A transport node real-time monitoring and early warning system for logistics management
Patent Information
- Application Number
- CN202610880798.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-17
- Publication Date
- 2026-09-15
Smart Images
Figure CN122761561A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of logistics management technology, specifically to a real-time monitoring and early warning system for transportation nodes in logistics management. Background Technology
[0002] As the scale of the domestic logistics network continues to expand, transportation nodes such as ports, logistics parks, and transit warehouses, as core hubs of the supply chain, directly affect the overall logistics efficiency through their operational stability and security management capabilities. Currently, logistics monitoring systems in the industry typically adopt a centralized cloud architecture. This architecture collects logistics data from all transportation nodes using sensors and uploads the collected sensor data to a cloud server for centralized processing and early warning. However, this architecture is highly dependent on the cloud and the public network in practical applications. Once a network outage or cloud failure occurs, the monitoring capability for all transportation nodes is completely lost. This phenomenon is more frequent in remote logistics stations and other areas with unstable public network coverage. Furthermore, the transmission mechanisms in existing technologies have significant flaws. Existing solutions do not prioritize collected data, leading to critical alarm information being blocked by ordinary data during network congestion, resulting in significant delays or even data loss. Moreover, existing technologies generally rely on data from individual sensors for anomaly detection, leading to a high false alarm rate. The lack of a global coordination mechanism between different transportation nodes makes it impossible to detect cascading risks in the supply chain in advance. When an anomaly occurs at one transportation node, the corresponding upstream and downstream nodes often cannot respond in a timely manner. Therefore, existing technologies have significant shortcomings. Summary of the Invention
[0003] The purpose of this invention is to provide a real-time monitoring and early warning system for transportation nodes in logistics management, so as to solve the problems mentioned in the background art.
[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a real-time monitoring and early warning system for transportation nodes for logistics management, comprising: multiple transportation node monitoring units, a regional collaborative server, and a cloud management platform; The transportation node monitoring unit includes a multi-sensor acquisition module, a local edge controller, a local early warning module, and a first communication module; The multi-sensor acquisition module collects environmental data, equipment status data, and cargo status data within the transportation node in real time. After binding the collected raw data with timestamps and geographical location information, it sends the data to the local edge controller. The local edge controller performs real-time preprocessing and anomaly detection on the received raw data through a preset anomaly judgment rule base and a business process timing rule base. When an anomaly event is detected, a local anomaly warning signal is generated and sent to the local warning module for alarm. In this invention, the local edge controller can independently complete the collection, processing, storage and warning of data of this node in a state of complete disconnection from the regional collaborative server and cloud management platform, without relying on external computing resources. The first communication module is used to divide all data output by the local edge controller into multiple priorities according to preset rules, and dynamically allocate transmission bandwidth according to the current network bandwidth status, prioritizing the transmission of high-priority data; during network interruption, all untransmitted data is cached in the local storage center according to priority order; when the network is restored, incremental synchronization transmission is performed in order of priority from high to low; the first communication module is also used to receive regional collaboration early warning information sent by the regional collaboration server and rule update data issued by the cloud management platform, and forward them to the local edge controller; The regional collaboration server includes a regional node management module and a regional early warning collaboration module; The regional node management module is used to divide the transportation node monitoring units within a preset geographical range into multiple regional collaboration groups. Each regional collaboration group contains multiple transportation nodes that are geographically adjacent and closely related in business. It also maintains the online status of all transportation node monitoring units within the preset geographical range and the regional collaboration group division information. The regional early warning and coordination module is used to receive abnormal event data sent by a monitoring unit of a certain transportation node within a preset geographical range. Based on the type, severity, and location of the corresponding abnormal event, it calculates the impact level of the corresponding abnormal event on other transportation nodes, and sends regional coordinated early warning information of different levels to the monitoring units of other affected transportation nodes according to the impact level. In this invention, when a monitoring unit of a certain transportation node detects an abnormal event, it first sends the abnormal event data to other monitoring units of the transportation nodes in its regional coordination group, and then sends it to the regional coordination server. The cloud management platform includes a relational graph construction module and a global early warning analysis module; The association graph construction module is used to construct and update the weighted association graph between all transportation nodes in the entire logistics network in real time. The weighted association graph includes the upstream nodes, downstream nodes, cargo dependencies and corresponding association weight values of each transportation node. The global early warning analysis module is used to receive abnormal event data sent by the regional collaboration server, and calculate the impact of the corresponding abnormal event on each upstream and downstream node based on the severity coefficient of the obtained abnormal event and the correlation weight value in the weighted correlation graph; when the impact value is greater than or equal to the preset early warning threshold, global chain early warning information is sent to the corresponding transportation node and the regional collaboration server. In this invention, the cloud management platform only stores global summary data and abnormal event data, and does not store the original data of all nodes.
