Simulation-based metering equipment distribution vehicle operation scene construction method

Abstract

The invention discloses a metering equipment distribution vehicle operation scene construction method based on simulation, and belongs to the technical field of data processing and simulation, and the method comprises the steps: obtaining vehicle parameters of a distribution vehicle team, attribute information of to-be-distributed metering equipment, task distribution time sequence information, and dynamic traffic environment data; a multi-modal data simulation model is constructed based on the data to simulate the operation process of the distribution vehicle, the vibration load spectrum acting on the metering equipment in the transportation process is calculated, the health degree of the equipment is predicted according to the vibration load spectrum, finally, an optimized distribution scheme is generated based on health degree evaluation, and dynamic operation scene data of the distribution process is obtained. The multi-modal data fusion technology, the health degree prediction based on the vibration load spectrum and the hierarchical adaptive evaluation method are adopted, the safety of the metering equipment in the transportation process can be effectively guaranteed, the distribution path and the resource configuration are optimized, and the efficiency and the reliability of logistics distribution are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and simulation, and in particular to a method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation. Background Art

[0002] In the field of data processing and simulation technology, modeling, simulation, and optimization of complex systems have become a research hotspot. In particular, in logistics and equipment management scenarios, the use of digital computing methods to accurately plan the operation of delivery vehicles and monitor and manage the status of transported metering equipment places higher demands on data processing methods.

[0003] However, existing technologies suffer from widespread technical flaws. Traditional path planning algorithms, typically based on the shortest path or fixed sequence, struggle to adapt to dynamic changes in vehicle load and energy consumption. Existing methods for assessing the status of metering equipment often rely on a single data source or post-analysis, lacking mechanisms for effectively integrating and processing heterogeneous data from multiple sources, making it impossible to conduct real-time, dynamic risk prediction and decision-making. These flaws prevent existing methods from implementing integrated modeling to accurately predict the dynamic operating status of equipment during simulated delivery processes, limiting overall efficiency and reliability. Summary of the Invention

[0004] To solve the above problems, the present invention provides a simulation-based method for constructing an operation scenario for a metering equipment delivery vehicle. It adopts a multimodal data simulation model, a health index prediction based on a vibration load spectrum, and a hierarchical adaptive evaluation method. It can integrate vehicle operation, equipment status, and environmental dynamics into modeling and accurate evaluation, generate the most optimized distribution plan, significantly improve the efficiency and reliability of logistics distribution, and ensure the safety of metering equipment during transportation.

[0005] The above objectives can be achieved through the following solutions: A method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation comprises: obtaining vehicle parameters of a delivery fleet, attribute information of metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data, wherein the vehicle parameters include vehicle load and energy consumption characteristics; constructing a multimodal data simulation model based on the vehicle parameters, attribute information of metering equipment to be delivered, and dynamic traffic environment data to simulate the operation process of the delivery vehicle on different paths; calculating, based on the simulation model, the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation; predicting the health index of the metering equipment at the end of delivery based on the vibration load spectrum; evaluating the driving path plan of the delivery vehicle based on the health index and task delivery timing information to generate several optimized delivery plans; and obtaining dynamic operation scenario data of the delivery process based on the optimized delivery plan.

[0006] Optionally, the acquisition of vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data includes: extracting and constructing attribute information of the metering equipment from technical documents, historical operation logs, and unstructured and structured data of failure cases of the metering equipment; acquiring vehicle parameters such as real-time load and energy consumption of the vehicle from the onboard bus of the delivery vehicle; acquiring task delivery timing information from the task management office, and acquiring dynamic traffic environment data from the traffic data platform; Optionally, the construction of the multimodal data simulation model includes: constructing a digital road network model that simulates the vehicle's driving path based on the vehicle parameters and dynamic traffic environment data; constructing a knowledge graph representing the characteristics of the metering equipment based on the attribute information of the metering equipment to be delivered; and fusing the digital road network model with the knowledge graph to generate a multimodal data simulation model that can simulate the operation process of the delivery vehicle.

[0007] Optionally, the construction of the knowledge graph representing the characteristics of the metering equipment includes: generating knowledge data of the metering equipment based on the attribute information of the metering equipment to be delivered; performing data preprocessing on the knowledge data to obtain a cleaned structured data set; performing knowledge extraction on the structured data set to obtain explicit knowledge and implicit knowledge; and performing knowledge fusion on the explicit knowledge and implicit knowledge to obtain a knowledge graph representing the characteristics of the metering equipment.

[0008] Optionally, fusing the digital road network model with the knowledge graph includes: using the digital road network model as the underlying physical simulation layer to simulate the kinematic and dynamic characteristics of the delivery vehicle; using the knowledge graph as the high-level semantic decision layer to provide fault association rules and risk levels of metering equipment under specific road conditions or operations; using the simulation results of the underlying physical simulation layer as the input of the high-level semantic decision layer, and the output of the high-level semantic decision layer as the dynamic constraint of the underlying physical simulation layer to generate a multimodal data simulation model.

