Tank truck intelligent monitoring unloading management system based on Internet of Things

Abstract

The invention provides a tank truck intelligent monitoring unloading management system based on the Internet of Things, and relates to the technical field of intelligent monitoring. The system comprises a discharging strategy module used for collecting various data, setting initial discharging parameters according to material type data, adjusting the initial discharging parameters according to transportation influence data to obtain adjusted discharging parameters, and generating a discharging strategy according to the adjusted discharging parameters. And the curve generation module is used for extracting microscopic particle motion data, tank body pressure and material flow velocity data from the internal data of the tank body, and generating a dynamic unloading curve by using a multi-parameter dynamic curve method. And the scheme adjusting module is used for carrying out anomaly identification analysis on the dynamic unloading curve to obtain an anomaly reason, and adjusting the unloading strategy according to the anomaly reason to obtain an optimized unloading scheme. The unloading strategy is dynamically adjusted according to the material characteristics, the transportation influence and the change of the internal data of the tank body, different working conditions and variable requirements are rapidly adapted, and the unloading effect is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring, and particularly relates to a liquid tank truck intelligent monitoring unloading management system based on Internet of Things. BACKGROUND

[0002] In the field of modern logistics transportation, the liquid tank truck is the main transportation tool for liquid materials such as polymer colloidal solution, high-viscosity oil and fat materials, and food raw materials, and the intelligent management of the unloading process of the liquid tank truck has always been the focus of the industry. The traditional liquid tank truck unloading system mainly relies on the experience of the operator to set the unloading parameters (such as air pressure, vibration frequency, flow rate, etc.), and the unloading state is monitored by manually observing the pressure gauge, flow meter and other single parameters.

[0003] In the prior art, the initial unloading parameters are usually set based on the general experience value of the material type, without fully considering the material characteristics (such as water content, particle size distribution), the influence of the transportation process vibration and the internal state of the tank. During the unloading process, the material flow state is dynamically changed (such as the transportation vibration may cause the local compaction of the material) due to the influence of factors such as transportation vibration and environmental temperature, but the traditional system lacks the real-time sensing and parameter self-adaptive adjustment capability for such changes. The existing monitoring system only collects macro data such as pressure and flow rate, and cannot obtain the micro particle motion characteristics inside the tank, which leads to the inability to identify potential risks such as fluidization failure and caking in advance. When unloading abnormalities occur, the traditional technology relies on the operator to determine the cause through the change of a single parameter, and lacks correlation analysis of multiple parameters such as pressure, flow rate and particle motion.

[0004] In view of the above defects, the present application provides a liquid tank truck intelligent monitoring unloading management system based on Internet of Things. SUMMARY

[0005] The present application provides a liquid tank truck intelligent monitoring unloading management system based on Internet of Things, which solves the defects in the prior art that there is a lack of accurate analysis and strategy optimization of the unloading process, the unloading strategy cannot be dynamically adjusted, and it is difficult to adapt to changing unloading demands.

[0006] The present application provides a liquid tank truck intelligent monitoring unloading management system based on Internet of Things, which includes: an unloading strategy module for collecting material type data, transportation influence data and tank internal data, setting initial unloading parameters according to the material type data, adjusting the initial unloading parameters to obtain adjusted unloading parameters according to the transportation influence data, and generating an unloading strategy according to the adjusted unloading parameters.

[0007] A curve generation module is used to extract micro particle motion data, tank pressure and material flow rate data from the tank internal data, and generate a dynamic unloading curve using a multi-parameter dynamic curve method.

[0008] The scheme adjustment module is configured to identify and analyze the abnormal reasons of the dynamic unloading curve by using the curve abnormal diagnosis method, and adjust the unloading strategy according to the abnormal reasons to obtain an optimized unloading scheme.

[0009] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things, the step of setting the initial unloading parameter includes: According to the material type data, the characteristic information and the recommended flow rate range of different materials are determined, and the optimal unloading pressure range of the material under different environmental conditions is determined to obtain the basic parameter.

