Article damage assessment method and device
By deploying sensors in the warehousing and logistics environment to monitor and analyze sensor data, damaged items can be automatically identified and assessed, solving the problem of untimely damage assessment and achieving efficient damage assessment and handling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-19
- Publication Date
- 2026-04-07
AI Technical Summary
In the existing technology, when items are damaged during storage and transportation, they cannot be detected and assessed in a timely manner, resulting in untimely handling.
By deploying sensors in the warehousing and logistics environment, the system monitors whether the sensor data exceeds the preset range. It uses different types of sensors to acquire data within the monitoring range, analyzes whether the data meets the damage assessment conditions, and automatically determines the items to be assessed for damage assessment and processing.
It enables timely assessment and handling of damaged items, reduces human intervention, and improves the efficiency and accuracy of damage assessment.
Smart Images
Figure CN121809824A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of computer, and in particular, to an article damage evaluation method and device. BACKGROUND
[0002] Warehouse logistics refers to logistics activities such as storage, custody, loading and unloading, and distribution of articles. In the process of storing, storing, loading and unloading, and distributing articles, the articles may be damaged due to improper storage or transportation. Usually, the damaged articles can only be found by the operating personnel through manual observation or after receiving customer complaints, which is not conducive to timely damage evaluation and processing of damaged articles. SUMMARY
[0003] Therefore, the embodiments of the present application provide an article damage evaluation method and device, which can timely evaluate and process damaged articles.
[0004] In a first aspect, the embodiments of the present application provide an article damage evaluation method, comprising: In response to monitoring that the sensor data collected by the sensor in the warehouse logistics environment is outside the preset range, determining the monitoring range of the sensor; According to the type of the sensor, obtaining the range monitoring data corresponding to the monitoring range; According to the range monitoring data, determining whether the monitoring range meets the damage evaluation condition; In response to the monitoring range meeting the damage evaluation condition, determining the article to be evaluated from the monitoring range, and performing damage evaluation processing on the article to be evaluated.
[0005] Optionally, according to the type of the sensor, obtaining the range monitoring data corresponding to the monitoring range, comprises: In response to the type of the sensor being an environmental monitoring type, determining the current time, and determining a first monitoring period according to the current time; Obtaining first monitoring data of the monitoring range within the first monitoring period; wherein the first monitoring data is collected by the environmental monitoring type sensor.
[0006] Optionally, according to the range monitoring data, determining whether the monitoring range meets the damage evaluation condition, comprises: According to the first monitoring data, determining the duration of the monitoring range in the abnormal environment; In response to the duration exceeding the duration threshold, determining that the monitoring range meets the damage evaluation condition.
[0007] Optionally, according to the type of the sensor, obtaining the range monitoring data corresponding to the monitoring range, comprises: In response to the type of the sensor being a vehicle-mounted type, determining the current time, and determining a second monitoring period according to the current time; acquire second monitoring data of the monitoring range in a second monitoring period; wherein the second monitoring data is acquired by the vision sensor.
[0008] Optionally, determining whether the monitoring range meets the damage assessment condition according to the range monitoring data comprises: performing visual analysis processing on the second monitoring data; in response to the visual analysis processing result indicating that an abnormal event occurs in the monitoring range, determining that the monitoring range meets the damage assessment condition; determining the to-be-assessed item from the monitoring range comprises: determining an item range involved in the abnormal event; determining the to-be-assessed item from the item range.
[0009] Optionally, determining whether the monitoring range meets the damage assessment condition according to the range monitoring data comprises: acquiring third monitoring data of the monitoring range in a second monitoring period; wherein the third monitoring data is acquired by the vehicle-mounted sensor; determining a visual abnormality score corresponding to the second monitoring data; determining a vehicle-mounted abnormality score corresponding to the sensing data; determining a total risk score according to the visual abnormality score and the vehicle-mounted abnormality score; in response to the total risk score being greater than a score threshold, determining that the monitoring range meets the damage assessment condition.
[0010] Optionally, before acquiring the second monitoring data of the monitoring range in the second monitoring period, the method further comprises: acquiring vehicle-mounted sensing data acquired by the vehicle-mounted sensor in a first historical period; acquiring visual sensing data acquired by the vision sensor in a second historical period; performing time alignment and / or space alignment processing on the vehicle-mounted sensing data and the visual sensing data; acquiring the second monitoring data of the monitoring range in the second monitoring period comprises: acquiring the second monitoring data corresponding to the monitoring range from the aligned visual sensor.
