An Internet of Things risk interaction traceability and evaluation system integrating UWB transmission tracking

Abstract

The present invention discloses an Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking, which relates to the technical field of UWB transmission. The real-time positioning and status data of goods are collected through the UWB tag module, and the UWB base station module receives and statistically analyzes the communication density parameter and the channel occupancy efficiency parameter. The central server module establishes a conflict degree evaluation model based on these data, generates a real-time conflict degree score, and the regulation strategy generation unit dynamically formulates channel allocation, transmission frequency adjustment, and priority allocation strategies according to the evaluation results. The dynamic regulation module executes these strategies to effectively alleviate communication conflicts through channel hopping, frequency optimization, and priority adjustment. At the same time, the traceability evaluation module combines the conflict score with the historical data of abnormal goods to accurately trace the problem and generate a risk assessment report, realizing efficient exception handling and system optimization, and significantly improving the reliability, flexibility of system communication, and warehouse management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of UWB transmission technology, and particularly to an Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking. Background Art

[0002] The Internet of Things risk interaction traceability evaluation integrating UWB (Ultra-Wideband) transmission and tracking is a comprehensive method that utilizes the high-precision positioning and low-power data transmission capabilities of UWB technology to establish a risk information interaction mechanism among Internet of Things devices, and to trace the source of potential problems in real time and evaluate their impacts. This evaluation identifies risks and takes targeted measures by tracking the operating status, location, and historical data of devices, thereby enhancing the security and reliability of the Internet of Things system.

[0003] For example, in intelligent warehousing, goods are equipped with UWB tags to achieve precise positioning. When some goods are damaged or the temperature is abnormal, the UWB system can trace the movement trajectory of the goods (such as when the abnormality starts during which transportation section), and notify relevant staff of the problem in a timely manner through the Internet of Things network. The risk interaction evaluation can determine whether the problem is due to equipment failure, environmental problems, or human operation errors.

[0004] The existing technologies have the following deficiencies:

[0005] 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 ability, it may still fail in an extremely high-density environment. In addition, communication conflicts will cause some goods data to be lost or delayed. For example, during the peak-period warehousing loading and unloading process, the status data of some key goods fails to be uploaded, resulting in information loss or monitoring interruption. Once this problem occurs frequently, it will lead to a decline in the overall performance of the system and even require manual intervention to suspend operation. Summary of the Invention

[0006] The purpose of the present invention is to provide an Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking to solve the deficiencies in the background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking, characterized by comprising a UWB tag module, a UWB base station module, a central server module, a dynamic regulation module, and a traceability evaluation module;

[0008] The UWB tag module is arranged on the goods and is used for collecting the real-time positioning data and environmental status data of the goods, and sending them to the UWB base station through ultra-wideband signals;

[0009] The UWB base station module is deployed in a warehousing or transportation environment and is used to receive the positioning data and status data sent by the UWB tag module, and to statistically calculate the communication density parameters within a unit time, including the number of active UWB tags and the data transmission frequency; at the same time, record the channel occupancy efficiency parameters, including the proportion of successfully transmitted data packets in the total channel occupancy time;

[0010] The central server module is communicatively connected to the UWB base station module and includes:

[0011] The data analysis unit is used to analyze the communication density parameters and the channel occupancy efficiency parameters, establish a conflict degree evaluation model, and generate a real-time conflict degree score;

[0012] The regulation strategy generation unit is used to dynamically generate an optimized channel allocation plan, a transmission frequency adjustment strategy, and a priority allocation plan according to the conflict degree score;

[0013] The dynamic regulation module is communicatively connected to the UWB tag module and the central server module, and is used to receive and execute the regulation plan generated by the regulation strategy generation unit, specifically including:

[0014] Dynamically adjust the working channels of the UWB tags, and adopt a frequency hopping mechanism to disperse the communication load in high-density areas; reduce the data transmission frequency for non-critical cargo tags and maintain a high frequency transmission for critical cargo tags; dynamically allocate channel priorities according to the importance of the cargo, and give priority to ensuring the data transmission of critical cargo;

[0015] The traceability evaluation module is deployed in the central server module and is used to analyze the historical positioning data and status data of abnormal cargo, and combine the conflict degree score to trace the source of the problem and generate a risk assessment report.

