Warehouse material identification method based on data analysis

Abstract

The application relates to the technical field of intelligent warehousing, and particularly discloses a warehouse material identification method based on data analysis, which realizes real-time collection of operation event data and material coordinate information through a UHF-RFID read-write array and a visual positioning system, generates operation event time sequences, calculates material mixing risk values, storage and taking frequency factors and storage and taking confusion degree indexes, analyzes coordinate data to detect displacement mutation and out-of-bound events, generates position abnormality degree indexes, fuses multiple indexes to obtain a storage location inspection priority score, generates an abnormal inspection instruction and labels abnormal types such as mixed storage, deviation and out-of-bound when the score exceeds a threshold value. The application realizes real-time collection and multidimensional analysis of operation event data and material coordinate position information of a warehouse storage location, combines operation event time sequence characteristics and position dynamic changes, constructs a quantitative index system to identify storage location abnormalities, realizes accurate identification of warehouse materials and timely early warning of abnormal states, and thus improves the fine level and operation efficiency of warehouse management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent warehousing, in particular to a warehouse material identification method based on data analysis. BACKGROUND

[0002] In the modern warehousing management system, efficient and accurate identification and management of warehouse materials are the key links to ensure smooth logistics and reduce operating costs. However, traditional warehouse material management and identification methods have many drawbacks and cannot meet the growing demand for warehousing business and the requirements of fine management.

[0003] In information collection, barcodes or manual records are relied on. Barcodes are easily damaged and need to be scanned at close range, which is low in efficiency. Manual records are prone to errors and cannot track material locations in real time, resulting in inaccurate inventory data and affecting subsequent links. In terms of material control, mixed storage risks are difficult to be warned in real time, manual inventory is time-consuming and prone to missing mixed storage problems, and there is a lack of position monitoring. When material displacement mutates or exceeds the boundary, traditional methods cannot intervene in real time, and the safety risk is high. In terms of data utilization and inspection planning, multi-source data is isolated and difficult to analyze collaboratively, manual inspection lacks quantitative standards, and resources are allocated unreasonably, so high-risk storage locations are prone to be missed.

[0004] Therefore, there is an urgent need for a warehouse material identification method based on data analysis to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a warehouse material identification method based on data analysis, comprising the following steps:

[0006] Through the UHF-RFID reader array and the visual positioning system, real-time collection of warehouse storage location operation event data and material coordinate position information is realized, the operation event data includes material SKU code and operation type, and the continuously collected operation event data is sorted by timestamp to generate an operation event time sequence;

[0007] According to the difference between the adjacent event SKU codes in the operation time sequence and the operation type, the material mixing risk value is calculated;

[0008] The number of operation events per unit time is obtained to generate an access frequency factor, and the material mixing risk value and the access frequency factor are fused to generate an access confusion degree index;

[0009] The continuous coordinate position data is analyzed to detect displacement mutation events, the material coordinate position is compared with the preset electronic fence boundary to obtain an out-of-bound event marker level, etc.

[0010] The displacement mutation event intensity and the out-of-bound event marker are fused to generate a position abnormality degree index, and the storage location inspection priority score is obtained based on the access confusion degree index, the position abnormality degree index, and the access frequency factor.

[0011] When the priority score exceeds a preset threshold, a storage location anomaly checking instruction is generated and an anomaly type label is marked, the anomaly type including at least one of material mixed storage anomaly, position offset anomaly, and out-of-bound stacking anomaly.

[0012] Further, the step of calculating the material mixing risk value according to the difference in SKU code of adjacent events in the operation time sequence and the operation type includes:

[0013] Extract all operation events of the target storage location within a set time window, and calculate the average access frequency per unit time;

[0014] Analyze the material SKU code of adjacent operation events pair by pair, and calculate the proportion of the same SKU code as the material similarity;

[0015] According to the proportion of the total volume of the material in the adjacent operation events to the maximum capacity of the storage location, a space load coefficient is calculated;

[0016] The material similarity and the space load coefficient are inversely related to generate a mixed risk weighting value;

[0017] Based on the product operation of the average access frequency and the mixed risk weighting value, a material mixing risk value is generated.

[0018] Further, the step of obtaining the number of operation events per unit time, generating an access frequency factor, and fusing the material mixing risk value and the access frequency factor to generate an access confusion degree index includes:

[0019] Based on the operation event time sequence, the total number of operation events of the target storage location within a preset period is counted, and the operation frequency per unit time is calculated;

[0020] Obtain the standard volume and the standard weight of a single piece of material;

[0021] Calculate a first proportion value of the material standard volume to the maximum volume of the storage location, and a second proportion value of the material standard weight to the safe bearing threshold of the storage location;

[0022] Obtain a material attribute influence coefficient according to the first proportion value and the second proportion value;

[0023] Obtain an access frequency factor according to the material attribute influence coefficient and the operation frequency per unit time;

[0024] Convert the material mixing risk value to a relative risk proportion value, and convert the access frequency factor to a relative frequency proportion value;

[0025] Obtain an access confusion degree index according to the relative risk proportion value and the relative frequency proportion value.

[0026] Further, the step of analyzing the continuous coordinate position data, detecting displacement mutation events, comparing the material coordinate position with the preset electronic fence boundary, and obtaining the boundary crossing event marking level comprises:

[0027] The continuous three-dimensional coordinate data stream collected by the visual positioning system is divided into trajectory segments at fixed time intervals, and the linear distance change between adjacent time points is calculated;

[0028] The average moving speed of each trajectory segment is calculated, and if the average moving speed exceeds the preset speed threshold, it is marked as a displacement mutation event, and if the average moving speed does not exceed the preset speed threshold, it is not marked;

[0029] The speed change rate of adjacent trajectory segments is calculated as the instantaneous acceleration;

[0030] The boundary vertex coordinate set of the preset electronic fence is obtained, and the vertical distance from the material coordinate to the nearest fence boundary is calculated in real time;

[0031] When the vertical distance is less than zero, a boundary crossing mark is generated, when the vertical distance is within the safe warning range, a warning mark is generated, and when a displacement mutation event is detected, a UHF-RFID reader is triggered synchronously;

[0032] Verify whether the current material SKU code matches the storage location registration information;

[0033] If the SKU code does not match, the warning level of the boundary crossing event mark is raised;

[0034] If the SKU code matches, the credibility weight of the displacement mutation event is reduced.

[0035] Further, the step of fusing the displacement mutation event intensity and the boundary crossing event mark to generate a position abnormality index, and obtaining a storage location inspection priority score based on the access confusion index, the position abnormality index, and the access frequency factor comprises:

[0036] Based on the displacement mutation event, the absolute value of the instantaneous acceleration and the total duration of the event are extracted;

[0037] The absolute value of the instantaneous acceleration and the duration are combined according to a preset proportion to generate a displacement event intensity value;

[0038] According to the boundary crossing event mark level, a corresponding weight coefficient is obtained;

[0039] According to the displacement event intensity value multiplied by the weight coefficient, a position abnormality index is obtained;

[0040] According to the access confusion index, the position abnormality index, and the access frequency factor, a storage location inspection priority score is obtained.

[0041] Further, when the inspection priority score exceeds the preset threshold, a storage location abnormality inspection instruction is generated and an abnormality type label is marked, the abnormality type including at least one of material mixed storage abnormality, position offset abnormality, and out-of-bound stacking abnormality.

[0042] When the storage location inspection priority score exceeds the preset threshold, multi-source consistency verification is performed according to the displacement mutation event coordinates, the RFID verification result, the position abnormality degree index, and the access confusion degree index.

