Whole-process informationization operation and maintenance management system for smart logistics

Abstract

The present application relates to the technical field of logistics operation and maintenance, in particular to a full-process informationized operation and maintenance management system for intelligent logistics, comprising a logistics information acquisition unit, a logistics rule generation unit, a logistics path optimization unit and a logistics operation and maintenance management unit. The present application realizes real-time mapping of physical equipment and virtual model by taking digital twin as the core, collects structured data such as cargo ID, temperature and humidity through a multi-modal sensing module, constructs a dynamic digital twin in combination with environmental parameter benchmark values, ensures accurate perception of logistics status and compliance judgment of environment, and provides a data cornerstone for full-process decision-making; the particle swarm algorithm is used to realize dynamic optimization of rules, maps parameters such as safety threshold, equipment failure rate and logistics timeliness into a three-dimensional parameter space, finds the globally optimal rule combination through particle iteration, reversely optimizes in combination with the execution result of the digital twin, forms a rule iteration closed loop, and improves the rule adaptability and execution efficiency.

Description

Technical Field

[0001] This invention relates to the field of logistics operation and maintenance technology, specifically to a full-process information-based operation and maintenance management system for smart logistics. Background Technology

[0002] Logistics operations and maintenance management refers to the systematic management and optimization of the operation and maintenance of various links in the logistics system, such as transportation, warehousing, and distribution. Its main objectives are to improve logistics efficiency, reduce costs, ensure timely and accurate delivery of goods, and guarantee the normal operation of logistics facilities and equipment. Logistics operations and maintenance management involves the coordination and control of human resources, information systems, material flow, inventory management, and other aspects. Through planning, implementation, monitoring, and adjustment, it ensures the smooth and efficient operation of the logistics chain.

[0003] However, existing technologies have several shortcomings. First, traditional systems rely too heavily on manual inspections and single sensors, resulting in insufficient perception capabilities and difficulty in comprehensively monitoring changes in logistics status, thus reducing real-time response capabilities. Second, the management and maintenance processes are fragmented, and information flow is not smooth, leading to delayed decision-making and affecting operational efficiency. Furthermore, traditional path planning algorithms perform poorly in dynamic environments, failing to flexibly respond to real-time changes, impacting logistics efficiency. Fixed safety thresholds are not dynamically adjusted, also leading to resource waste and safety hazards. In addition, traditional systems lack traceability in data management, making it difficult to quickly locate the source of problems when faults occur, prolonging fault handling time. These deficiencies restrict the intelligence level and overall operational effectiveness of smart logistics systems.

[0004] Based on this, the present invention provides a full-process information-based operation and maintenance management system for smart logistics to solve the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a full-process information-based operation and maintenance management system for smart logistics. This invention finds the lowest energy consumption resource allocation scheme through energy state evolution, and dynamically adjusts parameter mapping and weight coefficients by combining real-time environmental data to achieve dynamic and accurate matching of path optimization. Combined with a hierarchical response mechanism, it realizes rapid fault handling and path optimization linkage, and finally forms a smart logistics management system that is rule-driven and data-driven, which significantly improves logistics efficiency, reduces equipment failure rate, enhances adaptability, and realizes precise operation and maintenance management with full-process information and intelligence.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a full-process information-based operation and maintenance management system for smart logistics, including a logistics information collection unit, a logistics rule generation unit, a logistics route optimization unit, and a logistics operation and maintenance management unit. Specifically: The logistics information collection unit acquires structured real-time data streams of logistics equipment and the environment, constructs a digital twin mapping the logistics equipment and environment using the structured real-time data streams, extracts cargo IDs and logistics information, determines whether the logistics environment is compliant, and outputs a logistics status report. The logistics rule generation unit parses the logistics status report, generates logistics rules based on the parameters of the digital twin, simulates the operation effect of the logistics rules, performs iterative optimization, obtains the optimized logistics rules, executes them, and sends the execution results back to the digital twin. The logistics route optimization unit maps logistics parameters, constructs an objective function, derives a resource allocation scheme, dynamically adjusts the resource allocation scheme based on the real-time environmental data of the digital twin, outputs a quantum optimization scheme, and generates neuromorphic collaborative instructions. The logistics operation and maintenance management unit outputs safety thresholds based on historical data, performs tiered responses when a fault occurs, feeds back to the logistics route optimization unit, and updates the quantum optimization scheme.