[0011] Optionally, performing damage assessment processing on the to-be-assessed item comprises: acquiring at least one modality data of the to-be-assessed item; inputting the at least one modality data into a damage assessment model to obtain damage assessment data of the to-be-assessed item; in response to the damage assessment data meeting a damage judgment condition, obtaining factor scores of a plurality of liability factors according to the at least one modality data of the to-be-assessed item and the carrier data; Based on the factor scores of the plurality of liability factors, a carrier liability percentage for damage to the item to be evaluated is determined.
[0012] In a second aspect, an embodiment of the present application provides an item damage evaluation device, comprising: A range determination module is configured to determine a monitoring range of a sensor in response to monitoring that sensor-collected sensing data in a warehouse logistics environment is outside a preset range. A data acquisition module is configured to acquire range monitoring data corresponding to the monitoring range according to a type of the sensor. A condition determination module is configured to determine whether the monitoring range meets a damage evaluation condition according to the range monitoring data. An evaluation processing module is configured to determine an item to be evaluated from the monitoring range and perform damage evaluation processing on the item to be evaluated in response to the monitoring range meeting the damage evaluation condition.
[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising: One or more processors; A storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any of the above embodiments.
[0014] In a fourth aspect, an embodiment of the present application provides a computer readable medium having stored thereon a computer program, the program being executed by a processor to implement the method of any of the above embodiments.
[0015] In a fifth aspect, an embodiment of the present application provides a computer program product comprising a computer program, the computer program being executed by a processor to implement the method of any of the above embodiments.
[0016] An embodiment of the above application has the following advantages or beneficial effects: when it is monitored that sensor-collected sensing data in a warehouse logistics environment is outside a preset range, a monitoring range of a sensor is determined. Range monitoring data corresponding to the monitoring range is acquired to determine whether the monitoring range meets a damage evaluation condition. If the monitoring range meets the damage evaluation condition, there is a high probability that damaged items exist in the monitoring range. An item to be evaluated is determined from the monitoring range, and damage evaluation processing is performed on the item to be evaluated. The scheme of the embodiment of the present application can timely perform damage evaluation and subsequent processing on damaged items.
[0017] Further effects of the above non-conventional optional mode will be described in the following with reference to the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS
[0018] The accompanying drawings are used to better understand the present application and do not constitute an improper limitation on the present application. Among them: Figure 1 is a schematic diagram of a flow of a method for evaluating damage of an article provided by an embodiment of the present application; Figure 2 is a schematic diagram of a flow of a method for evaluating damage of an article provided by another embodiment of the present application; Figure 3 is a schematic diagram of a flow of a method for evaluating damage of an article provided by yet another embodiment of the present application; Figure 4 is a schematic diagram of a flow of a method for evaluating damage of an article provided by still another embodiment of the present application; Figure 5 is a structural schematic diagram of an apparatus for evaluating damage of an article provided by an embodiment of the present application; Figure 6 is a structural schematic diagram of a computer system of a terminal device or a server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION
[0019] Exemplary embodiments of the present application are described herein with reference to the accompanying drawings, which are meant to be exemplary and not limiting. Therefore, it should be recognized that many changes and modifications can be made to the embodiments described herein, without departing from the spirit and scope of the application. Also, for the purpose of clarity and a concise description, descriptions of well-known functions and constructions are omitted from the following disclosure.
[0020] It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solutions of the embodiments of the present application comply with relevant provisions of national laws and regulations.
[0021] Figure 1 is a schematic diagram of a flow of a method for evaluating damage of an article provided by an embodiment of the present application. As shown in Figure 1 , the method comprises: Step 101: determining a monitoring range of a sensor in response to monitoring that sensor data collected by the sensor in a warehouse logistics environment is outside a preset range.
[0022] The warehouse logistics environment includes an article storage environment, an article storage environment, an article loading and unloading environment, and an article distribution environment, etc. The sensor includes a gravity sensor, a gyroscope, a temperature and humidity sensor, etc. The preset range of the sensor is set according to the sensor, the warehouse logistics environment, and the stored article, etc. If the sensor data collected by the sensor is outside the preset range, the article in the monitoring range of the sensor may be damaged, and the monitoring range of the sensor is determined.
[0023] Step 102: acquiring range monitoring data corresponding to the monitoring range according to the type of the sensor.
[0024] The types of sensors include: environment monitoring type and vehicle-mounted type, etc. The types of sensors are different, and the range monitoring data corresponding to the monitoring range to be acquired are also different.
[0025] Step 103: According to the range monitoring data, it is determined whether the monitoring range meets the damage assessment condition.
