Label data interaction-based intelligent package adaptation method and device for inspection vehicle

Abstract

The invention relates to an inspection vehicle intelligent package adaptation method and device based on label data interaction. The method comprises the steps of obtaining structured label data embedded in a package based on label data interaction; analyzing the structured label data to obtain a physical characteristic vector corresponding to the package; generating a corresponding transportation control strategy according to the physical characteristic vector; the transportation control strategy comprises a control instruction and an operation parameter; the control instruction is used for instructing the inspection vehicle to operate according to the operating parameters. By adopting the method, data driving of control behaviors can be realized by utilizing the interaction process of the structured label data and the inspection workshop, and the limitation of traditional operation depending on a scheduling preset or fixed mode is broken through, so that the understanding capability of a transportation system on an actual task is stronger, a response mechanism is more flexible, and the transportation efficiency is improved. According to the method, the intelligent coping capacity of inspection tour to different package adaptive scenes is improved, automatic generation and dynamic execution of the transportation strategy are completed on the premise of not depending on manually set templates, and the method has good universality and expandability.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control of logistics assembly, and in particular relates to an intelligent packaging adaptation method and device for an inspection vehicle based on tag data interaction. Background Art

[0002] With the development of intelligent logistics and Internet of Things identification technology, technologies have emerged that use Radio Frequency Identification (RFID) technology and Bluetooth Low Energy (BLE) tags to manage the status of packaged items and dynamically control the transportation process. These technologies have the characteristics of contactless identification, low-power transmission, and support for embedded deployment. They can be used to track items, collect attribute data, and assist back-end systems in making information-based decisions.

[0003] Traditionally, RFID / BLE-based intelligent packaging identification technology can, to a certain extent, automatically number, track, and store items, achieving initial success in static processes such as express delivery and warehousing. In particular, during the packaging storage or sorting phase, using fixed readers to identify tag information enables rapid diversion and classification of materials.

[0004] However, in dynamic scenarios like logistics and mobile inspections, there's often a lack of effective linkage between packaging information and transport control. Inspection vehicles and transport vehicles rely primarily on manual configuration of transport parameters or simple adaptation using pre-set transport templates. The physical properties of packaging, such as compression and shock resistance, are often not read by the system, nor can they dynamically influence transport strategies. Even in partially automated scenarios, only packaging coding information can be read for tracking and registration, making it difficult to adjust specific operations such as shock absorption mode and speed limit strategies based on packaging status. Summary of the Invention

[0005] Based on this, it is necessary to provide an intelligent packaging adaptation method and device for inspection vehicles based on label data interaction, which can perform intelligent transportation adjustments based on the information of the packaging label, in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for intelligent packaging adaptation of an inspection vehicle based on tag data interaction, comprising:

[0007] Obtain structured tag data embedded in the package based on tag data interaction;

[0008] Parse the structured tag data to obtain the physical property vector corresponding to the packaging;

[0009] A corresponding transportation control strategy is generated according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters; the control instructions are used to instruct the inspection vehicle to operate according to the operating parameters.

[0010] In one embodiment, the structured label data is obtained by the following method:

[0011] Receive physical data of the package; the physical data includes pressure threshold, stacking limit, seismic level, temperature and humidity tolerance, and packaging type code;

[0012] Encode the physical data to obtain a tag protocol data body;

[0013] A key-value corresponding structure is constructed based on a preset data attribute set and bound to the corresponding package to obtain structured label data; the structured label data includes a unique identification label and physical property data.

[0014] In one embodiment, structured tag data embedded in the package is obtained based on the tag data interaction, including:

[0015] Generate a broadcast signal through a preset period; the broadcast signal is used to wake up the packaging tag and instruct the packaging tag to feedback tag data;

[0016] Receive the structured label data returned by the packaging label and confirm the label identity based on the unique identifier.

[0017] In one embodiment, parsing the structured tag data to obtain a physical property vector corresponding to the package includes:

[0018] Decode the tag data according to the preset data protocol to obtain the packaging characteristic set; the packaging characteristic set includes pressure threshold, stacking limit, seismic level, temperature and humidity tolerance, and packaging type code;

[0019] Mapping the package feature set to the internal feature vector yields the physical feature vector.

[0020] In one embodiment, generating a corresponding transportation control strategy based on the physical characteristic vector includes:

[0021] The rule matching engine is used to query and match the physical characteristic vector and the control strategy mapping table to obtain the control sub-parameter set;

[0022] Dynamically adjust the control sub-parameter set according to the current environmental variable data to obtain the control parameter set;

[0023] Generate a transportation control strategy based on the control parameter set.