[0016] Preferably, in the UWB base station module, the base station identifies the number of active UWB tags within the current time according to the tag ID in the received data packet, judges whether the tag is active by the periodic transmission of the tag signal, the base station records the number of data packets sent by each tag within a unit time, calculates its transmission frequency, and comprehensively calculates the communication density parameters within a unit time based on the data transmission frequencies of all active tags;

[0017] The base station records each successfully received data packet and statistically calculates the total number of data packets received within a unit time. By using a data packet sequence number or a verification mechanism to distinguish between repeated transmissions and new data packets, the base station accumulatively calculates the total channel occupancy time within a unit time according to the reception time and transmission duration of each recorded data packet, and calculates the channel occupancy efficiency parameters.

[0018] Preferably, in the central server module, after analyzing the communication density parameter within a unit time, a communication density anomaly score value is generated. The method for obtaining the communication density anomaly score value is as follows: Set the communication density data set as , and each data point represents the communication density within a unit time; for each data point , calculate the distance between it and all other points ; find the distance to the k-th nearest neighbor point, defined as: ; where, is the k-nearest neighbor set of the data point , including all points whose distance to is not greater than , and k is the number of neighbors; for and , calculate the reachable distance , and the expression is: ; if is less than , then take as the reachable distance, and the local reachability density of the data point is defined as the reciprocal mean of the reachable distances of its neighbor points: ; is the number of points of ; the local outlier factor of the data point is defined as the ratio of the average of the local reachability densities of its neighbor points to its own local reachability density: ; according to , generate the communication density anomaly score value Se, and the expression is: ; MaxLOF is the maximum LOF value among all current points.

[0019] Preferably, after analyzing the calculated channel occupancy efficiency parameter, a channel occupancy efficiency fluctuation score value is generated. The method for obtaining the channel occupancy efficiency fluctuation score value is as follows: Let the channel occupancy efficiency data be a time series ; starting from the z-th data point, where z is the size of the sliding window, construct a sliding window containing z data points : ; each time the window is moved forward by one time point to form a new subset, within the window , 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 change rate of the standard deviation: ; is the standard deviation of the channel occupancy efficiency at time t; take the change rate Mapped to the channel occupancy efficiency fluctuation score value EG, with the scoring range from 0 to 100: ; where is the preset maximum fluctuation rate.

[0020] Preferably, a weighted non - linear combination model is used to calculate the conflict degree score : ; where: α and β are weight parameters, satisfying α + β = 1, and are used to adjust the contribution ratio of Se and EG to ; f(Se) is the non - linear mapping function of the communication density anomaly score value, and g(EG) is the non - linear mapping function of the channel efficiency fluctuation score value;

[0021] The non - linear mapping f(Se) of the communication density anomaly score value uses an exponential function to map the non - linear growth characteristic of Se: ; where: = 100: the normalized maximum value of the communication density score value, and e is the natural constant;

[0022] The non - linear mapping g(EG) of the channel occupancy efficiency fluctuation score value uses an exponential mapping: ; where: = 100: the normalized maximum value of the channel efficiency fluctuation score value.

[0023] Preferably, the control strategy generation unit generates the corresponding conflict range and control strategy according to the conflict degree score , specifically including:

[0024] High conflict > 70: Switch the label channel in the high - load area to ensure the transmission priority of critical cargo labels;

[0025] Medium conflict 40 ≤ ≤ 70: Reduce the transmission frequency of non - critical labels and gradually reduce the transmission rate of low - priority labels;

[0026] Low conflict < 40: Restore normal channel allocation and transmission frequency, and maintain the real - time transmission of high - priority labels.

[0027] Preferably, in the traceability evaluation module, the positioning data includes the historical location information of the goods ;

[0028] The status data includes environmental status data ;

[0029] The conflict degree score includes the calculated historical time series;

[0030] Align the positioning data, status data, and conflict degree scores according to the timestamp to form a complete time series: ;

[0031] According to the positioning data Draw the movement trajectory of the goods, mark its stop points and movement directions, compare the trajectory with the predetermined route, and identify the deviation points; perform threshold detection on the status data to identify the out-of-standard points: ; is the maximum value of the preset environmental temperature data, is the maximum value of the preset environmental humidity data, check the value during the abnormal status period. If overlaps with the status anomaly, mark it as an anomaly affected by the communication environment: ; Determine the time period and location where the problem occurs according to the anomaly mark.