[0026] The damage assessment condition is set according to the category of the goods and the warehouse logistics environment. If the monitoring range meets the damage assessment condition, there is a high probability that there is damaged goods in the monitoring range, and the to-be-evaluated goods need to be determined from the monitoring range for damage assessment processing.
[0027] Step 104: In response to the monitoring range meeting the damage assessment condition, the to-be-evaluated goods are determined from the monitoring range, and the to-be-evaluated goods are subjected to damage assessment processing.
[0028] The to-be-evaluated goods can be randomly determined from the monitoring range. The to-be-evaluated goods can also be selected from the monitoring range by the staff.
[0029] In the scheme of the embodiment of the application, when it is monitored that the sensor data collected by the sensor in the warehouse logistics environment is outside the preset range, the monitoring range of the sensor is determined. The range monitoring data corresponding to the monitoring range is acquired to determine whether the monitoring range meets the damage assessment condition. If the monitoring range meets the damage assessment condition, the goods in the monitoring range have a high probability of being damaged goods. The to-be-evaluated goods are determined from the monitoring range, and the to-be-evaluated goods are subjected to damage assessment processing. The scheme of the embodiment of the application can timely perform damage assessment and subsequent processing on damaged goods.
[0030] Figure 2 is a flowchart of an article damage assessment method provided by another embodiment of the application. As shown in Figure 2 , the method comprises: Step 201: In response to monitoring that the sensor data collected by the sensor in the warehouse logistics environment is outside the preset range, the monitoring range of the sensor is determined.
[0031] Step 202: In response to the type of the sensor being the environment monitoring type, the current time is determined, and a first monitoring period is determined according to the current time.
[0032] The sensor of the environment monitoring type is used to monitor the static environment in the warehouse logistics environment. The sensor of the environment monitoring type includes: a temperature sensor, a humidity sensor, and a pressure sensor, etc.
[0033] The first monitoring period can be flexibly set. For example, the first monitoring period can be a period with the current time as the starting point and a half hour as the time length. The first monitoring period can also be a period with the current time as the ending point and two hours as the time length. The first monitoring period can also be a period between a first time point and a second time point. The first time point is located before the current time and is a time point one half hour away from the current time. The second time point is located after the current time and is a time point one hour away from the current time.
[0034] Step 203: Obtain first monitoring data in the monitoring range in the first monitoring period; wherein the first monitoring data is collected by an environmental monitoring sensor.
[0035] The first monitoring data is collected by an environmental sensor of the environmental monitoring type. The environmental sensor has the same sensor type as the sensor in step 201.
[0036] Step 204: Determine the duration of the monitoring range in the abnormal environment according to the first monitoring data.
[0037] The abnormal environment can be set according to the storage requirements of the goods. For example, if the air temperature is higher than the air temperature threshold, it is determined that the monitoring range is in an abnormal environment. If the humidity is higher than the humidity threshold, it is determined that the monitoring range is in an abnormal environment. If the pressure is higher than the pressure threshold, it is determined that the monitoring range is in an abnormal environment, and so on.
[0038] According to the first monitoring data, the duration of the monitoring range in the abnormal environment is determined. For example, the first monitoring data is collected by a temperature sensor. The air temperature threshold is 0 degrees Celsius. The air temperature of multiple consecutive time points is obtained from the first monitoring data. The duration of the abnormal environment is determined. The air temperature of each time point in the duration is lower than 0 degrees Celsius. The duration of the duration is determined as the duration of the monitoring range in the abnormal environment.
[0039] Step 205: In response to the duration exceeding the time length threshold, it is determined that the monitoring range meets the damage evaluation condition.
[0040] If the duration exceeds the time length threshold, it is determined that the monitoring range meets the damage evaluation condition, and step 206 is performed. If the duration does not exceed the time length threshold, it is determined that the monitoring range does not meet the damage evaluation condition, and the goods damage evaluation process is ended.
[0041] Step 206: Determine the to-be-evaluated goods from the monitoring range, and perform damage evaluation processing on the to-be-evaluated goods.
[0042] In the scheme of the embodiment of the present application, the environment monitoring type sensor is used to monitor the warehouse logistics environment. If it is monitored that the sensor data is out of the preset range, the first monitoring data of the monitoring range is acquired. The first monitoring data is acquired by using the sensor of the same sensor type. According to the first monitoring data, the duration of the monitoring range in the abnormal environment is determined. In the case that the duration exceeds the duration threshold, the damage evaluation processing is performed on the to-be-evaluated items in the monitoring range.
[0043] Figure 3 is a schematic diagram of a flow of an item damage evaluation method provided by another embodiment of the present application. As shown in Figure 3 , the method comprises: Step 301: in response to monitoring that the sensor data collected by the sensor in the warehouse logistics environment is out of the preset range, determining the monitoring range of the sensor.