[0010] According to the behavior characteristics of the material in the flow state, the basic parameter is dynamically supplemented to obtain the basic characteristic parameter, and the material inherent frequency and the initial unloading speed are obtained by analyzing the basic characteristic parameter.

[0011] According to the fluid mechanics, the minimum air pressure of the material fluidization is determined.

[0012] According to the moisture content of the material, the environmental temperature and the material particle size, the minimum initial air pressure is corrected to obtain the minimum initial air pressure.

[0013] According to the resonance theory, the material bridging is destroyed, and the vibration parameter is calculated by matching the material inherent frequency.

[0014] According to the initial unloading speed and based on the particle sedimentation theory, the critical flow rate is calculated.

[0015] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things, the step of obtaining the transportation influence data includes: The execution of the transportation task is tracked in real time, and the key information of the goods and the vehicle operation data are obtained.

[0016] The vehicle acceleration is extracted from the vehicle operation data and is subjected to fast Fourier transform, the power spectral density is analyzed, the vibration main frequency is identified, and the vibration influence data are obtained.

[0017] The pressure data are extracted from the key information of the goods, the variation coefficient is calculated through the pressure mean value and the standard deviation, and it is judged whether the variation coefficient is greater than a preset value, if yes, the pressure distribution data are output, otherwise the key information of the goods is continuously collected.

[0018] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things, the step of adjusting the unloading parameter includes: According to the vibration influence data, the influence of the vibration degree of the vehicle in the transportation process on the liquid material is analyzed, and the minimum initial air pressure is adjusted according to the change of the liquid material flow caused by the air pressure distribution of the pressure distribution data to obtain the adjusted air pressure.

[0019] According to the vibration influence data, the closeness of the vehicle vibration frequency and the natural vibration frequency of the liquid material in the transportation process is analyzed, and the vibration parameters are adjusted to obtain the corrected vibration parameters.

[0020] The flowability of the liquid material is analyzed in combination with the vibration influence data and the pressure distribution data, and the critical flow velocity is adjusted in combination with the transportation time to obtain the adjusted flow velocity.

[0021] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of generating the unloading strategy include: The adjusted air pressure, the corrected vibration parameters and the adjusted flow velocity are mapped to a unified scale to remove the dimensional differences to obtain the parameter combination.

[0022] The parameter combination is classified into multiple category combinations by using K-means clustering, and a strategy template library is constructed based on the material type data and the vehicle operation data.

[0023] According to the parameter characteristics of different category combinations, the optimal strategy template in the strategy template library is selected as the unloading strategy through fuzzy logic rules.

[0024] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of obtaining multiple category combinations include: The within-cluster sum of squares under different k values is calculated by the elbow rule, and the k value corresponding to the falling inflection point is selected as the number of clusters.

[0025] A first centroid is randomly selected, and subsequent centroids are repeatedly selected as the data points farthest from the selected centroids until a preset number of centroids are selected.

[0026] The Euclidean distance of the parameter combination to the preset number of centroids is calculated and assigned to the nearest cluster, and the formula is expressed as: In the formula, is the parameter combination vector, is the first centroid, and the parameter combination vector is the Euclidean distance of the parameter combination vector to the first centroid, is the first feature value of the parameter combination vector, is the first feature value of the first centroid.

[0027] In the formula, is the number of data points of the first cluster, is the first cluster data set.

[0028] When the centroid position changes less than a preset threshold, stop assigning, and calculate the contour coefficient, which is expressed by the formula: In the formula, is the average distance of the point to other points in the same cluster, is the average distance of the point to the nearest different cluster, is the contour coefficient of the first data point, is the maximum value of and .

[0029] According to the contour coefficient, analyze the parameter mean value and distribution of each cluster, and classify the parameter combinations to obtain multiple classified combinations.

[0030] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of extracting micro-particle motion data, tank pressure and material flow rate data include: Obtain particle sensor data, pressure sensor data and flow rate sensor data through tank internal data.

[0031] Through time series analysis of the particle sensor data, the motion trajectory and speed distribution of the powder particles in the tank are extracted, the flow mode and motion characteristics of the particles are analyzed, and the micro-particle motion data is determined in combination with the aggregation area and distribution law of the particles.