[0007] It should be noted that in this embodiment, edge computing gateways need to be deployed according to the logistics nodes. These gateways communicate directly with the devices through physical interfaces to ensure the real-time performance and reliability of data collection. Furthermore, data transmission rules are set based on protocols such as MQTT, which ensures that the data collection frequency of different devices meets the actual needs. For example, high-frequency collection of AGV status and low-frequency collection of temperature and humidity.

[0008] The logistics information collection unit includes a basic data module and a multimodal sensing module, wherein: The basic data module initializes environmental parameters and equipment baseline values ​​as safety thresholds to construct a digital twin. The multimodal sensing module identifies the goods, extracts the goods' ID and batch number, acquires the equipment's operating sound waves and the goods' temperature and humidity data, associates the goods' ID with the temperature and humidity data, makes a preliminary judgment, and outputs a logistics status report.

[0009] The logistics rule generation unit includes a rule establishment module and a simulation iteration module, wherein: The rule establishment module: parses the logistics status report, combines it with the security thresholds in the digital twin to generate initial logistics rules, optimizes the rule triggering thresholds, assigns a unique ID to each rule, defines the triggering conditions, and obtains the logistics rules; The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles, simulates the operation effect of the logistics rules through the particle swarm algorithm, performs iterative optimization to obtain optimized rules, executes them, and sends the execution results back to the digital twin.

[0010] The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles. It then simulates the operational effect of the logistics rules using a particle swarm optimization algorithm, iteratively optimizes the rules to obtain an optimized version, executes it, and sends the execution results back to the digital twin. The specific steps are as follows: A1: Correlate the safety thresholds within the logistics rules with equipment failure rates and logistics timeliness data from historical data to construct a three-dimensional parameter space; Among them, the safety threshold in the logistics rules is mapped to the X-axis coordinate point, the equipment failure rate is mapped to the Y-axis coordinate point, and the logistics timeliness data is mapped to the Z-axis coordinate point; A2: Generate multiple parameter combinations to obtain multiple particles, verify whether the particles are within the valid range of the parameter space, and remove invalid particles; A3: Set iteration rules, and each particle adjusts its speed and position in each iteration based on its own historical best position and the group's global best position; A4: Repeat the above steps until iterates to n times, converges to the global optimal solution, obtains the optimized logistics rules and executes them, and sends the execution results back to the digital twin.

[0011] The speed update formula in step A3 is:

[0012] In the formula: w is the inertia weight, c1 and c2 are acceleration coefficients, and rand() is a random number. Let be the velocity value of the i-th particle in the k-th iteration along dimension d. Let be the historical best position of the i-th particle in dimension d. This represents the globally optimal position of the entire particle swarm in dimension d. Let be the position value of the i-th particle in the k-th iteration along dimension d.

[0013] The logistics route optimization unit includes a resource allocation module and an event-driven collaborative module, wherein: The resource allocation module maps logistics parameters to the initial state of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme in conjunction with the real-time environmental data of the digital twin. The event collaboration-driven module analyzes the characteristics of sudden events in the logistics process in real time, and when a congested path is identified, it feeds back to the resource allocation module.