[0044] Step 302: in response to the type of the sensor being the vehicle-mounted type, determining the current time, and determining the second monitoring period according to the current time.
[0045] The vehicle-mounted sensor is installed on the transportation vehicle of the item. The monitoring range of the vehicle-mounted sensor can be the entire vehicle compartment or part of the area in the vehicle compartment. The vehicle-mounted sensor includes a gravity sensor, a gyroscope, etc.
[0046] The sensor data collected by the gravity sensor can be the acceleration of the vehicle in any direction. Through the perceived acceleration, the abnormal situations such as sudden braking and violent collision can be identified.
[0047] The sensor data collected by the gyroscope can be the angular velocity and the inclination change of the vehicle driving. If the angular velocity or the inclination change is too fast, the items loaded on the vehicle have the risk of rolling or tilting.
[0048] The second monitoring period can be flexibly set. For example, the second monitoring period can be a period with the current time as the starting point and ten minutes as the duration. The second monitoring period can also be a period with the current time as the end point and ten seconds as the duration. The second monitoring period can also be a period between the third time point and the fourth time point. The third time point is located before the current time and is five seconds away from the current time. The fourth time point is located after the current time and is five seconds away from the current time.
[0049] Step 303: acquiring the second monitoring data of the monitoring range in the second monitoring period; wherein the second monitoring data is collected by using the visual type sensor.
[0050] The visual type sensor and the vehicle-mounted sensor are installed on the same transportation vehicle. The visual type sensor includes a camera, a laser radar, etc. The visual type sensor is used to collect the image or video of the loaded items on the transportation vehicle.
[0051] Step 304: performing visual analysis processing on the second monitoring data.
[0052] Step 305: in response to the visual analysis processing result representing that an abnormal event occurs in the monitoring range, determining that the monitoring range meets the damage assessment condition.
[0053] The abnormal event is an event that may cause damage to the goods. The abnormal event can include: a trajectory mutation of the monitoring object, damage to the monitoring object, a state change of the monitoring object, collapse of a goods pile, and a large amount of goods scattering, etc. The monitoring object can include: goods, a packaging box loaded with goods, a transportation vehicle, etc. The trajectory mutation of the monitoring object includes: the monitoring object moving from point A to point B in the warehouse logistics environment. The state change of the monitoring object includes: the monitoring object changing from A face-up to B face-up, etc.
[0054] Step 306: determining a goods range involved in the abnormal event; and determining the goods to be assessed from the goods range.
[0055] The goods range is a range that can be affected by the abnormal event. For example, the monitored abnormal event is a trajectory mutation of a packaging box, and all goods loaded in the packaging box are determined as the goods range. The monitored abnormal event is collapse of a goods pile, and all goods in the goods pile are determined as the goods range.
[0056] Step 307: performing damage assessment processing on the goods to be assessed.
[0057] In the scheme of the embodiment of the application, a vehicle-mounted sensor is used to monitor the warehouse logistics environment. If the monitoring data is outside the preset range, second monitoring data of the monitoring range is obtained. The second monitoring data is collected by a visual sensor. According to the second monitoring data, it is determined whether an abnormal event occurs in the monitoring range. If an abnormal event occurs in the monitoring range, damage assessment processing is performed on the goods to be assessed involved in the abnormal event.
[0058] In an embodiment of the application, according to the range monitoring data, it is determined whether the monitoring range meets the damage assessment condition, including: determining a visual abnormal score corresponding to the second monitoring data; determining a vehicle-mounted abnormal score corresponding to the monitoring data; determining a total risk score according to the visual abnormal score and the vehicle-mounted abnormal score; and in response to the total risk score being greater than a score threshold, determining that the monitoring range meets the damage assessment condition.
[0059] The second monitoring data is processed by visual analysis to determine the abnormal event in the monitoring range. The abnormal event is compared with an event score mapping relationship to determine a visual abnormal score corresponding to the second monitoring data. If no abnormal event occurs in the monitoring range, the visual abnormal score is determined as a default value.
[0060] The acceleration, angular velocity and other sensor data are continuously collected by various sensors, and the sensor data is uniformly converted into a score between 0 and 1. For example, if an acceleration of 3g is detected, the acceleration anomaly score is determined to be 0.6. The vehicle-mounted anomaly score can include: acceleration anomaly score, angular velocity anomaly score, etc.
[0061] The environmental data in the monitoring range within the second monitoring period, such as temperature, humidity, pressure, weather severity, road bump index, etc. is obtained. According to the environmental data, the environmental anomaly score is determined.
