A sealing ring warehouse logistics management and control system based on multi-dimensional environment perception

Abstract

The application discloses a sealing ring warehouse logistics management and control system based on multi-dimensional environment perception, relates to the warehouse logistics management and control field, and comprises a multi-source feature acquisition and analysis module, inherent attribute parameters and specification constraint conditions of a target object are acquired, a space trajectory sequence is extracted, and a prediction matrix of an environment parameter along a route is synchronously generated; an environment stress quantitative evaluation module converts inherent attributes into an environment tolerance boundary model, combines a risk evaluation function to output a comprehensive environment stress risk vector; a flexible intervention and configuration execution module generates physical protection medium configuration signaling and carrier scheduling signaling and executes them; and a state observation and closed-loop evolution module outputs an utility constraint instruction dynamic adjustment strategy matching boundary based on actual delivered physical attenuation observation data and comprehensive performance dissipation data, so that the application realizes accurate prediction and self-adaptive protection of environment risks in the whole process of sealing ring warehouse logistics, and reduces performance loss and comprehensive cost.

Description

Technical Field

[0001] This invention relates to the field of warehouse logistics management and control, specifically a sealed ring warehouse logistics management and control system based on multi-dimensional environmental perception. Background Technology

[0002] Sealing rings (especially those made of polymeric elastomers such as rubber, silicone, and fluororubber) are highly sensitive to environmental factors (such as temperature, humidity, air pressure, ultraviolet radiation, and corrosive gases) during warehousing and logistics transportation. Extreme high temperatures accelerate the thermo-oxidative aging of materials, leading to decreased elasticity and increased permanent compression set; low-temperature environments may trigger the glass transition of materials, increasing their brittleness and causing sealing performance failure; high-humidity environments easily cause moisture absorption and swelling, dimensional changes, and even electrochemical corrosion with metal components. Traditional warehouse logistics management systems typically only focus on basic information such as order flow, inventory location, and transportation trajectory, lacking the systematic perception and forward-looking assessment capabilities of changes in the microenvironment along the route.

[0003] In actual contract fulfillment, most companies still adopt static, uniform packaging and transportation rules, such as using ordinary plastic packaging and less-than-truckload (LTL) logistics for all sealing ring orders. This "one-size-fits-all" approach is ill-suited to addressing the dynamic risks brought about by seasonal changes, regional climate differences, and sudden extreme weather events, leading to two common problems: first, insufficient protection, resulting in irreversible performance degradation by the time goods arrive, causing returns, reshipments, or customer complaints; second, excessive protection, using high-cost protective materials and dedicated transportation lines even for low-risk orders, resulting in a waste of packaging and logistics resources. Furthermore, existing systems lack a closed-loop feedback mechanism, making it impossible to reverse-engineer risk assessment models and protection strategies based on actual delivery quality data, hindering the autonomous evolution of risk prediction capabilities.

[0004] Therefore, there is an urgent need for a sealed-ring warehousing and logistics management system that can integrate multi-dimensional environmental perception, stress quantification assessment, flexible defense execution, and closed-loop evolution learning. This system can accurately identify environmental risks before shipment, dynamically match the optimal protection and transportation strategies, and reduce the fulfillment costs of the entire supply chain while ensuring product quality. Summary of the Invention

[0005] The purpose of this invention is to provide a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, comprising:

[0007] The multi-source feature acquisition and analysis module is used to obtain the inherent attribute parameters and specification constraints of the target object, extract the spatial trajectory sequence from the shipping node to the receiving node, and call the external interface based on the spatial trajectory sequence to synchronously generate the prediction matrix of the environmental parameters along the way aligned in the spatiotemporal dimension.

[0008] The environmental stress quantification assessment module is used to receive the inherent attribute parameters and convert them into an environmental tolerance boundary model. At the same time, it inputs the prediction matrix of the environmental parameters along the route into a preset risk assessment function, performs mapping calculations with the environmental tolerance boundary model as a constraint, and outputs the quantified comprehensive environmental stress risk vector.