[0024] In one embodiment, the control sub-parameter set is dynamically adjusted according to the current environment variable data to obtain a control parameter set, including:

[0025] Calculate the current environmental variable data and determine whether the current environmental variable data exceeds the packaging tolerance range to obtain dynamic adjustment requirements; dynamic adjustment requirements include whether dynamic adjustment is required or not;

[0026] If the dynamic adjustment requirement is that dynamic adjustment is required, the control sub-parameter set is modified based on the fuzzy principle to obtain the control parameter set;

[0027] If the dynamic adjustment requirement is that no dynamic adjustment is required, the control sub-parameter set is determined to be the control parameter set.

[0028] In one embodiment, the method further comprises:

[0029] Obtain real-time operating status data and external environment data of the inspection vehicle and obtain feedback information;

[0030] Conduct status evaluation on feedback information based on operating parameters to obtain evaluation results; the evaluation structure includes deviation and no deviation;

[0031] If the evaluation result shows that there is a deviation, a correction parameter is generated and the transportation control strategy is corrected according to the correction parameter.

[0032] In a second aspect, the present application also provides an inspection vehicle intelligent packaging adaptation device based on tag data interaction, comprising:

[0033] A data interaction module is used to obtain structured tag data embedded in the package based on tag data interaction;

[0034] The data parsing module is used to parse the structured label data and obtain the physical characteristic vector corresponding to the packaging;

[0035] The control adaptation module is used to generate a corresponding transportation control strategy according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters.

[0036] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any of the steps of the above-mentioned inspection vehicle intelligent packaging adaptation method based on label data interaction.

[0037] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps of the above-mentioned inspection vehicle intelligent packaging adaptation method based on tag data interaction.

[0038] The above-mentioned intelligent packaging adaptation method and device for patrol vehicles based on tag data interaction achieves a closed-loop mapping from static packaging data to dynamic transportation strategies through a data link between the packaging end and the patrol vehicle's transportation end. This system possesses environmental perception, behavior adjustment, and contextual adaptation capabilities, building a highly adaptive intelligent logistics infrastructure. By utilizing the interaction between structured tag data and the patrol workshop to achieve data-driven control of behavior, this system breaks through the limitations of traditional reliance on scheduling presets or fixed-mode operations, enabling the transportation system to have a stronger understanding of actual tasks, a more flexible response mechanism, and a higher level of automation and intelligence. This system enhances the dominant role of packaging information in transportation behavior and improves the patrol vehicle's intelligent response capabilities to different packaging adaptation scenarios. It automatically generates and dynamically executes transportation strategies without relying on manually set templates, demonstrating excellent versatility and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 Schematic diagram of the process of the inspection vehicle intelligent packaging adaptation method based on tag data interaction of the present invention;

[0041] Figure 2 This is a schematic diagram of the structured label data generation process of the inspection vehicle intelligent packaging adaptation method based on label data interaction of the present invention;

[0042] Figure 3 Schematic diagram of the step-by-step process of step S103;

[0043] Figure 4 This is a structural diagram of the inspection vehicle intelligent packaging adapter device based on tag data interaction of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0045] In one embodiment, Figure 1As shown, a method for intelligent packaging adaptation of inspection vehicles based on tag data interaction is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0046] S101. Acquire structured tag data embedded in a package based on tag data interaction.

[0047] Schematically, tag data interaction establishes a data connection with a passive or low-power active electronic tag pre-installed on the packaging through wireless communication protocols such as RFID ultra-high frequency interfaces and Bluetooth low energy (BLE) communication links, completing bidirectional data exchange. Optionally, the tag is pre-embedded during the packaging process and attached to a load-bearing component such as a packaging box, pallet, or sealing film by welding, laminating, or modular insertion. Its physical form can be a flexible electronic tag, a hard-shell encapsulated RFID card, or a composite BLE module containing an environmental sensing unit.