[0032] The pressure sensor data is analyzed in time domain, the change trend and fluctuation of the internal pressure of the tank are extracted, and the distribution law and difference of the pressure in the tank are analyzed to obtain the tank pressure.

[0033] According to the flow rate sensor data, the flow rate of the liquid material in the unloading pipeline is calculated, and the stability and uniformity of the flow rate are analyzed by calculating the fluctuation range and change amount of the flow rate to obtain the material flow rate data.

[0034] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of generating a dynamic unloading curve include: From the particle speed distribution and particle trajectory distribution, the micro-particle motion data is analyzed to obtain the liquid state index and collision energy as motion characteristics.

[0035] From the pressure change trend and distribution, the tank pressure is analyzed to obtain the pressure fluctuation coefficient and pressure gradient as pressure field characteristic parameters.

[0036] The time-averaged flow rate and flow rate decay rate are obtained by analyzing the material flow rate data from the flow rate variation trend and flow rate distribution as flow rate characteristic parameters.

[0037] The mutual relationship among the motion characteristic, pressure field characteristic parameter and flow rate characteristic parameter is analyzed, and the curve parameters are determined according to the dynamic characteristics of the unloading process.

[0038] According to the curve parameters, the dynamic unloading curve is drawn with the unloading time as the horizontal axis and the category combination as the vertical axis.

[0039] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of analyzing the abnormal reasons include: The real-time data are obtained by extracting the features of the dynamic unloading curve, and the real-time data are monitored to determine whether the data points exceed the preset threshold, and if so, an abnormal alarm is triggered to output the abnormal points.

[0040] The abnormal features are obtained by observing the change of the abnormal points on the curve and the influence on the curve shape.

[0041] The preliminary judgment is made based on the experience of the operating personnel and combined with the abnormal features, and the abnormal reasons are determined in combination with the running state of the unloading equipment and the material characteristics.

[0042] According to the liquid tank truck intelligent monitoring unloading management system based on the Internet of Things provided by the application, the steps of adjusting to obtain the optimization strategy scheme include: The multiple abnormal category reasons are obtained by identifying the abnormal types of the abnormal reasons, and the influence range of the local abnormality and the systematic abnormality of the multiple abnormal category reasons is evaluated.

[0043] The category combination that needs to be modified is determined according to the abnormal reasons, and the adjustment range is determined according to the influence range.

[0044] The optimal strategy template is determined according to the category combination that needs to be modified, and the unloading strategy is adjusted according to the adjustment range to obtain the optimization strategy scheme.

[0045] The present invention provides an intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things. It uses intelligent algorithms to accurately adjust unloading parameters based on transportation impact data and tank internal data. By using resonance theory and fluid mechanics principles to optimize vibration parameters and air pressure, it solves the problem of not being able to fully integrate material characteristics, transportation process and real-time data inside the tank, lacking accurate analysis and strategy optimization of the unloading process, resulting in unreasonable unloading parameter settings and affecting the unloading effect. It achieves the beneficial effect of dynamically adjusting the unloading strategy, quickly adapting to different working conditions and changing needs, and ensuring the unloading effect. By comprehensively considering multiple parameters, using K-means clustering, fuzzy logic and other methods to classify and process data, a strategy template library is constructed. Based on real-time data matching, the optimal strategy template is realized to achieve accurate formulation and efficient execution of unloading strategies. It solves the problem of being unable to dynamically adjust the unloading strategy based on real-time data and complex working conditions, being difficult to adapt to changing unloading needs, and being unable to ensure the efficiency and stability of the unloading process. It achieves the beneficial effect of reducing labor costs, improving management levels, and enhancing intelligence and automation levels. By monitoring the unloading curve in real time, anomalies can be quickly identified. Combining operating experience and equipment and material status, we conduct in-depth analysis of abnormal causes. The real-time monitoring and abnormality identification functions can promptly detect and handle abnormalities in the unloading process, prevent blockages, leakages and other problems, reduce safety and environmental risks, ensure the safety of personnel and equipment, and reduce environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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.