[0009] Optionally, the calculation of the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation includes: generating time domain jitter data of the delivery vehicle during driving based on the simulation model; performing fast Fourier transform on the time domain jitter data to obtain corresponding frequency domain vibration components; performing spectral analysis on the frequency domain vibration components to extract the amplitude and energy distribution characteristics of the frequency domain vibration components in a preset frequency band to generate a vibration load spectrum.

[0010] Optionally, predicting the health index of the metering equipment at the end of delivery includes: obtaining a standard vibration load spectrum in the attribute information of the metering equipment to be delivered; comparing the calculated vibration load spectrum with the standard vibration load spectrum to obtain a vibration characteristic difference value; and determining the health index of the metering equipment based on the vibration characteristic difference value, wherein the larger the vibration characteristic difference value, the worse the health index.

[0011] Optionally, the evaluation of the driving route plan of the delivery vehicle includes: performing a first-level evaluation on several route plans generated in the simulation model, wherein the first-level evaluation screens out candidate plans that meet basic requirements based on the energy consumption and task delivery timing information of the delivery vehicle; performing a second-level risk assessment on each candidate plan screened out by the first-level evaluation, wherein the second-level risk assessment identifies high-risk sections in the route plan based on the health index of the metering equipment and the fault association rules extracted from the knowledge graph; performing a local amplification simulation on the high-risk section to simulate the vibration impact caused by the delivery vehicle on the metering equipment when driving on the high-risk section, and dynamically updating the health index; re-evaluating and re-ranking the candidate plans based on the dynamically updated health index to generate several optimized delivery plans.

[0012] Optionally, obtaining dynamic operation scenario data of the delivery process includes: performing data statistics and summarizing on the optimized delivery plan; generating an evaluation index set based on the statistical and summary results, wherein the index set includes total mileage, total delivery energy consumption, total delivery time and health indicators of metering equipment; outputting the evaluation index set to generate dynamic operation scenario data.

[0013] Based on the same inventive concept, the present invention also provides a simulation-based metering equipment delivery vehicle operation scenario construction system, the system including: a data acquisition module for acquiring the vehicle parameters of the delivery fleet, the attribute information of the metering equipment to be delivered, the task delivery timing information and the dynamic traffic environment data; a model construction module for constructing a multimodal data simulation model based on the vehicle parameters, the attribute information of the metering equipment to be delivered and the dynamic traffic environment data; a load calculation module for calculating the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation based on the multimodal data simulation model; a health prediction module for predicting the health index of the metering equipment at the end of delivery based on the vibration load spectrum; a scheme evaluation module for evaluating the driving path scheme of the delivery vehicle based on the health index and the task delivery timing information, and generating several optimized delivery schemes; a scenario generation module for obtaining the dynamic operation scenario data of the delivery process based on the optimized delivery scheme.

[0014] Compared with the prior art, the present invention has the following advantages: 1. This invention provides a data processing method that integrates vehicle operation data with metering equipment attribute data through multimodal fusion to construct an integrated simulation model. This method addresses the disconnect between traditional route planning and equipment management, enabling comprehensive and accurate modeling of transportation scenarios and providing a reliable data foundation for intelligent decision-making.

[0015] 2. This invention calculates the vibration load spectrum during transportation and uses this information to predict the health indicators of metering equipment, filling a gap in existing technology for predicting dynamic equipment risks. This method dynamically evaluates and generates optimized distribution plans based on the prediction results, not only improving logistics efficiency but also fundamentally ensuring the safety of metering equipment during transportation, significantly enhancing distribution reliability.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of a method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to an embodiment of the present invention.

[0019] Figure 2 1 is a diagram of time-domain vibration data and frequency-domain spectrum according to an embodiment of the present invention.

[0020] Figure 3 This is a heat map of distribution plan evaluation indicators according to an embodiment of the present invention.

[0021] Figure 4 It is a structural diagram of a system for constructing a metering equipment distribution vehicle operation scenario based on simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0023] Reference Figure 1One embodiment of the present invention proposes a simulation-based method for constructing an operation scenario for a metering equipment delivery vehicle. By adopting a multimodal data simulation model, a health index prediction based on a vibration load spectrum, and a hierarchical adaptive evaluation method, it is able to integrate vehicle operation, equipment status, and environmental dynamics into modeling and accurately evaluate them, generate an optimized delivery plan, significantly improve the efficiency and reliability of logistics distribution, and ensure the safety of metering equipment during transportation.