[0048] In order to achieve standardized reading, structured tag data should be stored inside the tag, that is, an ordered data body that uses standard fields or protocol structures to represent the physical properties of the package. Specifically, structured tag data includes not only basic identification data such as a unique label, batch information, and cargo code, but also key parameters such as the packaging's pressure threshold, stacking layer limit, maximum acceleration allowed, adapted transportation mode number, seismic grade index, and optional temperature and humidity tolerance range. Furthermore, data items are written into the storage area of the tag chip using a custom TLV format (Type-Length-Value) or CBOR (Concise Binary Object Representation) encoding rule to ensure that they can be efficiently decoded according to the protocol at the reading end. The writing of tag data can be performed before the package leaves the factory, or the initialization binding can be completed via a handheld terminal before the goods are integrated into the logistics system.

[0049] Optionally, the reading module driving the inspection vehicle periodically broadcasts a request signal. Once a tag is activated, it responds to the request at a preset frequency and returns the stored data. To avoid collisions, a TDMA (Time Division Multiple Access) time slot or frequency hopping mechanism is used to manage the response sequence of multiple tags, complete data exchange with the target tag, and obtain the complete structured data within the tag.

[0050] S102: Parse the structured tag data to obtain a physical characteristic vector corresponding to the package.

[0051] Indicatively, the acquired structured tag data is parsed at the protocol level to extract a physical characteristic vector that characterizes the packaging status and transportation characteristics. The physical characteristic vector is an ordered vector set consisting of multiple representative physical properties, including but not limited to packaging material grade, upper pressure limit, recommended number of stacking layers, required seismic level for transportation, speed limit range, and controlled environmental conditions. Specifically, based on the identification header and corresponding length information of the data field, the original tag data is mapped into a standardized parameter set. Optionally, the parsing process supports automatic adaptation of multiple protocols, and can parse RFID structured fields as well as read GATT (Generic Attribute Profile) attribute service content embedded in BLE device broadcasts.

[0052] For example, to balance data scalability and versatility, parameter fields within tags can be configured according to predefined characteristic categories, value ranges, and control sensitivity modes. Specifically, for the seismic level field, the "Z3" level can be parsed, and its corresponding sensitive operation and control mode can be determined. After parsing, all parameters are organized according to their usage and impact dimensions to generate a complete package characteristic vector F', which serves as direct input for control strategy generation.

[0053] S103. Generate a corresponding transportation control strategy according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters; the control instructions are used to instruct the inspection vehicle to operate according to the operating parameters.

[0054] In principle, based on the physical characteristic vector F' obtained through analysis, the rule inference engine or strategy mapping module is called to generate a transportation control strategy that is adapted to it. The control strategy consists of two parts: control instructions and operating parameters. The control instructions mainly define the movement behavior mode that the inspection vehicle should adopt when transporting such packaging, including whether to enable shock-absorbing suspension, whether to enforce speed limit, whether to avoid specific road sections, etc. The operating parameters quantify specific movement indicators, including the maximum safe speed V MAX , minimum turning radius R MIN , acceleration and deceleration smooth curve k value, driving mode number, etc.

[0055] Optionally, a control strategy is typically generated by combining rule matching with environmental perception. Specifically, the control strategy mapping table is queried using the physical characteristic vector F' to retrieve the most suitable standard transport configuration. Parameters are then modified based on real-time environmental perception parameters such as road conditions, slope, and current load, ultimately forming the final strategy set. Furthermore, if the package tag incorporates a vibration or temperature sensor module, closed-loop adjustments can be made based on the real-time status data returned by the tag, dynamically updating or providing feedback corrections to the control strategy to adapt to sudden environmental changes.

[0056] For example, the structured data returned by the packaging label of the precision equipment indicates that its maximum load-bearing capacity is 200N, the seismic resistance level is IV, and the maximum allowable transport speed is 15km / h. At the same time, the built-in BLE tag transmits back in real time the excessive fluctuations in external vibration data. After generating the preliminary control strategy, it is found that the current inspection vehicle is running at 18km / h, which exceeds the packaging tolerance threshold. The operating parameters are automatically adjusted to limit the speed to below 12km / h, and the suspension buffer mode is enabled. At the same time, high slopes are dynamically avoided to achieve precise adaptation.