[0032] Preferably, according to the anomaly duration and the importance of the goods, evaluate the potential impact of the problem on the quality of the goods. Specifically: Combine the problem duration, the amplitude of the status anomaly, and the communication conflict degree to calculate the risk score : ; is the anomaly duration weight, is the status anomaly degree weight, is the average value of the conflict degree scores, is the preset weight coefficient, satisfying ;

[0033] According to value, divide the risk level: <40: Low risk; 40 ≤ <70: Medium risk; ≥70: High risk.

[0034] In the above technical solution, the technical effects and advantages provided by the present invention:

[0035] 1. The present invention constructs an Internet of Things risk interaction traceability evaluation system that combines UWB transmission tracking. Aiming at the communication conflict problem in high-density environments, a complete set of optimization and traceability solutions is proposed. The system includes a UWB tag module, a UWB base station module, a central server module, a dynamic regulation module, and a traceability evaluation module. Each module works in cooperation to collect and analyze the positioning data, status data, and communication parameters of the goods in real time, establish a conflict degree evaluation model, and dynamically generate regulation strategies. 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 for critical goods, and at the same time supports the accurate traceability and risk assessment of abnormal goods problems.

[0036] 2. The present invention optimizes the calculation of the communication density anomaly score value and the channel occupancy efficiency fluctuation score value through a variety of advanced algorithms, achieving precise quantification of the conflict degree. A weighted non-linear combination model is used to generate the conflict degree score value, and a targeted regulation strategy is provided according to the conflict range, realizing efficient and dynamic system optimization from channel switching in a high-conflict environment to recovery regulation in a low-conflict state. In addition, by combining historical location data, status data, and the conflict degree score value, the system can track the occurrence time, location, and source of influence of abnormal problems, evaluate the risk level, and generate a detailed report, providing strong support for intelligent warehousing and logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0038] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0040] For the embodiments, please refer to Figure 1 As shown, the Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking in this embodiment includes a UWB tag module, a UWB base station module, a central server module, a dynamic regulation module, and a traceability evaluation module;

[0041] The UWB tag module is set on the goods and is used to collect the real-time location data and environmental status data of the goods and send them to the UWB base station through ultra-wideband signals;

[0042] The UWB base station module is deployed in a warehousing or transportation environment and is used to receive the location data and status data sent by the UWB tag module, and statistically analyze the communication density parameters per unit time, including the number of active UWB tags and the data transmission frequency; at the same time, record the channel occupancy efficiency parameters, including the proportion of successfully transmitted data packets in the total channel occupancy time;

[0043] A central server module, communicatively connected to the UWB base station module, includes:

[0044] A data analysis unit, which analyzes the communication density parameter and the channel occupancy efficiency parameter, establishes a conflict degree evaluation model, and generates a real-time conflict degree score;

[0045] A regulation strategy generation unit, which dynamically generates an optimized channel allocation plan, a transmission frequency adjustment strategy, and a priority allocation plan according to the conflict degree score;

[0046] A dynamic regulation module, communicatively connected to the UWB tag module and the central server module, receives the regulation plan generated by the regulation strategy generation unit and executes it, specifically including:

[0047] Dynamically adjust the working channel of the UWB tag, and adopt a frequency hopping mechanism to disperse the communication load in high-density areas;

[0048] Reduce the data transmission frequency of non-critical goods tags and maintain high-frequency transmission for critical goods tags;

[0049] Dynamically allocate channel priorities according to the importance of goods, and give priority to ensuring the data transmission of critical goods;

[0050] A traceability evaluation module, deployed in the central server module, analyzes the historical location data and status data of abnormal goods, and combines the conflict degree score to trace the source of the problem and generate a risk assessment report.

[0051] The UWB tag module is an embedded device installed on goods, mainly used to collect real-time location data and environmental status data of goods, and transmit data through ultra-wideband signals. Its core functions include:

[0052] Location data collection: Realize centimeter-level accurate real-time location through ultra-wideband technology to track the movement trajectory of goods.

[0053] Environmental status monitoring: Collect environmental data such as temperature, humidity, and vibration around the goods to identify abnormal changes in the status of the goods.

[0054] Data transmission: Through the low-power, short-distance and high-bandwidth characteristics of UWB, the collected data is efficiently sent to the UWB base station.

[0055] 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: includes 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: adopting low-power design, usually powered by replaceable batteries or solar energy, supporting long-term operation.