[0005] Furthermore, in the process of the first communication module dividing all the data output by the local edge controller into multiple priorities according to preset rules, all the data output by the local edge controller is divided into first-level data, second-level data and third-level data. The primary data includes local anomaly warning signals, detailed data on anomaly events, equipment fault alarm data, and anomaly data on the status of dangerous goods. The secondary data includes key equipment operating status data, valuable goods status data, key business process node data, and node online status data; the categories of key equipment and valuable goods, as well as the key business process nodes, are all pre-set. The three-level data includes environmental monitoring data, operation log data, non-critical equipment status data, and historical statistical data; The specific method by which the first communication module dynamically allocates transmission bandwidth based on the current network bandwidth status includes: Periodically detect the uplink and downlink bandwidth of the current network, and take the minimum value of the obtained uplink and downlink bandwidth as the current available bandwidth; If the currently available bandwidth is greater than or equal to the preset first bandwidth threshold, then first-level data, second-level data, and third-level data will be transmitted simultaneously, with different bandwidths allocated to each level of data. The specific allocation method is as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset first allocation weight; the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset second allocation weight; the third-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset third allocation weight; and the sum of the first allocation weight, the second allocation weight, and the third allocation weight is equal to 1. If the currently available bandwidth is less than a preset first bandwidth threshold and the currently available bandwidth is greater than or equal to a preset second bandwidth threshold, then the transmission of level 3 data will be suspended, and only level 1 and level 2 data will be transmitted. The bandwidth allocated to each level of data will be adjusted as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fourth allocation weight; the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fifth allocation weight; and the sum of the fourth allocation weight and the fifth allocation weight is equal to 1. If the current available bandwidth is less than the preset second bandwidth threshold, only level 1 data will be transmitted, and the allocated bandwidth will be equal to the current available bandwidth.
[0006] Furthermore, during network interruption, the first communication module caches all untransmitted data in the local storage center according to priority order; when the network is restored, during the incremental synchronous transmission process in order of priority from high to low, a fixed-size circular buffer is divided in the local storage center, and the buffer capacity corresponding to each level of data is the product of the total buffer capacity and the preset weight ratio corresponding to the corresponding level of data, and the sum of the preset weights corresponding to each level of data is equal to 1. Specifically, the total capacity of the buffer is denoted as Ctotal, where the capacity of the first-level data buffer is C1 = Ctotal × 0.6, the capacity of the second-level data buffer is C2 = Ctotal × 0.3, and the capacity of the third-level data buffer is C3 = Ctotal × 0.1; During a network outage, untransmitted data is written to the corresponding level of the circular buffer according to priority. When a buffer level is full, the earliest data written in that buffer is automatically overwritten. Once the network is restored, first transmit all data in the first-level data buffer until the first-level data buffer is cleared; after the first-level data transmission is completed, transmit all data in the second-level data buffer until the second-level data buffer is cleared; finally, transmit all data in the second-level data buffer until the third-level data buffer is cleared. For data that fails to be transmitted, retransmission is performed according to the exponential backoff algorithm, and the corresponding retransmission interval is dynamically generated based on the number of retransmissions. The calculation formulas involved are as follows: Where Tn represents the retransmission interval for n retransmission attempts, and the time unit of Tn is milliseconds; when the retransmission attempt fails after reaching the preset number of attempts, the data transmission is abandoned and a log is recorded. In this invention, the first communication module integrates a BeiDou short message communication unit as a backup for public network communication; when public network communication is completely interrupted, primary data and basic node online status information are transmitted through the BeiDou short message communication unit; the BeiDou short message communication unit uses a custom compression encoding protocol to compress a complete alarm message to within a preset number of bytes; when public network communication is restored, it automatically switches back to public network communication mode.
[0007] Furthermore, the local edge controller has a built-in rule update unit, which is used to receive anomaly judgment rule update data and business process timing rule update data sent by the cloud management platform, and automatically update the local anomaly judgment rule library and business process timing rule library; the rule update data adopts an incremental update method, only transmitting the changed rule content, and the update data packet format is the concatenation result of rule ID, operation code and rule content; The local early warning module includes an audible and visual alarm unit, a local display unit, and a local handling suggestion generation unit; The audible and visual alarm unit is used to trigger audible and visual alarms of different frequencies and colors when receiving different levels of warning signals; The local display unit is used to display warning details, the location of the anomaly, and handling suggestions on the local display screen; The local handling suggestion generation unit has a built-in preset handling suggestion rule library, which is used to automatically generate corresponding handling suggestions according to the type and level of abnormal events; and receive handling feedback information from operators, and send the handling results to the local edge controller, which then reports them to the regional collaborative server and the cloud management platform.
[0008] In this invention, the local edge controller is also equipped with a data encryption unit, which is used to encrypt all the collected raw data and generated abnormal event data using a preset encryption algorithm before sending them to the first communication module; After receiving encrypted data, the regional collaborative server and cloud management platform automatically decrypt the data; the encryption key is managed uniformly by the cloud management platform and is automatically updated once every preset period.
[0009] Furthermore, during the data acquisition process of the multi-sensor acquisition module, when a certain sensor detects an anomaly, it automatically triggers all different types of sensors within a preset radius centered on that sensor to perform secondary verification. During the secondary verification process, the event in which the corresponding sensor detects an anomaly is determined to be a real anomaly only when at least one other type of sensor also detects an anomaly; otherwise, the event in which the corresponding sensor detects an anomaly is determined to be a sensor false alarm and no warning is triggered.