[0043] Based on the continuous three-dimensional coordinate data stream and the SKU code, the following spatial relationship analysis is performed:

[0044] The X, Y and Z axis offset amounts of the actual position of the material from the preset coordinates are calculated.

[0045] It is detected whether the distance between the surfaces of adjacent materials is less than a safe operation threshold.

[0046] It is detected whether the SKU codes of materials in the same area are the same.

[0047] According to the multi-source verification result, an abnormality type label is marked, and according to the position abnormality degree index, the

[0048] Risk level is divided.

[0049] The application also discloses a warehouse material identification system based on data analysis, which comprises:

[0050] A generation module is configured to collect operation event data and material coordinate position information of a warehouse storage location in real time through a UHF-RFID reader-writer array and a visual positioning system, the operation event data including a material SKU code and an operation type, and sort the continuously collected operation event data according to timestamps to generate an operation event time sequence.

[0051] A calculation module is configured to calculate a material mixing risk value according to the SKU code difference degree and the operation type of adjacent events in the operation time sequence.

[0052] A fusion module is configured to obtain the number of operation events per unit time to generate an access frequency factor, fuse the material mixing risk value and the access frequency factor, and generate an access confusion degree index.

[0053] An analysis module is configured to analyze continuous coordinate position data, detect displacement mutation events, compare the material coordinate position with a preset electronic fence boundary, and obtain an out-of-bound event label level.

[0054] An acquisition module is configured to fuse the displacement mutation event intensity and the out-of-bound event label to generate a position abnormality degree index, and obtain a storage location inspection priority score based on the access confusion degree index, the position abnormality degree index and the access frequency factor.

[0055] The output module is configured to generate a storage location anomaly checking instruction and mark an anomaly type label when the checking priority score exceeds a preset threshold, wherein the anomaly type comprises at least one of material mixed storage anomaly, position offset anomaly and out-of-bound stacking anomaly.

[0056] Further, the calculation module comprises:

[0057] The extraction unit is configured to extract all operation events of the target storage location within a set time window, and count the average access frequency per unit time;

[0058] The analysis unit is configured to analyze the material SKU codes of adjacent operation events pair by pair, and calculate the proportion of the same SKU code as the material similarity;

[0059] The calculation unit is configured to calculate the space load coefficient according to the proportion of the total volume of the materials in the adjacent operation events to the maximum capacity of the storage location;

[0060] The first generation unit is configured to inversely relate the material similarity and the space load coefficient to generate a mixed risk weighting value;

[0061] The second generation unit is configured to generate a material mixing risk value based on the product operation of the average access frequency and the mixed risk weighting value.

[0062] The application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above warehouse material identification method based on data analysis when executing the computer program.

[0063] The application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the above warehouse material identification method based on data analysis.

[0064] The application has the following beneficial effects:

[0065] The application realizes the real-time collection and multi-dimensional analysis of the operation event data and material coordinate position information of the warehouse storage location through the cooperative operation of the UHF-RFID reader array and the visual positioning system, combines the operation event time sequence characteristics and the position dynamic change, constructs a quantitative index system to identify the storage location anomaly, and finally realizes the accurate identification of the warehouse material and the timely early warning of the abnormal state, thereby improving the fine level and operation efficiency of the warehouse management. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The method flowchart is provided for an embodiment of the application.

[0067] Figure 2The system structure diagram for an embodiment of the present application.

[0068] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

[0070] As shown in the accompanying drawings, Figure 1 The present application provides a warehouse material identification method based on data analysis, comprising the following steps:

[0071] S1, real-time collection of operation event data and material coordinate position information of the warehouse location through the UHF-RFID reader array and the visual positioning system, wherein the operation event data includes material SKU code and operation type, and the continuously collected operation event data is sorted by timestamp to generate operation event time sequence;

[0072] S2, calculation of material mixing risk value according to the adjacent event SKU code difference and operation type in the operation time sequence;

[0073] S3, generation of access frequency factor by obtaining the number of operation events per unit time, fusion of material mixing risk value and access frequency factor, and generation of access confusion index;

[0074] S4, analysis of continuous coordinate position data, detection of displacement mutation event, comparison of material coordinate position with preset electronic fence boundary, and acquisition of out-of-bound event marking level;

[0075] S5, fusion of displacement mutation event intensity and out-of-bound event marking to generate position abnormality index, and acquisition of location inspection priority score based on access confusion index, position abnormality index and access frequency factor;

[0076] S6, generation of location abnormality inspection instruction and labeling of abnormal type marking when the inspection priority score exceeds the preset threshold, wherein the abnormal type includes at least one of material mixing abnormality, position offset abnormality and out-of-bound accumulation abnormality.

[0077] As described in the above steps S1-S6, the present application realizes real-time collection and multi-dimensional analysis of operation event data and material coordinate position information of the warehouse location through the cooperative operation of the UHF-RFID reader array and the visual positioning system, combines the operation event time sequence characteristics and the position dynamic change, constructs a quantitative index system to identify the location abnormality, and finally realizes the accurate identification of the warehouse material and the timely early warning of the abnormal state, thereby improving the fine level and operation efficiency of the warehouse management.

[0078] In the warehouse management scenario, the material access operation has high frequency and dynamics, the storage location state will change continuously during the operation process, and the difference of SKU code may cause material mixing, the deviation or out-of-bound of coordinate position may cause space utilization confusion and even safety risk. Real-time capture of these dynamic changes and accurate assessment of their impact on the storage location state are the core needs to ensure the orderly operation of the warehouse and reduce management costs.

[0079] In the traditional warehouse material management method, data collection relies on manual recording or single barcode scanning, which is not only inefficient, but also cannot associate operation events and location information in real time, lacks time sequence analysis for material mixing risk assessment, and cannot predict mixing trend through SKU difference of adjacent operations, and the detection of position abnormality only depends on static boundary judgment, ignoring the dynamic characteristics of displacement mutation, resulting in late abnormality discovery. The present application systematically solves these problems through multi-source data fusion and quantitative index construction.

[0080] In one embodiment, the step of calculating the material mixing risk value according to the SKU code difference degree and operation type of adjacent events in the operation time sequence comprises:

[0081] S21, extracting all operation events of the target storage location within a set time window, and counting the average access frequency per unit time;

[0082] S22, analyzing the material SKU codes of adjacent operation events pair by pair, and calculating the proportion of the same SKU code as the material similarity;

[0083] S23, calculating the space load coefficient according to the proportion of the total volume of the materials in the adjacent operation events to the maximum capacity of the storage location;

[0084] S24, inversely associating the material similarity and the space load coefficient to generate a mixing risk weighted value;

[0085] S25, generating a material mixing risk value based on the product operation of the average access frequency and the mixing risk weighted value.

[0086] As described in steps S21-S25 above, the present application calculates the material mixing risk value by performing time sequence analysis on the operation events of the target storage location within a specific time range, combining the material type difference, the storage space occupation condition and the operation frequency, so as to quantitatively evaluate the possibility of material mixing in the storage location, and provide accurate risk basis for subsequent identification of material mixing abnormality.

[0087] In actual warehouse operation, the risk of mixed storage of materials is not isolated, and is closely related to the operation frequency of the storage location, the change of material types and the utilization status of the storage location space. When the material storage and retrieval operation of a storage location is more frequent, each operation may introduce different types of materials, increasing the probability of mixed storage; the greater the difference in material types in adjacent operations, the higher the risk of management confusion and sorting errors after mixed storage. At the same time, if the occupancy rate of the storage location space is high, the materials are closely stacked, and once mixed storage occurs, it is difficult to quickly distinguish, and it may also exacerbate the disorder of space utilization, further increasing the negative impact of mixed storage. Therefore, it is necessary to quantitatively analyze these factors to build an index that can accurately reflect the risk of mixed storage of materials.