[0014] The resource allocation module maps logistics parameters to initial states of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme based on real-time environmental data from the digital twin. The specific process is as follows: B1: Mapping transport vehicles, route distances, and congestion probabilities to the initial states of qubits, and constructing an objective function to minimize total transport energy consumption, the formula of which is:

[0015] In the formula: w1, w2, w3 are the weighting coefficients for vehicles, congestion probability, and delivery time. For the number of transport vehicles, The path distance. For the probability of congestion, Delivery time; B2: By simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine, the optimal solution is obtained, and a resource allocation scheme is derived. B3: When the digital twin detects changes in environmental data, it adjusts the parameter mapping and weighting coefficients in real time and recalculates the energy function.

[0016] Step B2: Simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine to obtain the optimal solution and derive a resource allocation scheme. The specific process is as follows: The energy function value is calculated based on the initial state parameters and used as the baseline for annealing evolution; During m iterations, the path selection is first explored through the quantum tunneling effect, and then the congestion probability is further optimized. The qubits are gradually adjusted to the ground state, corresponding to the minimum value of the energy function, and the optimal solution is obtained.

[0017] The logistics operation and maintenance management unit includes a threshold adjustment module and a fault emergency module, wherein: The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and uses a vector machine algorithm to optimize the security threshold. The fault emergency module: when a fault occurs, it issues an early warning through a graded response, generates an emergency strategy, and feeds it back to the logistics route optimization unit to update the quantum optimization scheme.

[0018] The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and optimizes the security threshold using a vector machine algorithm. Specifically, it includes: The failure frequency of the equipment is extracted from historical data, and combined with the current environmental parameters and the real-time status of the equipment, the time series analysis method is used to analyze the upward trend of motor failure risk. By selecting ambient temperature, load rate, and historical failure interval as variables, the safety threshold is optimized, and its calculation formula is as follows:

[0019] In the formula: w1, w2, w3 are the weighting coefficients of ambient temperature, load rate and historical failure interval time, T is the ambient temperature, L is the load rate, F is the historical failure interval time and b is the bias term.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves real-time mapping between physical equipment and virtual models using a digital twin as its core. It collects structured data such as cargo ID, temperature, and humidity through multimodal sensing modules, and constructs a dynamic digital twin by combining it with environmental parameter benchmarks. This ensures accurate perception of logistics status and judgment of environmental compliance, providing a data foundation for end-to-end decision-making. A particle swarm optimization algorithm is employed for dynamic rule optimization. Parameters such as safety thresholds, equipment failure rates, and logistics timeliness are mapped into a three-dimensional parameter space. Particle iteration finds the globally optimal rule combination, and the results of the digital twin's execution are used for reverse optimization, forming a closed-loop rule iteration system that improves rule adaptability and execution efficiency. Furthermore, parameters such as vehicle count, path distance, and congestion probability are quantized, and the lowest energy consumption resource allocation scheme is found through energy state evolution. Combined with real-time environmental data, parameter mapping and weight coefficients are dynamically adjusted to achieve dynamic and precise matching for path optimization. A hierarchical response mechanism enables rapid fault handling and path optimization linkage, ultimately forming a rule-driven and data-driven intelligent logistics management system. This significantly improves logistics efficiency, reduces equipment failure rates, enhances adaptability, and achieves precise operation and maintenance management with full-process informatization and intelligence. Attached Figure Description

[0021] Figure 1 This is a system diagram of a full-process information-based operation and maintenance management system for smart logistics according to the present invention; Figure 2 This invention provides an optimized flowchart for a full-process information-based operation and maintenance management system for smart logistics. Figure 3 This is a core architecture diagram of a full-process information-based operation and maintenance management system for smart logistics according to the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example: like Figure 1 , Figure 2 and Figure 3 As shown, this embodiment provides a full-process information-based operation and maintenance management system for smart logistics, comprising a logistics information collection unit, a logistics rule generation unit, a logistics route optimization unit, and a logistics operation and maintenance management unit. Specifically: The logistics information collection unit acquires structured real-time data streams of logistics equipment and the environment, uses these data streams to construct a digital twin mapping the logistics equipment and environment, extracts cargo IDs and logistics information, determines whether the logistics environment is compliant, and outputs a logistics status report. The logistics rule generation unit parses the logistics status report, generates logistics rules based on the parameters of the digital twin, simulates the operation of the logistics rules, iteratively optimizes them, obtains the optimized logistics rules, executes them, and sends the execution results back to the digital twin. The logistics route optimization unit maps logistics parameters, constructs an objective function, derives a resource allocation scheme, dynamically adjusts the resource allocation scheme based on the real-time environmental data of the digital twin, outputs a quantum optimization scheme, and generates neuromorphic collaborative instructions. The logistics operation and maintenance management unit outputs safety thresholds based on historical data, performs tiered responses when a fault occurs, feeds back to the logistics route optimization unit, and updates the quantum optimization scheme.

