Internet of Things risk interaction tracing evaluation system integrated with UWB transmission tracking

Abstract

The invention discloses an internet of things risk interaction tracing evaluation system integrated with UWB transmission tracking, and relates to the technical field of UWB transmission, real-time positioning and state data of goods are collected through a UWB label module, a UWB base station module receives and counts communication density parameters and channel occupation efficiency parameters, and the communication density parameters and the channel occupation efficiency parameters are transmitted to the UWB label module. The central server module establishes a conflict degree evaluation model based on the data and generates a real-time conflict degree score, the regulation and control strategy generation unit dynamically formulates channel allocation, transmission frequency adjustment and priority allocation strategies according to an evaluation result, and the dynamic regulation and control module executes the strategies and controls the channel allocation, transmission frequency adjustment and priority allocation. Communication conflicts are effectively relieved through channel frequency hopping, frequency optimization and priority adjustment, meanwhile, the traceability evaluation module combines conflict scores and historical data of abnormal cargos, the problems are precisely traced, a risk evaluation report is generated, efficient exception handling and system optimization are achieved, and the system reliability is improved. And the reliability, flexibility and warehouse management efficiency of system communication are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of UWB transmission technology, and in particular to an Internet of Things risk interactive tracing and assessment system integrating UWB transmission tracking. Background Art

[0002] The IoT risk interactive traceability assessment that integrates UWB (ultra-wideband) transmission and tracking is a comprehensive method that uses the high-precision positioning and low-power data transmission capabilities of UWB technology to establish a risk information interaction mechanism between IoT devices, trace the source of potential problems in real time and assess their impact. This assessment tracks the operating status, location and historical data of the device, identifies risks and takes targeted measures, thereby improving the security and reliability of the IoT system.

[0003] For example, in smart warehousing, goods are equipped with UWB tags for precise positioning. When certain goods are damaged or have abnormal temperatures, the UWB system can trace the movement of the goods (for example, from which section of transportation the abnormality began) and promptly notify relevant staff of the problem through the IoT network. Risk interaction assessment can determine whether the problem is caused by equipment failure, environmental problems, or human error.

[0004] The prior art has the following deficiencies: When a large number of UWB tags used simultaneously in a warehousing system send signals to each other, communication conflicts may occur, resulting in some data packets not being correctly received or processed. Although UWB has a certain anti-collision capability, it may still fail in extremely high-density environments. In addition, communication conflicts can cause some cargo data to be lost or delayed. For example, during peak warehousing and loading and unloading processes, the status data of some key cargoes could not be uploaded, resulting in missing information or interrupted monitoring. Once this problem occurs frequently, it will cause the overall performance of the system to decline, and even require suspension of operation for manual intervention. Summary of the invention

[0005] The purpose of the present invention is to provide an Internet of Things risk interactive traceability assessment system integrating UWB transmission tracking to address the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an Internet of Things risk interactive tracing and evaluation system integrating UWB transmission tracking, comprising 1. an Internet of Things risk interactive tracing and evaluation system integrating UWB transmission tracking, characterized in that: it comprises a UWB tag module, a UWB base station module, a central server module, a dynamic control module and a tracing and evaluation module; The UWB tag module is installed on the cargo to collect the real-time location data and environmental status data of the cargo and send it to the UWB base station via an ultra-wideband signal; The UWB base station module is deployed in a storage or transportation environment to receive the positioning data and status data sent by the UWB tag module, and to count the communication density parameters per unit time, including the number of active UWB tags and the data transmission frequency; at the same time, the channel occupancy efficiency parameters are recorded, including the proportion of successfully transmitted data packets to the total channel occupancy time; The central server module is connected to the UWB base station module in communication, and includes: A data analysis unit, configured to establish a conflict degree evaluation model after analyzing the communication density parameter and the channel occupancy efficiency parameter, and generate a real-time conflict degree score; A control strategy generating unit, used for dynamically generating a channel allocation optimization scheme, a transmission frequency adjustment strategy and a priority allocation scheme according to the conflict degree score; The dynamic control module is connected to the UWB tag module and the central server module for receiving and executing the control scheme generated by the control strategy generation unit, and specifically includes: Dynamically adjust the working channel of UWB tags and use frequency hopping mechanism to disperse the communication load in high-density areas; reduce the data transmission frequency for non-critical cargo tags and maintain high-frequency transmission for critical cargo tags; dynamically assign channel priority according to the importance of the cargo and give priority to ensuring data transmission of critical cargo; The traceability assessment module is deployed on the central server module to analyze the historical location data and status data of abnormal goods, combine the conflict degree score, trace the source of the problem and generate a risk assessment report.