[0010] Furthermore, the regional early warning coordination module specifically includes: Based on the type of abnormal event, the corresponding influence radius for the abnormal event type is looked up from the preset influence radius lookup table and denoted as R; Based on the severity of the abnormal event, obtain the severity coefficient of the corresponding abnormal event, denoted as S; Specifically, the severity coefficient of an abnormal event is obtained by querying a pre-set severity coefficient mapping table for abnormal events. This mapping table contains the mapping relationship between the type of abnormal event, the severity of the corresponding abnormal event, and the severity coefficient of the abnormal event. In each mapping relationship in the table, the severity of the corresponding abnormal event corresponds to a sensor data monitoring interval, and the combination result of the type of abnormal event and the severity of the corresponding abnormal event in each mapping relationship corresponds to a unique severity of the abnormal event. Based on the location of the abnormal event, calculate the straight-line distance between the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs, and then calculate the straight-line distance Dg between the g-th transportation node among the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs. The following formulas are used to calculate the impact level of the corresponding abnormal event on other transportation nodes:
[0011] in, This represents the impact level coefficient of the corresponding abnormal event on the g-th transportation node among the remaining transportation nodes within the preset geographical range; max{} represents the operation to find the maximum value; This represents the impact factor of the corresponding abnormal event on the distance between the g-th transportation node and other transportation nodes within the preset geographical range; like If the value is greater than or equal to the threshold of the first impact level coefficient, then the regional early warning coordination module will send the first-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the threshold of the first influence level coefficient and If the value is greater than or equal to the threshold of the second impact level coefficient, then the regional early warning coordination module will send the second-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the second influence level coefficient threshold and If the value is greater than or equal to the threshold of the third impact level coefficient, then the regional early warning coordination module will send the third-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like If the value is less than the threshold of the third impact level coefficient, the regional early warning coordination module will not send regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range.
[0012] Furthermore, the specific process by which the relationship graph construction module constructs a weighted relationship graph between all transportation nodes in the entire logistics network is as follows: Based on the initial logistics network topology data and historical logistics business data, an initial weighted correlation graph is constructed. Newly added logistics business data is analyzed in real time to extract data on cargo flow direction, transportation frequency, average transportation time, and cargo value between each transportation node. Furthermore, the correlation weight values between corresponding transportation nodes are calculated by combining the cargo dependency relationships between each node. The calculation formulas are as follows:
[0013] in, This represents the weight value of the association between the i-th and j-th transportation nodes in the weighted association graph; This represents the number of transportations between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period; This represents the maximum number of transports between any two transport nodes in the weighted correlation graph within a preset statistical period. This represents the total value of goods transported between the i-th and j-th transport nodes in the weighted correlation graph within a preset statistical period. This represents the maximum total value of goods between any two transportation nodes in the weighted correlation graph within a preset statistical period. This represents the strength weight of the dependency relationship between the i-th and j-th transportation nodes obtained from the preset form based on the cargo type. When there is no business interaction between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period, the corresponding correlation weight value will be automatically adjusted. Set to 0.
[0014] Furthermore, the global early warning analysis module specifically includes: After receiving abnormal event data, the severity coefficient of the corresponding abnormal event and the transportation node to which the abnormal event occurred are obtained; in the obtained weighted association graph, a breadth-first search is performed starting from the transportation node to which the corresponding abnormal event occurred, traversing all upstream and downstream nodes with a search depth not exceeding a preset number of layers; The impact of each abnormal event on each traversed transportation node is calculated using the following formulas:
[0015] in, This represents the degree of impact of the corresponding abnormal event on the kth transport node being traversed. The time decay factor corresponding to the current time; , This indicates the preset attenuation coefficient; This indicates the time interval between the current time and the occurrence of the corresponding abnormal event; When the degree of impact of the corresponding abnormal event on the kth transport node being traversed is... If the warning threshold is greater than or equal to the preset warning threshold, then a global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred; otherwise, no global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred. Once the abnormal event is resolved, the sending of global chain warning information will automatically stop, and a warning cancellation notification will be sent to all transportation nodes that have received warnings.
[0016] Furthermore, the regional collaborative server sends heartbeat packets to all transportation node monitoring units within a preset geographical range at a preset period, and determines that the corresponding transportation node has failed if no heartbeat response is received for a preset number of consecutive times; when a transportation node monitoring unit fails, the corresponding regional collaborative server automatically and temporarily assigns the monitoring task of the transportation node to the backup transportation node monitoring unit of the transportation node; the backup transportation node monitoring unit of each failed transportation node is adjacent to the corresponding transportation node monitoring unit and its current load rate is less than a preset load rate threshold. The backup transport node monitoring unit receives sensor data from the faulty transport node for real-time processing and early warning. Once the faulty transport node recovers and sends a preset number of heartbeat packets, the monitoring task of the corresponding backup transport node monitoring unit is automatically revoked, and all data from the fault period is synchronized.