[0024] The method of this embodiment specifically includes: Obtaining vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data, wherein the vehicle parameters include vehicle load and energy consumption characteristics; Specifically, operating parameters such as vehicle load, energy consumption, location, and speed are acquired in real time through the vehicle's onboard bus. Simultaneously, information including customer location and delivery time is obtained from the task management center. Furthermore, dynamic traffic environment data, such as traffic congestion and accidents, is collected in real time from the traffic data platform. Attribute information for the metering equipment to be delivered is extracted from both unstructured and structured data, including technical documentation, historical operation logs, and failure cases.

[0025] Based on the vehicle parameters, attribute information of the metering equipment to be delivered, and dynamic traffic environment data, a multimodal data simulation model is constructed to simulate the operation process of the delivery vehicle on different routes; Specifically, the acquired vehicle parameters, the attributes of the metering equipment to be delivered, and dynamic traffic environment data are integrated to construct a multimodal data simulation model. This model combines the physical movement of the vehicle, the semantic characteristics of the metering equipment, and the road network environment information to simulate the operation of the delivery vehicle on different routes.

[0026] Based on the simulation model, the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation is calculated; Specifically, within the multimodal data simulation model, the vibration load spectrum acting on the metering equipment throughout the transportation process is calculated based on simulated vehicle trajectories and dynamic traffic environment data. This load spectrum quantitatively reflects the dynamic stress characteristics experienced by the equipment and helps assess potential damage risks during transportation.

[0027] predicting the health index of the metering equipment at the end of delivery based on the vibration load spectrum; Specifically, the calculated vibration load spectrum is used to predict the health index of the metering equipment after completing the delivery mission. This index is a forward-looking assessment based on simulation data, which can quantify the potential risk of damage or performance degradation of the equipment during transportation.

[0028] Based on the health index and task delivery timing information, the delivery vehicle's driving route plan is evaluated and several optimized delivery plans are generated; Specifically, the predicted health indicators are combined with task delivery timing information to conduct a comprehensive evaluation of multiple alternative driving route plans. The evaluation process aims to balance multiple objectives, such as ensuring equipment health, meeting delivery time windows, and minimizing energy consumption, to select the optimal delivery solution that balances efficiency and equipment safety.

[0029] According to the optimized delivery plan, dynamic operation scenario data of the delivery process is obtained.

[0030] Specifically, based on the evaluated and selected optimized delivery solutions, dynamic operational scenario data covering the entire delivery process is generated. This data includes not only the vehicle's dynamic driving path, but also a set of evaluation indicators such as total mileage, total delivery energy consumption, total delivery time, and the health of metering equipment.

[0031] By adopting multimodal data simulation models, calculations based on vibration load spectra, and prediction methods for health indicators, it is possible to integrate and fuse multi-source heterogeneous data into a model, achieve a forward-looking assessment of the status of metering equipment, and balance multiple goals such as equipment safety and distribution efficiency, significantly improving the efficiency and reliability of logistics distribution.

[0032] Optionally, the acquisition of vehicle parameters of the delivery fleet, attribute information of metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data includes: Extracting and constructing attribute information of the measuring equipment from the technical documentation, historical operation logs, and unstructured and structured data of failure cases of the measuring equipment; Specifically, we extract physical attributes such as model, weight, and dimensions from the measuring equipment's technical manuals, performance parameters from historical operation logs, and common failure modes and risk levels from past failure cases. By structured processing and integrating this multi-source data, we build a complete set of attribute information for the measuring equipment.

[0033] For example, for an electric energy metering device of model "MD2025", attribute information extracted from its technical documentation includes: net weight of 5 kg, maximum vibration frequency of 50 Hz, and main failure mode during transportation is loose internal wiring.

[0034] Obtain vehicle parameters such as real-time load and energy consumption from the delivery vehicle's onboard bus; Specifically, through the bus interface inside the vehicle, the vehicle's load sensor data, the energy consumption data of the battery management system, the position and speed data of the GPS module, and the vehicle's acceleration data are obtained in real time.

[0035] For example, when a delivery vehicle is performing a task, the on-board bus provides real-time feedback on vehicle parameters, such as: the vehicle's current load is 450 kg, the energy consumption per unit mileage is 0.2 kWh / km, and the vehicle is traveling at a speed of 50 km / h.

[0036] Obtain task delivery timing information from the task management office and dynamic traffic environment data from the traffic data platform; Specifically, the system receives a task queue containing multiple customer orders from the task management center. Each order specifies the customer's detailed location, the quantity to be delivered, and the expected delivery time. Simultaneously, it calls the traffic data platform's API to obtain real-time traffic congestion index, weather conditions, and emergency information for each road section.

[0037] For example, the Task Management Office issues a task instruction to deliver metering equipment to Customer A between 10:00 and 11:00 a.m. At the same time, the traffic data platform reports that a section of the delivery route is moderately congested due to a traffic accident, and the travel time is expected to increase by 20 minutes.