[0056] The communication interface includes: built-in ultra-wideband antenna, supporting wireless transmission of data. Supports specific communication protocols (such as IEEE 802.15.4a) to ensure compatibility and stability.

[0057] The UWB tag periodically sends ultra-wideband pulse signals. The signal bandwidth is usually above 500MHz, and the frequency range is between 3.1GHz and 10.6GHz. The UWB base station receives the tag signal and calculates the real-time position of the tag through TDOA (Time Difference of Arrival) or AOA (Angle of Arrival) algorithms. The tag can also be equipped with a base station cooperation mechanism (such as a backhaul mechanism) to confirm the positioning accuracy. The embedded processing unit in the tag parses the data fed back by the base station for positioning and encapsulates the real-time position of the goods into a data packet.

[0058] The tag is built with sensors to collect the temperature and humidity data around the goods in real time. For example, it detects whether the cold-chain goods are maintained within the set temperature range. Through the acceleration sensor or vibration sensor, it monitors whether the goods are subjected to external impacts or tipping. The tag preliminarily processes the collected data through built-in algorithms. For example, temperature and humidity beyond the set threshold or abnormal vibrations will trigger abnormal markings and elevate the data priority. The tag combines the environmental status data with the positioning data and encapsulates them into a complete data packet of the goods status information.

[0059] Transmission protocol: uses the ultra-wideband communication protocol (such as IEEE 802.15.4a) to transmit data, supporting high-speed and low-power data exchange. Data transmission usually adopts short-time high-bandwidth pulse communication, which can effectively avoid multipath interference. Transmission mechanism: The tag supports two modes of periodic transmission and event-triggered transmission: Periodic transmission: The tag sends positioning and environmental data at regular time intervals. Event-triggered transmission: For example, when abnormal temperature or vibration is detected, the tag immediately sends data and attaches an abnormal mark. Anti-interference ability: The UWB tag has strong anti-interference performance. Even in a high-density environment, it can reduce the collision probability through frequency hopping technology and signal coding.

[0060] Interaction with the UWB base station: The UWB tag sends data to the base station through ultra-wideband signals. The data received by the base station includes the real-time positioning information of the goods, environmental status data and abnormal marks. Interaction with the central server: The data transmitted by the tag is relayed by the base station and uploaded to the central server for further analysis and storage.

[0061] The UWB base station module is a core device deployed in a warehousing or transportation environment, mainly responsible for receiving positioning data and status data sent by UWB tag modules, and statistically analyzing communication environment parameters. Its specific functions include:

[0062] Receive and parse the ultra-wideband signals sent by multiple UWB tag modules to obtain positioning data and environmental status data. Statistically count the number of active UWB tags and their data transmission frequencies per unit time, and calculate the communication density parameter. Record the proportion of successfully transmitted data packets in 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.

[0063] Signal receiving component: Equipped with a high-sensitivity UWB receiving antenna, supporting the reception of ultra-wideband signals in the frequency range of 3.1 GHz to 10.6 GHz. Ensure the time accuracy of the received signals through timing synchronization technology, and support positioning calculations based on TDOA (Time Difference of Arrival) or AOA (Angle of Arrival).

[0064] Decode the data packets sent by the UWB tag module to extract positioning data (such as timestamp, tag ID, coordinates) and environmental status data (such as temperature, humidity, vibration, etc.). Support denoising processing of multipath signals to improve the data reception accuracy.

[0065] Based on the tag ID in the received data packet, the base station identifies the number of active UWB tags within the current time. Determine whether a tag is active through the periodic transmission of the tag signal (if no signal is received for more than a 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. By synthesizing the data transmission frequencies of all active tags, calculate the communication density parameter (CD) per unit time. 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 packets / second).

[0066] The base station records each successfully received data packet and statistically counts the total number of data packets received per unit time. Use data packet sequence numbers or verification mechanisms to distinguish between repeated transmissions and new data packets to ensure accurate statistical results. Total channel occupancy time calculation: The base station records the reception time and transmission duration of each data packet, and accumulatively calculates the total channel occupancy time per unit time. Channel occupancy efficiency parameter (CUE) calculation formula: ; is the total duration of successfully transmitted data packets per unit time. is the total occupancy duration of the channel per unit time.