[0024] The logistics information collection unit includes a basic data module and a multimodal sensing module, wherein: The basic data module initializes environmental parameters and equipment baseline values ​​as safety thresholds to construct a digital twin. The digital twin adopts a four-layer architecture. The physical layer consists of actual physical devices, such as temperature and humidity sensors, GPS positioning, and warehouses, to collect data in real time and execute control commands. The data layer includes dynamic data (such as temperature curves, location coordinates, and load rate) collected through IoT sensors, RFID, and AGV on-board systems. It also includes a historical database that stores operation logs, fault records, and maintenance files from the past three months.

[0025] The application layer consists of digital application modules for specific business scenarios, used to transform data into business decisions; The interaction layer consists of communication protocols (MQTT, 5G), API interfaces, and a digital twin visualization platform.

[0026] The multimodal sensing module identifies the goods, extracts the goods' ID and batch number, acquires the equipment's operating sound waves and the goods' temperature and humidity data, associates the goods' ID with the temperature and humidity data, makes a preliminary judgment, and outputs a logistics status report.

[0027] Preliminary assessment includes: performing spectrum analysis on abnormal equipment noise to determine the equipment's health status; Determine whether the temperature and humidity of the goods meet the storage standards. If the temperature and humidity exceed the standards or the equipment is abnormal, generate a specific problem description.

[0028] The logistics rule generation unit includes a rule establishment module and a simulation iteration module, wherein: The rule establishment module: parses the logistics status report, combines it with the security thresholds in the digital twin to generate initial logistics rules, optimizes the rule triggering thresholds, assigns a unique ID to each rule, defines the triggering conditions, and obtains the logistics rules; Furthermore, by analyzing the abnormal events in the multimodal report and combining them with the device parameters in the word twin, the ant colony algorithm is used to optimize the rule parameters and search for the optimal trigger threshold in the rule condition space. The specific steps are as follows: Using the device temperature threshold as the optimization variable, a continuous search space is constructed, with each threshold point corresponding to a path. The path length is determined by the objective function. Fifty ants are deployed, each representing a threshold adjustment scheme. Initially, the ants are randomly distributed in the parameter space. Each ant selects the next threshold point based on the correlation between historical failure rate and ambient temperature, and the process is iterated to achieve the effect of triggering the threshold by the optimization rule.

[0029] The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles, simulates the operation effect of the logistics rules through the particle swarm algorithm, performs iterative optimization to obtain optimized rules, executes them, and sends the execution results back to the digital twin.

[0030] The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles. It then simulates the operational effect of the logistics rules using a particle swarm optimization algorithm, iteratively optimizes the rules to obtain an optimized version, executes it, and sends the execution results back to the digital twin. The specific steps are as follows: A1: Correlate the safety thresholds within the logistics rules with equipment failure rates and logistics timeliness data from historical data to construct a three-dimensional parameter space; Among them, the safety threshold in the logistics rules is mapped to the X-axis coordinate point, the equipment failure rate is mapped to the Y-axis coordinate point, and the logistics timeliness data is mapped to the Z-axis coordinate point; A2: Generate multiple parameter combinations to obtain multiple particles, verify whether the particles are within the valid range of the parameter space, and remove invalid particles; A3: Set iteration rules, and each particle adjusts its speed and position in each iteration based on its own historical best position and the group's global best position; A4: Repeat the above steps until iterates to n times, converges to the global optimal solution, obtains the optimized logistics rules and executes them, and sends the execution results back to the digital twin. In this embodiment, n is 100.