[0062] The system assigns weights to different anomaly scores, and calculates the total risk score. For example, the total risk score = (acceleration anomaly score × 40%) + (angular velocity anomaly score × 30%) + (environmental anomaly score × 20%) + (visual anomaly score × 10%).
[0063] When the total risk score exceeds a preset risk threshold, such as 0.75, the system determines that the monitoring range meets the damage evaluation condition.
[0064] The weights of the anomaly scores corresponding to each sensor are dynamically adjusted. The weights can be assigned according to the historical error rate of the sensor. For example, the weight of the gravity sensor with an error rate <2% is set to 0.4. The weight of the temperature and humidity sensor with an error rate of 5% is set to 0.2. The distribution weights of each sensor can also be adjusted according to environmental information. For example, in heavy rain, the distribution weight of the visual anomaly score is reduced, and the distribution weight of the acceleration anomaly score is increased.
[0065] In an embodiment of the present application, before obtaining the second monitoring data of the monitoring range within the second monitoring period, it further includes: obtaining vehicle-mounted sensor data collected by vehicle-mounted sensors in a first historical period; obtaining visual sensor data collected by visual sensors in a second historical period; performing time alignment and / or space alignment processing on the vehicle-mounted sensor data and the visual sensor data; obtaining the second monitoring data of the monitoring range within the second monitoring period, including: obtaining the second monitoring data corresponding to the monitoring range from the aligned visual sensor data.
[0066] The sensor data from different sensors is uniformly processed to generate high-reliability item feature data. Time alignment is used to synchronize the time stamps in the sensor data. Weighted least squares method can be used to align the time stamps in the sensor data to solve the clock drift problem. Clock drift refers to the phenomenon that the local time of different sensors deviates from the standard time due to the frequency deviation of the hardware crystal oscillator.
[0067]
[0068] where w iis a precision weight of the sensor. For example, the precision weight of the gravity sensor can be set as 0.6, and the precision weight of the temperature and humidity sensor can be set as 0.2. i is a timestamp in the sensor data. is the aligned unified timestamp.
[0069] Spatial alignment is used to align the spatial data in the sensor data. By using the ICP (Iterative Closest Point) algorithm, the point cloud data of the object at different angles or time points can be aligned to the same coordinate system, and various problems such as detection of position change of the object before and after loading and unloading, fusion of multiple camera angles, monitoring of displacement of the object during transportation, etc. can be solved.
[0070] After the time alignment and / or spatial alignment processing of the vehicle-mounted sensor data and the visual sensor data, the method further includes: performing feature fusion processing on the vehicle-mounted sensor data and the visual sensor data. By using a cross-modal attention mechanism, a dynamic association between different sensor data is established, and feature fusion processing of multiple sensor data is realized. Through the fusion processing, the acceleration data is associated with the state of the object in the video frame, so that the abnormal event can be more accurately explained. Through data collection of multiple sensors, heterogeneous data from different sensors are uniformly processed to generate high-reliability object behavior features or object state features.
[0071] Figure 4 is a schematic diagram of a flow of an object damage assessment method according to another embodiment of the present application. As shown in Figure 4 , the method comprises: Step 401: In response to monitoring that the sensor data collected by the sensor in the warehouse logistics environment is outside the preset range, determining the monitoring range of the sensor.
[0072] Step 402: According to the type of the sensor, obtaining range monitoring data corresponding to the monitoring range.
[0073] Step 403: According to the range monitoring data, determining whether the monitoring range meets the damage assessment condition.
[0074] Step 404: In response to that the monitoring range meets the damage assessment condition, determining the object to be assessed from the monitoring range.
[0075] Step 405: Obtaining at least one modality data of the object to be assessed.
[0076] The modality data can include sensor data indicators, visual data indicators, auxiliary data indicators, etc. The sensor data indicators include acceleration data, angular velocity data, environmental sensor data, etc.
[0077] Visual data metrics are derived from images or videos of the goods. These images can be captured by visual sensors or uploaded by the recipient. Visual data metrics are obtained through image segmentation algorithms, point cloud analysis, and color space analysis. These metrics include: the percentage of damaged area on the outer packaging, the degree of structural deformation, the number of broken sealing strips, liquid leakage detection, and component displacement distance.
[0078] Environmental sensor data is used to assess the impact of temperature changes, evaluate humidity risks, and detect airtightness. Supporting data indicators include: fragility rating, maximum permissible impact, road bump index, weather severity level, and average damage rate along the same route.
[0079] Step 406: Input at least one modal data into the damage assessment model to obtain damage assessment data of the item to be assessed.
