A method for monitoring the transportation environment of heavy asphalt

Abstract

The invention discloses a method for monitoring the transportation environment of heavy traffic asphalt, which belongs to the field of transportation environment monitoring and regulation, and comprises: determining a monitoring multi-element group based on the transportation environment based on the characteristics of heavy traffic asphalt; constructing multiple transportation environments and conducting simulation drills to determine key monitoring nodes; determining strong constraint conditions based on the storage and transportation status based on the characteristics of heavy traffic asphalt, and configuring limit constraints; setting front-end self-adjustment rules, combining key monitoring nodes and limit constraints, determining a joint adjustment mode, and constructing an adaptive monitoring and control module; setting a preset monitoring cycle, conducting periodic continuous monitoring, combining an adaptive monitoring and control module to conduct transportation status evaluation and adjustment decisions, and determining environmental control data; the control system of the transport vehicle responds to the environmental control data to control the transportation environment of the heavy traffic asphalt, thereby improving the pertinence and adaptability of transportation environment monitoring, and performing precise control according to different environmental states to ensure the transportation quality of the heavy traffic asphalt.

Description

Technical Field

[0001] The invention relates to the field of transportation environment monitoring and control, and in particular to a transportation environment monitoring method for heavy asphalt. Background Art

[0002] During the transportation of heavy traffic asphalt, the monitoring and regulation of the transportation environment is extremely important, which directly affects the transportation quality and performance of heavy traffic asphalt. However, the transportation environment monitoring of heavy traffic asphalt in the existing technology generally has the problems of poor pertinence and adaptability, and it is difficult to achieve precise regulation, resulting in poor transportation quality of heavy traffic asphalt. Specifically, traditional transportation environment monitoring is difficult to fully obtain multi-dimensional information of the transportation environment. At the same time, it lacks special design for the characteristics of heavy traffic asphalt, and cannot accurately capture the key environmental factors that have the greatest impact on the quality of heavy traffic asphalt; in addition, the existing transportation environment monitoring of heavy traffic asphalt mostly adopts fixed threshold judgment and control strategies, lacks adaptive capabilities, and cannot be dynamically adjusted according to the real-time status and environmental changes of heavy traffic asphalt. Therefore, due to the inaccuracy of monitoring and regulation, heavy traffic asphalt often has quality problems such as too high or too low temperature, moisture and agglomeration during transportation, which affects subsequent construction and use. Summary of the invention

[0003] The present application provides a method for monitoring the transportation environment of heavy traffic asphalt, aiming to solve the technical problems in the prior art that the monitoring of the transportation environment of heavy traffic asphalt has poor pertinence and adaptability, is difficult to achieve precise control, and leads to poor transportation quality of heavy traffic asphalt.

[0004] The present application discloses a method for monitoring the transportation environment of heavy-duty asphalt, comprising: determining a monitoring multi-group based on the transportation environment based on the characteristics of the heavy-duty asphalt; traversing the monitoring multi-group, constructing multiple transportation environments and conducting simulation drills, and determining key monitoring nodes, wherein the key monitoring nodes are marked with a multi-group of environmental characteristic values; determining strong constraints based on the storage and transportation state based on the characteristics of the heavy-duty asphalt, and configuring a limit constraint, wherein the storage and transportation state is in liquid form and includes a preset melting point span of the storage and transportation state; setting a front-end self-adjustment rule, combining the key monitoring nodes with the limit constraint, determining a joint adjustment mode, conducting data-driven training, and constructing an adaptive monitoring and control module; setting a preset monitoring period, conducting periodic continuous monitoring, combining the adaptive monitoring and control module to conduct transportation state class evaluation and adjustment decisions, and determining environmental control data; the control system of the transport vehicle responds to the environmental control data to control the transportation environment of the heavy-duty asphalt, wherein the transportation environment includes the carriage environment and the storage container environment.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: Based on the characteristics of heavy traffic asphalt, a monitoring multi-group based on the transportation environment is determined. By analyzing the physical and chemical characteristics of heavy traffic asphalt, the environmental factors that have the greatest impact on its transportation quality are identified, and a monitoring multi-group is constructed, so as to collect the most critical environmental data in a targeted manner and improve the effectiveness of monitoring; the monitoring multi-group is traversed, multiple transportation environments are constructed and simulated drills are conducted to determine key monitoring nodes. The key monitoring nodes are marked with a multi-group of environmental characteristic values. Through simulation of different transportation environments, the changing patterns and sensitivity of each monitoring parameter are comprehensively evaluated, and the critical state points that have the greatest impact on the quality of heavy traffic asphalt are identified to form key monitoring nodes, so as to find the focus of monitoring and regulation and optimize the monitoring strategy; based on the characteristics of heavy traffic asphalt, strong constraints based on storage and transportation status are determined, and limit constraints are configured. According to the characteristics of heavy traffic asphalt, a safe temperature and viscosity range are set. Quality problems may occur beyond this range. The configuration of limit constraints realizes risk warning and safety prevention in the entire storage and transportation process. Control; set front-end self-adjustment rules, combine key monitoring nodes and limit constraints, determine the joint adjustment mode, conduct data-driven training, build an adaptive monitoring and control module, and establish an intelligent adaptive monitoring and control module through learning historical data and simulation data. According to the environmental parameters collected in real time, the control strategy is dynamically adjusted to achieve precise control; set a preset monitoring cycle, conduct periodic continuous monitoring, combine the adaptive monitoring and control module to conduct transportation status evaluation and adjustment decisions, generate targeted environmental control instructions, and the control system of the transport vehicle responds and executes them, and the environment of the carriage and storage container is adjusted in real time to ensure the technical solution of the transportation quality of heavy-duty asphalt. This solves the technical problems of poor pertinence and adaptability of heavy-duty asphalt transportation environment monitoring in the existing technology, making it difficult to achieve precise control, resulting in poor transportation quality of heavy-duty asphalt, and achieves the technical effect of improving the pertinence and adaptability of transportation environment monitoring, performing precise control according to different environmental conditions, and ensuring the transportation quality of heavy-duty asphalt.