[0088] Traditional material mixed storage risk assessment mainly relies on manual inspection and experience judgment. This method is not only inefficient and cannot follow the dynamic changes of storage location operation in real time, but also cannot comprehensively consider the operation frequency, material difference and space occupancy and other factors, resulting in a lag and subjectivity in the judgment of mixed storage risk, which is often discovered when the mixed storage problem is already serious, increasing the cost of subsequent arrangement and error correction. The present application realizes dynamic and objective evaluation of the risk of mixed storage of materials by systematic analysis of the time sequence of operation events, extraction of key influencing factors and quantitative calculation, effectively making up for the shortcomings of traditional methods.

[0089] Specifically, all operation events of the target storage location within a set time window are extracted, and the average access frequency per unit time is counted. The set time window can be determined according to the operation rhythm of the warehouse, for example, 1 hour, 8 hours (one shift) or 24 hours are selected as the analysis period, all storage, retrieval, shifting and other operation events occurring at the target storage location within the time window are collected by UHF-RFID reader array, and then the total number of operation events is divided by the duration of the time window to obtain the average access frequency. This frequency value directly reflects the busy degree of the storage location, for example, a storage location has 10 operations in 1 hour, the average access frequency is 10 times / hour, high-frequency operation means that materials are moved frequently, and the potential opportunity for mixed storage is more, which is an important basic factor constituting the risk of mixed storage.

[0090] The SKU codes of the materials of the adjacent operation events are analyzed pair by pair, and the proportion of the same SKU codes is calculated as the material similarity. In the time sequence of operation events, whether the SKU codes of the materials involved in the two adjacent operations (such as the previous operation is warehousing A material and the next operation is delivery B material) are the same directly reflects the continuity of the material types. By traversing each pair of adjacent events in the time sequence, the number of pairs of adjacent events with the same SKU codes is counted, and the number is divided by the total number of pairs of adjacent events to obtain the material similarity. For example, in 10 pairs of adjacent operation events, the SKU codes of 3 pairs are the same, and the material similarity is 30%. The lower the material similarity, the higher the proportion of different types of materials introduced by adjacent operations, and the greater the possibility of mixed storage. This index provides a judgment basis for mixed risk from the perspective of material type difference.

[0091] The space load coefficient is calculated according to the proportion of the total volume of the materials in the adjacent operation events to the maximum capacity of the storage location. For each pair of adjacent operation events, the volume data of the materials involved in the operation (which can be retrieved from the material information database of the warehouse through the SKU code of the material, and each SKU code corresponds to a unique standard volume of the material) is obtained, and the total volume of the materials in the storage location after the two operations is calculated. Then, the total volume is divided by the maximum volume (a preset parameter) of the storage location to obtain the space load coefficient. For example, the total volume of the materials after the adjacent operations is 5 cubic meters, and the maximum capacity of the storage location is 8 cubic meters, so the space load coefficient is 62.5%. The higher the space load coefficient, the more occupied the space of the storage location, and at this time, if there are different types of materials, the difficulty of arranging and the risk of chaos after mixed storage will also be higher. This coefficient quantifies the mixed risk from the perspective of the degree of space utilization.

[0092] The material similarity and the space load coefficient are inversely related to generate a mixed risk weighted value. Since the material similarity is negatively correlated with the mixed risk (low similarity means high risk), and the space load coefficient is positively correlated with the mixed risk (high coefficient means high risk), an inverse correlation method is adopted, that is, by multiplying (1-material similarity) and the space load coefficient, the mixed risk weighted value is obtained.

[0093] The calculation formula of the mixed risk weighted value is: wherein Q represents the mixed risk weighted value, represents the material similarity, represents the space load coefficient. For example, when the material similarity is 30% and the space load coefficient is 62.5%, the mixed risk weighted value is (1-30%) x 62.5% = 43.75%. This correlation method makes the situation of large material type difference and tight space occupation get a higher weighted value, accurately reflecting the potential mixed risk level under the joint action of the two factors.

[0094] The material mixing risk value is generated based on the product operation of the average access frequency and the hybrid risk weighted value. The average access frequency reflects the frequency of operation, and the hybrid risk weighted value reflects the potential risk under the combination of single operation. The product of the two comprehensively reflects the influence of the two factors on the final hybrid risk. For example, the average access frequency is 10 times / hour, and the hybrid risk weighted value is 43.75%, and the material mixing risk value is 10*43.75%=4.375. The higher the value, the greater the risk of material mixing of the storage location under the current operation mode. Through this quantitative index, the material mixing risk of the storage location can be objectively and timely evaluated, which provides a key basis for subsequent generation of access confusion index and determination of storage location inspection priority, and effectively improves the early identification ability and management efficiency of the warehouse for material mixing abnormality.

[0095] In one embodiment, the step of obtaining the number of operation events per unit time, generating an access frequency factor, and fusing the material mixing risk value and the access frequency factor to generate an access confusion index includes:

[0096] S31, based on the operation event time sequence, the total number of operation events of the target storage location in a preset period is counted, and the operation frequency per unit time is calculated.

[0097] S32, the standard volume and the standard weight of a single piece of material are obtained.

[0098] S33, the first proportion value of the standard volume of the material to the maximum volume of the storage location is calculated, and the second proportion value of the standard weight of the material to the safety bearing threshold of the storage location is calculated.

[0099] S34, the material attribute influence coefficient is obtained according to the first proportion value and the second proportion value.

[0100] S35, the access frequency factor is obtained according to the material attribute influence coefficient and the operation frequency per unit time.

[0101] S36, the material mixing risk value is converted into a relative risk proportion value, and the access frequency factor is converted into a relative frequency proportion value.

[0102] S37, the access confusion index is obtained according to the relative risk proportion value and the relative frequency proportion value.

[0103] As described in steps S31-S37 above, the present application generates an access confusion index by combining the operation frequency of the target storage location, the influence of the material properties on the occupancy of the storage location, and the material mixing risk, to quantitatively reflect the overall confusion degree of the storage location in the material access process, and to provide a comprehensive evaluation basis for subsequent storage location abnormality identification.

[0104] In warehouse management, the degree of access confusion of a storage location is not only determined by the frequency of operation, but also closely related to the volume and weight of the material. When the storage location is frequently operated, the material is repeatedly moved, and if the material has a large volume and high weight, its placement in the storage location is easily affected by the operation and becomes disordered. At the same time, if there is already a certain risk of material mixing, frequent operation will further exacerbate the confusion. Therefore, the operation frequency, material attribute influence and mixing risk need to be comprehensively considered to accurately judge the access confusion state of the storage location.

[0105] The traditional method for evaluating the confusion degree of the storage location only focuses on the number of operations, ignoring the influence of material attributes on the occupation of the storage location, resulting in deviation in the judgment of the confusion degree. For example, two storage locations with the same operation frequency, one stores small and light material, and the other stores large and heavy material close to the weight threshold of the storage location. The latter is more likely to be placed in disorder due to space limitations during operation, but the traditional method cannot distinguish this difference, which affects the accuracy of the judgment of the abnormality of the storage location. The present application introduces a material attribute influence coefficient, combines the operation frequency with the actual occupation pressure of the material on the storage location, and further integrates the mixing risk to realize the accurate quantification of the access confusion degree, which makes up for the shortcomings of the traditional method.