[0031] The speed update formula in step A3 is:

[0032] In the formula: w is the inertia weight, with a value range of 0.4 ≤ w ≤ 1.2; c1 and c2 are acceleration coefficients, with a value range of [0, 4], which can be selected according to the actual situation; rand() is a random number generator. Let be the velocity value of the i-th particle in the k-th iteration along dimension d. Let be the historical best position of the i-th particle in dimension d. This represents the globally optimal position of the entire particle swarm in dimension d. Let be the position value of the i-th particle in the k-th iteration along dimension d.

[0033] The logistics route optimization unit includes a resource allocation module and an event-driven collaborative module, wherein: The resource allocation module maps logistics parameters to the initial state of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme in conjunction with the real-time environmental data of the digital twin. The event collaboration-driven module analyzes the characteristics of sudden events in the logistics process in real time, and when a congested path is identified, it feeds back to the resource allocation module.

[0034] The resource allocation module maps logistics parameters to initial states of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme based on real-time environmental data from the digital twin. The specific process is as follows: B1: Mapping transport vehicles, route distances, and congestion probabilities to the initial states of qubits, and constructing an objective function to minimize total transport energy consumption, the formula of which is:

[0035] In the formula: w1, w2, w3 are the weighting coefficients for vehicles, congestion probability, and delivery time. For the number of transport vehicles, The path distance. For the probability of congestion, In this embodiment, w1, w2, and w3 are set to 0.5, 0.3, and 0.2, respectively, to represent delivery time. As a concrete example, the transport vehicles are represented by 3 qubits (000 = 1 vehicle, 001 = 2 vehicles, 010 = 3 vehicles), and the path distance is represented by 2 qubits (00 = path A, 01 = path B, 10 = path C). B2: By simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine, the optimal solution is obtained, and a resource allocation scheme is derived. B3: When the digital twin detects changes in environmental data, it adjusts the parameter mapping and weighting coefficients in real time and recalculates the energy function.

[0036] Assuming rainfall affects path A, the distance coefficient is adjusted from 1.0 to 1.2, and the congestion probability of path B is updated from 30% to 35%. The quantum algorithm remaps the parameters as follows: The quantum bit encoding for path A is adjusted from 00 to 01, and the quantum bit encoding for path C is increased from 10 to 11. Adjust the weighting coefficients, keeping the vehicle weighting coefficient unchanged, increase the congestion probability weight from 0.3 to 0.4, set the delivery time to 0.1, and then re-execute the annealing process.

[0037] Step B2: Simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine to obtain the optimal solution and derive a resource allocation scheme. The specific process is as follows: The energy function value is calculated based on the initial state parameters and used as the baseline for annealing evolution; During the m iterations, the path selection is first explored through the quantum tunneling effect, and then the congestion probability is further optimized. The qubits are gradually adjusted to the ground state, corresponding to the minimum value of the energy function, to obtain the optimal solution. In this embodiment, m is taken as 800.

[0038] The logistics operation and maintenance management unit includes a threshold adjustment module and a fault emergency module, wherein: The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and uses a vector machine algorithm to optimize the security threshold. The fault emergency module: when a fault occurs, it issues an early warning through a graded response, generates an emergency strategy, and feeds it back to the logistics route optimization unit to update the quantum optimization scheme.

[0039] The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and optimizes the security threshold using a vector machine algorithm. Specifically, it includes: The failure frequency of the equipment is extracted from historical data, and combined with the current environmental parameters and the real-time status of the equipment, the time series analysis method is used to analyze the upward trend of motor failure risk. Time series analysis is a statistical technique used to analyze data sequences arranged in chronological order. This method helps identify trends, seasonality, periodicity, and sudden events by studying the patterns of change in data over time.