[0017] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) The present invention achieves complete autonomy of the transport nodes to a certain extent through the local edge controller. Even if the public network is completely interrupted, each transport node can still independently complete the data collection, processing and local early warning of its own transport nodes. (2) The present invention adopts a combined transmission mechanism of data priority classification, dynamic bandwidth allocation, hierarchical ring buffer and exponential backoff retransmission, which enables the effective transmission of first-level alarm information to be prioritized when network bandwidth is limited, thereby effectively reducing the feedback delay of alarm information. (3) This invention constructs a three-level early warning mechanism of local anomaly early warning, regional collaborative early warning and global chain early warning, and can accurately calculate the risk impact range of abnormal events by combining a weighted correlation graph with the corresponding time decay factor. Attached Figure Description
[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a real-time monitoring and early warning system for transportation nodes used in logistics management according to the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Please see Figure 1 This embodiment provides a real-time monitoring and early warning system for transportation nodes in logistics management, including: multiple transportation node monitoring units, a regional collaborative server, and a cloud management platform; The transportation node monitoring unit includes a multi-sensor acquisition module, a local edge controller, a local early warning module, and a first communication module; The multi-sensor acquisition module collects environmental data, equipment status data, and cargo status data within the transportation node in real time. After binding the collected raw data with timestamps and geographical location information, it sends the data to the local edge controller. During the data acquisition process of the multi-sensor acquisition module, when a sensor detects an anomaly, it automatically triggers secondary verification of all different types of sensors within a preset radius centered on that sensor. During the secondary verification process, the event of the corresponding sensor detecting an anomaly is determined to be a real anomaly only when at least one other type of sensor also detects an anomaly; otherwise, the event of the corresponding sensor detecting an anomaly is determined to be a sensor false alarm and no warning is triggered.
[0021] The local edge controller performs real-time preprocessing and anomaly detection on the received raw data through a preset anomaly judgment rule base and business process timing rule base. When an anomaly event is detected, a local anomaly warning signal is generated and sent to the local warning module for alarm. The local edge controller can independently complete the collection, processing, storage and warning of local node data in a state of complete disconnection from the regional collaborative server and cloud management platform, without relying on external computing resources. The local edge controller has a built-in rule update unit, which is used to receive anomaly judgment rule update data and business process timing rule update data sent by the cloud management platform, and automatically update the local anomaly judgment rule library and business process timing rule library; the rule update data adopts an incremental update method, only transmitting the changed rule content, and the update data packet format is the concatenation result of rule ID, operation code and rule content; In this invention, the local edge controller is also equipped with a data encryption unit, which is used to encrypt all the collected raw data and generated abnormal event data using a preset encryption algorithm before sending them to the first communication module; After receiving encrypted data, the regional collaborative server and cloud management platform automatically decrypt the data; the encryption key is managed uniformly by the cloud management platform and is automatically updated once every preset period.
[0022] The local early warning module includes an audible and visual alarm unit, a local display unit, and a local handling suggestion generation unit; The audible and visual alarm unit is used to trigger audible and visual alarms of different frequencies and colors when receiving different levels of warning signals; The local display unit is used to display warning details, the location of the anomaly, and handling suggestions on the local display screen; The local handling suggestion generation unit has a built-in preset handling suggestion rule library, which is used to automatically generate corresponding handling suggestions according to the type and level of abnormal events; and receive handling feedback information from operators, and send the handling results to the local edge controller, which then reports them to the regional collaborative server and the cloud management platform.
[0023] The first communication module is used to divide all data output by the local edge controller into multiple priorities according to preset rules, and dynamically allocate transmission bandwidth according to the current network bandwidth status, prioritizing the transmission of high-priority data; during network interruption, all untransmitted data is cached in the local storage center according to priority order; when the network is restored, incremental synchronization transmission is performed in order of priority from high to low; the first communication module is also used to receive regional collaboration early warning information sent by the regional collaboration server and rule update data issued by the cloud management platform, and forward them to the local edge controller; In the process of the first communication module dividing all the data output by the local edge controller into multiple priorities according to preset rules, all the data output by the local edge controller is divided into first-level data, second-level data and third-level data. The primary data includes local anomaly warning signals, detailed data on anomaly events, equipment fault alarm data, and anomaly data on the status of dangerous goods. The secondary data includes key equipment operating status data, valuable goods status data, key business process node data, and node online status data; the categories of key equipment and valuable goods, as well as the key business process nodes, are all pre-set. The three-level data includes environmental monitoring data, operation log data, non-critical equipment status data, and historical statistical data; The specific method by which the first communication module dynamically allocates transmission bandwidth based on the current network bandwidth status includes: Periodically detect the uplink and downlink bandwidth of the current network, and take the minimum value of the obtained uplink and downlink bandwidth as the current available bandwidth; If the currently available bandwidth is greater than or equal to the preset first bandwidth threshold, then first-level data, second-level data, and third-level data will be transmitted simultaneously, with different bandwidths allocated to each level of data. The specific allocation method is as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset first allocation weight; the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset second allocation weight; the third-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset third allocation weight, and the sum of the first allocation weight, the second allocation weight and the third allocation weight is equal to 1; in this embodiment, the first allocation weight is 0.5, the second allocation weight is 0.3, and the third allocation weight is 0.2. If the currently available bandwidth is less than a preset first bandwidth threshold and the currently available bandwidth is greater than or equal to a preset second bandwidth threshold, then the transmission of level 3 data will be suspended, and only level 1 and level 2 data will be transmitted. The bandwidth allocated to each level of data will be adjusted as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fourth allocation weight, and the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fifth allocation weight, and the sum of the fourth allocation weight and the fifth allocation weight is equal to 1; in this embodiment, the fourth allocation weight is 0.6 and the fifth allocation weight is 0.4. If the current available bandwidth is less than the preset second bandwidth threshold, only level 1 data will be transmitted, and the allocated bandwidth will be equal to the current available bandwidth.