[0038] Optionally, the constructing of a multimodal data simulation model includes: Based on the vehicle parameters and dynamic traffic environment data, a digital road network model simulating the vehicle's travel path is constructed; Specifically, based on GPS data and road topology, a digital road network model is constructed, consisting of edges representing road segments and nodes representing intersections. By associating real-time traffic environment data, such as congestion index and road condition information, with road segments in the road network, the kinematic and dynamic characteristics of vehicles can be dynamically simulated.

[0039] For example, in the digital road network model, a 5km urban road section is marked as "high congestion", and the model's simulation speed parameter will be reduced accordingly, while its vibration coefficient will be adjusted according to the road surface roughness information.

[0040] Constructing a knowledge graph representing the characteristics of the metering equipment based on the attribute information of the metering equipment to be delivered; Specifically, a knowledge graph is constructed by extracting and integrating knowledge from the technical documentation, historical failure cases, and operational data of the measuring equipment. This knowledge graph uses components, failure types, and operating procedures of the measuring equipment as entities, and hierarchical and associative relationships as relationships, to comprehensively describe the characteristics of the measuring equipment.

[0041] For example, in the knowledge graph, the entity "Electricity Meter A" is connected to the entity "Loose Internal Line" through the relationship "Fault Mode", and the entity "Loose Internal Line" is connected to the entity "High Frequency Vibration" through the relationship "May be caused by...", and its physical properties such as weight and size are also recorded as entity attributes.

[0042] The digital road network model is integrated with the knowledge graph to generate a multimodal data simulation model that can simulate the operation process of the delivery vehicle.

[0043] Specifically, the digital road network model and the knowledge graph are deeply integrated. The digital road network model provides dynamic information about the physical world, while the knowledge graph provides semantic and decision-making information about metering equipment. This integration enables the model to not only simulate the physical movement of vehicles on the road network, but also make high-level decisions and predict status based on knowledge about metering equipment, forming a unified multimodal data simulation model.

[0044] For example, the fused simulation model can predict the risk of health degradation of electricity meter A when driving on the road section based on the "bumpy road section" information in the road network model and the "vibration threshold" of "electricity meter A" in the knowledge graph, and adjust the driving strategy of the simulated vehicle accordingly.

[0045] Optionally, constructing a knowledge graph representing characteristics of a metering device includes: generating knowledge data of the metering equipment based on the attribute information of the metering equipment to be delivered; Specifically, knowledge data about metering equipment is acquired from a variety of heterogeneous data sources. This data includes unstructured documents such as technical manuals or operating procedures in PDF format, structured data such as historical operating parameters and fault reports obtained from databases, and text information extracted from historical fault cases using natural language processing technology.

[0046] For example, for a certain model of electric energy metering equipment, its knowledge data includes a PDF manual describing its physical parameters and wiring specifications, and a database log containing all abnormal records in the past three years.

[0047] Performing data preprocessing on the knowledge data to obtain a cleaned structured data set; Specifically, we perform word segmentation and entity recognition on unstructured text, align structured data in different formats, remove redundant and abnormal data, and finally obtain a unified structured data set.

[0048] For example, after word segmentation of a historical fault report text, key entities such as "voltage abnormality" and "loose internal wiring" can be identified and associated with the device ID in the database to form a structured data record.

[0049] performing knowledge extraction on the structured data set to obtain explicit knowledge and implicit knowledge; Specifically, explicit knowledge is obtained by directly extracting tuples of entities and relationships from structured datasets. Meanwhile, implicit knowledge is obtained by inferring potential relationships between entities from unstructured data such as historical failure cases through a pre-set knowledge extraction model or rule engine.

[0050] For example, the explicit knowledge may be "the net weight of the electric energy meter A is 5 kg", while the implicit knowledge may be the association rule inferred from the fault case that "high frequency vibration may cause the internal wiring of the electric energy meter A to loosen".

[0051] The explicit knowledge and implicit knowledge are fused to obtain a knowledge graph representing the characteristics of the measuring equipment.

[0052] Specifically, the explicit and implicit knowledge extracted are integrated. Explicit knowledge provides certain facts and relationships, while implicit knowledge supplements logical reasoning and potential associations. The fusion of the two forms a knowledge graph that comprehensively characterizes the characteristics of metrological equipment.

[0053] For example, the knowledge graph integrates the explicit knowledge "electricity meter A contains line components" with the implicit knowledge "high-frequency vibration will cause the line to loosen" to form a complete knowledge chain.

[0054] Optionally, fusing the digital road network model with the knowledge graph includes: The digital road network model is used as the underlying physical simulation layer to simulate the kinematic and dynamic characteristics of the delivery vehicle; Specifically, in the underlying physical simulation layer, the delivery vehicle's kinematic characteristics, such as speed, acceleration, mileage, and energy consumption, are calculated and simulated in real time based on vehicle parameters and road network attributes. Furthermore, combined with road surface smoothness and environmental data, the vehicle's time-domain jitter data generated during driving is simulated as a physical manifestation of the vibration experienced by the equipment.