[0009] The flexible intervention and configuration execution module is used to match a unique corresponding response strategy in the preset defensive fulfillment stack through table lookup or logical optimization, and generate interrelated physical protection medium configuration signaling and carrier scheduling signaling according to the response strategy, and send them down to the underlying warehousing and transportation execution units.

[0010] The state observation and closed-loop evolution module is used to extract physical attenuation observation data and comprehensive performance dissipation data at the time of actual delivery. Based on the deviation between the physical attenuation observation data and the corresponding historical predicted comprehensive environmental stress risk vector, the state observation and closed-loop evolution module generates a gradient correction signal and sends it back to the environmental stress quantification assessment module to update the weight parameters of the risk assessment function. Based on the comprehensive performance dissipation data, it generates utility constraint instructions and sends them to the flexible intervention and configuration execution module to dynamically adjust the strategy matching boundary.

[0011] Preferably, the environmental parameter prediction matrix along the route includes temperature and humidity coupled data vectors of multiple passing nodes arranged in a time series; the risk assessment function calculates the difference between the temperature and humidity coupled data vector of each passing node and the environmental tolerance boundary model to generate the local deviation of each node, and performs weighted integration with the expected residence time coefficient of the corresponding node to accumulate and generate the comprehensive environmental stress risk vector of the whole for this mission.

[0012] Preferably, the generated physical protection medium configuration signaling and the carrier scheduling signaling form a strongly coupled control pair; when the comprehensive environmental stress risk vector exceeds the preset critical threshold, the control pair generated by the module forces the underlying unit to simultaneously activate the high-barrier physical protection assembly process and the high-efficiency constant temperature carrier scheduling process, and if either process fails to verify, the system's delivery blocking mechanism is triggered.

[0013] Preferably, the specific method for the state observation and closed-loop evolution module to update the weight parameters of the risk assessment function includes the following steps:

[0014] The built-in reinforcement learning correction unit continuously calculates the residual between the expected safety rate and the actual physical decay rate of the target object under the same specification constraints and specific environmental parameters along the way; when the residual continuously exceeds the lower limit of the confidence interval, a penalty mechanism is triggered, and the sensitivity weight tensor of the corresponding environmental parameter in the risk assessment function is automatically increased.

[0015] Preferably, the specific method for adjusting the matching boundary of the state observation and closed-loop evolution module includes the following steps:

[0016] The built-in fulfillment resource dissipation calculation unit intercepts the response strategy intended by the flexible intervention and configuration execution module in real time, and extracts the estimated fulfillment dissipation value required to execute the strategy; when the estimated fulfillment dissipation value exceeds the utility profit threshold bound to the order, it feeds back an out-of-bounds blocking signal to the flexible intervention and configuration execution module.

[0017] Preferably, the flexible intervention and configuration execution module executes the following interaction strategy after receiving the out-of-bounds blocking signal:

[0018] The current configuration signaling and scheduling signaling are suspended, and a spatiotemporal variable reconstruction request is initiated to the multi-source feature acquisition and analysis module. The multi-source feature acquisition and analysis module generates a new test trajectory set by extending the departure time on the time axis or switching the starting node on the spatial axis. The environmental stress quantification and evaluation module traverses the test trajectory set until it outputs an alternative optimization solution signaling that does not trigger an out-of-bounds blocking signal.

[0019] Preferably, the interaction mechanism for the performance resource dissipation calculation unit to maintain the long-term game balance of the system is as follows: real-time monitoring of performance tasks under low environmental stress risk vectors, extraction of resource surplus points generated by triggering baseline degradation protection, and dynamic injection of the resource surplus points into the global utility subsidy pool; before receiving the out-of-bounds blocking signal, the flexible intervention and configuration execution module first initiates a difference reconciliation request to the global utility subsidy pool, and if the reconciliation is successful, ignores the out-of-bounds blocking signal and forcibly executes a high-level defense strategy.