[0006] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A flow chart of a method for monitoring the transportation environment of heavy asphalt is provided for an embodiment of the present application; Figure 2 A flow chart of determining a joint adjustment mode in a method for monitoring the transportation environment of heavy asphalt is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0008] The overall idea of ​​the technical solution provided by this application is as follows: The embodiment of the present application provides a method for monitoring the transportation environment of heavy traffic asphalt. First, based on the characteristics of heavy traffic asphalt, a multi-dimensional monitoring multi-element group is determined, covering the key environmental factors that have the greatest impact on the transportation quality. Then, through the simulation of different transportation environments, the critical state points that have the greatest impact on the quality are identified, forming key monitoring nodes. At the same time, according to the characteristics of heavy traffic asphalt, a safe temperature and viscosity range is set, and a limit constraint is configured to achieve risk warning and safety control throughout the transportation process. In addition, through the study of historical data and simulation data, an adaptive monitoring and control module is established, and the control strategy is dynamically adjusted according to the environmental parameters collected in real time to achieve precise control. Afterwards, through periodic continuous monitoring and adaptive control, targeted environmental control instructions are generated in real time, and the control system of the transport vehicle responds and executes, and the environment of the carriage and storage container is controlled in real time to fully ensure the transportation quality of heavy traffic asphalt.

[0009] After introducing the basic principles of the present application, the non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the drawings in the specification.

[0010] like Figure 1 As shown, the embodiment of the present application provides a method for monitoring the transportation environment of heavy asphalt, the method comprising: S1: Based on the characteristics of heavy traffic asphalt, determine the monitoring multi-group based on the transportation environment.

[0011] Specifically, since heavy traffic asphalt has special physical and chemical properties during transportation, such as high viscosity, volatility, temperature sensitivity, etc., transportation environment monitoring is designed based on the characteristics of heavy traffic asphalt. First, analyze the various characteristics of heavy traffic asphalt, such as melting point, flash point, viscosity, density, etc., and determine the key monitoring parameters related to the transportation environment based on the characteristics of heavy traffic asphalt, such as temperature, humidity, pressure, vibration frequency, etc. Then, combine these monitoring parameters to form a monitoring multi-group. The monitoring multi-group is a multi-dimensional parameter set, and each dimension represents an environmental parameter that needs to be monitored. By setting up the monitoring multi-group, the environmental state of heavy traffic asphalt during transportation can be fully reflected, providing a data basis for subsequent simulation exercises, key node determination and other steps, improving the pertinence of transportation environment monitoring, and laying the foundation for precise regulation.

[0012] S2: traverse the monitoring tuple, construct a multi-transportation environment and conduct a simulation exercise, and determine key monitoring nodes, wherein the key monitoring nodes are identified by an environmental feature value tuple.

[0013] Specifically, after determining the monitoring multi-tuple, multiple transport environments are constructed and key monitoring nodes are determined through simulation exercises. First, the determined monitoring multi-tuple is traversed, and a variety of different transport environments are constructed according to the monitoring multi-tuple to obtain a multiple transport environment. Among them, each monitoring parameter in the monitoring multi-tuple has its own range of values. By arranging and combining the key value points of different monitoring parameters, a series of environmental states are constructed. Each environmental state is composed of a set of specific monitoring parameter values, representing a typical environmental condition that may be encountered during the transportation of heavy-duty asphalt, thereby obtaining a multiple transport environment. A multiple transport environment refers to a series of transport environments corresponding to the monitoring multi-tuple. Each transport environment has a specific combination of environmental parameters, reflecting the various environmental states that may be encountered during the transportation of heavy-duty asphalt.

[0014] After constructing multiple transport environments, simulated drills are conducted for each transport environment. Specifically, physical simulation, numerical simulation and other technical means are used to simulate the transportation process of heavy traffic asphalt under different transport environments, and the changes in various environmental parameters are monitored in real time. Through simulated drills, the impact of different transport environments on the quality of heavy traffic asphalt is comprehensively evaluated. Subsequently, based on the simulated drills, key monitoring nodes are determined. Key monitoring nodes refer to environmental status points that have the greatest impact on the quality of heavy traffic asphalt during transportation and that most require key monitoring and regulation. Each key monitoring node corresponds to a specific set of environmental parameter combinations, which is a multi-tuple of environmental characteristic values, reflecting the environmental status characteristics at the key monitoring node.

[0015] By traversing the monitoring multi-tuple to construct a multi-transport environment, and conducting simulation drills in the multi-transport environment, we can find out the key environmental state combination that affects the quality of heavy-duty asphalt, that is, the key monitoring nodes. Each key monitoring node is marked with a set of environmental characteristic value multi-tuples, which provides an important basis for subsequent adaptive monitoring and control.