[0067] Use reliable Internet of Things transmission protocols (such as MQTT, HTTP) to transmit the statistically obtained communication density parameter (CD) and channel occupancy efficiency parameter (CUE) to the central server module. The data packet contains a timestamp, base station ID, communication parameters, and the received tag data for subsequent analysis and dynamic regulation. A compression algorithm is adopted to reduce the amount of transmitted data, improve bandwidth utilization, and ensure real-time performance in a high-density environment.

[0068] A data analysis unit, which analyzes the communication density parameter and the channel occupancy efficiency parameter, establishes a conflict degree evaluation model, and generates a real-time conflict degree score;

[0069] After analyzing the communication density parameter within a unit time, a communication density anomaly score value is generated. The method for obtaining the communication density anomaly score value is as follows:

[0070] Set the communication density data set as , and each data point represents the communication density within a unit time; for each data point , calculate the distance between it and all other points ; find the distance to the k-th nearest neighbor point, defined as: ; where is the set of k nearest neighbors of data point , including all points whose distance from is not greater than , and k is the number of neighbors; it is used to define the data range for local density calculation, usually 5 - 10.

[0071] For and , calculate the reachable distance , and the expression is: ; if is less than , then take as the reachable distance to prevent points with too high density from having too much influence on anomaly points. The local reachable density of data point is defined as the reciprocal mean of the reachable distances of its neighbor points: ; is the number of points of ; the local anomaly factor of data point is defined as the ratio of the average of the local reachable densities of its neighbor points to its own local reachable density: ; when ≈1, it indicates that the density of is similar to the density of its neighbor points, and it is a normal point; when >1, it indicates that the density of Its density is significantly lower than that of neighboring points and it is a potential outlier.

[0072] According to Generate the communication density anomaly score value Se, and the expression is: ; MaxLOF is the maximum LOF value among all current points, which is used to normalize the anomaly score value.

[0073] After analyzing the calculated channel occupancy efficiency parameters, generate the channel occupancy efficiency fluctuation score value. The method for obtaining the channel occupancy efficiency fluctuation score value is:

[0074] Let the channel occupancy efficiency data be a time series ; Starting from the z-th data point, where z is the size of the sliding window (i.e., the number of data points included, such as 5 time points), construct a sliding window containing k data points : ; Each time the window is moved forward by one time point to form a new subset. Within the window , 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 change rate of the standard deviation : ; is the standard deviation of the channel occupancy efficiency at time t; if is large, it indicates that the fluctuation amplitude has changed significantly and there may be an anomaly. Map the change rate of the standard deviation to the channel occupancy efficiency fluctuation score value EG, and the scoring range is from 0 to 100: ; In the formula, is the preset maximum fluctuation change rate, which is used to normalize the score value. The higher it is, the more significant the fluctuation of the channel occupancy efficiency is.

[0075] Use a weighted non-linear combination model to calculate the conflict degree score : ; Among them: α and β are weight parameters, satisfying α + β = 1, which are used to adjust the contribution ratio of Se and EG to (for example, α = 0.6, β = 0.4 means that the communication density anomaly is more important). f(Se) is the non-linear mapping function of the communication density anomaly score value, and commonly used exponential functions or piecewise linear functions are used. g(EG) is the non-linear mapping function of the channel efficiency fluctuation score value, and similarly, exponential or piecewise linear functions are used.

[0076] The communication density anomaly score value mapping f(Se) uses an exponential function to map the non-linear growth characteristics of Se: ; Among them: = 100: The normalized maximum value of the communication density score (consistent with the range of Se), where e is the natural constant for exponential mapping. f(Se) indicates that the higher Se is, the greater its contribution to is, showing a non-linear growth. The channel occupancy efficiency fluctuation score is mapped by g(EG) using a similar exponential mapping: ; where: = 100: The normalized maximum value of the channel efficiency fluctuation score (consistent with the range of EG). g(EG) indicates that the higher EG is, the greater its contribution to is.

[0077] The regulation strategy generation unit is used to dynamically generate an optimized channel allocation plan, a transmission frequency adjustment strategy, and a priority allocation plan according to the conflict degree score. Specifically:

[0078] The regulation strategy generation unit generates the corresponding conflict range and regulation strategy according to the conflict degree score specifically including:

[0079] High conflict > 70: Channel allocation optimization: Switch the label channel in the high-load area. Priority allocation: Ensure the transmission priority of critical cargo labels.