[0106] Specifically, based on the time sequence of operation events, the total number of operation events of the target storage location in a preset period is counted, and the operation frequency per unit time is calculated. The preset period can be set according to the warehouse management requirements, such as 1 hour, 4 hours, etc. All storage, withdrawal, displacement and other operation events of the target storage location in the preset period are collected by the UHF-RFID reader array, and the total number of operation events is divided by the length of the preset period to obtain the operation frequency per unit time. For example, the preset period is 2 hours, and the total number of operation events is 16, then the operation frequency per unit time is 8 times / hour, which directly reflects the operation intensity of the storage location and is the basic parameter for measuring the confusion degree.

[0107] The standard volume and standard weight of a single piece of material are obtained. These data are stored in the material information database of the warehouse, and can be retrieved by the SKU code of the material. Each SKU code corresponds to unique material specification parameters, such as a material with a SKU code corresponding to a standard volume of 0.5 m³ and a standard weight of 50 kg. This provides basic data for analyzing the influence of material on the occupation of the storage location.

[0108] The first proportion value of the standard volume of the material to the maximum volume of the storage location and the second proportion value of the standard weight of the material to the safety bearing threshold of the storage location are calculated. The maximum volume and the safety bearing threshold of the storage location are preset parameters. For example, the maximum volume of a certain storage location is 10 m³, the safety bearing threshold is 1000 kg, and for the above-mentioned material, the standard volume is 0.5 m³, which accounts for 5% (the first proportion value) of the maximum volume of the storage location, and the standard weight is 50 kg, which accounts for 5% (the second proportion value) of the safety bearing threshold of the storage location. These two proportion values reflect the basic occupation degree of a single material to the storage location from the dimensions of space occupation and bearing pressure.

[0109] The material attribute influence coefficient is obtained according to the first proportion value and the second proportion value. The coefficient is generated by comprehensively considering the occupation pressure of the volume and weight of the material to the storage location, and is usually the maximum value or the weighted average value of the two proportion values. For example, the first and second proportion values are both 5%, and the material attribute influence coefficient is 5% (0.05). If the first proportion value of a certain material is 30% and the second proportion value is 20%, the coefficient can be 30% (0.3) to highlight the greater impact of volume occupation on the operation of the storage location. The coefficient directly reflects the restriction degree of the material attribute to the storage access operation, so the higher the coefficient, the more likely the material will cause the storage location to be placed in disorder due to the volume or weight during the operation.

[0110] The access frequency factor is obtained according to the material attribute influence coefficient and the operation frequency per unit time. By multiplying the two, a comprehensive frequency index that takes into account the operation intensity and the influence of the material attribute can be obtained. For example, the operation frequency per unit time is 8 times / hour, and the material attribute influence coefficient is 0.05, so the access frequency factor is 0.4. When the operation frequency is still 8 times / hour and the coefficient is 0.3, the factor is 2.4. This result shows that under the same operation frequency, the material with a higher volume or weight ratio will produce a higher access frequency factor, which can more truly reflect the actual impact of the operation on the disorder degree of the storage location.

[0111] The material mixing risk value is converted into a relative risk proportion value, and the access frequency factor is converted into a relative frequency proportion value. The relative risk proportion value is the ratio of the risk value to the preset maximum risk value. For example, the mixing risk value of a certain storage location is 6, and the preset maximum value is 10, so the relative risk proportion value is 60%. The relative frequency proportion value of the access frequency factor is calculated in a similar manner. For example, the factor is 2.4, and the preset maximum value is 5, so the relative frequency proportion value is 48%. This conversion can eliminate the difference in the dimensions of different indicators, making them comparable.

[0112] According to the relative risk ratio value and the relative frequency ratio value, an access confusion degree index is obtained, and by performing a weighted operation on the two ratio values (the weights can be set according to the importance of the warehouse to the mixed risk and the operation frequency, for example, 50% each), an index that comprehensively reflects the confusion degree of the storage location access can be obtained. For example, the relative risk is 60% and the relative frequency is 48%, and after weighted calculation, the access confusion degree index is 54%, and the higher the access confusion degree index, the more chaotic the access state of the storage location under the joint action of material mixing and operation frequency.

[0113] Through the above steps, the quantification and evaluation of the access confusion degree of the storage location are realized, the frequency of operation is considered, the actual influence of the material attribute on the storage location is taken into account, and the potential risk of material mixing is integrated. Compared with the traditional evaluation method that only depends on the number of operations, the real chaotic state of the storage location can be more accurately reflected, a scientific basis is provided for the subsequent judgment of the priority of the storage location inspection, and the identification efficiency of the warehouse for the access exception is effectively improved.

[0114] In one embodiment, the step of analyzing the continuous coordinate position data, detecting a displacement mutation event, comparing the material coordinate position with a preset electronic fence boundary, and obtaining a boundary crossing event marking level comprises:

[0115] S41, the continuous three-dimensional coordinate data stream collected by the visual positioning system is divided into trajectory segments at fixed time intervals, and the linear distance change between adjacent time points is calculated;

[0116] S42, the average moving speed of each trajectory segment is calculated, if the average moving speed exceeds a preset speed threshold, a displacement mutation event is marked, and if the average moving speed does not exceed the preset speed threshold, no marking is performed;

[0117] S43, the speed change rate of adjacent trajectory segments is calculated as the instantaneous acceleration;

[0118] S44, the boundary vertex coordinate set of the preset electronic fence is obtained, and the perpendicular distance from the material coordinate to the nearest fence boundary is calculated in real time;

[0119] S45, when the perpendicular distance is less than zero, a boundary crossing mark is generated, when the perpendicular distance is within a safe warning range, a warning mark is generated, and when a displacement mutation event is detected, a UHF-RFID reader / writer is triggered synchronously;

[0120] S46, it is verified whether the current material SKU code matches the storage location registration information;

[0121] S47, if the SKU code does not match, the warning level of the boundary crossing event marking is raised;

[0122] S48, if the SKU code matches, the credibility weight of the displacement mutation event is reduced.

[0123] As described in steps S41-S48, the application dynamically analyzes the continuous coordinate position data of the material through the visual positioning system, detects the displacement mutation event, obtains the out-of-bound event mark by comparing with the preset electronic fence boundary, and adjusts the warning level by verifying the material identity through the UHF-RFID reader, to realize accurate identification and grading of material position abnormalities, and provide reliable basis for subsequent generation of position abnormality index.

[0124] In the warehouse scene, the position state of the material directly affects the space utilization efficiency and operation safety. The material may suddenly displace (displacement mutation) due to forklift collision, unstable stacking, etc., or exceed the preset storage location boundary (out-of-bound) due to improper placement. If these situations are not discovered in time, it will lead to chaotic storage location space, insufficient distance between adjacent materials, and even cause safety accidents. At the same time, whether the material belongs to the current storage location (i.e. whether the SKU code and storage location registration match) will directly affect the severity of the position abnormality, and the risk level of temporary displacement of the material in the current storage location is completely different from that of the out-of-bound material, therefore, the dynamic displacement characteristics, boundary compliance and identity information of the material need to be comprehensively considered to accurately judge the nature and level of the position abnormality.

[0125] Traditional position monitoring methods mostly rely on static boundary checking (such as only judging whether it is out-of-bound), which cannot capture the mutation characteristics in the displacement process (such as a violent displacement of 1 meter in 0.5 seconds), nor does it combine material identity verification, resulting in one-sidedness in the judgment of abnormalities. For example, a material in the current storage location may temporarily exceed the boundary due to normal handling, which is significantly different from the risk of long-term out-of-bound of an out-of-bound material, but the traditional method may mark them equally, resulting in insufficient warning accuracy, and the neglect of displacement mutation may lead to delayed discovery of potential hazards such as material tilting caused by collision. The application realizes all-round and accurate identification of position abnormalities through the cooperation of dynamic trajectory analysis, boundary comparison and identity verification, and makes up for the defects of traditional methods.