[0057] In the aforementioned intelligent packaging adaptation method for inspection vehicles based on tag data interaction, low-power RFID / BLE tags are embedded to structure the physical characteristics of the package and bind them to a unique identifier. Automatic identification is achieved when the inspection vehicle approaches, enabling the vehicle to obtain the characteristic data corresponding to the package. This data interaction eliminates the need for manual identification, improving the linkage and adaptability between packaging and transportation, enabling automatic adaptation of packaging and transportation modes, and enhancing transportation personalization. Parsing structured tag data accurately captures sensitive indicators such as pressure thresholds, stacking limits, and seismic resistance levels. Based on these indicators, targeted transportation control strategies are generated, such as limiting vehicle speed, switching to shock absorption modes, and controlling the number of stacking layers. This significantly reduces the likelihood of package damage or internal damage caused by improper transportation conditions. Control strategy generation is based on regular matching or mapping of physical characteristic vectors, resulting in a high degree of automation, fast response, and strong logical consistency. This avoids transportation strategy configuration errors caused by insufficient human experience or misjudgment, improving stability and controllability. The characteristic information of different packages is written into the structured tag data. The inspection vehicle can independently analyze, respond, and control each packaging unit. It has good diversity compatibility and is suitable for multi-package mixed transportation scenarios, improving the flexibility and intelligence level of the transportation system.

[0058] In one embodiment, Figure 2 As shown, the structured label data is obtained by the following method:

[0059] S201. Receive physical data of the package; the physical data includes pressure threshold, stacking limit, seismic resistance level, temperature and humidity tolerance, and package type code.

[0060] Schematically, packaging physical data refers to a set of basic indicators that directly describe the properties of packaging materials, transportation response characteristics, and environmental tolerances, usually including but not limited to pressure thresholds, stacking limits, seismic resistance levels, temperature and humidity tolerances, and packaging type codes, where packaging type codes can be used to identify packaging materials and structural forms such as cartons, foam pallets, and metal shells. Optionally, physical data can be obtained in a variety of ways. It can be output by testing equipment in the packaging manufacturing process, manually input in the quality acceptance process, or automatically synchronized with MES systems (Manufacturing Execution System) and WMS (Warehouse Management System). To ensure data consistency, the data source can be standardized and verified, and after collection, it can be uniformly converted into a structured set of physical attribute fields, namely physical data.

[0061] S202: Encode the physical data to obtain a label protocol data body.

[0062] Schematically, an encoding operation is performed on the above-mentioned physical data to form a tag protocol data body that can be written and interacted in the electronic tag. The encoding process needs to convert the physical properties into a recognizable binary or hexadecimal data stream based on the preset data communication protocol. Among them, the data communication protocol can be EPCglobal (RFID tag universal standard), GATT or a custom CBOR compression structure. Specifically, each attribute field will be assigned a unique attribute code, such as 0x01 for the pressure threshold, 0x02 for the seismic resistance level, and then fill in its value range and length in turn to generate a tag protocol data body. Optionally, in order to adapt to the capacity and interface type of different tag chips, the data body should have compression capabilities, allowing the field to have default or expandable segments.

[0063] For example, the physical properties of a packaging unit are a pressure threshold of 300N, seismic resistance level III, a stacking limit of 5 layers, a temperature tolerance range of 0-40°C, and a packaging type code of CTN-B2. The data is encoded by field, with 0x01-02BC representing 300N, 0x02-03 representing seismic resistance level III, 0x03-05 and 0x04-0028 / 0050 representing the upper and lower temperature limits in hexadecimal format, and 0x05-43544E2D4232 representing ASCII encoding. This ultimately forms a continuous protocol data body for subsequent writing to the tag storage area.

[0064] S203: Construct a key-value corresponding structure based on a preset data attribute set, and bind it to the corresponding package to obtain structured label data; the structured label data includes a unique identification label and physical property data.

[0065] Schematically, a key-value pair structure is constructed based on a preset set of data attributes and bound to the target package to obtain the final structured tag data. This structure is a collection of data descriptions, including unique identification fields and physical characteristic fields, and is organized and stored in an extensible format such as JSON, XML, or Protocol Buffers. During the binding process, the unique tag of each packaging unit is confirmed and a mapping table is established in a database or written locally to the chip, achieving a one-to-one correspondence between the package and the structure.

[0066] In one embodiment, structured tag data embedded in the package is obtained based on the tag data interaction, including:

[0067] S11. Generate a broadcast signal through a preset period; the broadcast signal is used to wake up the packaging tag and instruct the packaging tag to feedback tag data.