[0016] S3: Based on the characteristics of the heavy traffic asphalt, determine the strong constraint conditions based on the storage and transportation state, and configure a limit constraint, wherein the storage and transportation state is in liquid form and includes a preset melting point span of the storage and transportation state.

[0017] Specifically, after determining the key monitoring nodes, the characteristics of heavy-duty asphalt are further considered, and strong constraints and limit constraints are introduced for its storage and transportation status to ensure the safety and controllability of the transportation process. Heavy-duty asphalt needs to be kept in liquid form during transportation, which requires that its temperature must be maintained within a certain melting range. If the temperature is too low, the heavy-duty asphalt will solidify, causing pipeline blockage; if the temperature is too high, it may cause safety hazards. Therefore, based on the melting characteristics of heavy-duty asphalt in the characteristics of heavy-duty asphalt, a preset melting span is set as the temperature constraint condition for its storage and transportation status.

[0018] After the strong constraints are determined, the corresponding limit constraints are configured. The limit constraint can be a hardware device or software module that monitors the temperature changes of heavy traffic asphalt in real time, and issues a warning signal when the temperature exceeds the preset melting point span, while triggering corresponding control measures, such as heating or cooling, to maintain the temperature within a safe range. The introduction of the limit constraint can effectively prevent transportation accidents caused by abnormal temperature and improve the safety and reliability of the transportation process. At the same time, the trigger signal of the limit constraint also provides an important basis for subsequent adaptive monitoring and control, so that the control measures can be more timely and accurate.

[0019] In view of the special storage and transportation requirements of heavy traffic asphalt, strong constraints based on the melting point span are set, and corresponding limit constraints are configured to achieve safe monitoring and timely regulation of the transportation process and ensure the stability of the transportation quality of heavy traffic asphalt.

[0020] S4: Set the front-end self-adjustment rules, combine the key monitoring nodes with the limit constraints, determine the joint adjustment mode, perform data-driven training, and build an adaptive monitoring and control module.

[0021] Specifically, first, the front-end self-adjustment rules are set. The front-end self-adjustment rules are rules for general environmental parameter fluctuations, such as small changes in temperature and humidity. Through the preset threshold range and adjustment strategy, the front-end self-adjustment rules can automatically trigger corresponding control measures when abnormal parameters are detected, such as adjusting the temperature control system of the car, changing the working state of the ventilation device, etc., to maintain the relative stability of environmental parameters. Secondly, the identified key monitoring nodes and the configured limit constraints are fully considered and integrated with the front-end self-adjustment rules to form a joint adjustment mode. In the joint adjustment mode, the optimal control strategy is dynamically selected according to the change state of the environmental parameters: when the parameter fluctuation is small, the front-end self-adjustment rules are preferred; when the early warning signal of the key node is detected, the intelligent analysis and decision-making function of the back-end is started to perform more accurate parameter control; if the limit constraint is triggered, the emergency control measures are immediately implemented to prevent the deterioration of the quality of the heavy asphalt. In order to realize the joint adjustment mode, the data-driven method is used to train the adaptive monitoring and control module. Specifically, various environmental parameter data and quality inspection data during the transportation of heavy-duty asphalt are collected, and corresponding to different control strategies and effect evaluation indicators to form a training data set. Then, the adaptive monitoring and control module is trained and optimized using machine learning algorithms, such as deep neural networks and reinforcement learning, so that it can autonomously select the optimal control strategy based on real-time monitoring data, and continuously improve the accuracy and effectiveness of control through continuous self-learning and iteration. After the above training, the adaptive monitoring and control module can dynamically adjust various environmental parameters according to the real-time monitoring data during the transportation of heavy-duty asphalt to ensure the stability of transportation quality. At the same time, the module can also quickly respond to the early warning signals of key monitoring nodes and the trigger signals of limit constraints, and execute corresponding emergency control measures to minimize transportation risks.

[0022] By setting front-end self-adjustment rules, integrating key monitoring nodes and limit constraints, building a joint adjustment mode, and introducing a data-driven training mechanism, an intelligent adaptive monitoring and control module is constructed. This can fully perceive the changes in environmental parameters during the transportation of heavy asphalt, autonomously execute the optimal control strategy, and ensure the stability and controllability of transportation quality.

[0023] S5: Setting a preset monitoring period, performing periodic continuous monitoring, combining the adaptive monitoring and control module to perform transport status assessment and adjustment decisions, and determining environmental control data.

[0024] Specifically, first, set the preset monitoring cycle. According to the actual needs and environmental characteristics of heavy-duty asphalt transportation, select appropriate time intervals, such as every minute, every 10 minutes or every hour, as the basic cycle for monitoring data collection and analysis, so as to ensure real-time performance while avoiding the waste of resources caused by too frequent data collection. After determining the preset monitoring cycle, the heavy-duty asphalt transportation process is periodically and continuously monitored. Through various sensors and monitoring devices installed on transportation vehicles and storage containers, environmental parameter data such as temperature, humidity, and pressure are collected in real time, and summarized and reported according to the preset monitoring cycle for periodic and continuous monitoring. After the monitoring data is reported, the adaptive monitoring and control module analyzes and processes the collected data, evaluates the current transportation status, determines whether there is an abnormal risk, and forms a comprehensive transportation status class, such as "normal", "warning", "dangerous", etc. Based on the results of the transportation status class evaluation, the adaptive monitoring and control module further executes the corresponding adjustment decision. For the "normal" state, the current transport environment parameters are maintained according to the front-end self-adjustment rules; for the "warning" state, the intelligent analysis and dynamic optimization functions of the back-end are started according to the preset conditions of the key monitoring nodes, and the corresponding parameter adjustment instructions are given; and for the "dangerous" state, the limit restraint is triggered and emergency control measures are immediately implemented, such as emergency braking, rapid cooling, etc., to prevent the occurrence of transport accidents. The result of the adjustment decision forms a set of specific environmental control data, including the adjustment set values ​​of the temperature, humidity, pressure and other parameters of the transport vehicles and storage containers, as well as the corresponding execution time, duration and other control instructions.