[0007] Preferably, in the UWB base station module, the base station identifies the number of active UWB tags in the current time according to the tag ID in the received data packet, determines whether the tag is in an active state through the periodic transmission of the tag signal, and records the number of data packets sent by each tag per unit time, calculates its sending frequency, and calculates the communication density parameter per unit time by combining the data sending frequencies of all active tags; The base station records each successfully received data packet and counts the total number of data packets received per unit time. It uses the data packet sequence number or verification mechanism to distinguish repeated transmissions from new data packets. The base station accumulates the total channel occupancy time per unit time based on the reception time and transmission duration of each recorded data packet, and calculates the channel occupancy efficiency parameter.

[0008] Preferably, in the central server module, the communication density parameter within a unit time is analyzed to generate a communication density anomaly score value, and the communication density anomaly score value is obtained by: setting the communication density data set to , each data point Represents the communication density per unit time; for each data point , calculate the distance between it and all other points ; Find the distance to the kth nearest neighbor, defined as: ; In the formula, For data points The k nearest neighbor set of Distance not greater than For all points of and , calculate the reachable distance , the expression is: ;if Less than , then take As the reachable distance, the data point The local reachable density It is defined as the reciprocal mean of the reachable distances of its neighboring points: ; yes number of points; data points The local anomaly factor It is defined as the ratio of the average of the local reachable density of its neighboring points to its own local reachable density: ;according to Generate the communication density anomaly score Se, the expression is: ; MaxLOF is the maximum LOF value among all current points.

[0009] Preferably, the calculated channel occupancy efficiency parameters are analyzed to generate a channel occupancy efficiency fluctuation score value, and the channel occupancy efficiency fluctuation score value is obtained by: assuming that the channel occupancy efficiency data is a time series ; Starting from the zth data point, z is the size of the sliding window, and a sliding window containing z data points is constructed : ; Each time the window is moved forward by one time point, a new subset is formed. Calculate the mean of the channel occupancy efficiency and the standard deviation of the channel occupancy efficiency within the window; for time t and the previous time , calculate the rate of change of the standard deviation : ; is the standard deviation of the channel occupancy efficiency at time t; the rate of change of the standard deviation Mapped to the channel occupancy efficiency fluctuation score EG, with a score range of 0 to 100: ; In the formula, The preset maximum fluctuation rate.

[0010] Preferably, a weighted nonlinear combination model is used to calculate the conflict degree score : ; Among them: α and β are weight parameters, satisfying α+β=1, which are used to adjust Se and EG contribution ratio; f(Se) is the nonlinear mapping function of the communication density anomaly score value, and g(EG) is the nonlinear mapping function of the channel efficiency fluctuation score value; The communication density anomaly score value mapping f(Se) uses an exponential function to map the nonlinear growth characteristics of Se: ;in: =100: the normalized maximum value of the communication density score, e is a natural constant; The channel occupancy efficiency fluctuation score value mapping g(EG) adopts exponential mapping: ;in: =100: the normalized maximum value of the channel efficiency fluctuation score.

[0011] Preferably, the control strategy generation unit scores the conflict degree. Generate the corresponding conflict scope and control strategy, including: High Conflict >70: Switch the tag channel in the high-load area to ensure the transmission priority of key cargo tags; Medium conflict 40 ≤ ≤70: Reduce the transmission frequency of non-critical tags and gradually reduce the transmission rate of low-priority tags; Low Conflict <40: Restore normal channel allocation and transmission frequency to maintain real-time transmission of high-priority tags.

[0012] Preferably, in the traceability assessment module, the location data includes the historical location information of the goods ; Status data includes environmental status data ; The conflict score includes the history of the calculation Time series; Align the positioning data, status data, and conflict score by timestamp to form a complete time series: ; Based on positioning data Draw the moving trajectory of the goods, mark their stop points and movement direction, compare the trajectory with the planned route, and identify the deviation points; perform threshold detection on the status data and identify the exceeding points: ; is the maximum value of the preset ambient temperature data. The maximum value of the preset environmental humidity data is used to check the abnormal status period. Value, if Overlapping with status anomalies, marked as anomalies affected by the communication environment: ; Based on the abnormal mark, determine the time period and location where the problem occurred.