[0080] Medium conflict 40 ≤ ≤ 70: Transmission frequency adjustment: Reduce the transmission frequency of non-critical labels. Priority allocation: Dynamic scheduling, gradually reducing the transmission rate of low-priority labels.

[0081] Low conflict < 40: Restore normal channel allocation and transmission frequency, and maintain the real-time transmission of high-priority labels.

[0082] The dynamic regulation module is communicatively connected to the UWB tag module and the central server module, and is used to receive and execute the regulation plan generated by the regulation strategy generation unit. Specifically including:

[0083] Dynamically adjust the working channel of the UWB tag: By dynamically adjusting the working channel of the UWB tag, use the frequency hopping mechanism to disperse the communication load and reduce the channel congestion degree in the high-density area. Utilize the multi-channel characteristics supported by UWB technology to switch the tags in the high-load area to low-load channels to avoid conflicts caused by too many tags on the same channel.

[0084] 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 rate). Determine the distribution of high-load channels and low-load channels. According to the regulation scheme, reallocate some tags in the high-load channels to the low-load channels. Execute the channel switching instruction: The base station sends a channel switching command to the tags, and the tags adjust the channel frequency of the transmitted signal according to the received instruction.

[0085] In a high-conflict environment, enable the frequency hopping mechanism to let the tags switch between multiple channels at a predetermined time interval to further disperse the channel load. Channel allocation optimization objective: ; 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 collision probability of high-load channels. Improving channel utilization and optimizing communication performance.

[0086] Dynamically adjust the data transmission frequency: By adjusting the data transmission frequency of UWB tags, reduce the communication pressure of non-critical cargo tags while maintaining high-frequency transmission of critical cargo to ensure the real-time nature of its data. The tags dynamically adjust the transmission frequency according to their importance. High-priority cargo maintains high-frequency transmission, and low-priority cargo appropriately reduces the transmission frequency.

[0087] Obtain tag classification information from the regulation scheme and divide the tags into critical tags and non-critical tags according to the importance of the cargo. For critical tags, maintain the current transmission frequency (such as 1 time / second).

[0088] For non-critical tags, reduce the frequency through the formula: ; is the new transmission frequency of non-critical tags. is the transmission frequency of critical tags (set value, such as 1 time / second). When the conflict level decreases (such as <40), gradually restore the frequency of non-critical tags until the default value is reached. The execution effects include: reducing non-critical data traffic and reducing channel load. Ensuring the high-frequency data transmission requirements of critical cargo.

[0089] Dynamically allocate channel priorities: Dynamically allocate channel priorities according to the importance of the cargo, and give priority to ensuring the data transmission of critical cargo to avoid data loss caused by conflicts. Assign a priority to each tag, and tags with a higher priority have a higher occupancy right in channel resource allocation.

[0090] Obtain cargo importance information from the regulation scheme and divide the tags into high, medium, and low priorities. High-priority tags correspond to critical cargo, and low-priority tags correspond to non-critical cargo. When there is a conflict in channel resources, give priority to allocating time slots to high-priority tags. For low-priority tags, delay their data transmission time or reduce the transmission frequency.

[0091] Dynamically adjust the priority allocation rules according to real-time data and the conflict degree score. When the conflict degree decreases, gradually resume the transmission of low-priority tags. Priority weight calculation: ; is the priority weight of tag i, is the importance weight (predefined) of the tag. The execution effects include: giving priority to ensuring the data transmission of critical goods. Optimize the channel resource allocation in a conflict environment and reduce data loss.

[0092] The traceability and evaluation module, deployed in the central server module, is used to analyze the historical location data and status data of abnormal goods, and combine with the conflict degree score to trace the source of the problem and generate a risk assessment report.

[0093] Location data: The historical location information of the goods ;

[0094] Status data: Environmental status data (such as temperature, humidity) .

[0095] Conflict degree score: The historical time series calculated by the system, reflecting the real-time state of the communication environment.

[0096] Align the location data, status data and conflict degree score according to the timestamp to form a complete time series: .

[0097] According to the location data Draw the movement trajectory of the goods, mark its stop points and movement directions. Compare the trajectory with the predetermined route to identify the deviation points (such as exceeding the normal transportation area).

[0098] Perform threshold detection on the status data to identify the points exceeding the standard: ; is the maximum value of the preset environmental temperature data, is the maximum value of the preset environmental humidity data, and combine with the timestamp to mark the time period and location where the abnormal state occurs. Visualization display: Generate a trajectory map and a status data curve, highlighting the abnormal points.