[0055] For example, the underlying physical simulation layer simulates a delivery truck driving through a bumpy road section at a speed of 60 km / h, and outputs a data stream of acceleration and vibration amplitude on the road section lasting 5 minutes.

[0056] The knowledge graph is used as a high-level semantic decision layer to provide fault association rules and risk levels of metering equipment under specific road conditions or operations; Specifically, the high-level semantic decision layer uses vibration and energy consumption data output by the underlying physical simulation layer as input and matches it with the metering equipment fault association rules stored in the knowledge graph. Through reasoning, this layer can identify potential failure modes and assess the risk level based on pre-set rules. For example, it can associate high-frequency vibration with loose internal wiring faults and mark them as high risk.

[0057] Exemplarily, the high-layer semantic decision layer receives high-amplitude time-domain jitter data from the bottom-layer simulation, and determines that the current road section has a "high risk" to the measurement equipment carried according to the association rule "high-frequency vibration causes line loosening" in the knowledge graph.

[0058] The simulation result of the bottom-layer physical simulation layer is taken as the input of the high-layer semantic decision layer, and the output of the high-layer semantic decision layer is taken as the dynamic constraint of the bottom-layer physical simulation layer, to generate a multi-modal data simulation model.

[0059] Specifically, in the fused model, the bottom-layer physical simulation layer first generates simulation data and passes it to the high-layer semantic decision layer. After analysis and reasoning, the high-layer semantic decision layer obtains the risk level or decision instruction, such as "slow down" or "detour", and feeds it back to the bottom-layer physical simulation layer as a dynamic constraint. The bottom-layer simulation layer adjusts its simulation parameters according to these constraints, such as reducing the vehicle speed, thereby forming a closed-loop adaptive dynamic simulation process.

[0060] Exemplarily, the bottom-layer simulation layer simulates that a certain path section will produce high-amplitude vibration, and the high-layer decision layer determines that there is a high risk accordingly, and issues a dynamic constraint "limit the vehicle speed to below 40km / h" to the bottom-layer simulation layer. The bottom-layer simulation layer then modifies the vehicle driving parameters according to this constraint and re-simulates the running condition of the road section.

[0061] Optionally, the vibration load spectrum of the computing distribution vehicle acting on the measurement equipment during transportation includes: Based on the simulation model, time-domain jitter data of the distribution vehicle during driving is generated; Specifically, in the multi-modal data simulation model, a series of acceleration data continuously changing on the time axis is generated according to the simulated vehicle driving speed, road flatness and vehicle suspension characteristics. These acceleration data are recorded in a high-frequency sampling manner, directly reflecting the vibration of the measurement equipment during transportation.

[0062] Exemplarily, the simulation model simulates that the vehicle drives at a speed of 40km / h on a section of urban road, and generates a section of acceleration time series data lasting for 1 minute, recording the vibration amplitude on X, Y and Z axes every millisecond.

[0063] Performing fast Fourier transform on the time-domain jitter data to obtain corresponding frequency-domain vibration components; Specifically, the generated time-domain jitter data is processed by fast Fourier transform. This processing converts the time-domain signal into a frequency-domain signal, thereby decomposing the complex vibration waveform into sinusoidal wave components of different frequencies and amplitudes, facilitating the analysis of the energy distribution of vibration at different frequencies. For example, Figure 2As shown in the figure, the upper part is the time domain jitter data of the simulated delivery vehicle during driving, and the lower part is the frequency domain spectrum after fast Fourier transform processing, which clearly shows the distribution of vibration energy at different frequencies. The fast Fourier transform formula can be expressed as: , in, Indicates the first Vibration components, Indicates the first jitter data sampling points, Indicates the total number of jitter data sampling points, Indicates the frequency number, represents the imaginary unit, The square is -1.

[0064] For example, after performing fast Fourier transform calculation on a period of time domain vibration data, a spectrum diagram is obtained. During the calculation process, if the data sampling point N is set to 1000, the calculated frequency domain vibration component exist 、 and Several frequency points show significant amplitudes, indicating that the vibration data has higher energy at frequencies such as 10 Hz, 30 Hz and 80 Hz.

[0065] The frequency domain vibration component is subjected to spectrum analysis to extract the amplitude and energy distribution characteristics of the frequency domain vibration component in a preset frequency band to generate a vibration load spectrum.

[0066] Specifically, the converted frequency domain vibration components are subjected to spectrum analysis. This analysis focuses on the preset frequency bands that have potential impact on the metering equipment, extracts the vibration amplitude and energy distribution characteristics within these frequency bands, and combines these characteristic data to form the final vibration load spectrum. The spectrum energy calculation formula is: , in, Indicates the total energy within the preset frequency band. Indicates the first Vibration components, and Respectively represent the start and end frequency numbers of the preset frequency band.