[0013] Preferably, the potential impact of the problem on the quality of the goods is evaluated based on the duration of the abnormality and the importance of the goods. Specifically, the risk score is calculated by combining the duration of the problem, the magnitude of the abnormal state, and the degree of communication conflict. : ; is the abnormal duration weight, is the weight of the abnormality degree of the state, is the average value of the conflict degree score, is the preset weight coefficient, satisfying ; according to Values ​​are divided into risk levels: <40: low risk; 40≤ <70: medium risk; ≥70: High risk.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention proposes a complete set of optimization and tracing solutions for communication conflict problems in high-density environments by constructing an Internet of Things risk interactive traceability assessment system that integrates UWB transmission tracking. The system includes a UWB tag module, a UWB base station module, a central server module, a dynamic control module, and a traceability assessment module. Each module works together to collect and analyze the positioning data, status data, and communication parameters of the goods in real time, establish a conflict degree assessment model, and dynamically generate a control strategy. Through channel hopping, transmission frequency optimization, and dynamic priority allocation, the system significantly alleviates the communication congestion problem in high-density scenarios, ensures the real-time and integrity of data transmission of key goods, and supports accurate tracing and risk assessment of abnormal goods problems.

[0015] 2. The present invention optimizes the calculation of communication density anomaly scores and channel occupancy efficiency fluctuation scores through a variety of advanced algorithms, achieving accurate quantification of the degree of conflict. A weighted nonlinear combination model is used to generate conflict degree scores, and targeted control strategies are provided according to the scope of the conflict, switching channels from high-conflict environments to recovery control in low-conflict states, achieving efficient and dynamic system optimization. In addition, combined with historical positioning data, status data and conflict degree scores, the system can track the time, location and source of abnormal problems, assess risk levels and generate detailed reports, providing strong support for intelligent warehousing and logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] For examples, see Figure 1 As shown, the IoT risk interactive traceability assessment system integrating UWB transmission tracking described in this embodiment includes a UWB tag module, a UWB base station module, a central server module, a dynamic control module and a traceability assessment module; The UWB tag module is installed on the cargo to collect the real-time location data and environmental status data of the cargo and send it to the UWB base station via an ultra-wideband signal; The UWB base station module is deployed in a storage or transportation environment to receive the positioning data and status data sent by the UWB tag module, and to count the communication density parameters per unit time, including the number of active UWB tags and the data transmission frequency; at the same time, the channel occupancy efficiency parameters are recorded, including the proportion of successfully transmitted data packets to the total channel occupancy time; The central server module is connected to the UWB base station module in communication, and includes: A data analysis unit, configured to establish a conflict degree evaluation model after analyzing the communication density parameter and the channel occupancy efficiency parameter, and generate a real-time conflict degree score; A control strategy generating unit, used for dynamically generating a channel allocation optimization scheme, a transmission frequency adjustment strategy and a priority allocation scheme according to the conflict degree score; The dynamic control module is connected to the UWB tag module and the central server module for receiving and executing the control scheme generated by the control strategy generation unit, and specifically includes: Dynamically adjust the working channel of UWB tags and use frequency hopping mechanism to disperse the communication load in high-density areas; Reduce the data transmission frequency for non-critical cargo labels and maintain high-frequency transmission for critical cargo labels; Dynamically assign channel priorities based on the importance of goods, giving priority to ensuring data transmission of critical goods; The traceability assessment module is deployed on the central server module to analyze the historical location data and status data of abnormal goods, combine the conflict degree score, trace the source of the problem and generate a risk assessment report.

[0020] The UWB tag module is an embedded device installed on the cargo, which is mainly used to collect the real-time positioning data and environmental status data of the cargo and transmit the data through ultra-wideband signals. Its core functions include: Positioning data collection: Real-time positioning with centimeter-level accuracy is achieved through ultra-wideband technology to track the movement of goods.

[0021] Environmental status monitoring: Collect environmental data such as temperature, humidity, vibration, etc. around the goods to identify abnormal changes in the status of the goods.

[0022] Data transmission: Through the low power consumption, short distance and high bandwidth characteristics of UWB, the collected data can be efficiently sent to the UWB base station.

[0023] The core components include: UWB chip: used to generate and send ultra-wideband signals, supporting high-precision positioning functions (such as TDOA, AOA positioning). Sensor module: including temperature sensor, humidity sensor and vibration sensor, used to collect environmental status data. Embedded processing unit: used to process positioning and environmental data and encapsulate them into data packets for transmission. Power module: adopts low-power design, usually powered by replaceable batteries or solar energy, and supports long-term operation.