[0025] Through periodic continuous monitoring, combined with the transport status assessment and adjustment decision-making functions of the adaptive monitoring and control module, a closed-loop monitoring and control process is formed to dynamically respond to environmental changes during the transportation of heavy asphalt, automatically optimize the control plan, and ensure the quality stability, safety and reliability of the entire transportation process.

[0026] S6: The control system of the transport vehicle responds to the environmental control data to control the transport environment of the heavy-duty asphalt, wherein the transport environment includes the vehicle compartment environment and the storage container environment.

[0027] Specifically, the transport vehicle is equipped with a complete control system, which establishes a real-time communication connection with the adaptive monitoring and control module. When the adaptive monitoring and control module generates new environmental control data, the control system of the transport vehicle can receive the environmental control data in a timely manner, and start the corresponding actuator according to the environmental control data to control the transportation environment. Among them, the transportation environment includes the carriage environment and the storage container environment. The carriage environment control mainly targets the temperature, humidity, pressure and other parameters inside the carriage. By adjusting the working status of the heating, cooling, ventilation and other equipment in the carriage, a stable and suitable transportation space is maintained; the storage container environment control focuses on the temperature, viscosity and other indicators of the heavy traffic asphalt liquid itself, and ensures that the heavy traffic asphalt is always in the best physical and chemical state by controlling the heating, stirring and other devices of the container.

[0028] After receiving the environmental control data, the control system of the transport vehicle analyzes the transport environment control and extracts specific control instructions for the compartment environment and storage container environment, such as temperature setting value, humidity adjustment range, pressure control threshold, heating power, stirring speed, etc. Subsequently, the control system sends these instructions to the actuators of the compartment and storage container respectively to trigger the corresponding environmental control operations. During the entire control process, the control system of the transport vehicle continuously monitors the changes in environmental parameters and feeds back real-time data to the adaptive monitoring and control module. Based on the actual control effect, the adaptive monitoring and control module dynamically optimizes the control strategy to form a closed-loop feedback control process to ensure the optimal management of the heavy asphalt transportation environment.

[0029] Through the execution of environmental control data by the transport vehicle control system, the carriage environment and storage container environment during the transportation of heavy-duty asphalt can be accurately controlled, various quality risk factors can be eliminated, the safety, efficiency and reliability of the transportation process can be ensured, and the full-process optimization management of the transportation quality of heavy-duty asphalt can be achieved.

[0030] Furthermore, the embodiment of the present application also includes: Interactive target transportation routes, determine a transportation environment library, the transportation environment library includes an external environment and a road condition environment; in combination with the transportation environment library, extract and determine multi-environment feature values ​​based on the monitoring multi-group, wherein one type of transportation environment corresponds to a group of multi-environment feature values; integrate the multi-environment feature values ​​and establish a mapping with the transportation environment to determine the multiple transportation environments.

[0031] In a feasible implementation, first, the target transportation route of heavy asphalt transportation is obtained through interaction with the user or the upper system. The target transportation route includes data of multiple dimensions such as the starting point, the end point, the passing place, the mileage, and the road grade. After obtaining the target transportation route, the corresponding transportation environment library is matched and generated according to the route characteristics of the target transportation route. The transportation environment library not only includes the external environmental factors related to the target transportation route, such as meteorological conditions, terrain characteristics, etc., but also considers the road conditions of the target transportation route itself, such as road surface type, slope, curve, speed limit, etc. By constructing a comprehensive transportation environment library, a solid data foundation is provided for the subsequent construction of multiple transportation environments. Then, based on the obtained monitoring multi-group, combined with the constructed transportation environment library, the multi-environment characteristic values ​​are further extracted and determined. The multi-environment characteristic value refers to a set of data reflecting the typical values ​​or distribution characteristics of each monitoring parameter under a specific transportation environment. For example, for the plateau hypoxia environment, the multi-environment characteristic values ​​include low air pressure, low oxygen partial pressure, etc.; while for the high cold environment, the multi-environment characteristic values ​​include ultra-low temperature, large temperature difference, etc. Then, the corresponding relationship between the transportation environment and the multi-environment characteristic values ​​is established, that is, one type of transportation environment corresponds to a set of specific multi-environment characteristic values, forming a multi-transportation environment.

[0032] By modeling the refined transportation environment of heavy-duty asphalt transportation routes, constructing a multi-transportation environment, and carefully depicting the transportation characteristics of heavy-duty asphalt under different geographical, road, and meteorological conditions, it provides important data support for the follow-up and helps to achieve more accurate and intelligent heavy-duty asphalt transportation environment management.

[0033] Furthermore, the embodiment of the present application also includes: Identify the characteristics of the heavy traffic asphalt and determine the characteristic thresholds based on the storage and transportation status, wherein the characteristic thresholds at least include a volatility point threshold, a sealing level threshold, and a melting point range threshold; based on the characteristic thresholds, perform indicator conversion based on the transportation environment to determine the environmental indicators; traverse the environmental indicators, perform expansion processing in the indicator fault tolerance direction, and integrate to determine the strong constraints, wherein the expansion ranges of various environmental indicators are different.