[0068] Schematically, a tag data reading unit, such as a Bluetooth Low Energy (BLE) communication module or a UHF RFID (ultra-high frequency) reader / writer, within the inspection vehicle periodically generates a broadcast signal. This broadcast signal can take the form of a BLE broadcast frame or a UHF RFID wake-up signal. For example, if the embedded tag is a Bluetooth Low Energy (BLE) chip that supports BLE, the broadcast signal can be configured as a broadcast packet containing a preset service UUID (Universally Unique Identifier) and a data feedback request. For UHF (ultra-high frequency) passive RFID tags, the broadcast signal is a short pulse of energy that activates the tag's internal voltage-controlled rectifier circuit, forcing it into a response state. The broadcast period can be adaptively adjusted based on the packaging density and read range, ensuring complete coverage while avoiding excessive energy consumption. The primary function of this broadcast signal is to wake the tag and trigger its active response behavior. Upon receiving the wake-up broadcast, each tag enters data upload mode and broadcasts its internally stored structured tag data within a specified time window.

[0069] S12. Receive the structured label data returned by the packaging label and confirm the label identity based on the unique identifier.

[0070] Receive the return signal and complete data parsing and identity confirmation. Schematically, the receiving process takes into account multi-tag concurrent scenarios. A listening queue can be set up to perform real-time filtering and caching of the unique identification field carried in the data frame. For each frame of returned data, the unique identifier is first extracted and the local packaging identification database is queried to confirm whether the tag is included in the planned transport mission. If a match is found, the structured tag data is stored in the inspection vehicle's local memory and combined with information such as the current timestamp and inspection location to form a packaging data entry to be processed.

[0071] Furthermore, to ensure communication security and data integrity, data packet decoding and integrity verification can be performed. Specifically, encryption structures can be used to protect tag content, or a checksum can be appended to the end of the structure to verify data consistency before and after transmission. Data frames that do not meet the verification criteria or fail decoding are marked as abnormal entries and re-requested later to avoid mismatches in control strategy matching.

[0072] In one embodiment, parsing the structured tag data to obtain a physical property vector corresponding to the package includes:

[0073] S21. Decode the tag data according to a preset data protocol to obtain a packaging characteristic set; the packaging characteristic set includes a pressure threshold, a stacking limit, a seismic resistance level, a temperature and humidity tolerance, and a packaging type code.

[0074] Decode structured tag data based on the adopted data protocol. Structured tag data, as a standard information format stored within packaging tags, typically uses key-value pairs to store multiple physical characteristic fields of the package. Its transmission format can be lightweight structures such as CBOR and JSON compressed packages. Upon receiving this data structure, decoding is performed based on the field mapping table and encoding method defined by the protocol. Specifically, the key list within the structure is extracted and matched against the predefined protocol field dictionary, revealing the actual meaning of each field. For example, "CL" maps to the compression limit, "SG" maps to the shock grade, and "SL" maps to the stack limit. Temperature and humidity tolerance may be represented as a four-tuple consisting of maximum temperature, minimum temperature, maximum humidity, and minimum humidity. The package type code is a classification code based on a unified numbering system. Once successfully decoded, a set of package characteristics with clear physical semantics is obtained. This characteristic set is temporarily stored in a structured local cache and serves as input to the subsequent feature extraction module.

[0075] S22. Map the packaging characteristic set to the internal characteristic vector to obtain a physical characteristic vector.

[0076] Schematically, the decoded packaging characteristic set is converted into a physical characteristic vector for model calculation, that is, the scattered heterogeneous physical property values are standardized into a set of numerical vectors with fixed dimensions and uniform scales, so that the subsequent control strategy determination module can accurately respond to the packaging adaptation scheme through rule matching, model reasoning or control parameter search.

[0077] Optionally, the mapping method uses attribute normalization, encoding conversion, and multidimensional feature combination. Specifically, the pressure threshold is a real value, which can be retained directly as the original value or converted into a level vector by interval classification; the stacking limit is a discrete integer, which can be directly input as an integer feature; the seismic resistance level uses the level enumeration value, which is converted into a vector representation through one-hot encoding; the temperature and humidity tolerance quadruple is linearly normalized to between 0 and 1; the packaging type code can be mapped to a material strength vector based on its corresponding material parameters. All fields will be combined into a set of physical property vectors.

[0078] In one embodiment, Figure 3 As shown, the corresponding transportation control strategy is generated according to the physical characteristic vector, including:

[0079] S301: Use a rule matching engine to query and match the physical characteristic vector and the control strategy mapping table to obtain a control sub-parameter set.