[0024] Communication interfaces include: built-in ultra-wideband antenna to support wireless data transmission. Support for specific communication protocols (such as IEEE 802.15.4a) to ensure compatibility and stability.

[0025] UWB tags periodically send ultra-wideband pulse signals, with a signal bandwidth of usually more than 500MHz and a frequency range of 3.1GHz to 10.6GHz. The UWB base station receives the tag signal and calculates the real-time location of the tag through the TDOA (time difference of arrival) or AOA (angle of arrival) algorithm. Tags can also be equipped with base station cooperation mechanisms (such as return link mechanisms) to confirm positioning accuracy. The tag's embedded processing unit parses the data feedback from the base station positioning and encapsulates the real-time location of the goods into data packets.

[0026] The built-in sensor of the tag collects temperature and humidity data around the goods in real time, for example, to detect whether the cold chain goods are kept within the set temperature range. The acceleration sensor or vibration sensor is used to monitor whether the goods are impacted or tipped over by external forces. The tag performs preliminary processing on the collected data through the built-in algorithm. For example, temperature and humidity exceeding the set threshold or abnormal vibration will trigger an abnormal mark and increase the data priority. The tag combines the environmental status data with the positioning data and encapsulates it into a complete cargo status information data packet.

[0027] Transmission protocol: Use ultra-wideband communication protocols (such as IEEE 802.15.4a) to transmit data, supporting high-speed, low-power data exchange. Data transmission usually uses short-term, high-bandwidth pulse communication, which can effectively avoid multipath interference. Transmission mechanism: Tags support two modes: periodic transmission and event-triggered transmission: Periodic transmission: The tag sends positioning and environmental data at regular intervals. Event-triggered transmission: If abnormal temperature or vibration is detected, the tag immediately sends data and attaches an abnormal mark. Anti-interference ability: UWB tags have strong anti-interference performance, and can reduce the probability of conflict even in high-density environments through frequency hopping technology and signal coding.

[0028] Interaction with UWB base station: UWB tags send data to the base station via ultra-wideband signals. The data received by the base station includes real-time location information of the goods, environmental status data, and abnormal markings. Interaction with the central server: The data transmitted by the tag is transferred through the base station and uploaded to the central server for further analysis and storage.

[0029] The UWB base station module is a core device deployed in a storage or transportation environment. It is mainly responsible for receiving the positioning data and status data sent by the UWB tag module, and for counting and analyzing the communication environment parameters. The specific functions include: Receive and parse ultra-wideband signals sent by multiple UWB tag modules to obtain positioning data and environmental status data. Count the number of active UWB tags per unit time and their data transmission frequency, and calculate the communication density parameter. Record the proportion of successfully transmitted data packets to the total channel occupancy time, and calculate the channel occupancy efficiency parameter. Transmit the received data and calculation results to the central server module for further analysis and decision-making.

[0030] Signal receiving component: Equipped with a high-sensitivity UWB receiving antenna, it supports ultra-wideband signal reception in the frequency range of 3.1GHz to 10.6GHz. The timing synchronization technology is used to ensure the time accuracy of the received signal, and supports positioning calculation based on TDOA (time difference of arrival) or AOA (angle of arrival).

[0031] Decode the data packets sent by the UWB tag module and extract positioning data (such as timestamp, tag ID, coordinates) and environmental status data (such as temperature, humidity, vibration, etc.). Support denoising of multipath signals to improve the accuracy of data reception.

[0032] The base station identifies the number of active UWB tags in the current time based on the tag ID in the received data packet. The tag is determined to be active through periodic transmission of the tag signal (if no signal is received for more than the set time, it is marked as inactive). The base station records the number of data packets sent by each tag per unit time and calculates its transmission frequency. The communication density parameter (CD) per unit time is calculated based on the data transmission frequency of all active tags. The calculation formula is: CD=N×F; N is the number of active tags per unit time. F is the average transmission frequency of each tag (unit: data packet / second).

[0033] The base station records each successfully received data packet and counts the total number of data packets received per unit time. The data packet sequence number or verification mechanism is used to distinguish repeated transmissions from new data packets to ensure accurate statistical results. Calculation of total channel occupancy time: The base station records the reception time and transmission time of each data packet, and accumulates and calculates the total channel occupancy time per unit time. Channel occupancy efficiency parameter (CUE) calculation formula: ; It is the total time of successfully transmitting data packets per unit time. It is the total occupation time of the channel per unit time.