[0034] In a preferred embodiment, first, the characteristics of heavy traffic asphalt are identified, and the corresponding characteristic thresholds are determined based on the characteristic performance of heavy traffic asphalt under storage and transportation conditions. These characteristic thresholds reflect the key physical and chemical indicators of heavy traffic asphalt during storage and transportation, including the volatility point threshold, the sealing level threshold and the melting point range threshold. Among them, the volatility point threshold reflects the volatility characteristics of heavy traffic asphalt, that is, under what temperature conditions will significant volatility occur; the sealing level threshold reflects the requirements of heavy traffic asphalt for the sealing performance of storage and transportation containers, that is, the sealing level of the container needs to reach what level to effectively prevent the leakage or contamination of heavy traffic asphalt; the melting point range threshold reflects the melting characteristics of heavy traffic asphalt, that is, within what temperature range the heavy traffic asphalt can maintain a stable molten state to ensure normal storage, transportation and use. Then, based on the determined characteristic thresholds, the indicator conversion based on the transportation environment is performed to obtain environmental indicators directly related to the transportation environment. For example, the volatility point threshold is converted into the upper threshold of the ambient temperature, that is, when the ambient temperature exceeds this threshold, the heavy traffic asphalt will over-volatilize and produce harmful gases, thus affecting transportation safety and quality; the sealing level threshold is converted into the limiting conditions of ambient humidity or pressure, that is, the sealing performance of the storage and transportation container needs to meet certain humidity or pressure requirements to effectively prevent the external environment from affecting the quality of heavy traffic asphalt; the melting point range threshold is converted into a reasonable range of ambient temperature, that is, within this temperature range, the heavy traffic asphalt can maintain a stable molten state to ensure normal storage and use. Through indicator conversion, the environmental indicators are determined, which provides an important basis for the subsequent determination of strong constraints.

[0035] Subsequently, based on the obtained environmental indicators, further expansion processing in the direction of indicator fault tolerance is carried out to integrate and determine strong constraints. The so-called expansion processing in the direction of indicator fault tolerance refers to relaxing the restriction range of environmental indicators on the premise of meeting the requirements of heavy traffic asphalt characteristics to increase the fault tolerance and adaptability of constraints. According to the importance and sensitivity of different environmental indicators, the corresponding expansion interval is set. For example, for key indicators closely related to the quality of heavy traffic asphalt, such as temperature indicators, the expansion interval should be relatively small to strictly control the fluctuation of ambient temperature; while for secondary indicators that have relatively little impact on the quality of heavy traffic asphalt, such as humidity indicators, the expansion interval can be appropriately enlarged to improve the flexibility of constraints. Through differentiated indicator expansion processing and comprehensive consideration of the expansion results of various environmental indicators, strong constraints are obtained, which can not only meet the core requirements of heavy traffic asphalt quality control, but also take into account the variability and complexity of the transportation environment, and improve the practicality and robustness of constraints.

[0036] By establishing a precise relationship between the characteristics of heavy traffic asphalt and the transportation environment, and obtaining strong constraints on this basis, we can effectively guide the environmental monitoring and regulation of the heavy traffic asphalt transportation process, and provide support for ensuring transportation quality.

[0037] Further, such as Figure 2 As shown, the embodiment of the present application also includes: Based on the front-end self-adjustment rules, a first adjustment mode is determined, and the first adjustment mode is the front-end autonomous adjustment; for the key monitoring node, a second adjustment mode is determined, and the second adjustment mode is the back-end decision adjustment; based on the limit constraint, a third adjustment mode is determined, and the third adjustment mode is the front-end strong constraint adjustment; the first adjustment mode, the second adjustment mode and the third adjustment mode are combined, and the mode environment state constraints are performed to determine the joint adjustment mode.

[0038] In a preferred embodiment, first, based on the front-end self-adjustment rule, a first adjustment mode, i.e., the front-end autonomous adjustment mode, is determined. The front-end self-adjustment rule means that in the process of heavy-duty asphalt transportation, for general, small fluctuations in environmental parameters, there is no need for the intervention of the back-end system, and the environmental parameters are automatically adjusted according to the preset adjustment logic and thresholds only through the control system of the transport vehicle or storage container itself. The front-end autonomous adjustment mode can quickly respond to conventional environmental changes, reduce the processing load of the back-end system, and improve the real-time performance of the adjustment.

[0039] Then, for the key monitoring nodes, determine the second adjustment mode, that is, the back-end decision adjustment mode. Different from the front-end autonomous adjustment, the back-end decision adjustment mainly deals with complex and critical environmental parameter anomalies, which requires intelligent analysis and decision-making of the back-end system. When the key monitoring node is triggered during the transportation environment monitoring process, that is, when an environmental parameter anomaly occurs that may seriously affect the quality of heavy traffic asphalt or transportation safety, it automatically switches to the back-end decision adjustment mode. In the back-end decision adjustment mode, the monitoring data of the transport vehicle or storage container will be transmitted to the back-end system in real time. The back-end system will comprehensively analyze the environmental status, evaluate the severity and scope of the anomaly, and generate corresponding adjustment instructions based on the preset decision rules and optimization algorithms to guide the front-end to perform precise and effective environmental control to eliminate the impact of the anomaly and ensure transportation quality and safety.