[0067] For example, spectrum analysis of the frequency domain vibration components identifies a continuous high-amplitude energy peak in the 20Hz to 40Hz frequency band. If there are three main vibration components with amplitudes of 5, 10, and 8 in this frequency band, the total energy in this frequency band can be obtained by calculating the square of 5 plus the square of 10 plus the square of 8. It is 189.

[0068] Optionally, the health indicator of the predicted metering equipment at the end of delivery includes: Obtaining a standard vibration load spectrum from the attribute information of the metering equipment to be delivered; Specifically, the attribute information of the metering equipment to be delivered stores a standard vibration load spectrum of the equipment under normal operating conditions. This standard load spectrum can be based on quality control tests conducted before the equipment leaves the factory, or historical data obtained under a standard laboratory environment.

[0069] For example, for a certain type of measuring equipment, the standard value of its vibration energy within a specific frequency band (such as 20 Hz-40 Hz) is 1500, and this data serves as an important parameter of the standard vibration load spectrum.

[0070] Comparing the calculated vibration load spectrum with a standard vibration load spectrum to obtain a vibration characteristic difference value; Specifically, the vibration load spectrum calculated based on the simulation model is quantitatively compared with the standard vibration load spectrum. The comparison method can be to calculate the energy difference, peak difference, or Euclidean distance of the statistical characteristics of the two load spectra in the same frequency band. The result is a vibration characteristic difference value that can reflect the degree of deviation between the current state of the equipment and the standard state. The vibration characteristic difference value calculation formula is: , in, represents the vibration characteristic difference value, The calculated vibration load spectrum is characteristic parameters, The first part of the standard vibration load spectrum characteristic parameters, Indicates the total number of feature parameters.

[0071] For example, if within a certain frequency band, the characteristic parameter vector of the calculated vibration load spectrum is , and the characteristic parameter vector of the standard vibration load spectrum is , then according to , and the vibration characteristic difference value D is 0.11.

[0072] A health index of the measuring device is determined according to the vibration characteristic difference value, wherein the larger the vibration characteristic difference value is, the worse the health index is.

[0073] Specifically, the vibration characteristic difference value is used to determine the health index of the measuring equipment through a preset function or rule table. This index is a normalized value or grade classification that intuitively represents the health status of the equipment, and the health index is negatively correlated with the vibration characteristic difference value. The health index calculation formula is: , wherein, represents a health index, represents a decay coefficient for adjusting the sensitivity of the health index, is an exponential function with the natural constant e as the base.

[0074] Exemplarily, if the vibration feature difference value is 0.11, and the decay coefficient is set to 5, then the health index is obtained as 0.577 according to This value indicates that the device health is at a warning level, and corresponding measures need to be taken.

[0075] Optionally, the method further comprises: performing a first-level evaluation on the plurality of path schemes generated by the simulation model, wherein the first-level evaluation is based on the energy consumption of the delivery vehicle and the task delivery timing information, and filters out candidate schemes that meet basic requirements; Specifically, each path scheme generated by the simulation model is preliminarily filtered. The filtering process filters out schemes that cannot complete delivery within a specified time or exceed the vehicle's power endurance according to indicators such as the energy consumption of the delivery vehicle and the matching degree of the task delivery timing information, and retains all candidate schemes that meet the basic constraint conditions.

[0076] Exemplarily, the simulation generates 100 path schemes, and the first-level evaluation filters out 15 schemes that are expected to be delayed by more than 30 minutes or need to be charged halfway, leaving 85 candidate schemes that meet the timing and energy consumption constraints.

[0077] For each candidate scheme filtered out by the first-level evaluation, a second-level risk evaluation is performed, wherein the second-level risk evaluation is based on the health index of the metering device and the fault association rules extracted from the knowledge graph, and identifies high-risk road segments in the path scheme; Specifically, each path in the candidate scheme filtered out by the first-level evaluation is traversed, and the fault association rules stored in the knowledge graph are used to compare the road segment types on the path with the health index of the metering device. If the physical characteristics of a road segment match the device fault risk conditions defined in the knowledge graph, the road segment is marked as a high-risk road segment.

[0078] Exemplarily, there is a rule in the knowledge graph defined as "road surface bumping is associated with high-frequency vibration", if a path scheme contains a road segment of "road surface bumping", and the current health index of the metering device is lower than the preset threshold, then the road segment is identified as a high-risk road segment.

[0079] ​​​​​​​​​​​​​​​​​​​Perform a localized magnification simulation on the high-risk road section to simulate the vibration impact on the metering equipment caused by the delivery vehicle driving on the high-risk road section, and dynamically update the health index; Specifically, once a high-risk road section is identified, the simulation model performs a higher-precision zoomed-in simulation of that section. During this process, the simulation model more precisely calculates the vehicle's kinematic and dynamic behavior on that road section, generating more accurate vibration impact data. Based on this new data, the health indicators of the metering equipment are recalculated and dynamically updated.