[0034] Use reliable IoT transmission protocols (such as MQTT, HTTP) to transmit the statistical communication density parameters (CD) and channel occupancy efficiency parameters (CUE) to the central server module. The data packet contains timestamp, base station ID, communication parameters, and received tag data for subsequent analysis and dynamic control. Use compression algorithms to reduce the amount of transmitted data, improve bandwidth utilization, and ensure real-time performance in high-density environments.

[0035] A data analysis unit, configured to establish a conflict degree evaluation model after analyzing the communication density parameter and the channel occupancy efficiency parameter, and generate a real-time conflict degree score; After analyzing the communication density parameters within a unit time, a communication density anomaly score is generated. The method for obtaining the communication density anomaly score is as follows: Set the communication density dataset to , each data point Represents the communication density per unit time; for each data point , calculate the distance between it and all other points ; Find the distance to the kth nearest neighbor, defined as: ; In the formula, For data points The k nearest neighbor set of Distance not greater than All points of , k is the number of neighbors; it is used to define the data range for local density calculation, usually 5~10.

[0036] for and , calculate the reachable distance , the expression is: ;if Less than , then take As the reachable distance, it prevents points with too high density from having too great an impact on outliers. The local reachable density It is defined as the reciprocal mean of the reachable distances of its neighboring points: ; yes number of points; data points The local anomaly factor It is defined as the ratio of the average of the local reachable density of its neighboring points to its own local reachable density: ;when ≈1, indicating The density of is similar to that of its neighboring points, which is a normal point. >1, indicating The density of is significantly lower than that of its neighboring points, and it is a potential outlier.

[0037] according to Generate the communication density anomaly score Se, the expression is: ; MaxLOF is the maximum LOF value among all current points, which is used to normalize the anomaly score value.

[0038] After analyzing the calculated channel occupancy efficiency parameters, a channel occupancy efficiency fluctuation score value is generated. The method for obtaining the channel occupancy efficiency fluctuation score value is as follows: Assume that the channel occupancy efficiency data is a time series ; Starting from the zth data point, z is the size of the sliding window (that is, the number of data points included, such as 5 time points), a sliding window containing k data points is constructed : ; Each time the window is moved forward by one time point, a new subset is formed. Calculate the mean of the channel occupancy efficiency and the standard deviation of the channel occupancy efficiency within the window; for time t and the previous time , calculate the rate of change of the standard deviation : ; is the standard deviation of the channel occupancy efficiency at time t; if If the standard deviation is large, it means that the fluctuation range has changed significantly and there may be anomalies. Mapped to the channel occupancy efficiency fluctuation score EG, with a score range of 0 to 100: ; In the formula, It is the preset maximum fluctuation rate, which is used to normalize the score value. The higher it is, the more significant the fluctuation of channel occupancy efficiency is.

[0039] The conflict degree score is calculated using a weighted nonlinear combination model : ; Among them: α and β are weight parameters, satisfying α+β=1, which are used to adjust Se and EG The contribution ratio of the channel efficiency fluctuation score is (for example, α=0.6, β=0.4 means that the communication density anomaly is more important). f(Se) is the nonlinear mapping function of the communication density anomaly score value, which is usually an exponential function or a piecewise linear function. g(EG) is the nonlinear mapping function of the channel efficiency fluctuation score value, which is similarly an exponential or piecewise linear function.

[0040] The communication density anomaly score value mapping f(Se) uses an exponential function to map the nonlinear growth characteristics of Se: ;in: =100: the normalized maximum value of the communication density score (consistent with the range of Se), e is a natural constant used for exponential mapping. f(Se) means that the higher the Se, the higher the The greater the contribution, the nonlinear growth. The channel occupancy efficiency fluctuation score value mapping g(EG) adopts a similar exponential mapping: ;in: =100: the normalized maximum value of the channel efficiency fluctuation score (consistent with the range of EG). g(EG) means that the higher the EG, the better the The greater the contribution.

[0041] The control strategy generation unit is used to dynamically generate a channel allocation optimization plan, a transmission frequency adjustment strategy and a priority allocation plan according to the conflict degree score, specifically: The control strategy generation unit scores the conflict degree. Generate the corresponding conflict scope and control strategy, including: High Conflict >70: Channel allocation optimization: switch tag channels in high-load areas. Priority allocation: ensure the transmission priority of key cargo tags.

[0042] Medium conflict 40 ≤ ≤70: Transmission frequency adjustment: reduce the transmission frequency of non-critical tags. Priority allocation: dynamic scheduling, gradually reduce the transmission rate of low-priority tags.