[0040] Subsequently, based on the limit constraint, the third adjustment mode, namely the front-end strong constraint adjustment mode, is determined. The limit constraint means that during the transportation of heavy-duty asphalt, for certain extreme environmental parameter abnormalities that exceed the safety threshold, mandatory adjustment measures need to be taken immediately to prevent major accidents or losses. When the limit constraint is triggered during the transportation environment monitoring process, that is, when an extreme abnormality that seriously threatens the quality of heavy-duty asphalt or transportation safety occurs, it automatically switches to the front-end strong constraint adjustment mode. In the front-end strong constraint adjustment mode, there is no need to wait for the decision of the back-end system. The control system of the transport vehicle or storage container will directly execute the preset mandatory adjustment measures, such as emergency braking, rapid cooling, power cut-off, etc., to curb the further deterioration of the abnormality as quickly as possible and ensure the safety of the transportation process.

[0041] Subsequently, the first adjustment mode, the second adjustment mode and the third adjustment mode are combined, and the mode environment state constraints are performed to finally determine the combined adjustment mode. The core of the combined adjustment mode is to dynamically select the optimal combination of adjustment modes according to different environmental states to maximize the adjustment effect. When performing mode environment state constraints, the real-time monitoring values, change trends, abnormality levels and other factors of various environmental parameters are comprehensively considered, and they are matched with the preset environmental states to identify the current environmental state type, such as normal state, abnormal state, dangerous state, etc. On this basis, according to the preset mode selection logic, the enabling conditions and priorities of each adjustment mode are dynamically determined. For example, in the normal environmental state, the front-end autonomous adjustment mode is enabled; in the abnormal state, the back-end decision-making adjustment is enabled; in the dangerous state, the front-end strong constraint adjustment mode is enabled. Through the combined adjustment mode, according to the real-time state of the heavy asphalt transportation environment, the adjustment strategy and execution method are adaptively adjusted to improve the transportation environment management effect.

[0042] Furthermore, the embodiment of the present application also includes: Acquire environmental monitoring data, extract the environmental indicators and determine whether they meet the key monitoring nodes, wherein the determination is made by calibrating the environmental characteristic value tuple; if they meet, transmit the environmental monitoring data back to the back end of the adaptive monitoring and control module, evaluate the transportation status and make environmental adjustment decisions, and determine the environmental control data.

[0043] In a feasible implementation, first, environmental monitoring data is obtained, that is, various sensors and monitoring devices deployed on transport vehicles or storage containers are used to collect various parameters related to the heavy asphalt transportation environment in real time, such as temperature, humidity, pressure, etc. After obtaining the environmental monitoring data, environmental indicators corresponding to strong constraints are further extracted, such as temperature indicators, humidity indicators, pressure indicators, etc. Subsequently, the extracted environmental indicators are compared with the preset key monitoring nodes to determine whether the current environmental state has reached the conditions for triggering the key monitoring nodes. Among them, the judgment process is achieved by calibrating the environmental characteristic value multi-group. The environmental characteristic value multi-group is obtained by simulating multiple transport environments, reflecting the characteristic value combination of the environmental parameters corresponding to each key monitoring node. By matching the real-time monitored environmental indicators with the environmental characteristic value multi-group, it is possible to accurately identify whether the current environmental state meets the triggering conditions of a key monitoring node, providing a reliable basis for subsequent control decisions.

[0044] If the judgment result shows that the current environmental status meets a certain key monitoring node, the back-end decision-making and adjustment mode is automatically triggered. Specifically, the currently collected environmental monitoring data is transmitted back to the back-end of the adaptive monitoring and control module in real time through the wireless communication network. After receiving the environmental monitoring data, the back-end first pre-processes and extracts features of the data, and conducts a comprehensive assessment of the current transportation status. It not only considers the degree of abnormality of a single environmental parameter, but also analyzes the correlation and joint impact between the parameters, and comprehensively judges the risk level and abnormality type of the transportation environment. Based on the results of the transportation status assessment, the back-end further executes environmental adjustment decisions, generates the optimal combination of control instructions in real time according to the preset adjustment strategy library and optimization algorithm, and encapsulates it as environmental control data, which is sent to the control system of the transportation vehicle or storage container to guide it to perform targeted environmental adjustments to eliminate transportation environment abnormalities and ensure the transportation quality of heavy asphalt.

[0045] Through precise perception and intelligent control of the heavy-duty asphalt transportation environment, we can not only promptly discover and respond to key anomalies in the transportation environment, but also generate environmental control data through intelligent decision-making at the back end, thereby maximizing the quality of heavy-duty asphalt transportation and improving transportation efficiency and safety.

[0046] Furthermore, the embodiment of the present application also includes: Establish a communication connection between the limit constraint and the monitoring equipment group, and set the limit trigger adjustment rule; the limit constraint front end identifies the environmental monitoring data, and generates a trigger adjustment instruction if any one of the strong constraint conditions is triggered; based on the trigger adjustment instruction, determine the environmental control data with the limit trigger adjustment rule.