[0080] A rule matching engine is used to perform correlation analysis between the physical characteristic vector and the control strategy mapping table. In principle, the matching engine can be implemented based on a Boolean rule tree, a weight matching table, or a decision matrix, mapping the key indicators in the input vector to a set of control sub-parameters according to the set conditions. The control strategy mapping table pre-defines the transportation parameter templates corresponding to each physical characteristic interval. For example, when the pressure threshold is lower than 100N, the maximum speed limit is set to 0.5m / s; when the seismic resistance level is 1, the high-level shock absorption mode is turned on; when the packaging type is flexible material and the stacking limit is less than 3 layers, stacking is prohibited.

[0081] By traversing the fields in the input vector and triggering matching conditions one by one, the corresponding control sub-parameter set is gradually selected. This sub-parameter set typically includes preliminary speed limits, acceleration limits, whether active suspension is enabled, and the turning radius correction factor used in path planning.

[0082] S302: Dynamically adjust the control sub-parameter set according to the current environment variable data to obtain a control parameter set.

[0083] Due to the highly dynamic nature of the transportation environment, control parameters based solely on static packaging characteristics may not guarantee safety and adaptability in actual operation. Therefore, a dynamic adjustment mechanism for environmental variables is introduced. Environmental variables include, but are not limited to, ground vibration levels, current ground slope, air temperature and humidity, and cabin temperature. These variables can be collected in real time by the inspection vehicle's built-in sensor system. These environmental variable data, as external input variables, are used to readjust the control sub-parameter set, resulting in a control parameter set that better suits the current conditions.

[0084] S303: Generate a transportation control strategy based on the control parameter set.

[0085] The transport control strategy is a set of goals executed by the bottom-level control module of the inspection vehicle, and includes two core parts: a control instruction set and an operating parameter group. Among them, the control instruction set describes the logical sequence and triggering conditions of the control behavior, such as starting the vehicle's active shock absorber unit, limiting steering acceleration, detouring according to the path number, etc.; while the operating parameter group provides numerical support for the actual execution of these instructions. Schematically, the control strategy is transmitted to the inspection vehicle chassis controller or path planning unit in the form of a message queue, control frame or behavior tree to achieve automatic adaptive operation throughout the process. For example, if a package is judged to be of a high-fragility, high-stack risk type and the current vibration value exceeds the limit, the control strategy may include starting the high-sensitivity shock absorption mode, switching to the obstacle avoidance priority path, prohibiting stacking, and the operating speed not exceeding 0.45m / s.

[0086] In one embodiment, the control sub-parameter set is dynamically adjusted according to the current environment variable data to obtain a control parameter set, including:

[0087] S41. Calculate current environmental variable data, and determine whether the current environmental variable data exceeds a packaging tolerance range, thereby obtaining dynamic adjustment requirements. Dynamic adjustment requirements include whether dynamic adjustment is required or not.

[0088] Schematically, the current environmental variable data is collected from the multi-source sensor system carried by the inspection vehicle itself, including but not limited to the ground vibration value, air temperature, air humidity, slope, vehicle acceleration and navigation status on the inspection path. After collecting the complete environmental variable data set, it is compared one by one with the packaging tolerance interval contained in the structured label data to determine whether there is a packaging risk in the current transportation environment. Among them, the packaging tolerance interval refers to the maximum or minimum threshold that the packaging can withstand for the external physical environment, usually including the maximum acceleration value corresponding to the seismic resistance level, the temperature and humidity interval limited by the humidity and heat tolerance, the slope tolerance value, etc. Based on the above comparison, the environmental data status is classified as requiring dynamic adjustment or not, and the corresponding dynamic adjustment requirements are output. Optionally, the judgment logic is implemented in the form of rule judgment or interval mapping, and can adopt hard threshold comparison, logical combination judgment, or use a fuzzy interval-based risk scoring system in complex implementations.

[0089] S42. If the dynamic adjustment requirement is that dynamic adjustment is required, the control sub-parameter set is modified based on the fuzzy principle to obtain a control parameter set.

[0090] Illustratively, when dynamic adjustment is determined, the original set of control sub-parameters is modified based on fuzzy control principles to form the final set of control parameters. Specifically, fuzzy control is used to optimize parameters under unpredictable or nonlinear environmental conditions. Fuzzy membership functions and rule tables are used as a foundation to softly modify adjustment amplitudes and parameter target values. For example, if the slope exceeds a preset tolerance but the temperature is normal, only the path planning parameters are adjusted to avoid high-slope areas without enforcing a speed limit.