[0126] Specifically, the continuous three-dimensional coordinate data stream collected by the visual positioning system is divided into trajectory segments at fixed time intervals, and the linear distance change between adjacent time points is calculated. The visual positioning system collects the three-dimensional coordinates of the material in real time (such as (x, y, z)), and the fixed time interval can be set according to the monitoring accuracy requirement (such as 0.1 seconds, 0.5 seconds), and a segment of coordinate data is intercepted as a trajectory segment every time the interval passes. For example, with an interval of 0.1 seconds, the coordinates at time t are set as (x1, y1, z1), , , ), the coordinates at time t+0.1 are set as (x2, y2, z2), , , If the straight-line distance between the two adjacent time points is L, then the formula for calculating the change in straight-line distance between adjacent time points is:

[0127] ;

[0128] For example, when the time coordinate is (1.2, 0.8, 0.5), the time is (1.3, 0.9, 0.5), and the change in straight-line distance between adjacent time points is [(1.3-1.2)²+(0.9-0.8)²+(0.5-0.5)²]≈0.14 meters, which reflects the movement distance of the material in a short time and is the basic data for detecting displacement mutation.

[0129] The average moving speed of each trajectory segment is calculated, and if the average moving speed exceeds the preset speed threshold, it is marked as a displacement mutation event. The average moving speed is the straight-line distance change of the trajectory segment divided by the fixed time interval, and the preset speed threshold is set according to the material characteristics and operation specifications (such as 0.5 meters / second, used to distinguish between normal slow movement and sudden violent displacement). For example, the straight-line distance change of a certain trajectory segment is 0.3 meters, the time interval is 0.1 seconds, the average moving speed is 3 meters / second, which exceeds the threshold of 0.5 meters / second, and it is marked as a displacement mutation event. This marking can accurately capture the rapid movement of the material caused by accidents and provide key signals for subsequent risk assessment.

[0130] The speed change rate of adjacent trajectory segments is calculated as the instantaneous acceleration, which is obtained by dividing the difference between the average moving speeds of the two adjacent trajectory segments by the time interval. For example, the average speed of the previous trajectory segment is 0.2 meters / second, the current trajectory segment is 3 meters / second, and the time interval is 0.1 seconds, then the instantaneous acceleration is (3-0.2) / 0.1=28 meters / second². The instantaneous acceleration reflects the degree of change in displacement speed, and the larger the value, the more significant the external force acting on the material (such as violent collision), which is a core parameter for measuring the intensity of displacement mutation.

[0131] The boundary vertex coordinate set of the preset electronic fence is obtained, and the vertical distance from the material coordinate to the nearest fence boundary is calculated in real time. The electronic fence is a three-dimensional boundary set according to the size of the storage location, and its vertex coordinate set is a preset parameter (such as the x-axis boundary of a certain storage location electronic fence is 0-3 meters, the y-axis is 0-2 meters, and the z-axis is 0-1.8 meters, and the vertex coordinates include (0, 0, 0), (3, 0, 0), (3, 2, 0), etc.). The vertical distance from the material coordinate to the nearest fence boundary is the shortest distance from the current coordinate of the material to each boundary, for example, the material coordinate is (3.2, 1.5, 1.2), the vertical distance to the x-axis 3-meter boundary is -0.2 meters (negative sign indicates exceeding the boundary), and the distance to the y-axis 0-meter boundary is 1.5 meters, so the nearest vertical distance is -0.2 meters, which directly reflects whether the material has crossed the boundary and the degree of crossing.

[0132] Generate out-of-bound marker or pre-warning marker according to vertical distance, and trigger UHF-RFID reader synchronously when detecting displacement mutation event. When vertical distance is less than zero (such as -0.2 meters), it indicates that the material has crossed the electronic fence, and the out-of-bound marker is generated; when the vertical distance is within the safe pre-warning range (such as 0-0.3 meters, i.e. close to the boundary but not crossing the boundary), the pre-warning marker is generated; and when the displacement mutation event is detected, the system immediately triggers the UHF-RFID reader array to read the SKU code of the material, preparing for subsequent identity verification, ensuring that the material identity information can be quickly associated when a position anomaly occurs.

[0133] Verify whether the current material SKU code matches the storage location registration information. The storage location registration information is stored in the warehouse management system and records the material SKU codes allowed to be stored in the storage location (such as A storage location registered as SKU001, SKU002). By comparing the material SKU code read by the RFID with the registration information, it can be determined whether the material belongs to the current storage location.

[0134] Adjust the pre-warning level according to the matching result: if the SKU code does not match (such as SKU003 appearing in A storage location), it indicates that it is an out-of-bound material, and the pre-warning level of the out-of-bound event marker needs to be raised (such as from general out-of-bound to serious out-of-bound); if the SKU code matches (such as SKU001 in A storage location), it indicates that it is a material of the current storage location, which may be temporarily displaced due to normal operation, so the credibility weight of the displacement mutation event is reduced (such as the influence weight on risk assessment is reduced from 0.8 to 0.3), avoiding excessive pre-warning for normal operation.

[0135] Through the above steps, the present application realizes dynamic and multi-dimensional analysis of material position anomaly, capturing the mutation characteristics of displacement (speed, acceleration) and judging the boundary compliance (vertical distance), and further distinguishing the nature of anomaly by combining material identity verification. Compared with the traditional method of relying only on static boundary check, the accuracy and pertinence of position anomaly recognition are significantly improved, providing a comprehensive and reliable basis for subsequent risk classification, effectively reducing the operational risk and safety hazards caused by position anomaly.

[0136] In one embodiment, the step of fusing the displacement mutation event intensity and the out-of-bound event marker to generate a position anomaly degree index, and obtaining a storage location inspection priority score based on the access confusion degree index, the position anomaly degree index, and the access frequency factor, comprises:

[0137] S51, based on the displacement mutation event, extracting the instantaneous acceleration absolute value and the total duration of the event;

[0138] S52, combining the instantaneous acceleration absolute value and the duration according to a preset proportion to generate a displacement event intensity value;

[0139] S53, obtaining a corresponding weight coefficient according to the boundary crossing event marking level;

[0140] S54, obtaining a position abnormality degree index according to the displacement event intensity value multiplied by the weight coefficient;

[0141] S55, obtaining a storage location inspection priority score according to the access confusion degree index, the position abnormality degree index and the access frequency factor.

[0142] As described in steps S51-S55, the present application extracts key dynamic parameters of displacement mutation events, combines the marking level of boundary crossing events, generates a quantitative position abnormality degree index, and then fuses the access confusion degree index and the access frequency factor to calculate a storage location inspection priority score, thereby realizing accurate quantification and sorting of the position abnormality risk of the storage location and providing a scientific basis for the priority allocation of warehouse resources.

[0143] In warehouse management, the position abnormality risk of materials is not only related to whether it crosses the boundary, but also closely related to the severity of the displacement process. The greater the acceleration of displacement mutation and the longer the duration, the more significant the impact on the stability of the material and the surrounding environment. At the same time, the severity level of the boundary crossing event (such as slight warning, serious boundary crossing) directly reflects the degree of space violation. Relying on a single dimension (such as only looking at whether it crosses the boundary) cannot comprehensively evaluate the actual risk of position abnormality. The inspection priority of the storage location also needs to be combined with its access confusion state and operation frequency. The storage location with high confusion degree and frequent operation needs to be processed first because the position abnormality may cause more serious chain problems. Therefore, the fusion of multi-dimensional indexes is needed to realize accurate quantification of the position abnormality risk and the inspection priority.