[0024] Furthermore, during network interruption, the first communication module caches all untransmitted data in the local storage center according to priority order; when the network is restored, during the incremental synchronous transmission process in order of priority from high to low, a fixed-size circular buffer is divided in the local storage center, and the buffer capacity corresponding to each level of data is the product of the total buffer capacity and the preset weight ratio corresponding to the corresponding level of data, and the sum of the preset weights corresponding to each level of data is equal to 1. Specifically, the total capacity of the buffer is denoted as Ctotal, where the capacity of the first-level data buffer is C1 = Ctotal × 0.6, the capacity of the second-level data buffer is C2 = Ctotal × 0.3, and the capacity of the third-level data buffer is C3 = Ctotal × 0.1; During a network outage, untransmitted data is written to the corresponding level of the circular buffer according to priority. When a buffer level is full, the earliest data written in that buffer is automatically overwritten. Once the network is restored, first transmit all data in the first-level data buffer until the first-level data buffer is cleared; after the first-level data transmission is completed, transmit all data in the second-level data buffer until the second-level data buffer is cleared; finally, transmit all data in the second-level data buffer until the third-level data buffer is cleared. For data that fails to be transmitted, retransmission is performed according to the exponential backoff algorithm, and the corresponding retransmission interval is dynamically generated based on the number of retransmissions. The calculation formulas involved are as follows: Where Tn represents the retransmission interval for n retransmission attempts, and the time unit of Tn is milliseconds; when the retransmission attempt fails after reaching the preset number (3 times in this embodiment), the data transmission is abandoned and a log is recorded. In this invention, the first communication module integrates a BeiDou short message communication unit as a backup for public network communication; when public network communication is completely interrupted, primary data and basic node online status information are transmitted through the BeiDou short message communication unit; the BeiDou short message communication unit uses a custom compression encoding protocol to compress a complete alarm message to within a preset number of bytes; when public network communication is restored, it automatically switches back to public network communication mode.
[0025] The regional collaboration server includes a regional node management module and a regional early warning collaboration module; The regional node management module is used to divide the transportation node monitoring units within a preset geographical range into multiple regional collaboration groups. Each regional collaboration group contains multiple transportation nodes that are geographically adjacent and closely related in business. It also maintains the online status of all transportation node monitoring units within the preset geographical range and the regional collaboration group division information. The regional early warning and coordination module is used to receive abnormal event data sent by a monitoring unit of a certain transportation node within a preset geographical range. Based on the type, severity, and location of the corresponding abnormal event, it calculates the impact level of the corresponding abnormal event on other transportation nodes, and sends regional coordinated early warning information of different levels to the monitoring units of other affected transportation nodes according to the impact level. In this invention, when a monitoring unit of a certain transportation node detects an abnormal event, it first sends the abnormal event data to other monitoring units of the transportation nodes in its regional coordination group, and then sends it to the regional coordination server. The regional early warning and coordination module specifically includes: Based on the type of abnormal event, the corresponding influence radius for the abnormal event type is looked up from the preset influence radius lookup table and denoted as R; Based on the severity of the abnormal event, obtain the severity coefficient of the corresponding abnormal event, denoted as S; Specifically, the severity coefficient of an abnormal event is obtained by querying a pre-set severity coefficient mapping table for abnormal events. This mapping table contains the mapping relationship between the type of abnormal event, the severity of the corresponding abnormal event, and the severity coefficient of the abnormal event. In each mapping relationship in the table, the severity of the corresponding abnormal event corresponds to a sensor data monitoring interval, and the combination result of the type of abnormal event and the severity of the corresponding abnormal event in each mapping relationship corresponds to a unique severity of the abnormal event. Based on the location of the abnormal event, calculate the straight-line distance between the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs, and then calculate the straight-line distance Dg between the g-th transportation node among the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs. The following formulas are used to calculate the impact level of the corresponding abnormal event on other transportation nodes:
[0026] in, This represents the impact level coefficient of the corresponding abnormal event on the g-th transportation node among the remaining transportation nodes within the preset geographical range; max{} represents the operation to find the maximum value; This represents the impact factor of the corresponding abnormal event on the distance between the g-th transportation node and other transportation nodes within the preset geographical range; like If the value is greater than or equal to the threshold of the first impact level coefficient, then the regional early warning coordination module will send the first-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the threshold of the first influence level coefficient and If the value is greater than or equal to the threshold of the second impact level coefficient, then the regional early warning coordination module will send the second-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the second influence level coefficient threshold and If the value is greater than or equal to the threshold of the third impact level coefficient, then the regional early warning coordination module will send the third-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like If the value is less than the threshold of the third impact level coefficient, the regional early warning coordination module will not send regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range.