[0043] Low Conflict <40: Restore normal channel allocation and transmission frequency to maintain real-time transmission of high-priority tags.

[0044] The dynamic control module is connected to the UWB tag module and the central server module for receiving and executing the control scheme generated by the control strategy generation unit, and specifically includes: Dynamically adjust the working channel of UWB tags: By dynamically adjusting the working channel of UWB tags, the frequency hopping mechanism is used to disperse the communication load and reduce the channel congestion in high-density areas. Using the multi-channel characteristics supported by UWB technology, tags in high-load areas are switched to low-load channels to avoid conflicts caused by too many tags on the same channel.

[0045] Receive real-time channel load information from the central server and calculate the current load of each channel (such as the number of tags or channel utilization). Determine the distribution of high-load channels and low-load channels. According to the control plan, reallocate some tags in the high-load channels to the low-load channels. Execute channel switching instructions: The base station sends a channel switching command to the tag, and the tag adjusts the channel frequency of the transmitted signal according to the received instruction.

[0046] In a high-conflict environment, the frequency hopping mechanism is enabled to allow tags to switch between multiple channels at predetermined time intervals to further disperse the channel load. Channel allocation optimization goals: ; Where M is the total number of channels, is the load of channel i, is the capacity of channel i. The execution effects include: reducing the probability of conflict in high-load channels, improving channel utilization, and optimizing communication performance.

[0047] Dynamically adjust data transmission frequency: By adjusting the data transmission frequency of UWB tags, the communication pressure of non-critical goods tags is reduced, while maintaining high-frequency transmission of critical goods to ensure the real-time data. Tags dynamically adjust the transmission frequency according to their importance, high-priority goods maintain high-frequency transmission, and low-priority goods appropriately reduce the transmission frequency.

[0048] Obtain label classification information from the control plan and divide it into key labels and non-key labels according to the importance of the goods. For key labels, maintain the current transmission frequency (such as 1 time / second).

[0049] For non-critical tags, the frequency is reduced by the formula: ; New transmission frequency for non-critical tags. is the transmission frequency of the key tag (set value, such as 1 time / second). <40), gradually restore the frequency of non-critical tags until it reaches the default value. The execution effects include: reducing non-critical data traffic and channel load. Ensuring the high-frequency data transmission requirements of critical goods.

[0050] Dynamically assign channel priorities: Dynamically assign channel priorities based on the importance of the goods, prioritize data transmission of critical goods, and avoid data loss due to conflicts. Assign priorities to each tag, and tags with higher priorities enjoy higher occupancy rights in channel resource allocation.

[0051] Obtain cargo importance information from the control scheme and classify tags into high, medium, and low priorities. High-priority tags correspond to critical cargo, and low-priority tags correspond to non-critical cargo. When channel resources conflict, time slots are allocated to high-priority tags first. For low-priority tags, their data transmission time is delayed or the transmission frequency is reduced.

[0052] According to real-time data and conflict scores, the priority allocation rules are adjusted dynamically. When the conflict level decreases, the transmission of low-priority tags is gradually restored. Priority weight calculation: ; is the priority weight of label i, The importance weight of the tag (predefined). The execution effects include: giving priority to data transmission of key goods. Optimizing channel resource allocation in conflict environments to reduce data loss.

[0053] The traceability assessment module is deployed on the central server module to analyze the historical location data and status data of abnormal goods, combine the conflict degree score, trace the source of the problem and generate a risk assessment report.

[0054] Location data: historical location information of goods ; Status data: Environmental status data (such as temperature, humidity) .

[0055] Conflict Score: A History of System Calculation Time series,reflects the real-time status of the communication environment.

[0056] Align the positioning data, status data, and conflict score by timestamp to form a complete time series: .

[0057] Based on positioning data Plot the movement of goods, noting their stops and directions of movement. Compare the trajectory with the planned route and identify deviations (such as those outside the normal transport area).

[0058] Perform threshold detection on status data to identify exceeding points: ; is the maximum value of the preset ambient temperature data. The maximum value of the preset environmental humidity data is combined with the timestamp to mark the time period and location where the abnormal state occurs. Visual display: Generate trajectory diagrams and status data curves to highlight abnormal points.

[0059] Check the abnormal status during the period Value, if Overlapping with status anomalies, marked as anomalies that may be affected by the communication environment: ; According to the abnormal mark, determine the time period and location where the problem occurred. The cause classification of the problem includes: Equipment failure: Check the sensor status (such as power, signal interruption) and Environmental issues: Combined with status data, analyze whether the abnormality is caused by excessive temperature and humidity or vibration. Communication interference: If Significantly higher than normal, possibly caused by communication congestion. Human Error: Compare the trajectory to the planned path to check for deviations or unusual stops.