[0080] The damage assessment model outputs damage assessment data for the item to be assessed. This data includes core output indicators and detailed classification outputs. Core output indicators include: damage probability, damage severity, and liability confidence level. Detailed classification outputs include: impact damage contribution, crush damage contribution, vibration damage contribution, temperature influence score, humidity influence score, and air pressure influence score.
[0081] The damage assessment model employs a multimodal data fusion architecture. It utilizes a cross-modal attention mechanism to align and fuse sensor and visual data metrics. A 3D convolutional neural network is used to analyze the spatiotemporal characteristics of damaged object photographs. A time-series signal processing module processes time-series data from vehicle sensors such as gravity sensors and gyroscopes, and compensates for environmental noise such as weather and road conditions. The damage assessment model's processing flow consists of the following three stages: The data preprocessing stage is used to denoise and align multiple viewpoints of the uploaded damaged photos. It also involves time-frequency analysis of sensor data and standardization of environmental data.
[0082] The feature extraction stage uses an improved ResNet-50 to extract features of the damaged regions. Improvements to ResNet-50 include adding coordinate attention to the Attention Gate and replacing ordinary convolutions with dynamic convolutions. The feature extraction stage also uses an LSTM network to extract temporal anomaly features.
[0083] The fusion decision-making stage calculates the correlation weights between modalities through a cross-attention mechanism and outputs damage probability scores and damage type classifications.
[0084] Step 407: In response to the damage assessment data meeting the damage liability criteria, factor scores for multiple liability factors are obtained based on at least one modal data of the item to be assessed and carrier data.
[0085] Damage liability determination criteria can be set according to business needs. Damage liability determination criteria may include at least one of the following: damage probability exceeds a probability threshold, damage severity exceeds a severity threshold, liability confidence level exceeds a confidence threshold, etc.
[0086] At least one modal data point of the item to be evaluated, along with carrier data, is input into a multi-factor liability assessment engine to obtain factor scores for multiple liability factors. These factors include: transportation behavior factor, item condition factor, environmental adaptability factor, and historical credit factor. The transportation behavior factor is obtained through anomaly detection based on onboard sensor data. The item condition factor is output by the damage assessment model. The environmental adaptability factor characterizes the carrier's performance in responding to adverse environments. The historical credit factor is derived from the carrier's historical records.
[0087] The multi-factor accountability engine is a decision-making system based on a hybrid of data and machine learning. It adaptively adjusts weights using a Bayesian optimization algorithm. The evidence credibility assessment module within the multi-factor accountability engine is used to score the reliability of various evidence sources. When different pieces of evidence contradict each other, the multi-factor accountability engine provides an arbitration strategy. The multi-factor accountability engine includes the following three-stage processing flow: During the evidence collection and preprocessing stage, sensor data is collected in real time through sensors; photos or videos of items are analyzed and processed; and environmental data such as the carrier's credit score and weather and road conditions are collected.
[0088] In the evidence credibility assessment stage, various types of evidence are evaluated based on factors such as completeness, timeliness, and source credibility to obtain the credibility of each type of evidence.
[0089] The responsibility factor output stage is used to output the factor scores of multiple responsibility factors.
[0090] Step 408: Determine the carrier's liability for damage to the item to be assessed based on factor scores of multiple liability factors.
[0091] The factor scores for each liability factor are normalized, converting them to a range of 0 to 1. Weights for each liability factor are dynamically assigned based on the current scenario type, including general transportation, fragile goods transportation, and transportation in severe weather. Finally, the carrier's liability ratio for damage to the assessed goods is obtained using the formula for calculating the damage liability ratio. The formula for calculating the damage liability ratio is as follows: Responsibility ratio = Σ(Factor weight × Factor score) × Correction coefficient The correction factor is calculated as min(1, average credibility / 0.8). Here, average credibility is the mean of the credibility of various types of evidence.
[0092] If the factor score for the transportation behavior factor is 0.8, and its weight is 40%; the factor score for the goods status factor is 0.9, and its weight is 30%; the factor score for the environmental adaptation factor is 0.5, and its weight is 20%; and the factor score for the historical credit factor is 0.7, and its weight is 10%, then the liability ratio is (0.8). 0.4 + 0.9 0.3 + 0.5 0.2 + 0.7 0.1) × 0.95 = 76%.
[0093] The responsibility ratio is calculated based on a multi-factor liability assessment engine. Penalty information is then generated according to this ratio. This information is then sent to the carrier and shipper's systems via SMS or application.