[0099] Check the value during the period of abnormal status. If overlaps with the abnormal status, mark it as an abnormality that may be affected by the communication environment: ; According to the abnormal mark, determine the time period and location where the problem occurs. The classification of problem causes includes: Equipment failure: Check the sensor status (such as battery power, signal interruption) and Relevance of values. Environmental issues: Analyze in combination with status data whether anomalies are caused by excessive temperature and humidity or vibrations. Communication interference: If is significantly higher than the normal value, it may be caused by communication congestion. Human error: Compare the trajectory with the predetermined path to check for any deviation or abnormal stay.

[0100] Evaluate the potential impact of the problem on the quality of the goods based on the duration of the anomaly and the importance of the goods. Calculate the risk score by integrating the duration of the problem, the amplitude of the status anomaly, and the degree of communication conflict : ; is the weight of the anomaly duration, is the weight of the degree of status anomaly (such as the amplitude of temperature exceeding the standard), is the average or peak value of the conflict degree score. is the preset weight coefficient, satisfying . According to value, divide the risk level: <40: Low risk; 40 ≤ <70: Medium risk; ≥70: High risk.

[0101] The content of the evaluation 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 description of abnormal points. Impact of conflict environment: Analysis of the degree of communication conflict and the possibility of anomaly overlap. Risk assessment: Risk score, impact scope and level classification. Suggested measures: Solutions provided for the problem source (such as channel optimization, equipment replacement). Report format: Support forms such as PDF, visual charts, etc., for easy archiving and decision-making.

[0102] In this embodiment, it includes a UWB tag module, a UWB base station module, a central server module, a dynamic regulation module, and a traceability evaluation module, which achieve efficient communication and anomaly traceability in a high-density environment through collaborative work. The UWB tag module collects the real-time positioning data and environmental status data of goods and sends them to the UWB base station module through ultra-wideband signals. The UWB base station module receives the data and statistics the communication density parameter and channel occupancy efficiency parameter to reflect the communication environment status. The central server module analyzes these parameters through the data analysis unit, establishes a conflict degree evaluation model and generates a real-time conflict degree score. The regulation strategy generation unit dynamically formulates an optimized channel allocation plan, a transmission frequency adjustment strategy, and a priority allocation plan based on this score. The dynamic regulation 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 critical 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 score of abnormal goods, providing support for anomaly handling and system optimization.