[0144] Traditional methods for evaluating position abnormality mostly stay in qualitative judgment (such as "has crossed the boundary" and "has not crossed the boundary"), which neither quantifies the intensity of displacement mutation nor combines the boundary crossing level for comprehensive analysis, resulting in a rough judgment of the risk. At the same time, the inspection priority of the storage location relies on manual experience and lacks objective quantitative standards, often resulting in missed detection of high-risk storage locations and excessive inspection of low-risk storage locations. The present application solves these problems by constructing a position abnormality degree index and fusing multiple factors to calculate the priority, thereby realizing fine evaluation of the position abnormality risk and reasonable allocation of inspection resources.

[0145] Based on the displacement mutation event, the absolute value of instantaneous acceleration and the total duration of the event are extracted. The displacement mutation event is a marked result whose average moving speed exceeds a preset threshold. The instantaneous acceleration is the rate of change of speed of adjacent trajectory segments in the event (for example, in a certain displacement mutation event, the average speed of the previous trajectory segment is 0.3 m / s, the current trajectory segment is 3.5 m / s, the time interval is 0.1 second, and the instantaneous acceleration is (3.5-0.3) / 0.1=32 m / s², and the absolute value is 32). The total duration of the event is the time span from the beginning to the end of the displacement mutation (for example, the event starts at t=10.2s and ends at t=10.5s, and the total duration is 0.3 seconds). These two parameters respectively reflect the severity and influence range of the displacement mutation, so the greater the acceleration, the more intense the external force acting on the material, the longer the duration, and the greater the possibility of the material deviating from the normal position. Both of them together constitute the core dimension of evaluating the impact of displacement mutation.

[0146] The instantaneous acceleration absolute value and the duration are combined according to a preset proportion to generate a displacement event intensity value. The preset proportion is set according to the importance of acceleration (reflecting impact intensity) and duration (reflecting cumulative impact) in the warehouse. In this embodiment, the acceleration proportion is 70%, and the duration proportion is 30%.

[0147] The calculation formula of the displacement event intensity value is: ;

[0148] Among them, represents the displacement event intensity value, represents the absolute value of instantaneous acceleration, represents the acceleration proportion, represents the duration, represents the duration proportion.

[0149] Taking the above data as an example, the absolute value of instantaneous acceleration is 32, and the duration is 0.3 seconds. Then the displacement event intensity value = 32x0.7+0.3x0.3=22.4+0.09=22.49. The greater the value, the more significant the disturbance of the displacement mutation to the material position, providing a quantitative dynamic displacement parameter for subsequent risk assessment.

[0150] According to the boundary crossing event marking level, the corresponding weight coefficient is obtained. The boundary crossing event marking level comes from the above classification result (for example, the "warning mark" of the vertical distance in the safety warning range is level 1, and the "boundary crossing mark" according to the exceeding degree is divided into level 2 and level 3), different levels correspond to different weight coefficients (for example, level 1 weight 0.4, level 2 weight 0.7, level 3 weight 1.0), the higher the level, the greater the weight, so as to highlight the contribution of serious boundary crossing to position anomaly. For example, a certain material boundary crossing mark is level 3, and the corresponding weight coefficient is 1.0, which directly reflects the high risk of space violation.

[0151] The position abnormality degree index is obtained according to the displacement event intensity value multiplied by the weight coefficient, the position abnormality degree index fuses the dynamic displacement intensity and the static boundary crossing level, and comprehensively reflects the actual risk of the position abnormality. For example, the displacement event intensity value is 22.49, the boundary crossing mark is level 3 (weight 1.0), and then the position abnormality degree index = 22.49 x 1.0 = 22.49; if another material displacement event intensity value is 15, the boundary crossing mark is level 1 (weight 0.4), and then the index = 15 x 0.4 = 6, obviously, the position abnormality risk of the former is higher, and the index provides a comparable quantitative standard for the position abnormality risk of different storage locations.

[0152] The storage location inspection priority score is obtained according to the access confusion degree index, the position abnormality degree index and the access frequency factor. The three are weighted and summed through a preset weight (for example, the position abnormality degree accounts for 40%, the access confusion degree accounts for 30%, and the access frequency factor accounts for 30%) to obtain the priority score. For example, the position abnormality degree of a certain storage location is 22.49, the access confusion degree is 8.5, and the access frequency factor is 5.2, and then the priority score = 22.49 x 0.4 + 8.5 x 0.3 + 5.2 x 0.3 = 8.996 + 2.55 + 1.56 = 13.106, the higher the score, the higher the urgency of the inspection of the storage location due to the position abnormality, the access confusion and the frequent operation.

[0153] Through the above steps, the present application realizes multi-dimensional quantification of the position abnormality risk, not only captures the intensity of dynamic displacement through the displacement event intensity, but also reflects the severity of space violation through the boundary crossing level weight, and finally fuses the overall confusion state and the operation frequency of the storage location to generate an objective inspection priority score. Compared with the traditional qualitative evaluation and experience judgment, the method can accurately distinguish the risk levels of different storage locations, ensure that high-risk storage locations are inspected first, and significantly improve the efficiency and pertinence of warehouse abnormality processing.

[0154] In one embodiment, when the inspection priority score exceeds a preset threshold, a storage location abnormality inspection instruction is generated and an abnormality type mark is marked, the abnormality type includes at least one of material mixed storage abnormality, position offset abnormality and boundary crossing accumulation abnormality, and the step includes:

[0155] When the storage location inspection priority score exceeds a preset threshold, multi-source consistency verification is performed according to the displacement mutation event coordinates, the RFID verification result, the position abnormality degree index and the access confusion degree index.

[0156] S61, based on the continuous three-dimensional coordinate data stream and the SKU code, the following spatial relationship analysis is performed:

[0157] S62, the X, Y and Z axis offset of the actual position of the material from the preset coordinates is calculated, and if the axis offset is greater than a design threshold, it is determined as a position offset abnormality.

[0158] S63, detecting whether the distance between adjacent material surfaces is less than a safe operation threshold value, the safe operation threshold value being 0.5 m, and determining that the abnormal stacking out of boundary exists if the distance is less than the safe operation threshold value;

[0159] S64, detecting whether the SKU codes of the materials in the same area are the same, and determining that the abnormal mixed storage exists if the SKU codes of the materials in the same area are different;

[0160] S65, marking the abnormal type according to the multi-source verification result, and dividing the risk level according to the position abnormality index

[0161]

[0162] As described in steps S61-S65, the application accurately identifies abnormal types such as mixed storage, position deviation, and abnormal stacking out of boundary by performing multi-source data consistency verification and three-dimensional space relationship analysis on the storage locations with priority score exceeding the threshold value, and divides the risk level based on the position abnormality index, generates targeted abnormal inspection instructions, realizes accurate positioning and hierarchical disposal of warehouse abnormalities, and improves the efficiency and accuracy of abnormal processing.

[0163] In warehouse operation, when the priority score of the storage location exceeds the preset threshold value, it indicates that the storage location has a high abnormal risk, but the specific abnormal type (mixed storage, deviation, or abnormal stacking out of boundary) and the risk level still need to be determined in order to take effective intervention measures. Different abnormal types have different causes and effects: mixed storage may cause sorting errors, position deviation may affect space utilization, and abnormal stacking out of boundary may cause safety accidents. If the types cannot be accurately distinguished, the treatment measures will lack pertinence, and a single data source may have errors (such as occasional deviation in visual positioning), which needs to be verified by multiple sources to ensure the reliability of abnormal judgment. Therefore, multi-dimensional verification and analysis are needed to accurately identify abnormal types and risk levels.