[0094] The solution in this invention requires no human intervention. By integrating various sensor data and environmental data, it automatically calculates the liability ratio for damaged goods. Through modular design, the processing logic for different sensor data is separated. The influence of each factor is dynamically adjusted to obtain the liability ratio, and the factor score of each liability factor is returned to obtain more accurate information on carrier penalties.
[0095] Figure 5 This is a schematic diagram of the structure of an article damage assessment device provided in one embodiment of the present invention. Figure 5 As shown, the device includes: The range determination module 501 is used to determine the monitoring range of the sensor in response to the detection that the sensor data collected by the sensor in the warehousing and logistics environment is outside the preset range. The data acquisition module 502 is used to acquire range monitoring data corresponding to the monitoring range according to the type of sensor; The condition determination module 503 is used to determine whether the monitoring range meets the damage assessment conditions based on the range monitoring data. The assessment processing module 504 is used to identify the item to be assessed from the monitoring range in response to the monitoring range meeting the damage assessment conditions, and to perform damage assessment processing on the item to be assessed.
[0096] Optionally, the data acquisition module 502 is specifically used for: In response to the sensor being classified as an environmental monitoring type, the current time is determined, and based on the current time, the first monitoring period is determined; Acquire the first monitoring data within the first monitoring period of the monitoring scope; wherein, the first monitoring data is collected using environmental monitoring sensors.
[0097] Optionally, the condition determination module 503 is specifically used for: Based on the first monitoring data, determine the duration of the monitoring range under abnormal conditions; If the duration exceeds the duration threshold, the monitoring range is determined to meet the damage assessment criteria.
[0098] Optionally, the data acquisition module 502 is specifically used for: In response to the sensor being classified as vehicle-mounted, the current time is determined, and based on the current time, a second monitoring period is determined. Acquire second monitoring data within the second monitoring period; wherein the second monitoring data is collected using a visual sensor.
[0099] Optionally, the condition determination module 503 is specifically used for: Visual analysis processing was performed on the second monitoring data; The visual analysis and processing results characterize abnormal events occurring within the monitoring range, and determine whether the monitoring range meets the damage assessment criteria. Optionally, the condition determination module 503 is specifically used for: Acquire third monitoring data within the second monitoring period; the third monitoring data is collected using vehicle-mounted sensors. Determine the visual anomaly score corresponding to the second monitoring data; Determine the vehicle anomaly score corresponding to the sensor data; The total risk score is determined based on the visual anomaly score and the vehicle-mounted anomaly score. If the total risk score is greater than the score threshold, the monitoring scope is determined to meet the damage assessment criteria.
[0100] The evaluation and processing module 504 is specifically used for: Determine the scope of items involved in the abnormal event; From the range of items, identify the items to be evaluated.
[0101] Optionally, the data acquisition module 502 is also used for: Acquire vehicle sensor data collected by vehicle-mounted sensors in the first historical time period; Acquire visual sensing data collected by visual sensors in the second historical time period; Perform time alignment and / or spatial alignment processing on vehicle sensor data and visual sensor data; The second monitoring data corresponding to the monitoring range is obtained from the aligned vision sensor.
[0102] Optionally, the evaluation processing module 504 is specifically used for: Obtain at least one modal data of the item to be evaluated; At least one modal data is input into the damage assessment model to obtain the damage assessment data of the item to be assessed. In response to the damage assessment data meeting the damage liability criteria, factor scores for multiple liability factors are obtained based on at least one modal data of the item to be assessed and carrier data. Based on factor scores of multiple liability factors, the proportion of the carrier's liability for damage to the items to be assessed is determined.
[0103] This invention provides an electronic device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the methods of any of the above embodiments.
[0104] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the above embodiments.
[0105] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing a terminal device of the present invention. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.
[0106] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.
[0107] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.
[0108] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.
[0109] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0111] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor, and for example, can be described as: a range determination module, a data acquisition module, a condition determination module, and an evaluation processing module. The names of these modules do not necessarily limit the module itself; for example, the range determination module can also be described as "a module that determines the monitoring range of a sensor in response to detecting that sensor data collected by a sensor in a warehousing and logistics environment is outside a preset range."
[0112] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: In response to the detection that the sensor data collected by the sensor in the warehousing and logistics environment is outside the preset range, the monitoring range of the sensor is determined; Based on the type of sensor, obtain the range monitoring data corresponding to the monitoring range; Based on the monitoring data, determine whether the monitoring area meets the damage assessment criteria; In response to the monitoring range meeting the damage assessment criteria, the items to be assessed are identified from the monitoring range, and damage assessment is performed on the items to be assessed.