[0020] As a preferred method, the method for generating a comprehensive environmental stress risk vector specifically includes the following steps:

[0021] Extract the time series variables and environmental stress variables from the predicted environmental parameters along the route, call the material degradation kinetics baseline equation corresponding to the target object, and map the discrete environmental stress during the predicted route period into continuous equivalent cumulative damage values ​​through time integration.

[0022] The risk assessment function compares the equivalent cumulative damage value with the failure threshold of the target object and outputs a structured multi-risk vector including thermal aging, cold embrittlement and hygrothermal swelling dimensions.

[0023] As a preferred option, an independent interaction mechanism is also included, which includes: listening to the outbound events of the order management system in real time through a message queue, suspending the outbound process in an intercepted state; after the flexible intervention and configuration execution module generates the physical protection medium configuration signaling, injecting it as an overwrite variable into the standard operating procedure database of the warehouse management system, and releasing the outbound process to dynamically rewrite the packaging guidance UI interface of the front-line operation terminal.

[0024] Preferably, the external signaling interaction gateway monitors the output status of the flexible intervention and configuration execution module in real time. When it captures an upgrade protection response strategy triggered by a high-risk event, it extracts the environmental anomaly parameters and incremental defense measures list corresponding to the strategy, maps and encapsulates them into a standard customer care data package, and automatically triggers the application programming interface to push it to the designated user node of the third-party customer relationship management system.

[0025] In summary, the beneficial effects of this invention are:

[0026] This invention breaks through the limitations of traditional logistics systems that only track trajectories. By introducing a multi-source environmental feature collection and stress quantification assessment mechanism, it can accurately predict environmental risks in transit before orders are shipped. The system transforms the subsequent physical loss costs into the pre-emptive precise protection costs by flexibly issuing dynamic protection configuration signals, thus eliminating compliance and financial risks caused by over-shipment from the source.

[0027] This invention pioneers a state observation and closed-loop evolution architecture. By continuously capturing real physical degradation data at the end of the compliance process through a multimodal feedback parsing unit, and utilizing a reinforcement learning mechanism to calculate the loss function for prediction bias, the system can autonomously correct the feature weights in the risk assessment function. With the accumulation of compliance data, the system can automatically generate a highly accurate environmental degradation knowledge graph for specific materials, achieving unsupervised evolution free from human intervention.

[0028] This invention incorporates a dynamic resource accounting and cross-compensation mechanism. Instead of blindly upgrading protection, the system quantifies the boundary performance costs generated by defensive actions in real time and negotiates them against profit limits. By injecting the resource points saved from downgrading protection under low-risk conditions into a "global utility subsidy pool," the system specifically subsidizes excess defense expenditures under high-risk conditions. This improves overall performance security while achieving a net reduction in overall packaging and logistics costs throughout the entire lifecycle.

[0029] When extreme environmental stresses cause defense costs to exceed profit margins, the system's spatiotemporal self-healing unit can decisively trigger a blocking mechanism, achieving low-cost risk mitigation by delaying shipments or inter-regional allocation. Simultaneously, through an external signaling gateway, the system automatically maps internal crisis intervention actions into proactive customer care and early warning information, successfully transforming uncontrollable weather disadvantages into service premiums that enhance customer loyalty, significantly expanding the commercial boundaries of the logistics management system. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the overall architecture and data flow structure of a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to the present invention.

[0032] Figure 2 This is a business control flowchart of a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, according to the present invention.

[0033] Figure 3 This is a schematic diagram of the environmental stress quantification assessment algorithm for a sealing ring storage and logistics management system based on multi-dimensional environmental perception, according to the present invention.

[0034] Figure 4 This is a schematic diagram of the underlying execution timing of a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, according to the present invention.

[0035] Figure 5 This is a flowchart of the performance dissipation calculation for a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, according to the present invention.