[0164] Traditional methods rely on manual on-site investigation after discovering high-risk storage locations, which lacks multi-source data verification (such as relying on visual judgment of abnormal stacking out of boundary while ignoring RFID identity information) and systematic analysis of spatial relationships (such as ignoring axial deviation or adjacent distance), resulting in ambiguous abnormal type judgment, arbitrary risk level division, and low processing efficiency. The application solves these problems systematically through multi-source consistency verification and structured space analysis, realizing the automation and precision of abnormal identification.

[0165] ​When the storage location inspection priority score exceeds the preset threshold value, multi-source consistency verification is performed according to the displacement mutation event coordinates, the RFID verification result, the position abnormality degree index, and the access confusion degree index. The storage location inspection priority score comes from the above calculation result, and the preset threshold value is set according to the warehouse risk bearing capacity (for example, 10 points, exceeding which triggers inspection); the displacement mutation event coordinates are three-dimensional coordinates when the marked mutation event occurs (for example, (3.5, 1.2, 0.8)); the RFID verification result is the matching condition of the material SKU code and the storage location registration information (for example, not matching); the position abnormality degree index is 22.49, and the access confusion degree index is 8.5. Multi-source consistency verification is achieved by comparing whether these data point to the same abnormal trend: if the displacement mutation coordinates show that the boundary is exceeded, the RFID verification is not matched, the position abnormality degree is high, and the access confusion degree is high, then the verification is consistent, and it is confirmed that the abnormality really exists; if there is a contradiction in some data (for example, the position abnormality degree is high, but the RFID verification is matched and there is no displacement mutation), rechecking is required to avoid misjudgment.

[0166] Based on the continuous three-dimensional coordinate data stream, spatial relationship analysis is performed. The continuous three-dimensional coordinate data stream is collected in real time by the visual positioning system and includes the (x, y, z) coordinates of the material at each time. The specific analysis includes three aspects: first, the X, Y, and Z axis offset amounts of the actual position of the material and the preset coordinates are calculated. The preset coordinates are the standard storage position planned by the storage location (for example, (2.0, 1.0, 0.5)). The difference between the actual position and the preset coordinates is the offset amount (for example, X axis offset 0.8 m, Y axis offset -0.3 m, and Z axis offset 0.2 m). This value reflects the degree of deviation of the material from the standard position. Second, whether the distance between the surfaces of adjacent materials is less than the safe operation threshold value (≥0.5 m) is detected. Whether the operation safety is affected is determined by calculating the three-dimensional spatial distance of the current material and the nearest surrounding material (for example, 0.3 m). Third, whether the SKU codes of the materials in the same area are the same is detected. The safe limit of the storage location is the preset maximum Z axis height (for example, 1.5 m). If the Z coordinate of the top of the material is 1.8 m, it is determined that the limit is exceeded.

[0167] According to the multi-source verification result, the abnormal type label is marked, and the risk level is divided according to the position abnormality degree index. The abnormal type label is determined based on the verification and analysis results: if the access confusion degree is high and the RFID verification exists, the SKU is not matched, and the label is marked as “material mixed storage abnormality”; if the axis offset amount exceeds the preset allowed range (for example, ±0.5 m), the label is marked as “position offset abnormality”; if the boundary is exceeded and the stacking height is over the limit, the label is marked as “boundary stacking abnormality” (multiple types can be marked at the same time). The risk level is divided according to the position abnormality degree index, and the specific division method is as follows:

[0168] The position abnormality degree ∈ [0, 0.3) → I-level risk (yellow warning);

[0169] Position abnormality degree ∈ [0.3, 0.7) -> II level risk (orange alert);

[0170] Position abnormality degree >= 0.7 -> III level risk (red alert).

[0171] The higher the level, the stronger the demand for emergency treatment of the abnormality.

[0172] Through the above steps, the application realizes a closed loop from high-risk storage location identification to specific abnormality type confirmation, multi-source consistency verification ensures the reliability of abnormality judgment, avoids misjudgment caused by single data error, three-dimensional space relationship analysis comprehensively captures details such as position deviation, insufficient spacing and stacking over-limit, provides an objective basis for abnormality type labeling, and risk level division clearly defines the processing priority. Compared with the traditional manual inspection, this method significantly improves the accuracy and efficiency of abnormality identification, so that the warehouse management personnel can quickly locate the problem and take targeted measures, effectively reducing the operational risk caused by abnormality.

[0173] The application also discloses a warehouse material identification system based on data analysis, comprising:

[0174] A generation module 1 is configured to collect operation event data and material coordinate position information of a warehouse storage location in real time through a UHF-RFID reader array and a visual positioning system, the operation event data includes material SKU code and operation type, and sort the continuously collected operation event data according to time stamp to generate an operation event time sequence;

[0175] A calculation module 2 is configured to calculate a material mixing risk value according to the SKU code difference and operation type of adjacent events in the operation time sequence;

[0176] A fusion module 3 is configured to obtain the number of operation events per unit time to generate an access frequency factor, fuse the material mixing risk value and the access frequency factor to generate an access confusion degree index;

[0177] An analysis module 4 is configured to analyze continuous coordinate position data, detect displacement mutation events, compare the material coordinate position with a preset electronic fence boundary to obtain a boundary crossing event mark level, and the like.

[0178] An acquisition module 5 is configured to fuse the displacement mutation event intensity and the boundary crossing event mark to generate a position abnormality degree index, and obtain a storage location inspection priority score based on the access confusion degree index, the position abnormality degree index and the access frequency factor.

[0179] An output module 6 is configured to generate a storage location abnormality inspection instruction and label an abnormality type mark when the inspection priority score exceeds a preset threshold, the abnormality type includes at least one of material mixing abnormality, position deviation abnormality and boundary crossing stacking abnormality.

[0180] Further, the computing module comprises:

[0181] An extraction unit is configured to extract all operation events of the target storage location within a set time window, and count the average access frequency per unit time;

[0182] An analysis unit is configured to analyze the material SKU codes of adjacent operation events pair by pair, and calculate the proportion of the same SKU code as the material similarity;

[0183] A computing unit is configured to calculate the space load coefficient according to the proportion of the total volume of the material in the adjacent operation events to the maximum capacity of the storage location;

[0184] A first generating unit is configured to inversely relate the material similarity and the space load coefficient to generate a hybrid risk weighting value;

[0185] A second generating unit is configured to generate a material hybrid risk value based on the product operation of the average access frequency and the hybrid risk weighting value.

[0186] The application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the warehouse material identification method based on data analysis when executing the computer program.

[0187] The application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the warehouse material identification method based on data analysis.

[0188] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, value library or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0189] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, so that a process, device, article or method that includes a list of elements not only includes those elements, but also includes other elements not expressly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, device, article or method that includes the element.

[0190] The above only describes the preferred embodiments of the present application, and does not limit the scope of the present application. Any equivalent results or equivalent process transformations obtained by using the content of the present application specification and drawings, or directly or indirectly applied to other related technical fields, are also included in the protection scope of the present application.