[0091] S43: If the dynamic adjustment requirement is that no dynamic adjustment is required, determine the control sub-parameter set as the control parameter set.

[0092] In one embodiment, the method further comprises:

[0093] S51. Acquire real-time operation status data and external environment data of the inspection vehicle and obtain feedback information.

[0094] Schematically, the real-time operating status data and external environment data of the inspection vehicle are obtained. Among them, the operating status data mainly include vehicle speed, steering angle, acceleration, vibration amplitude, obstacle avoidance behavior frequency, power consumption changes, etc.; the external environment data includes road conditions, temperature and humidity changes, light intensity, obstacle density and other information. Optionally, the above data are collected and uploaded at a preset period through on-board sensor systems such as IMU (Inertial Measurement Unit) modules, temperature and humidity sensors, cameras, radars and GPS (Global Positioning System) to form feedback information, dynamically describing the current behavior status and operating environment of the inspection vehicle.

[0095] S52. Perform a status evaluation on the feedback information based on the operating parameters to obtain an evaluation result; the evaluation structure includes a deviation and a no deviation.

[0096] Schematically, the feedback information is evaluated based on the operational parameters of the generated and issued transportation control strategy. The evaluation process can employ a deviation determination model based on parameter intervals to identify the degree of deviation between the strategy settings and actual operation. The evaluation mechanism is typically implemented as a deviation detection module, with the input being the difference between the target parameter values defined in the strategy and the actual sampled values.

[0097] S53. If the evaluation result shows that there is a deviation, a correction parameter is generated, and the transportation control strategy is corrected according to the correction parameter.

[0098] If the evaluation results indicate a deviation, new correction parameters are generated based on the error data. These correction parameters can be generated using dynamic compensation or fuzzy callback logic to minimize intervention in the control strategy while still being sufficiently effective. For example, if the vehicle speed is consistently low due to slippery conditions, the correction parameters might include increasing the traction control level or adjusting the route priority to avoid wet areas. If vibration levels exceed the limit, the vehicle speed may be further limited, the suspension response may be increased, or the suspension mode may be switched to high.

[0099] Optionally, the correction process can be completed locally on the controller or uploaded to the cloud-based scheduling system for policy optimization calculation and subsequent distribution. The revised transportation control strategy will immediately replace some of the operating parameters in the original strategy and reactivate the operating cycle, thus closing the entire adaptive control loop.

[0100] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0101] Based on the same inventive concept, the embodiments of the present application also provide a patrol vehicle intelligent packaging adaptation device based on tag data interaction for implementing the aforementioned patrol vehicle intelligent packaging adaptation method based on tag data interaction. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the patrol vehicle intelligent packaging adaptation device based on tag data interaction provided below can be found in the above-mentioned limitations of the patrol vehicle intelligent packaging adaptation method based on tag data interaction, and will not be repeated here.

[0102] In an exemplary embodiment, Figure 4 As shown, a smart packaging adapter device for an inspection vehicle based on tag data interaction is provided, comprising:

[0103] A data interaction module is used to obtain structured tag data embedded in the package based on tag data interaction;

[0104] The data parsing module is used to parse the structured label data and obtain the physical characteristic vector corresponding to the packaging;

[0105] The control adaptation module is used to generate a corresponding transportation control strategy according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters.

[0106] In one embodiment, it further includes:

[0107] A data acquisition module, for receiving physical data of the package;

[0108] A data encoding module is used to encode the physical data to obtain a label protocol data body;

[0109] The data structuring module is used to construct a key-value corresponding structure based on a preset data attribute set, and bind it with the corresponding package to obtain structured tag data.

[0110] In one embodiment, it further includes:

[0111] A communication module, configured to generate a broadcast signal at a preset period;

[0112] The label identification module is used to receive the structured label data returned by the packaging label and confirm the label identity based on the unique identifier.

[0113] In one embodiment, the data parsing module is further configured to decode the tag data according to a preset data protocol to obtain a packaging characteristic set; the data parsing module is further configured to map the packaging characteristic set to an internal feature vector to obtain a physical characteristic vector.

[0114] In one embodiment, it further includes:

[0115] A control strategy matching module is used to query and match the physical characteristic vector and the control strategy mapping table using a rule matching engine to obtain a control sub-parameter set;

[0116] A dynamic adjustment module is used to dynamically adjust the control sub-parameter set according to the current environmental variable data to obtain a control parameter set;

[0117] The control adaptation module is further used to generate a transportation control strategy according to the control parameter set.