[0036] Figure 6 This is a schematic diagram of machine learning update for a sealing ring storage and logistics management system based on multi-dimensional environmental perception, according to the present invention.

[0037] Figure 7 This is a schematic diagram showing the order transportation interface of a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to the present invention.

[0038] Figure 8 This is a schematic diagram illustrating the comparison of order transportation costs for a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, as described in this invention. Detailed Implementation

[0039] The present invention will now be described in further detail with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0040] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the invention will be more thorough and complete.

[0041] All features disclosed in this specification, or all steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps.

[0042] Any feature disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by other equivalent or similar features for a similar purpose, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.

[0043] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of at least two elements or the interaction relationship of at least two elements, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0044] Please see Figures 1-8 One embodiment of the present invention is a sealing ring warehousing and logistics management system based on multi-dimensional environmental perception. The system preferably adopts a microservice architecture in terms of software physical architecture and is deployed on a cloud server. The system is decoupled from the enterprise's existing order management system (OMS), warehouse management system (WMS), and transportation management system (TMS) through standard application programming interfaces or message queue middleware such as Kafka.

[0045] The core of this system comprises four highly collaborative modules: a multi-source feature acquisition and analysis module, an environmental stress quantification and assessment module, a flexible intervention and configuration execution module, and a state observation and closed-loop evolution module. Together, they form a closed-loop control chain, the details of which are as follows:

[0046] I. Multidimensional Environmental Perception and Stress Quantification

[0047] This embodiment details how the system transforms weather forecasts into engineering physical risks.

[0048] Step 1: Feature Acquisition. When a new order is generated by the enterprise's OMS, the multi-source feature acquisition and parsing module intercepts the order information by listening to the order event stream. It extracts the inherent attribute parameters of the "target object," such as the material (FKM fluororubber), hardness, dimensions, and specification constraints. Simultaneously, it calls the Gaode or Baidu Maps API to extract the predicted spatial trajectory sequence from the shipping location to the destination. Next, it calls a professional meteorological API to obtain the environmental parameter prediction matrix along the corresponding time axis, including the highest / lowest temperature, relative humidity, and extreme weather markers (heavy rain / snowstorm) for each node along the route within the next 72 hours.

[0049] Step 2: Stress quantification calculation. The environmental stress quantification assessment module receives the above data. Taking a specific target object as a polymer elastomer seal as an example, the module has a preset environmental tolerance boundary model for this material, such as the glass transition temperature of FKM. The temperature is -20℃, and the relative humidity sensitivity threshold is 75%.

[0050] The risk assessment function executes the following logic:

[0051] Calculate node deviation: The difference between the predicted extreme cold temperature passing through a certain node and the tolerance boundary is calculated to obtain the temperature deviation.

[0052] Integration and weighting: Multiply the temperature deviation by the dynamic temperature weight tensor of the material, and sum the results with the expected exposure time in the environment and the time integration coefficient.

[0053] Output: The system ultimately outputs a structured, comprehensive environmental stress risk vector, for example: .

[0054] II. Flexible Intervention and Implementation Through Bottom-Level Penetration

[0055] This embodiment illustrates how the system breaks static packaging rules to achieve flexible control. When the assessment module outputs a high-risk vector, the flexible intervention and configuration execution module begins to operate.

[0056] The flexible intervention and configuration execution module, based on the above output, = 88, high risk, match within the preset defensive fulfillment stack:

[0057] If the score is in the low risk range (0-30): trigger the baseline fulfillment configuration, such as ordinary PE self-sealing bag + 5-layer cardboard box, ordinary less-than-truckload logistics.

[0058] If the score is in the medium risk range (31-70): trigger enhanced instructions (such as adding a foamed buffer layer, specifying direct trunk logistics).

[0059] If the score is high-risk (above 71, 88 in this example): the extreme environmental isolation configuration is triggered. For polymer elastomers, the system generates a dynamic packaging BOM: forcibly calls aluminum foil composite moisture-proof bags, issues a vacuum heat-sealing command, and calculates the need to add 50 grams of silica gel desiccant. At the same time, on the logistics side, it forcibly matches SF Express air freight or temperature-controlled cold chain trucks.