Claims

1. Warehouse material identification method based on data analysis, characterized in that, The method comprises the following steps: Real-time collection of warehouse storage location operation event data and material coordinate position information through a UHF-RFID reader-writer array and a visual positioning system, the operation event data including material SKU codes and operation types, and sorting the continuously collected operation event data according to timestamps to generate an operation event time sequence; According to the SKU code difference degree and operation type of adjacent events in the operation event time sequence, a material mixing risk value is calculated, and the specific steps include: Extracting all operation events of the target storage location within a set time window, and counting the average access frequency per unit time; Pairwise analysis of the material SKU codes of adjacent operation events, and calculating the proportion of the same SKU code as the material similarity; According to the proportion of the total volume of the materials in the adjacent operation events to the maximum capacity of the storage location, a space load coefficient is calculated; The material similarity and the space load coefficient are inversely related to generate a mixing risk weighted value; Based on the product operation of the average access frequency and the mixing risk weighted value, a material mixing risk value is generated; Obtaining the number of operation events per unit time to generate an access frequency factor, and fusing the material mixing risk value and the access frequency factor to generate an access confusion degree index; Analyzing the continuous coordinate position data, detecting displacement mutation events, comparing the material coordinate position with the preset electronic fence boundary, and obtaining the boundary crossing event marker level; Fusing the displacement mutation event intensity and the boundary crossing event marker to generate a position abnormality index, and obtaining a storage location inspection priority score based on the access confusion degree index, the position abnormality index and the access frequency factor; When the inspection priority score exceeds a preset threshold, a storage location abnormality inspection instruction is generated and an abnormality type marker is marked, the abnormality type including at least one of material mixing abnormality, position offset abnormality and boundary crossing accumulation abnormality.

2. The data analysis based warehouse material identification method according to claim 1, characterized in that, The step of obtaining the number of operation events per unit time to generate an access frequency factor, and fusing the material mixing risk value and the access frequency factor to generate an access confusion degree index comprises: Based on the operation event time sequence, the total number of operation events of the target storage location within a preset period is counted to calculate the operation frequency per unit time; Obtaining the standard volume and the standard weight of a single piece of material; Calculating a first proportion value of the material standard volume to the maximum volume of the storage location, and calculating a second proportion value of the material standard weight to the safe bearing threshold of the storage location; According to the first proportion value and the second proportion value, a material attribute influence coefficient is obtained; According to the material attribute influence coefficient and the operation frequency per unit time, an access frequency factor is obtained; The material mixing risk value is converted into a relative risk proportion value, and the access frequency factor is converted into a relative frequency proportion value; According to the relative risk proportion value and the relative frequency proportion value, an access confusion degree index is obtained.

3. The data analysis based warehouse material identification method according to claim 1, characterized in that, The step of analyzing the continuous coordinate position data, detecting displacement mutation events, comparing the material coordinate position with the preset electronic fence boundary, and obtaining the boundary crossing event marker level comprises: The continuous three-dimensional coordinate data stream collected by the visual positioning system is divided into track segments at a fixed time interval, and the linear distance change between adjacent time points is calculated; Calculate the average moving speed of each trajectory segment, if the average moving speed exceeds the preset speed threshold, mark it as a displacement mutation event, if the average moving speed does not exceed the preset speed threshold, do not mark it; Calculate the speed change rate of adjacent trajectory segments as the instantaneous acceleration; Obtain the boundary vertex coordinate set of the preset electronic fence, and calculate the vertical distance from the material coordinate to the nearest fence boundary in real time; When the vertical distance is less than zero, generate an out-of-boundary marker, when the vertical distance is within a safe warning range, generate a warning marker, and when a displacement mutation event is detected, trigger a UHF-RFID reader simultaneously; Verify whether the current material SKU code matches the storage location registration information; If the SKU code does not match, increase the warning level of the out-of-boundary event marker; If the SKU code matches, reduce the credibility weight of the displacement mutation event.

4. The data analysis based warehouse material identification method of claim 1, wherein, The steps of fusing the displacement mutation event intensity and the out-of-boundary event marker to generate a position abnormality index, and obtaining a storage location inspection priority score based on the access confusion index, the position abnormality index, and the access frequency factor, include: Based on the displacement mutation event, extract the absolute value of the instantaneous acceleration and the total duration of the event; Combine the absolute value of the instantaneous acceleration and the duration according to a preset proportion to generate a displacement event intensity value; According to the out-of-boundary event marker level, obtain a corresponding weight coefficient; According to the displacement event intensity value multiplied by the weight coefficient, obtain a position abnormality index; According to the access confusion index, the position abnormality index, and the access frequency factor, obtain a storage location inspection priority score.

5. The data analysis based warehouse material identification method according to claim 3, wherein, The steps of generating a storage location abnormality inspection instruction and labeling an abnormality type marker when the inspection priority score exceeds a preset threshold, and the abnormality type includes at least one of material mixed storage abnormality, position offset abnormality, and out-of-boundary stacking abnormality, include: When the storage location inspection priority score exceeds the preset threshold, perform multi-source consistency verification according to the displacement mutation event coordinates and RFID verification results, the position abnormality index, and the access confusion index; Based on the continuous three-dimensional coordinate data stream and the SKU code, the following spatial relationship analysis is performed: Calculate the X, Y, and Z axis offset of the actual position of the material from the preset coordinates; Detect whether the distance between adjacent material surfaces is less than a safe operation threshold; Detect whether the SKU codes of materials in the same area are the same; According to the multi-source verification result, label an abnormality type marker, and divide the risk level according to the position abnormality index.

6. A warehouse material identification system based on data analysis, characterized by, It includes: A generation module is configured to collect operation event data and material coordinate position information of a warehouse storage location in real time through a UHF-RFID reader array and a visual positioning system, the operation event data including material SKU code and operation type, and sort the continuously collected operation event data according to timestamp to generate an operation event time sequence; A calculation module is configured to calculate a material mixing risk value according to the SKU code difference and operation type of adjacent events in the operation time sequence, and the specific steps include: Extract all operation events of the target storage location within a set time window, and calculate the average access frequency per unit time; The material SKU codes of adjacent operation events are analyzed pair by pair, and the proportion of the same SKU code is calculated as the material similarity; According to the proportion of the total volume of the material in the adjacent operation events to the maximum capacity of the storage location, the space load coefficient is calculated; The material similarity and the space load coefficient are inversely related to generate a mixed risk weighting value; Based on the product operation of the average access frequency and the mixed risk weighting value, a material mixed risk value is generated; The fusion module is used to obtain the number of operation events per unit time to generate an access frequency factor, and to fuse the material mixed risk value and the access frequency factor to generate an access confusion degree index; The analysis module is used to analyze the continuous coordinate position data, detect displacement mutation events, compare the material coordinate position with the preset electronic fence boundary, and obtain a boundary crossing event marker level; The acquisition module is used to fuse the displacement mutation event intensity and the boundary crossing event marker to generate a position abnormality index, and to obtain a storage location inspection priority score based on the access confusion degree index, the position abnormality index and the access frequency factor; The output module is used to generate a storage location abnormality inspection instruction and mark an abnormality type marker when the inspection priority score exceeds a preset threshold, the abnormality type including at least one of material mixed storage abnormality, position offset abnormality and boundary crossing accumulation abnormality.

7. The data analysis based warehouse material identification system of claim 6, wherein, The computing module includes: The extraction unit is used to extract all operation events of the target storage location within a set time window, and to count the average access frequency per unit time; The analysis unit is used to analyze the material SKU codes of adjacent operation events pair by pair, and to calculate the proportion of the same SKU code as the material similarity; The calculation unit is used to calculate the space load coefficient according to the proportion of the total volume of the material in the adjacent operation events to the maximum capacity of the storage location; The first generation unit is used to inversely relate the material similarity and the space load coefficient to generate a mixed risk weighting value; The second generation unit is used to generate a material mixed risk value based on the product operation of the average access frequency and the mixed risk weighting value.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 5.

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