[0047] In a preferred embodiment, first, a communication connection is established between the limit constraint and the monitoring equipment group. The monitoring equipment group refers to various sensors, monitoring devices and actuators deployed on the transport vehicle or storage container, which are responsible for real-time collection of environmental monitoring data and execution of corresponding adjustment operations according to the received environmental control data. While establishing the communication connection, preset limit trigger adjustment rules, clearly define the judgment criteria of various dangerous environmental states, and the corresponding adjustment measures and execution methods, to provide a decision-making basis for the work of the limit constraint. Then, the limit constraint deployed at the front end of the transport vehicle or storage container will identify the environmental monitoring data in real time, and compare the environmental monitoring data with the preset strong constraint conditions to determine whether the current transportation environment is in a dangerous situation. Once the environmental monitoring data triggers any one of the strong constraints, it indicates that the transportation environment has already appeared in a dangerous situation that seriously threatens the quality of heavy asphalt or transportation safety, and mandatory adjustment measures must be taken immediately. At this time, the limit constraint automatically generates a trigger adjustment instruction, executes an emergency control operation, and obtains the corresponding environmental control data.

[0048] Furthermore, the embodiment of the present application also includes: If the environmental monitoring data does not meet the requirements of the key monitoring nodes and does not trigger the strong constraint conditions, the environmental control data is determined based on the front-end self-adjustment rules.

[0049] In a feasible implementation, when the environmental monitoring data neither meets the conditions of the key monitoring nodes nor triggers the strong constraint conditions, although the transportation environment has experienced certain fluctuations or deviations, it has not yet reached the level where the back-end decision-making adjustment or the front-end strong constraint adjustment needs to be initiated, and is a relatively minor abnormality or fluctuation within the normal range. In this case, environmental regulation is performed based on the front-end self-regulation rules. When it is determined that the current environmental monitoring data falls within the scope of front-end self-regulation, the local control system of the transport vehicle or storage container will directly enable the front-end self-regulation rules, calculate and generate the corresponding environmental regulation data based on the real-time monitoring data, and control each actuator to adjust, and maintain the relative stability of the transportation environment by fine-tuning parameters such as temperature, humidity, and pressure, and avoid further development of abnormal fluctuations. In the front-end self-regulation, since the adjustment process is completed locally, there is no need for frequent interaction with the back-end, which reduces the communication and computing burden.

[0050] Through front-end self-regulation, fast and lightweight regulation is achieved, which effectively reduces the processing burden and improves the response speed. At the same time, through the organic combination with other regulation modes, a comprehensive, flexible and efficient environmental regulation system is constructed, which can effectively deal with various abnormal situations in the transportation of heavy asphalt and maximize the transportation quality.

[0051] Furthermore, the embodiment of the present application also includes: Carry out transportation tracking and obtain the weather forecast data of the target transportation route, conduct transportation risk assessment, and determine the risk coefficient; if the risk coefficient is greater than the risk threshold, optimize the transportation route in combination with the transportation positioning data.

[0052] In a preferred embodiment, first, the transportation of heavy-duty asphalt is tracked in real time by a GPS positioning device installed on the transport vehicle. At the same time, the meteorological forecast data of the target transport route is obtained, which includes the forecast information such as weather conditions, temperature changes, and precipitation probability along the transport route in the future. After obtaining the transport tracking data and meteorological forecast data, these data are comprehensively analyzed to evaluate the current transportation risk level. Specifically, the meteorological forecast data is matched with the constructed transportation environment library to identify the adverse meteorological conditions that may have an adverse impact on the transportation of heavy-duty asphalt, such as heavy rain, typhoon, high temperature, ice and snow, etc.; at the same time, considering the current transportation progress, road conditions, traffic conditions and other factors, the potential risks of the transportation process are evaluated from multiple dimensions. Subsequently, the results of the risk assessment are quantified into a risk coefficient. The higher the coefficient, the greater the uncertainty and potential danger faced by the transportation process. After that, the risk coefficient is compared with the preset risk threshold. The risk threshold is a critical value set according to the transportation safety requirements of heavy-duty asphalt, combined with a large amount of historical data and expert experience. When the risk coefficient exceeds the risk threshold, it means that the current transportation may have a large safety hazard and needs to be optimized and adjusted. Once it is confirmed that the risk factor exceeds the standard, the transportation route optimization process is automatically started to obtain the real-time positioning data of the transport vehicle, determine its current location and road section, and calculate the optimal path that avoids harsh environments and road conditions while taking into account transportation efficiency based on the road network topology of the target transportation route, meteorological conditions along the way, road conditions, etc., and generate path adjustment instructions, which are sent to the transport vehicle through the wireless communication network to guide the adjustment of the driving route, ensure the safety and smoothness of the transportation process, and maximize the safety and stability of heavy asphalt transportation.

[0053] In summary, the method for monitoring the transportation environment of heavy asphalt provided in the embodiment of the present application has the following technical effects: Based on the characteristics of heavy-duty asphalt, the monitoring multi-group based on the transportation environment is determined, and the monitoring parameters are determined in a targeted manner to improve the effectiveness and pertinence of the monitoring, laying the foundation for subsequent monitoring. Traverse the monitoring multi-group, build multiple transportation environments and conduct simulation drills to determine the key monitoring nodes. The key monitoring nodes are marked with environmental characteristic value multi-groups to provide guidance for subsequent key monitoring and regulation. Based on the characteristics of heavy-duty asphalt, determine the strong constraints based on the storage and transportation state, configure the limit constraint, where the storage and transportation state is in liquid form, including the preset melting point span of the storage and transportation state, set a safe temperature and viscosity range for the characteristics of heavy-duty asphalt, and trigger warning and control once the range is exceeded. Through the configuration of the limit constraint, risk warning and safety prevention and control are realized in the whole storage and transportation process. Set the front-end self-adjustment rules, combine the key monitoring nodes and the limit constraint, determine the joint adjustment mode, conduct data-driven training, build an adaptive monitoring and control module, dynamically adjust the control strategy according to the real-time collected environmental parameters, and realize precise regulation of the transportation environment. Set a preset monitoring cycle, conduct periodic continuous monitoring, combine the adaptive monitoring and control module to evaluate and make adjustment decisions on the transportation status, determine the environmental control data, continuously collect transportation environment data according to the preset monitoring cycle, combine the adaptive monitoring and control module to conduct real-time evaluation and decision-making, and generate targeted environmental control data to achieve dynamic monitoring and control of the entire transportation process. The control system of the transport vehicle responds to the environmental control data and controls the transportation environment of heavy-duty asphalt. The transportation environment includes the carriage environment and the storage container environment. According to the received environmental control data, the environmental parameters of the carriage and storage container, such as temperature and humidity, are automatically adjusted to ensure that the heavy-duty asphalt is transported under the best environmental conditions, thereby ensuring its quality and performance.