[0060] System-level interactive penetration: After the execution signal is generated, it is directly sent to the WMS via API. The static SOP and standard operating procedure originally set by the WMS for this product are forcibly overwritten by this signal. When the pickers on the front line of the warehouse scan the order barcode with a PDA, the UI interface that pops up on the PDA screen is no longer the regular packaging, but a customized anti-freeze and anti-crack packaging instruction highlighted in red, completely eliminating human judgment errors.

[0061] III. Performance Dissipation Calculation

[0062] This embodiment is the core of the business logic of the present invention, resolving the conflict between security and cost.

[0063] The state observation and closed-loop evolution module incorporates a fulfillment resource dissipation calculation unit. In the above embodiment, if the system plans to upgrade packaging and air-flying parts, the calculation unit will immediately calculate the "estimated boundary fulfillment cost" of this action, for example, an increase of 150 yuan. The system then compares this 150 yuan with the profit margin of the order.

[0064] Self-healing interception intervention: A loss of 50 yuan is detected, triggering an out-of-bounds blocking signal. At this point, the spatiotemporal domain intervention self-healing unit intervenes, suspending the current packaging instructions and sending an alternative optimization plan to the planning department:

[0065] Option A (Time Domain Avoidance): It is recommended to postpone shipment for 48 hours until the peak of the cold wave in Harbin has passed;

[0066] Option B (Spatial Domain Avoidance): It is recommended to switch the shipping location from the Dongguan warehouse to the Shenyang backup warehouse to shorten the exposure time to harsh environments.

[0067] If an order is extremely urgent and cannot be delayed, the system will query the "Global Utility Subsidy Pool." This pool collects the surplus funds saved from 10,000 orders placed during low-risk weather conditions such as spring / autumn when the system automatically downgraded packaging, for example, by omitting aluminum foil bags. If there is sufficient funds in the pool, the system will automatically initiate a reimbursement process, using the saved money to subsidize the excess cost of 150 yuan, and force the order to be shipped, thereby minimizing overall costs.

[0068] IV. Customer Relationship Management and Machine Learning

[0069] At the moment the aforementioned high-risk orders leave the warehouse, the external signaling interaction gateway extracts the data packets of "extreme cold warning" and "free upgrade to vacuum packaging and air cargo" and automatically pushes them to the enterprise's Salesforce or self-developed CRM system. The CRM system then sends service care notifications to customers' mobile phones or WeChat in the tone of a salesperson, turning disaster prevention measures into brand marketing.

[0070] After the order is signed for, the reinforcement learning correction unit continues to operate. If, despite the use of advanced protection, the customer still reports physical attenuation observation data such as "micro-cracks appearing in 3 sealing rings" in the after-sales system, the system receives this residual data and determines that the original risk function's prediction of "cold embrittlement" is still insufficient. The system's prediction bias correction center extracts the historical predicted risk vector at the time of order shipment and finds that the system's calculated "cold embrittlement score" at that time was only 60 points, not triggering the highest level of protection. At this point, the system calculates the loss function between the predicted value and the actual physical attenuation value. .

[0071] When the accumulated loss exceeds the preset confidence interval, the system automatically updates the temperature deviation weight parameter W in the Arrhenius equation using the gradient descent method.

[0072]

[0073] in With a preset learning rate, through the iteration of this formula, when the system encounters the exact same origin, destination and weather conditions again, the "cold brittleness score" will be automatically corrected to 85 points, thereby accurately triggering the high-strength thermal insulation packaging mechanism. When encountering similar conditions again, the system will trigger the highest defense level earlier, achieving unsupervised evolution that becomes smarter with use.