[0103] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0104] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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, computer, server, or data center to another website, computer, server, or data center in a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0105] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking, characterized in that: It includes a UWB tag module, a UWB base station module, a central server module, a dynamic regulation module, and a traceability evaluation module; The UWB tag module is set on the goods and is used to collect the real-time positioning data and environmental status data of the goods, and send them to the UWB base station through ultra-wideband signals; The UWB base station module is deployed in a warehousing or transportation environment and is used to receive the positioning data and status data sent by the UWB tag module, and statistically calculate the communication density parameters per unit time, including the number of active UWB tags and the data transmission frequency. The specific 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, and CD is the communication density parameter per unit time; at the same time, record the channel occupancy efficiency parameter, including the proportion of successfully transmitted data packets in the total channel occupancy time; The central server module is communicatively connected to the UWB base station module and includes: The data analysis unit is used to analyze the communication density parameter and the channel occupancy efficiency parameter, establish a conflict degree evaluation model, and generate a real-time conflict degree score; Specifically, it includes: calculating the conflict degree score using a weighted non-linear combination model : ; where: α and β are weight parameters, satisfying α + β = 1, and are used to adjust the contribution ratio of Se and EG to ; f(Se) is a non-linear mapping function of the communication density anomaly score value, and g(EG) is a non-linear 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 non-linear growth characteristic of Se: ; where: = 100: the normalized maximum value of the communication density score value, and e is the natural constant; The channel occupancy efficiency fluctuation score value mapping g(EG) uses an exponential mapping: ; where: = 100: the normalized maximum value of the channel efficiency fluctuation score value; Among them, the method for obtaining the communication density anomaly score value is as follows: Set the communication density data set as , and each data point represents the communication density within a unit time; for each data point , calculate the distance between it and all other points ; find the distance to the k-th nearest neighbor point, defined as: ; in the formula, is the set of k nearest neighbors of the data point , including all points whose distance from is not greater than , and k is the number of neighbors; for and , calculate the reachable distance , and the expression is: ; if is less than , then take as the reachable distance, and the local reachability density of the data point is defined as the reciprocal mean of the reachable distances of its neighbor points: ; is the number of points; the local outlier factor of the data point is defined as the ratio of the average of the local reachability densities of its neighbor points to its own local reachability density: ; generate the communication density anomaly score value Se according to , and the expression is: ; MaxLOF is the maximum LOF value among all current points; The method for obtaining the channel occupancy efficiency fluctuation score value is as follows: Let the channel occupancy efficiency data be a time series ; Starting from the th data point, where z is the size of the sliding window, construct a sliding window containing z data points : ; Each time the window is moved forward by one time point to form a new subset. Within the window , 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 change rate of the standard deviation : ; is the standard deviation of the channel occupancy efficiency at time t; Map the change rate of the standard deviation to the channel occupancy efficiency fluctuation score value EG, and the scoring range is from 0 to 100: ; In the formula, is the preset maximum fluctuation change rate; The regulation strategy generation unit is used to dynamically generate an optimized channel allocation plan, a transmission frequency adjustment strategy, and a priority allocation plan according to the conflict degree score; The dynamic regulation module is communicatively connected to the UWB tag module and the central server module and is used to receive and execute the regulation plan generated by the regulation strategy generation unit. Specifically, it includes: Dynamically adjust the working channel of the UWB tag, and use the frequency hopping mechanism to disperse the communication load in the high-density area; reduce the data transmission frequency of non-critical goods tags and maintain high-frequency transmission for critical goods tags; dynamically allocate channel priorities according to the importance of the goods, and give priority to ensuring the data transmission of critical goods; The traceability evaluation module is deployed in the central server module and is used to analyze the historical positioning 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. An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking according to claim 1, characterized in that: In the UWB base station module, the base station identifies the number of active UWB tags within the current time according to the tag ID in the received data packet, judges whether the tag is in an active state by 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 transmission frequency, and comprehensively calculates the communication density parameter per unit time based on the data transmission frequencies of all active tags; The base station records each successfully received data packet and statistically calculates the total number of data packets received per unit time. Using the data packet sequence number or verification mechanism to distinguish between repeated transmissions and new data packets, the base station accumulatively calculates the total channel occupancy time per unit time according to the reception time and transmission duration of each recorded data packet, and calculates the channel occupancy efficiency parameter.

3. An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking according to claim 1, characterized in that: The regulation strategy generation unit generates a corresponding conflict range and regulation strategy according to the conflict degree score which specifically includes: High conflict >70: Switch the label channel of the high-load area to ensure the transmission priority of critical cargo labels; Medium conflict 40 ≤ ≤ 70: Reduce the transmission frequency of non-critical tags and gradually decrease the transmission rate of low-priority tags; Low conflict <40: Restore the normal channel allocation and transmission frequency, and maintain the real-time transmission of high-priority tags.

4. An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking according to claim 1, characterized in that: In the retrospective evaluation module, the positioning data includes the historical location information of the goods ; The status data includes environmental status data ; The conflict degree score includes the calculated history time series; Align the positioning data, status data, and conflict degree scores according to the timestamp to form a complete time series: ; According to the positioning data Draw the movement trajectory of the goods, mark its stopping points and movement directions, compare the trajectory with the predetermined route, and identify the deviation points; perform threshold detection on the status data to identify the out-of-standard points: ; is the maximum value of the preset environmental temperature data, is the maximum value of the preset environmental humidity data, check the value during the abnormal status period. If overlaps with the abnormal status, mark it as an abnormality affected by the communication environment: ; According to the abnormality mark, determine the time period and location where the problem occurred.

5. An Internet of Things risk interaction traceability evaluation system integrating UWB transmission tracking, characterized in that: Evaluate the potential impact of the problem on the quality of the goods based on the abnormal duration and the importance of the goods. Specifically: Calculate the risk score by integrating the problem duration, the amplitude of the status abnormality, and the degree of communication conflict. : ; is the weight of the abnormal duration. is the weight of the degree of status abnormality. is the average value of the conflict degree score. is the preset weight coefficient, satisfying ; According to value, divide the risk level: <40: Low risk; 40 ≤ <70: Medium risk; ≥ 70: High risk.

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