[0080] For example, for a 500-meter high-risk section on a route, the simulation model will be rerun at a higher sampling rate to accurately simulate the vibration data of the vehicle passing over each uneven road surface, and dynamically update the equipment health index to the recalculated equipment health index based on this data.

[0081] Based on the dynamically updated health index, candidate plans are re-evaluated and sorted to generate several optimized delivery plans.

[0082] Specifically, the dynamically updated health index and the previous first-level evaluation index are used to conduct a final comprehensive evaluation of all candidate solutions. The evaluation process will recalculate a comprehensive score for each solution and sort all solutions according to the score, ultimately obtaining one or more optimized delivery solutions that perform best in terms of efficiency and equipment safety, such as Figure 3 As shown in the figure, each row represents a final optimized delivery solution, and each column displays multi-dimensional evaluation indicators. The depth of the color directly reflects the specific quantitative value of each indicator, providing a dense information presentation of the solution's advantages and disadvantages.

[0083] For example, suppose that after the health index is dynamically updated, the plan originally ranked first is downgraded because the equipment health is significantly reduced due to a certain road section, while the plan originally ranked second is promoted to the first place because of the smaller impact on health, becoming the final optimized delivery plan.

[0084] Optionally, obtaining dynamic operation scenario data of the delivery process includes: Performing data statistics and summarizing on the optimized distribution plan; Specifically, the simulation model collects data from the entire delivery process simulated by the optimal delivery solution. This process records vehicle mileage, speed, energy consumption, and the real-time health status of metering equipment on each road section, and then compiles and summarizes this data.

[0085] For example, during a complete delivery simulation, the system collects statistics on the data of a vehicle traveling 50 km and taking 2 hours, and summarizes the average speed and total energy consumption of the vehicle.

[0086] Generate an evaluation index set based on the statistical and summary results, wherein the index set includes total mileage, total delivery energy consumption, total delivery time, and health indicators of metering equipment; Specifically, based on the statistical and aggregated data from the delivery process, an evaluation index set consisting of multiple key performance indicators is generated. This index combines transportation efficiency indicators such as total mileage, total delivery energy consumption, and total delivery time with the health of metering equipment at the end of the delivery process to comprehensively quantify the quality of the delivery plan.

[0087] For example, based on the statistical results, an evaluation indicator set is generated including a total mileage of 65 kilometers, a total time of 2.5 hours, a total energy consumption of 20 kWh, and a final equipment health index of 0.92.

[0088] Output the evaluation indicator set and generate dynamic operation scenario data.

[0089] Specifically, the evaluation index set is taken as the core content and integrated with other detailed dynamic operation data, such as the vehicle's coordinates, speed, energy consumption change curve at each time point, to form a complete dynamic operation scenario data.

[0090] For example, a data file in JSON format is generated, which contains a field named "metrics" whose content is the evaluation indicator set, and also contains a field named "trajectory" whose content is the sequence of spatiotemporal coordinate points of the vehicle on the entire path.

[0091] Based on the same inventive concept, Figure 4 As shown, the present invention also provides a system for constructing an operation scenario of a metering equipment delivery vehicle based on simulation, the system comprising: A data acquisition module is used to obtain vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data; A model building module, configured to build a multimodal data simulation model based on the vehicle parameters, attribute information of the metering equipment to be delivered, and dynamic traffic environment data; A load calculation module, configured to calculate a vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation based on the multimodal data simulation model; A health prediction module, configured to predict the health index of the metering equipment at the end of delivery based on the vibration load spectrum; A plan evaluation module is used to evaluate the driving route plan of the delivery vehicle based on the health index and task delivery timing information, and generate several optimized delivery plans; The scenario generation module is used to obtain dynamic operation scenario data of the delivery process according to the optimized delivery plan.

[0092] It should be noted that the electrical connections between the above-mentioned units do not necessarily mean direct connections of lines. Indirect connections are applicable to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above description is only an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention.

[0093] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

Claims

1. A method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation, characterized in that: The method comprises: Obtain vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data; Based on the vehicle parameters, attribute information of the metering equipment to be delivered, and dynamic traffic environment data, a multimodal data simulation model is constructed to simulate the operation process of the delivery vehicle on different routes; Based on the simulation model, the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation is calculated; predicting the health index of the metering equipment at the end of delivery based on the vibration load spectrum; Based on the health index and task delivery timing information, the delivery vehicle's driving route plan is evaluated and several optimized delivery plans are generated; According to the optimized delivery plan, dynamic operation scenario data of the delivery process is obtained.

2. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 1, characterized in that: The acquisition of vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data includes: Extracting and constructing attribute information of the metering equipment to be delivered from technical documents, historical operation logs, and unstructured and structured data of failure cases of the metering equipment to be delivered; Obtain vehicle parameters such as real-time load and energy consumption from the delivery vehicle's onboard bus; Obtain task delivery timing information from the task management office and obtain dynamic traffic environment data from the traffic data platform.

3. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 1, characterized in that: The constructing of the multimodal data simulation model comprises: Based on the vehicle parameters and dynamic traffic environment data, a digital road network model simulating the vehicle's travel path is constructed; Constructing a knowledge graph representing the characteristics of the metering equipment based on the attribute information of the metering equipment to be delivered; The digital road network model is integrated with the knowledge graph to generate a multimodal data simulation model that can simulate the operation process of the delivery vehicle.

4. The method for constructing a metering equipment delivery vehicle operation scenario based on simulation according to claim 3 is characterized in that: The construction of the knowledge graph representing the characteristics of the measuring equipment includes: generating knowledge data of the metering equipment based on the attribute information of the metering equipment to be delivered; Performing data preprocessing on the knowledge data to obtain a cleaned structured data set; performing knowledge extraction on the structured data set to obtain explicit knowledge and implicit knowledge; The explicit knowledge and implicit knowledge are fused to obtain a knowledge graph representing the characteristics of the measuring equipment.

5. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 3, characterized in that: The fusion of the digital road network model and the knowledge graph includes: The digital road network model is used as the underlying physical simulation layer to simulate the kinematic and dynamic characteristics of the delivery vehicle; The knowledge graph is used as a high-level semantic decision layer to provide fault association rules and risk levels of metering equipment under specific road conditions or operations; The simulation results of the underlying physical simulation layer serve as input to the high-level semantic decision layer, and the output of the high-level semantic decision layer serves as the dynamic constraint of the underlying physical simulation layer to generate a multimodal data simulation model.

6. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 1, characterized in that: The calculation of the vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation includes: Based on the simulation model, generating time domain jitter data of the delivery vehicle during driving; Performing a fast Fourier transform on the time domain jitter data to obtain a corresponding frequency domain vibration component; The frequency domain vibration component is subjected to spectrum analysis to extract the amplitude and energy distribution characteristics of the frequency domain vibration component in a preset frequency band to generate a vibration load spectrum.

7. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 1, characterized in that: The health indicators of the predicted metering equipment at the end of delivery include: Obtaining a standard vibration load spectrum from the attribute information of the metering equipment to be delivered; Comparing the calculated vibration load spectrum with a standard vibration load spectrum to obtain a vibration characteristic difference value; A health index of the measuring device is determined according to the vibration characteristic difference value, wherein the larger the vibration characteristic difference value is, the worse the health index is.

8. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 3, characterized in that: The evaluation of the driving path plan of the delivery vehicle includes: Performing a primary evaluation on the multiple routing schemes generated in the simulation model, wherein the primary evaluation is based on the energy consumption of the delivery vehicles and the task delivery timing information to select candidate schemes that meet basic requirements; Perform a second-level risk assessment on each candidate solution selected by the first-level assessment, wherein the second-level risk assessment is based on the health indicators of the metering equipment and the fault association rules extracted from the knowledge graph to identify high-risk sections in the route solution; Perform a localized magnification simulation on the high-risk road section to simulate the vibration impact on the metering equipment caused by the delivery vehicle driving on the high-risk road section, and dynamically update the health index; Based on the dynamically updated health index, candidate plans are re-evaluated and sorted to generate several optimized delivery plans.

9. The method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to claim 1, characterized in that: The dynamic operation scenario data of the distribution process is obtained including: Performing data statistics and summarizing on the optimized distribution plan; Generate an evaluation index set based on the statistical and summary results, wherein the index set includes total mileage, total delivery energy consumption, total delivery time, and health indicators of metering equipment; Output the evaluation indicator set and generate dynamic operation scenario data.

10. The system for constructing an operation scenario of a metering equipment delivery vehicle based on simulation is applied to the method for constructing an operation scenario of a metering equipment delivery vehicle based on simulation according to any one of claims 1 to 9, characterized in that: The system comprises: A data acquisition module is used to obtain vehicle parameters of the delivery fleet, attribute information of the metering equipment to be delivered, task delivery timing information, and dynamic traffic environment data; A model building module, configured to build a multimodal data simulation model based on the vehicle parameters, attribute information of the metering equipment to be delivered, and dynamic traffic environment data; A load calculation module, configured to calculate a vibration load spectrum of the delivery vehicle acting on the metering equipment during transportation based on the multimodal data simulation model; A health prediction module, configured to predict the health index of the metering equipment at the end of delivery based on the vibration load spectrum; A plan evaluation module is used to evaluate the driving route plan of the delivery vehicle based on the health index and task delivery timing information, and generate several optimized delivery plans; The scenario generation module is used to obtain dynamic operation scenario data of the delivery process according to the optimized delivery plan.

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