[0118] In one embodiment, it further includes:

[0119] The tolerance interval judgment module is used to calculate the current environmental variable data and determine whether the current environmental variable data exceeds the packaging tolerance interval to obtain dynamic adjustment requirements;

[0120] The dynamic adjustment module is used to modify the control sub-parameter set based on the fuzzy principle to obtain the control parameter set if the dynamic adjustment requirement is that dynamic adjustment is required; the dynamic adjustment module is also used to determine the control sub-parameter set as the control parameter set if the dynamic adjustment requirement is that dynamic adjustment is not required.

[0121] In one embodiment, the method further comprises:

[0122] Feedback data module, used to obtain the real-time operating status data and external environment data of the inspection vehicle and obtain feedback information;

[0123] An evaluation module is used to evaluate the status of feedback information based on operating parameters and obtain evaluation results; the evaluation structure includes deviation and no deviation;

[0124] The correction module is used to generate correction parameters if the evaluation result shows that there is a deviation, and to correct the transportation control strategy according to the correction parameters.

[0125] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0127] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0128] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A method for intelligent packaging adaptation of inspection vehicles based on tag data interaction, characterized in that: The method comprises: Obtain structured tag data embedded in the package based on tag data interaction; Parsing the structured tag data to obtain a physical property vector corresponding to the package; A corresponding transportation control strategy is generated according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters; the control instructions are used to instruct the inspection vehicle to operate according to the operating parameters.

2. The method according to claim 1, characterized in that The structured label data is obtained by the following method: Receiving physical data of the package; the physical data including pressure threshold, stacking limit, seismic level, temperature and humidity tolerance, and package type code; Encoding the physical data to obtain a label protocol data body; A key-value corresponding structure is constructed based on a preset data attribute set and bound to the corresponding package to obtain structured label data; the structured label data includes a unique identification label and physical property data.

3. The method according to claim 2, characterized in that The structured tag data embedded in the package is obtained based on the tag data interaction, including: Generate a broadcast signal through a preset period; the broadcast signal is used to wake up the packaging tag and instruct the packaging tag to feedback tag data; Receive the structured label data returned by the packaging label, and confirm the label identity according to the unique identifier.

4. The method according to claim 2, characterized in that The parsing of the structured tag data to obtain a physical characteristic vector corresponding to the package includes: Decoding the tag data according to a preset data protocol to obtain a packaging characteristic set; the packaging characteristic set includes the pressure threshold, the stacking limit, the seismic resistance level, the temperature and humidity tolerance, and the packaging type code; The packaging characteristic set is mapped to an internal characteristic vector to obtain the physical characteristic vector.

5. The method according to claim 1, wherein Generating a corresponding transportation control strategy according to the physical characteristic vector includes: Using a rule matching engine to query and match the physical characteristic vector and the control strategy mapping table to obtain a control sub-parameter set; Dynamically adjusting the control sub-parameter set according to current environmental variable data to obtain a control parameter set; A transportation control strategy is generated according to the control parameter set.

6. The method according to claim 5, characterized in that The dynamically adjusting the control sub-parameter set according to the current environment variable data to obtain the control parameter set includes: Calculating the current environmental variable data and determining whether the current environmental variable data exceeds a packaging tolerance range to obtain a dynamic adjustment requirement; the dynamic adjustment requirement includes whether dynamic adjustment is required or not; If the dynamic adjustment requirement is the need for dynamic adjustment, then modifying the control sub-parameter set based on a fuzzy principle to obtain the control parameter set; If the dynamic adjustment requirement is that dynamic adjustment is not required, the control sub-parameter set is determined to be the control parameter set.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Obtain real-time operating status data and external environment data of the inspection vehicle and obtain feedback information; Performing a status evaluation on the feedback information based on the operating parameters to obtain an evaluation result; the evaluation structure includes a deviation and a no deviation; If the evaluation result shows that there is a deviation, a correction parameter is generated, and the transportation control strategy is corrected according to the correction parameter.

8. An intelligent packaging adapter device for inspection vehicles based on tag data interaction, characterized in that: The device comprises: A data interaction module is used to obtain structured tag data embedded in the package based on tag data interaction; A data parsing module, configured to parse the structured tag data to obtain a physical characteristic vector corresponding to the package; A control adaptation module is used to generate a corresponding transportation control strategy according to the physical characteristic vector; the transportation control strategy includes control instructions and operating parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images