[0040] By selecting ambient temperature, load rate, and historical failure interval as variables, the safety threshold is optimized, and its calculation formula is as follows:

[0041] In the formula: w1, w2, w3 are the weighting coefficients of ambient temperature, load rate and historical failure interval time, T is the ambient temperature, L is the load rate, F is the historical failure interval time and b is the bias term.

[0042] In this embodiment, w1, w2, and w3 take values ​​of 0.4, 0.3, and 0.3, respectively, and the steps for determining the value of b are as follows: The loss function with regularization is minimized as the objective:

[0043] In the formula: w is the feature weight vector (weights of ambient temperature, load rate, and fault interval time are 0.4 / 0.3 / 0.3), b is the bias term to be solved, and C is the regularization coefficient to balance model complexity and fitting error. It is an insensitive loss function (allowing the predicted value to fluctuate within the range of ε without incurring loss).

[0044] Taking historical data from the past three months as an example (containing 100 samples), each sample includes ambient temperature, load rate, fault interval time, and the corresponding actual motor health threshold, which must meet the following requirements:

[0045]

[0046] In the formula: , All variables are slack variables (allowing samples to extend beyond the -ε band) and the risk of overfitting is controlled.

[0047] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0048] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A full-process information-based operation and maintenance management system for smart logistics, characterized in that, It includes a logistics information collection unit, a logistics rule generation unit, a logistics route optimization unit, and a logistics operation and maintenance management unit, among which: The logistics information collection unit acquires structured real-time data streams of logistics equipment and environment, uses the structured real-time data streams to construct digital twins that map to the logistics equipment and environment, extracts cargo IDs and logistics information, determines whether the logistics environment is compliant, and outputs a logistics status report. The logistics rule generation unit: parses the logistics status report, generates logistics rules by combining the parameters of the digital twin, simulates the operation effect of the logistics rules, performs iterative optimization, obtains the optimized logistics rules and executes them, and sends the execution results back to the digital twin; The logistics path optimization unit maps logistics parameters, constructs an objective function, derives a resource allocation scheme, dynamically adjusts the resource allocation scheme by combining real-time environmental data from the digital twin, outputs a quantum optimization scheme, and generates neuromorphic collaborative instructions. The logistics operation and maintenance management unit combines historical data to output safety thresholds. When a fault occurs, it performs a graded response and feeds back to the logistics route optimization unit to update the quantum optimization scheme.

2. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 1, characterized in that, The logistics information collection unit includes a basic data module and a multimodal sensing module, wherein: The basic data module initializes environmental parameters and equipment baseline values ​​as safety thresholds to construct a digital twin. The multimodal sensing module identifies the goods, extracts the goods' ID and batch number, acquires the equipment's operating sound waves and the goods' temperature and humidity data, associates the goods' ID with the temperature and humidity data, makes a preliminary judgment, and outputs a logistics status report.

3. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 1, characterized in that, The logistics rule generation unit includes a rule establishment module and a simulation iteration module, wherein: The rule establishment module: parses the logistics status report, combines it with the security thresholds in the digital twin to generate initial logistics rules, optimizes the rule triggering thresholds, assigns a unique ID to each rule, defines the triggering conditions, and obtains the logistics rules; The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles, simulates the operation effect of the logistics rules through the particle swarm algorithm, performs iterative optimization to obtain optimized rules, executes them, and sends the execution results back to the digital twin.

4. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 3, characterized in that, The simulation iteration module maps the safety thresholds within the logistics rules to obtain multiple particles. It then simulates the operational effect of the logistics rules using a particle swarm optimization algorithm, iteratively optimizes the rules to obtain an optimized version, executes it, and sends the execution results back to the digital twin. The specific steps are as follows: A1: Correlate the safety thresholds within the logistics rules with equipment failure rates and logistics timeliness data from historical data to construct a three-dimensional parameter space; Among them, the safety threshold in the logistics rules is mapped to the X-axis coordinate point, the equipment failure rate is mapped to the Y-axis coordinate point, and the logistics timeliness data is mapped to the Z-axis coordinate point; A2: Generate multiple parameter combinations to obtain multiple particles, verify whether the particles are within the valid range of the parameter space, and remove invalid particles; A3: Set iteration rules, and each particle adjusts its speed and position in each iteration based on its own historical best position and the group's global best position; A4: Repeat the above steps until iterates to n times, converges to the global optimal solution, obtains the optimized logistics rules and executes them, and sends the execution results back to the digital twin.

5. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 4, characterized in that, The speed update formula in step A3 is: ； In the formula: w is the inertia weight, c1 and c2 are acceleration coefficients, and rand() is a random number. Let be the velocity value of the i-th particle in the k-th iteration along dimension d. Let be the historical best position of the i-th particle in dimension d. This represents the globally optimal position of the entire particle swarm in dimension d. Let be the position value of the i-th particle in the k-th iteration along dimension d.

6. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 1, characterized in that, The logistics route optimization unit includes a resource allocation module and an event-driven collaborative module, wherein: The resource allocation module maps logistics parameters to the initial state of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme in conjunction with the real-time environmental data of the digital twin. The event collaboration-driven module analyzes the characteristics of sudden events in the logistics process in real time, and when a congested path is identified, it feeds back to the resource allocation module.

7. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 6, characterized in that, The resource allocation module maps logistics parameters to initial states of qubits, applies the quantum annealing algorithm to find the lowest energy state, derives a resource allocation scheme, and dynamically adjusts the resource allocation scheme based on real-time environmental data from the digital twin. The specific process is as follows: B1: Mapping transport vehicles, route distances, and congestion probabilities to the initial states of qubits, and constructing an objective function to minimize total transport energy consumption, the formula of which is: In the formula: w1, w2, w3 are the weighting coefficients for vehicles, congestion probability, and delivery time. For the number of transport vehicles, The path distance. For the probability of congestion, Delivery time; B2: By simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine, the optimal solution is obtained, and a resource allocation scheme is derived. B3: When the digital twin detects changes in environmental data, it adjusts the parameter mapping and weighting coefficients in real time and recalculates the energy function.

8. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 7, characterized in that, Step B2: Simulating the evolution of qubits from the initial state to the lowest energy state using a quantum annealing machine to obtain the optimal solution and derive a resource allocation scheme. The specific process is as follows: The energy function value is calculated based on the initial state parameters and used as the baseline for annealing evolution; During m iterations, the path selection is first explored through the quantum tunneling effect, and then the congestion probability is further optimized. The qubits are gradually adjusted to the ground state, corresponding to the minimum value of the energy function, and the optimal solution is obtained.

9. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 1, characterized in that, The logistics operation and maintenance management unit includes a threshold adjustment module and a fault emergency module, wherein: The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and uses a vector machine algorithm to optimize the security threshold. The fault emergency module: when a fault occurs, it issues an early warning through a graded response, generates an emergency strategy, and feeds it back to the logistics route optimization unit to update the quantum optimization scheme.

10. The end-to-end information-based operation and maintenance management system for smart logistics according to claim 9, characterized in that, The threshold adjustment module analyzes historical data and real-time data provided based on digital twins, and optimizes the security threshold using a vector machine algorithm. Specifically, it includes: The failure frequency of the equipment is extracted from historical data, and combined with the current environmental parameters and the real-time status of the equipment, the time series analysis method is used to analyze the upward trend of motor failure risk. By selecting ambient temperature, load rate, and historical failure interval as variables, the safety threshold is optimized, and its calculation formula is as follows: ； In the formula: w1, w2, w3 are the weighting coefficients of ambient temperature, load rate and historical failure interval time, T is the ambient temperature, L is the load rate, F is the historical failure interval time and b is the bias term.

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