1 to 9.

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