[0074] Furthermore, to more intuitively and comprehensively demonstrate the technical solution and practical application value of this invention, an end-to-end operational example will be constructed below. This example fully covers the entire lifecycle management process from order receipt, environmental risk assessment, dynamic strategy game to final closed-loop evolution;

[0075] Business Background: In mid-December, the Enterprise Order Management System (OMS) received a large B2B order destined for Harbin, Heilongjiang Province.

[0076] Target object extraction: The multi-source feature acquisition and analysis module automatically intercepts orders and extracts the inherent attributes of goods. This batch of goods is a high-performance fluororubber (FKM) sealing ring. The system calls the underlying material database to obtain its specification constraints: the glass transition temperature limit is -20°C and the relative humidity sensitive critical value is 75%.

[0077] refer to Figure 7 The system calls the map API to extract spatial trajectory and calls the meteorological API to generate a forecast matrix of environmental parameters along the route for the next 72 hours. The data shows that when the cargo passes through the North China to Northeast China section, it will encounter a strong cold wave, and the minimum temperature at the Harbin terminal unloading node is expected to drop sharply to -32°C.

[0078] 2. Quantitative assessment and high-risk early warning of environmental stress

[0079] The environmental stress quantification assessment module receives the above matrix and inputs it into the risk assessment function. The function calculates the difference between the extreme predicted temperature of each node and the tolerance boundary (-20°C) of the FKM material.

[0080] Based on the estimated dwell time, such as an estimated 8 hours of exposure at the Harbin transit station, time integration and weighting are performed, and the system outputs a comprehensive environmental stress risk vector: {"ThermalAging": 0.02, "ColdBrittle": 0.94, "Swelling": 0.10, "OverallRiskScore": 92}. Since the OverallRiskScore reaches 92, far exceeding the preset high-risk threshold of 71, the system determines that this order is in an "extremely high cold brittleness risk" state.

[0081] 3. Flexible intervention and the game of fulfilling contractual obligations through resources

[0082] refer to Figure 8 The flexible intervention and configuration execution module matches a high-level response strategy in the defensive fulfillment stack, and the system generates a strongly coupled control pair:

[0083] We abandoned conventional cardboard boxes and insisted on using a packaging BOM of "EPE foam insulation lining + vacuum heat sealing".

[0084] Carrier scheduling signaling: Force the upgrade of regular less-than-truckload (LTL) logistics to "SF Express air freight + last-mile temperature-controlled cold chain trucks".

[0085] Fulfillment resource dissipation calculation and boundary violation blocking: The built-in fulfillment resource dissipation calculation unit intercepts the intention in real time and calculates that upgrading packaging and logistics will generate an estimated incremental fulfillment dissipation value of 280 yuan. However, the utility profit threshold bound to this order is only 150 yuan. Because the cost exceeds the profit limit, the calculation unit sends a "boundary violation blocking signal" to the system, suspending the current instruction.

[0086] To ensure cargo safety and not affect delivery timeliness, the flexible intervention module initiated a request for difference reimbursement from the "global utility subsidy pool." This subsidy pool contained 8,500 yuan of resource points remaining from previous low-risk autumn weather conditions, achieved through downgraded protection, such as omitting aluminum foil bags and desiccants. The system successfully allocated 130 yuan from the pool to make up the difference, ignoring blocking signals, forcibly releasing the cargo and implementing the highest level of defense strategy.

[0087] 4. Integration of bottom-level penetration execution with customer care

[0088] The execution signal is injected as an overwrite variable into the Standard Operating Procedure (SOP) database of the Warehouse Management System (WMS). When a frontline picker scans the order with a PDA, the original standard packaging instructions on the terminal screen are forcibly locked, and a custom UI interface highlighted in red pops up: "WARNING: Extremely cold destination! Please be sure to use EPE insulated boxes and vacuum seal them!" Any failure in process verification (such as failure to scan the insulated box barcode) triggers the shipment blocking mechanism.