[0060] Based on the duration of the abnormality and the importance of the goods, the potential impact of the problem on the quality of the goods is assessed. The risk score is calculated by combining the duration of the problem, the magnitude of the abnormal status, and the degree of communication conflict. : ; is the abnormal duration weight, is the weight of the abnormal state (such as the temperature exceeding the standard), It is the average or peak value of the conflict degree score. is the preset weight coefficient, satisfying .according to Values ​​are divided into risk levels: <40: low risk; 40≤ <70: medium risk; ≥70: High risk.

[0061] The assessment report includes: Anomaly description: basic information of the goods, abnormal time period and location. Trajectory and status analysis: historical trajectory diagram, status data change curve and abnormal point description. Impact of conflict environment: analysis of the possibility of communication conflict degree and abnormal overlap. Risk assessment: risk score, impact scope and level classification. Recommended measures: solutions provided for the source of the problem (such as channel optimization, equipment replacement). Report format: supports PDF, visual charts and other formats for easy archiving and decision-making.

[0062] In this embodiment, a UWB tag module, a UWB base station module, a central server module, a dynamic control module and a traceability evaluation module are included, and efficient communication and abnormal traceability in a high-density environment are achieved through collaborative work. The UWB tag module collects the real-time positioning data and environmental status data of the goods, and sends them to the UWB base station module through an ultra-wideband signal. The UWB base station module receives data and counts communication density parameters and channel occupancy efficiency parameters to reflect the communication environment status. The central server module analyzes these parameters through a data analysis unit, establishes a conflict degree evaluation model and generates a real-time conflict degree score, and the control strategy generation unit dynamically formulates a channel allocation optimization plan, a transmission frequency adjustment strategy and a priority allocation plan based on the score. The dynamic control module executes these plans, including dynamically adjusting the working channel, optimizing the transmission frequency and allocating channel priorities to disperse the communication load and ensure the real-time data transmission of key goods. The traceability evaluation module traces the source of the problem and generates a risk assessment report by analyzing the historical positioning data, status data, and conflict degree scores of abnormal goods, providing support for abnormal handling and system optimization.

[0063] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0064] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0065] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An Internet of Things risk interactive traceability assessment system integrating UWB transmission tracking, characterized by: It includes UWB tag module, UWB base station module, central server module, dynamic control module and traceability evaluation module; The UWB tag module is installed on the cargo to collect the real-time location data and environmental status data of the cargo and send it to the UWB base station via an ultra-wideband signal; The UWB base station module is deployed in a storage or transportation environment to receive the positioning data and status data sent by the UWB tag module, and to count the communication density parameters per unit time, including the number of active UWB tags and the data transmission frequency; at the same time, the channel occupancy efficiency parameters are recorded, including the proportion of successfully transmitted data packets to the total channel occupancy time; The central server module is connected to the UWB base station module in communication, and includes: A data analysis unit, configured to establish a conflict degree evaluation model after analyzing the communication density parameter and the channel occupancy efficiency parameter, and generate a real-time conflict degree score; A control strategy generating unit, used for dynamically generating a channel allocation optimization scheme, a transmission frequency adjustment strategy and a priority allocation scheme according to the conflict degree score; The dynamic control module is connected to the UWB tag module and the central server module for receiving and executing the control scheme generated by the control strategy generation unit, and specifically includes: Dynamically adjust the working channel of UWB tags and use frequency hopping mechanism to disperse the communication load in high-density areas; reduce the data transmission frequency for non-critical cargo tags and maintain high-frequency transmission for critical cargo tags; dynamically assign channel priority according to the importance of the cargo and give priority to ensuring data transmission of critical cargo; The traceability assessment module is deployed on the central server module to analyze the historical location data and status data of abnormal goods, combine the conflict degree score, trace the source of the problem and generate a risk assessment report.

2. According to claim 1, the Internet of Things risk interactive tracing assessment system integrating UWB transmission tracking is characterized by: In the UWB base station module, the base station identifies the number of active UWB tags in the current time according to the tag ID in the received data packet, and determines whether the tag is in an active state through the periodic transmission of the tag signal. The base station records the number of data packets sent by each tag per unit time, calculates its sending frequency, and combines the data sending frequencies of all active tags to calculate the communication density parameter per unit time; The base station records each successfully received data packet and counts the total number of data packets received per unit time. It uses the data packet sequence number or verification mechanism to distinguish repeated transmissions from new data packets. The base station accumulates the total channel occupancy time per unit time based on the reception time and transmission duration of each recorded data packet, and calculates the channel occupancy efficiency parameter.

3. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 2 is characterized by: In the central server module, the communication density parameters within a unit time are analyzed to generate a communication density anomaly score value. The communication density anomaly score value is obtained by setting the communication density data set to , each data point Represents the communication density per unit time; for each data point , calculate the distance between it and all other points ; Find the distance to the kth nearest neighbor, defined as: ; In the formula, For data points The k nearest neighbor set of Distance not greater than For all points of and , calculate the reachable distance , the expression is: ;if Less than , then take As the reachable distance, the data point The local reachable density It is defined as the reciprocal mean of the reachable distances of its neighboring points: ; yes number of points; data points The local anomaly factor It is defined as the ratio of the average of the local reachable density of its neighboring points to its own local reachable density: ;according to Generate the communication density anomaly score Se, the expression is: ; MaxLOF is the maximum LOF value among all current points.

4. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 3 is characterized by: After analyzing the calculated channel occupancy efficiency parameters, a channel occupancy efficiency fluctuation score value is generated. The channel occupancy efficiency fluctuation score value is obtained by: assuming that the channel occupancy efficiency data is a time series ; Starting from the zth data point, z is the size of the sliding window, and a sliding window containing z data points is constructed : ; Each time the window is moved forward by one time point, a new subset is formed. Calculate the mean of the channel occupancy efficiency and the standard deviation of the channel occupancy efficiency within the window; for time t and the previous time , calculate the rate of change of the standard deviation : ; is the standard deviation of the channel occupancy efficiency at time t; the rate of change of the standard deviation Mapped to the channel occupancy efficiency fluctuation score EG, with a score range of 0 to 100: ; In the formula, The preset maximum fluctuation rate.

5. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 4 is characterized by: The conflict degree score is calculated using a weighted nonlinear combination model : ; Among them: α and β are weight parameters, satisfying α+β=1, which are used to adjust Se and EG contribution ratio; f(Se) is the nonlinear mapping function of the communication density anomaly score value, and g(EG) is the nonlinear mapping function of the channel efficiency fluctuation score value; The communication density anomaly score value mapping f(Se) uses an exponential function to map the nonlinear growth characteristics of Se: ;in: =100: the normalized maximum value of the communication density score, e is a natural constant; The channel occupancy efficiency fluctuation score value mapping g(EG) adopts exponential mapping: ;in: =100: the normalized maximum value of the channel efficiency fluctuation score.

6. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 5 is characterized by: The control strategy generation unit scores the conflict degree. Generate the corresponding conflict scope and control strategy, including: High Conflict >70: Switch the tag channel in the high-load area to ensure the transmission priority of key cargo tags; Medium conflict 40 ≤ ≤70: Reduce the transmission frequency of non-critical tags and gradually reduce the transmission rate of low-priority tags; Low Conflict <40: Restore normal channel allocation and transmission frequency to maintain real-time transmission of high-priority tags.

7. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 1 is characterized by: In the traceability assessment module, the location data includes the historical location information of the goods ; Status data includes environmental status data ; The conflict score includes the history of the calculation Time series; Align the positioning data, status data, and conflict score by timestamp to form a complete time series: ; Based on positioning data Draw the moving trajectory of the goods, mark their stop points and movement direction, compare the trajectory with the planned route, and identify the deviation points; perform threshold detection on the status data and identify the exceeding points: ; is the maximum value of the preset ambient temperature data. The maximum value of the preset environmental humidity data is used to check the abnormal status period. Value, if Overlapping with status anomalies, marked as anomalies affected by the communication environment: ; Based on the abnormal mark, determine the time period and location where the problem occurred.

8. The IoT risk interactive tracing and assessment system integrating UWB transmission tracking according to claim 7 is characterized by: According to the duration of the abnormality and the importance of the goods, the potential impact of the problem on the quality of the goods is evaluated. Specifically, the risk score is calculated by combining the duration of the problem, the magnitude of the abnormal state, and the degree of communication conflict. : ; is the abnormal duration weight, is the weight of the abnormality degree of the state, is the average value of the conflict degree score, is the preset weight coefficient, satisfying ; according to Values ​​are divided into risk levels: <40: low risk; 40≤ <70: medium risk; ≥70: High risk.

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