[0054] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0055] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these changes and variations.

Claims

1. A method for monitoring the transportation environment of heavy traffic asphalt, characterized in that: The method comprises: Based on the characteristics of heavy traffic asphalt, determine the monitoring multi-group based on the transportation environment; Traversing the monitoring tuple, constructing a multi-transportation environment and conducting a simulated exercise, and determining key monitoring nodes, wherein the key monitoring nodes are identified by a tuple of environmental characteristic values; Based on the characteristics of the heavy traffic asphalt, determine the strong constraint conditions based on the storage and transportation state, and configure the limit constraint, wherein the storage and transportation state is in liquid form and includes a preset melting point span of the storage and transportation state; Set the front-end self-adjustment rules, combine the key monitoring nodes with the limit constraint, determine the joint adjustment mode, perform data-driven training, and build an adaptive monitoring and control module; Set a preset monitoring period, conduct periodic continuous monitoring, combine the adaptive monitoring and control module to conduct transportation status assessment and adjustment decisions, and determine environmental control data; The control system of the transport vehicle responds to the environmental control data to control the transport environment of the heavy-duty asphalt, wherein the transport environment includes the vehicle compartment environment and the storage container environment.

2. The method for monitoring the transportation environment of heavy traffic asphalt according to claim 1, characterized in that: The construction of a multi-transport environment includes: Interact with the target transportation route to determine a transportation environment library, wherein the transportation environment library includes an external environment and a road condition environment; In combination with the transport environment library, based on the monitoring multi-group, extract and determine multi-environment feature values, wherein one type of transport environment corresponds to one group of multi-environment feature values; The multi-environment characteristic values ​​are integrated and mapped with the transportation environment to determine the multiple transportation environments.

3. The method for monitoring the transportation environment of heavy traffic asphalt according to claim 1, characterized in that: The determination of the strong constraint conditions based on the storage and transportation status includes: Identify the characteristics of the heavy traffic asphalt and determine the characteristic thresholds based on the storage and transportation status, wherein the characteristic thresholds at least include a volatility point threshold, a sealing level threshold, and a melting point range threshold; Based on the characteristic threshold, an indicator conversion based on the transportation environment is performed to determine the environmental indicator; The environmental indicators are traversed, expansion processing is performed in the indicator fault tolerance direction, and the strong constraint conditions are determined by integration, wherein the expansion intervals of the environmental indicators are different.

4. The method for monitoring the transportation environment of heavy traffic asphalt according to claim 1, characterized in that: The determining of the joint adjustment mode includes: Based on the front-end self-adjustment rule, determining a first adjustment mode, wherein the first adjustment mode is front-end autonomous adjustment; For the key monitoring node, determining a second adjustment mode, where the second adjustment mode is backend decision adjustment; Based on the limit constraint, determining a third adjustment mode, wherein the third adjustment mode is front-end strong constraint adjustment; The first adjustment mode, the second adjustment mode and the third adjustment mode are combined, and mode environment state constraints are performed to determine the combined adjustment mode.

5. The method for monitoring the transportation environment of heavy traffic asphalt according to claim 3, characterized in that: The determining of environmental control data includes: Acquire environmental monitoring data, extract the environmental indicators and determine whether they meet the key monitoring nodes, wherein the determination is made by calibrating the environmental characteristic value multi-tuple; If it meets the requirements, the environmental monitoring data is transmitted back to the back end of the adaptive monitoring and control module to evaluate the transportation status and make environmental adjustment decisions to determine the environmental control data.

6. A method for monitoring the transportation environment of heavy traffic asphalt as claimed in claim 5, characterized in that: The determining of environmental control data includes: Establishing a communication connection between the limit restraint and the monitoring equipment group, and setting limit trigger adjustment rules; The limit constraint front end identifies the environmental monitoring data and generates a trigger adjustment instruction if any one of the strong constraint conditions is triggered; Based on the trigger adjustment instruction, the environmental control data is determined according to the limit trigger adjustment rule.

7. A method for monitoring the transportation environment of heavy traffic asphalt as claimed in claim 5, characterized in that: If the environmental monitoring data does not meet the requirements of the key monitoring nodes and does not trigger the strong constraint conditions, the environmental control data is determined based on the front-end self-adjustment rules.

8. The method for monitoring the transportation environment of heavy traffic asphalt according to claim 2, characterized in that: The method further comprises: Conduct transportation tracking and obtain weather forecast data for the target transportation route, conduct transportation risk assessment, and determine the risk factor; If the risk factor is greater than the risk threshold, the transportation route is optimized in combination with the transportation positioning data.

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