[0027] The regional collaborative server sends heartbeat packets to all transportation node monitoring units within a preset geographical range at a preset period, and determines that the corresponding transportation node has failed if no heartbeat response is received for a preset number of consecutive times. When a transportation node monitoring unit fails, the corresponding regional collaborative server automatically assigns the monitoring task of the transportation node to the backup transportation node monitoring unit of the transportation node. The backup transportation node monitoring unit of each failed transportation node is adjacent to the corresponding transportation node monitoring unit and its current load rate is less than a preset load rate threshold. The backup transport node monitoring unit receives sensor data from the faulty transport node for real-time processing and early warning. Once the faulty transport node recovers and sends a preset number of heartbeat packets, the monitoring task of the corresponding backup transport node monitoring unit is automatically revoked, and all data from the fault period is synchronized.
[0028] The cloud management platform includes a relational graph construction module and a global early warning analysis module; The association graph construction module is used to construct and update the weighted association graph between all transportation nodes in the entire logistics network in real time. The weighted association graph includes the upstream nodes, downstream nodes, cargo dependencies and corresponding association weight values of each transportation node. The specific process by which the relationship graph construction module constructs a weighted relationship graph between all transportation nodes in the entire logistics network is as follows: Based on the initial logistics network topology data and historical logistics business data, an initial weighted correlation graph is constructed. Newly added logistics business data is analyzed in real time to extract data on cargo flow direction, transportation frequency, average transportation time, and cargo value between each transportation node. Furthermore, the correlation weight values between corresponding transportation nodes are calculated by combining the cargo dependency relationships between each node. The calculation formulas are as follows:
[0029] in, This represents the weight value of the association between the i-th and j-th transportation nodes in the weighted association graph; This represents the number of transportations between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period; This represents the maximum number of transports between any two transport nodes in the weighted correlation graph within a preset statistical period. This represents the total value of goods transported between the i-th and j-th transport nodes in the weighted correlation graph within a preset statistical period. This represents the maximum total value of goods between any two transportation nodes in the weighted correlation graph within a preset statistical period. This represents the strength weight of the dependency relationship between the i-th and j-th transportation nodes obtained from the preset form based on the cargo type. When there is no business interaction between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period, the corresponding correlation weight value will be automatically adjusted. Set to 0.
[0030] The global early warning analysis module is used to receive abnormal event data sent by the regional collaboration server, and calculate the impact of the corresponding abnormal event on each upstream and downstream node based on the severity coefficient of the obtained abnormal event and the correlation weight value in the weighted correlation graph; when the impact value is greater than or equal to the preset early warning threshold, global chain early warning information is sent to the corresponding transportation node and the regional collaboration server. The global early warning analysis module specifically includes: After receiving abnormal event data, the severity coefficient of the corresponding abnormal event and the transportation node to which the abnormal event occurred are obtained; in the obtained weighted association graph, a breadth-first search is performed starting from the transportation node to which the corresponding abnormal event occurred, traversing all upstream and downstream nodes with a search depth not exceeding a preset number of layers; The impact of each abnormal event on each traversed transportation node is calculated using the following formulas:
[0031] in, This represents the degree of impact of the corresponding abnormal event on the kth transport node being traversed. The time decay factor corresponding to the current time; , This indicates the preset attenuation coefficient; This indicates the time interval between the current time and the occurrence of the corresponding abnormal event; When the degree of impact of the corresponding abnormal event on the kth transport node being traversed is... If the warning threshold is greater than or equal to the preset warning threshold, then a global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred; otherwise, no global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred. Once the abnormal event is resolved, the sending of global chain warning information will automatically stop, and a warning cancellation notification will be sent to all transportation nodes that have received warnings.
[0032] In this embodiment, the cloud management platform only stores global summary data and abnormal event data, and does not store the original data of all nodes.
[0033] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0034] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A transport node real-time monitoring and early warning system for logistics management, characterized in that, include: Multiple transportation node monitoring units, a regional collaborative server, and a cloud management platform; The transportation node monitoring unit includes a multi-sensor acquisition module, a local edge controller, a local early warning module, and a first communication module; The multi-sensor acquisition module collects environmental data, equipment status data, and cargo status data within the transportation node in real time. After binding the collected raw data with timestamps and geographical location information, it sends the data to the local edge controller. The local edge controller performs real-time preprocessing and anomaly detection on the received raw data through a preset anomaly detection rule base and a business process timing rule base; when an anomaly event is detected, a local anomaly warning signal is generated and sent to the local warning module for alarm. The first communication module is used to divide all data output by the local edge controller into multiple priorities according to preset rules, and dynamically allocate transmission bandwidth according to the current network bandwidth status, prioritizing the transmission of high-priority data; During network outages, all untransmitted data is cached in the local storage center according to priority; when the network is restored, incremental synchronization is performed in order of priority from high to low; the first communication module is also used to receive regional collaborative early warning information sent by the regional collaborative server and rule update data issued by the cloud management platform, and forward them to the local edge controller. The regional collaboration server includes a regional node management module and a regional early warning collaboration module; The regional node management module is used to divide the transportation node monitoring units within a preset geographical range into multiple regional collaboration groups. Each regional collaboration group contains multiple transportation nodes that are geographically adjacent and closely related in business. It also maintains the online status of all transportation node monitoring units within the preset geographical range and the regional collaboration group division information. The regional early warning and coordination module is used to receive abnormal event data sent by a monitoring unit of a certain transportation node within a preset geographical range, calculate the impact level of the corresponding abnormal event on other transportation nodes according to the type, severity and location of the corresponding abnormal event, and send regional coordinated early warning information of different levels to the monitoring units of other affected transportation nodes according to the impact level. The cloud management platform includes a relational graph construction module and a global early warning analysis module; The association graph construction module is used to construct and update the weighted association graph between all transportation nodes in the entire logistics network in real time. The weighted association graph includes the upstream nodes, downstream nodes, cargo dependencies and corresponding association weight values of each transportation node. The global early warning analysis module is used to receive abnormal event data sent by the regional collaborative server, and calculate the impact of the corresponding abnormal event on each upstream and downstream node based on the severity coefficient of the obtained abnormal event and the correlation weight value in the weighted correlation graph. When the impact level is greater than or equal to the preset warning threshold, a global chain warning message is sent to the corresponding transportation node and regional collaboration server.