[0113] According to the technical solution of this embodiment of the invention, when the sensor data collected by the sensor in the warehousing and logistics environment is detected to be outside a preset range, the monitoring range of the sensor is determined. Monitoring data corresponding to the monitoring range is acquired to determine whether the monitoring range meets the damage assessment conditions. If the monitoring range meets the damage assessment conditions, the items within the monitoring range are highly likely to be damaged. The items to be assessed are identified from the monitoring range, and damage assessment processing is performed on these items. The solution of this embodiment of the invention can promptly perform damage assessment and other subsequent processing on damaged items.
[0114] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for assessing damage to goods, characterized in that, include: In response to the detection that the sensor data collected by the sensor in the warehousing and logistics environment is outside the preset range, the monitoring range of the sensor is determined; Based on the type of sensor, obtain the range monitoring data corresponding to the monitoring range; Based on the monitoring data of the aforementioned range, determine whether the monitoring range meets the damage assessment criteria; In response to the monitoring range meeting the damage assessment conditions, the item to be assessed is identified from the monitoring range, and the item to be assessed is subjected to damage assessment processing.
2. The method according to claim 1, characterized in that, The step of obtaining range monitoring data corresponding to the monitoring range based on the type of sensor includes: In response to the fact that the sensor is of the environmental monitoring type, the current time is determined, and a first monitoring period is determined based on the current time; Acquire first monitoring data within the first monitoring period for the monitoring range; wherein the first monitoring data is collected using environmental monitoring sensors.
3. The method according to claim 2, characterized in that, The step of determining whether the monitoring range meets the damage assessment conditions based on the monitoring data includes: Based on the first monitoring data, determine the duration of the monitoring range under abnormal conditions; In response to the duration exceeding a duration threshold, the monitoring range is determined to meet the damage assessment criteria.
4. The method according to claim 1, characterized in that, The step of obtaining range monitoring data corresponding to the monitoring range based on the type of sensor includes: In response to the fact that the sensor is a vehicle-mounted type, the current time is determined, and a second monitoring period is determined based on the current time; Acquire second monitoring data within the second monitoring period of the monitoring range; wherein the second monitoring data is collected using a visual sensor.
5. The method according to claim 4, characterized in that, The step of determining whether the monitoring range meets the damage assessment conditions based on the monitoring data includes: Visual analysis processing is performed on the second monitoring data; In response to the visual analysis processing results characterizing an abnormal event occurring within the monitoring range, it is determined that the monitoring range meets the damage assessment criteria; The process of identifying the items to be evaluated from the monitoring range includes: Determine the scope of items involved in the abnormal event; The item to be evaluated is determined from the range of items.
6. The method according to claim 4, characterized in that, The step of determining whether the monitoring range meets the damage assessment conditions based on the monitoring data includes: Determine the visual anomaly score corresponding to the second monitoring data; Determine the vehicle anomaly score corresponding to the sensor data; The total risk score is determined based on the visual anomaly score and the vehicle anomaly score. If the total risk score is greater than a score threshold, the monitoring range is determined to meet the damage assessment criteria.
7. The method according to claim 4, characterized in that, Before acquiring the second monitoring data within the second monitoring period for the monitoring range, the method further includes: Acquire vehicle sensor data collected by vehicle-mounted sensors in the first historical time period; Acquire visual sensing data collected by visual sensors in the second historical time period; Perform time alignment and / or spatial alignment processing on the vehicle-mounted sensor data and the visual sensor data; The acquisition of the second monitoring data within the second monitoring period for the monitoring range includes: The second monitoring data corresponding to the monitoring range is obtained from the aligned visual sensor.
8. The method according to claim 1, characterized in that, The damage assessment process for the item to be assessed includes: Obtain at least one modal data of the item to be evaluated; The at least one modal data is input into the damage assessment model to obtain the damage assessment data of the item to be assessed. In response to the damage assessment data meeting the damage liability criteria, factor scores for multiple liability factors are obtained based on at least one modal data of the item to be assessed and carrier data. Based on the factor scores of the multiple liability factors, the carrier's liability ratio for damage to the items to be assessed is determined.
9. A device for assessing damage to goods, characterized in that, include: The range determination module is used to determine the monitoring range of the sensor in response to the detection that the sensor data collected by the sensor in the warehousing and logistics environment is outside the preset range. The data acquisition module is used to acquire range monitoring data corresponding to the monitoring range according to the type of the sensor; The condition determination module is used to determine whether the monitoring range meets the damage assessment conditions based on the range monitoring data. An assessment processing module is used to, in response to the monitoring range meeting the damage assessment conditions, identify the item to be assessed from the monitoring range and perform damage assessment processing on the item to be assessed.
10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.
11. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.
12. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.