[0089] After the external signaling gateway detects a high-risk upgrade action, it extracts a list of incremental defense measures and maps and encapsulates them into a standard customer care data package. The CRM system automatically pushes a message to the customer via WeChat: "Dear customer, due to an early warning of extremely cold weather in your area, to prevent the sealing rings from freezing and cracking, our company has upgraded you to aviation temperature-controlled logistics and special insulated packaging free of charge to protect the safety of your goods." This successfully turns the weather disadvantage into a service premium.

[0090] 5. State observation and unsupervised closed-loop evolution

[0091] Two weeks after the goods were signed for, the multimodal feedback analysis unit continuously captured end-of-fulfillment data. The customer mentioned in after-sales feedback: "Although the goods were generally intact, the two outermost sealing rings on the packaging hardened slightly during installation, affecting the initial fit."

[0092] The reinforcement learning correction unit extracted the after-sales text feedback, quantified it as the actual physical decay rate, and compared it with the expected safety rate at the time of shipment. It was found that the residual exceeded the lower limit of the confidence interval.

[0093] The system determined that the original risk assessment function underestimated the sensitivity of "external cold embrittlement during multilayer stacking." The system then invoked the material degradation kinetics baseline equation to calculate the loss function L(W) of the prediction bias and automatically updated the weight parameter tensor using gradient descent.

[0094] ;

[0095] Through unsupervised evolution, the system increases the sensitivity weight tensor of the corresponding environmental parameters. When the system encounters similar spatiotemporal trajectories from temperate to frigid zones again in the future, the risk prediction score will be more accurate, and it may further trigger more refined protection strategies such as "multi-layered staggered insulation buffering" to achieve adaptive evolution.

[0096] The above description is merely a specific embodiment of the invention, but the scope of protection of the invention is not limited thereto. Any variations or substitutions conceived without inventive effort should be included within the scope of protection of the invention. Therefore, the scope of protection of the invention should be determined by the scope defined in the claims.

Claims

1. A sealing ring warehousing and logistics management system based on multi-dimensional environmental perception, characterized in that: include: The multi-source feature acquisition and analysis module is used to obtain the inherent attribute parameters and specification constraints of the target object, extract the spatial trajectory sequence from the shipping node to the receiving node, and call the external interface based on the spatial trajectory sequence to synchronously generate the prediction matrix of the environmental parameters along the way aligned in the spatiotemporal dimension. The environmental stress quantification assessment module is used to receive the inherent attribute parameters and convert them into an environmental tolerance boundary model. At the same time, it inputs the prediction matrix of the environmental parameters along the route into a preset risk assessment function, performs mapping calculations with the environmental tolerance boundary model as a constraint, and outputs the quantified comprehensive environmental stress risk vector. The flexible intervention and configuration execution module is used to match a unique corresponding response strategy in the preset defensive fulfillment stack through table lookup or logical optimization, and generate interrelated physical protection medium configuration signaling and carrier scheduling signaling according to the response strategy, and send them down to the underlying warehousing and transportation execution units. The state observation and closed-loop evolution module is used to extract physical attenuation observation data and comprehensive performance dissipation data at the time of actual delivery. Based on the deviation between the physical attenuation observation data and the corresponding historical predicted comprehensive environmental stress risk vector, the state observation and closed-loop evolution module generates a gradient correction signal and sends it back to the environmental stress quantification assessment module to update the weight parameters of the risk assessment function. Based on the comprehensive performance dissipation data, it generates utility constraint instructions and sends them to the flexible intervention and configuration execution module to dynamically adjust the strategy matching boundary.

2. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: The environmental parameter prediction matrix along the route includes temperature and humidity coupled data vectors of multiple passing nodes arranged in a time series; the risk assessment function calculates the difference between the temperature and humidity coupled data vector of each passing node and the environmental tolerance boundary model to generate the local deviation of each node, and performs weighted integration with the expected residence time coefficient of the corresponding node to accumulate and generate the comprehensive environmental stress risk vector of the whole for this mission.

3. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 2, characterized in that: The generated physical protection medium configuration signaling and the carrier scheduling signaling form a strongly coupled control pair; when the comprehensive environmental stress risk vector exceeds the preset critical threshold, the control pair generated by the module forces the underlying unit to simultaneously activate the high-barrier physical protection assembly process and the high-efficiency constant temperature carrier scheduling process, and if either process fails to verify, the system's delivery blocking mechanism will be triggered.

4. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 3, characterized in that: The specific method for updating the weight parameters of the risk assessment function by the state observation and closed-loop evolution module includes the following steps: The built-in reinforcement learning correction unit continuously calculates the residual between the expected safety rate and the actual physical decay rate of the target object under the same specification constraints and specific environmental parameters along the way; when the residual continuously exceeds the lower limit of the confidence interval, a penalty mechanism is triggered, and the sensitivity weight tensor of the corresponding environmental parameter in the risk assessment function is automatically increased.

5. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 4, characterized in that: The specific method for adjusting the matching boundary of the state observation and closed-loop evolution module includes the following steps: The built-in fulfillment resource dissipation calculation unit intercepts the response strategy intended by the flexible intervention and configuration execution module in real time, and extracts the estimated fulfillment dissipation value required to execute the strategy; when the estimated fulfillment dissipation value exceeds the utility profit threshold bound to the order, it feeds back an out-of-bounds blocking signal to the flexible intervention and configuration execution module.

6. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: Upon receiving the boundary-crossing blocking signal, the flexible intervention and configuration execution module executes the following interaction strategy: The current configuration signaling and scheduling signaling are suspended, and a spatiotemporal variable reconstruction request is initiated to the multi-source feature acquisition and analysis module. The multi-source feature acquisition and analysis module generates a new test trajectory set by extending the departure time on the time axis or switching the starting node on the spatial axis. The environmental stress quantification and evaluation module traverses the test trajectory set until it outputs an alternative optimization solution signaling that does not trigger an out-of-bounds blocking signal.

7. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: The interaction mechanism by which the performance resource dissipation calculation unit maintains the long-term game balance of the system is as follows: real-time monitoring of performance tasks under low environmental stress risk vectors, extraction of resource surplus points generated by triggering baseline degradation protection, and dynamic injection of the resource surplus points into the global utility subsidy pool; before receiving the out-of-bounds blocking signal, the flexible intervention and configuration execution module first initiates a difference reconciliation request to the global utility subsidy pool. If the reconciliation is successful, the out-of-bounds blocking signal is ignored and a high-level defense strategy is forcibly executed.

8. The sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: The method for generating a comprehensive environmental stress risk vector specifically includes the following steps: Extract the time series variables and environmental stress variables from the predicted environmental parameters along the route, call the material degradation kinetics baseline equation corresponding to the target object, and map the discrete environmental stress during the predicted route period into continuous equivalent cumulative damage values ​​through time integration. The risk assessment function compares the equivalent cumulative damage value with the failure threshold of the target object and outputs a structured multi-risk vector including thermal aging, cold embrittlement and hygrothermal swelling dimensions.

9. A sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: It also includes an independent interaction mechanism, which includes: listening to the outbound events of the order management system in real time through a message queue, suspending the outbound process in an intercepted state; after the flexible intervention and configuration execution module generates the physical protection medium configuration signaling, injecting it as an overwrite variable into the standard operating procedure database of the warehouse management system, and releasing the outbound process to dynamically rewrite the packaging guidance UI interface of the front-line operation terminal.

10. A sealing ring warehousing and logistics management system based on multi-dimensional environmental perception according to claim 1, characterized in that: The external signaling interaction gateway monitors the output status of the flexible intervention and configuration execution module in real time. When it captures an upgrade protection response strategy triggered by a high-risk event, it extracts the environmental anomaly parameters and incremental defense measures list corresponding to the strategy, maps and encapsulates them into a standard customer care data package, and automatically triggers the application programming interface to push it to the designated user node of the third-party customer relationship management system.

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