2. The real-time monitoring and early warning system for transport nodes for logistics management according to claim 1, characterized in that: In the process of the first communication module dividing all the data output by the local edge controller into multiple priorities according to preset rules, all the data output by the local edge controller is divided into first-level data, second-level data and third-level data. The primary data includes local anomaly warning signals, detailed data on anomaly events, equipment fault alarm data, and data on the abnormal status of dangerous goods. The secondary data includes key equipment operating status data, valuable goods status data, key business process node data, and node online status data; the categories of key equipment and valuable goods, as well as the key business process nodes, are all pre-set. The three-level data includes environmental monitoring data, operation log data, non-critical equipment status data, and historical statistical data; The specific method by which the first communication module dynamically allocates transmission bandwidth based on the current network bandwidth status includes: Periodically detect the uplink and downlink bandwidth of the current network, and take the minimum value of the obtained uplink and downlink bandwidth as the current available bandwidth; If the currently available bandwidth is greater than or equal to the preset first bandwidth threshold, then first-level data, second-level data, and third-level data will be transmitted simultaneously, with different bandwidths allocated to each level of data. The specific allocation method is as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset first allocation weight; the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset second allocation weight; the third-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset third allocation weight; and the sum of the first allocation weight, the second allocation weight, and the third allocation weight is equal to 1. If the currently available bandwidth is less than a preset first bandwidth threshold and the currently available bandwidth is greater than or equal to a preset second bandwidth threshold, then the transmission of level 3 data will be suspended, and only level 1 and level 2 data will be transmitted. The bandwidth allocated to each level of data will be adjusted as follows: The first-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fourth allocation weight; the second-level data allocation bandwidth is equal to the product of the current available bandwidth and the preset fifth allocation weight; and the sum of the fourth allocation weight and the fifth allocation weight is equal to 1. If the current available bandwidth is less than the preset second bandwidth threshold, only level 1 data will be transmitted, and the allocated bandwidth will be equal to the current available bandwidth.
3. The real-time monitoring and early warning system for transport nodes for logistics management according to claim 1, characterized in that: During a network interruption, the first communication module caches all untransmitted data in the local storage center according to priority order. When the network is restored, incremental synchronous transmission is performed in order of priority from high to low. During this process, a fixed-size circular buffer is divided in the local storage center. The buffer capacity corresponding to each level of data is the product of the total buffer capacity and the preset weight ratio of the corresponding level of data. The sum of the preset weights corresponding to each level of data is equal to 1. During a network outage, untransmitted data is written to the corresponding level of the circular buffer according to priority. When a buffer level is full, the earliest data written in that buffer is automatically overwritten. Once the network is restored, first transmit all data in the first-level data buffer until the first-level data buffer is cleared; after the first-level data transmission is completed, transmit all data in the second-level data buffer until the second-level data buffer is cleared; finally, transmit all data in the second-level data buffer until the third-level data buffer is cleared. For data that fails to be transmitted, it is retransmitted according to the exponential backoff algorithm, and the corresponding retransmission interval is dynamically generated based on the number of retransmissions.
4. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 1, characterized in that: The local edge controller has a built-in rule update unit, which is used to receive anomaly judgment rule update data and business process timing rule update data sent by the cloud management platform, and automatically update the local anomaly judgment rule library and business process timing rule library; the rule update data adopts an incremental update method, only transmitting the changed rule content, and the update data packet format is the concatenation result of rule ID, operation code and rule content; The local early warning module includes an audible and visual alarm unit, a local display unit, and a local handling suggestion generation unit; The audible and visual alarm unit is used to trigger audible and visual alarms of different frequencies and colors when receiving different levels of warning signals; The local display unit is used to display warning details, the location of the anomaly, and handling suggestions on the local display screen; The local handling suggestion generation unit has a built-in preset handling suggestion rule library, which is used to automatically generate corresponding handling suggestions according to the type and level of the abnormal event; It also receives feedback information from operators and sends the results to the local edge controller, which then reports them to the regional collaboration server and cloud management platform.
5. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 1, characterized in that: During the data acquisition process of the multi-sensor acquisition module, when a sensor detects an anomaly, it automatically triggers all different types of sensors within a preset radius centered on that sensor to perform secondary verification. During the secondary verification process, the event in which the corresponding sensor detects an anomaly is determined to be a real anomaly only when at least one other type of sensor also detects an anomaly. Conversely, if the corresponding sensor detects an abnormal event, it is determined to be a sensor false alarm and no warning is triggered.
6. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 1, characterized in that: The regional early warning and coordination module specifically includes: Based on the type of the abnormal event, the influence radius corresponding to the corresponding abnormal event type is looked up from the preset influence radius lookup table and denoted as R; based on the severity of the abnormal event, the severity coefficient of the corresponding abnormal event is obtained and denoted as S. Based on the location of the abnormal event, calculate the straight-line distance between the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs, and then calculate the straight-line distance Dg between the g-th transportation node among the remaining transportation nodes within the preset geographical range and the transportation node to which the corresponding abnormal event location belongs. The following formulas are used to calculate the impact level of the corresponding abnormal event on other transportation nodes: in, This represents the impact level coefficient of the corresponding abnormal event on the g-th transportation node among the remaining transportation nodes within the preset geographical range; max{} represents the operation to find the maximum value; This represents the impact factor of the corresponding abnormal event on the distance between the g-th transportation node and other transportation nodes within the preset geographical range; like If the value is greater than or equal to the threshold of the first impact level coefficient, then the regional early warning coordination module will send the first-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the threshold of the first influence level coefficient and If the value is greater than or equal to the threshold of the second impact level coefficient, then the regional early warning coordination module will send the second-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like Less than the second influence level coefficient threshold and If the value is greater than or equal to the threshold of the third impact level coefficient, then the regional early warning coordination module will send the third-level regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range. like If the value is less than the threshold of the third impact level coefficient, the regional early warning coordination module will not send regional coordinated early warning information based on the corresponding abnormal event to the g-th transportation node among the remaining transportation nodes within the preset geographical range.
7. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 6, characterized in that: The specific process by which the relationship graph construction module constructs a weighted relationship graph between all transportation nodes in the entire logistics network is as follows: Based on the initial logistics network topology data and historical logistics business data, an initial weighted correlation graph is constructed. Newly added logistics business data is analyzed in real time to extract data on cargo flow direction, transportation frequency, average transportation time, and cargo value between each transportation node. Furthermore, the correlation weight values between corresponding transportation nodes are calculated by combining the cargo dependency relationships between each node. The calculation formulas are as follows: in, This represents the weight value of the association between the i-th and j-th transportation nodes in the weighted association graph; This represents the number of transportations between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period; This represents the maximum number of transports between any two transport nodes in the weighted correlation graph within a preset statistical period. This represents the total value of goods transported between the i-th and j-th transport nodes in the weighted correlation graph within a preset statistical period. This represents the maximum total value of goods between any two transportation nodes in the weighted correlation graph within a preset statistical period. This represents the strength weight of the dependency relationship between the i-th and j-th transportation nodes obtained from the preset form based on the cargo type. When there is no business interaction between the i-th and j-th transportation nodes in the weighted correlation graph within a preset statistical period, the corresponding correlation weight value will be automatically adjusted. Set to 0.
8. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 7, characterized in that: The global early warning analysis module specifically includes: After receiving abnormal event data, the severity coefficient of the corresponding abnormal event and the transportation node to which the abnormal event occurred are obtained; in the obtained weighted association graph, a breadth-first search is performed starting from the transportation node to which the corresponding abnormal event occurred, traversing all upstream and downstream nodes with a search depth not exceeding a preset number of layers; The impact of each abnormal event on each traversed transportation node is calculated using the following formulas: in, This represents the degree of impact of the corresponding abnormal event on the kth transport node being traversed. The time decay factor corresponding to the current time; , This indicates the preset attenuation coefficient; This indicates the time interval between the current time and the occurrence of the corresponding abnormal event; When the degree of impact of the corresponding abnormal event on the kth transport node being traversed is... If the warning threshold is greater than or equal to the preset warning threshold, then a global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred; otherwise, no global chain warning message is sent to the transportation node and the corresponding regional collaboration server where the abnormal event occurred. Once the abnormal event is resolved, the sending of global chain warning information will automatically stop, and a warning cancellation notification will be sent to all transportation nodes that have received warnings.
9. A real-time monitoring and early warning system for transportation nodes in logistics management according to claim 1, characterized in that: The regional collaborative server sends heartbeat packets to all transportation node monitoring units within a preset geographical range at a preset period, and determines that the corresponding transportation node has failed if no heartbeat response is received for a preset number of consecutive times. When a transportation node monitoring unit fails, the corresponding regional collaborative server automatically assigns the monitoring task of the transportation node to the backup transportation node monitoring unit of the transportation node. The backup transportation node monitoring unit of each failed transportation node is adjacent to the corresponding transportation node monitoring unit and its current load rate is less than a preset load rate threshold. The backup transport node monitoring unit receives sensor data from the faulty transport node for real-time processing and early warning. Once the faulty transport node recovers and sends a preset number of heartbeat packets, the monitoring task of the corresponding backup transport node monitoring unit is automatically revoked, and all data from the fault period is synchronized.