[0047] Figure 1 It is a structural diagram of the liquid tank truck intelligent monitoring and unloading management system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] The following combination Figure 1 The present invention describes an intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things.

[0050] Figure 1It is a structural schematic diagram of a liquid tank truck intelligent monitoring unloading management system based on Internet of Things provided by the embodiment of the application.

[0051] As shown in the figure, the liquid tank truck intelligent monitoring unloading management system based on Internet of Things provided by the embodiment of the application comprises: Figure 1 An unloading strategy module, configured to collect material type data, transportation influence data and tank interior data, set initial unloading parameters according to the material type data, adjust the initial unloading parameters to obtain adjusted unloading parameters according to the transportation influence data, and generate an unloading strategy according to the adjusted unloading parameters. The step of setting the initial unloading parameters comprises: Determining characteristic information and recommended flow rate range of different materials according to the material type data, and determining a best unloading pressure range of the materials under different environmental conditions to obtain basic parameters. The characteristic information can include particle size distribution, bulk density, loose density and flowability parameters, etc.

[0052] According to the behavior characteristics of the materials in the flow state, the basic parameters are dynamically supplemented to obtain basic characteristic parameters, and the basic characteristic parameters are analyzed to obtain material natural frequency and initial unloading speed. The behavior characteristics can include vibration flowability and slump degree, etc.

[0053] According to the behavior characteristics of the materials in the flow state, the basic parameters are dynamically supplemented to obtain basic characteristic parameters, and the basic characteristic parameters are analyzed to obtain material natural frequency and initial unloading speed. The behavior characteristics can include vibration flowability and slump degree, etc.

[0054] According to fluid mechanics, the minimum air pressure of material fluidization is determined, and the formula is expressed as: In the formula, is the material density, is the acceleration of gravity, is the material height, is the friction coefficient between the material and the tank, is the flow coefficient, is the unloading port area, is the minimum air pressure.

[0055] According to the moisture content of the material, the environmental temperature and the material particle size, the minimum initial air pressure is corrected to obtain the minimum initial air pressure.

[0056] According to the resonance theory, the material arch is destroyed, and the vibration parameters are calculated by matching the material natural frequency, and the formula is expressed as: In the formula, is the material particle toughness coefficient, is the equivalent mass of the vibration unit, is the vibration parameter, is the fixed coefficient.

[0057] According to the initial unloading speed and based on the particle settling theory, the critical flow rate is calculated, which is expressed by the formula: In the formula, is the particle diameter, is the particle density, is the air density, is the drag coefficient, is the critical flow rate.

[0058] The step of obtaining transportation influence data includes: Real-time tracking of the execution of the transportation task, obtaining key information of the goods and vehicle operation data. The key information of the goods can include loading and unloading time, transportation time, route, estimated arrival time, etc. The vehicle operation data can include engine speed, fuel consumption, acceleration and fault code, etc.

[0059] Extract the vehicle acceleration from the vehicle operation data and perform fast Fourier transform, analyze the power spectral density, and identify the vibration main frequency to obtain vibration influence data.

[0060] Extract the pressure data from the key information of the goods, calculate the coefficient of variation through the mean and standard deviation of the pressure, and determine whether the coefficient of variation is greater than the preset value. If yes, output the pressure distribution data, otherwise continue to collect the key information of the goods.

[0061] The step of adjusting to obtain the adjusted unloading parameter includes: According to the vibration influence data, analyze the influence of the vibration degree of the vehicle in the transportation process on the liquid material, and adjust the minimum initial air pressure according to the change of the air pressure distribution data on the liquid flow to obtain the adjusted air pressure, which is expressed by the formula: In the formula, is the adjusted air pressure, is the temperature correction coefficient, is the temperature difference between the material temperature and the reference temperature, is the minimum initial air pressure, is the vibration compaction coefficient, is the pressure influence coefficient.

[0062] According to the vibration influence data, analyze the proximity of the vibration frequency of the vehicle in the transportation process to the natural vibration frequency of the liquid material, and adjust the vibration parameter to obtain the corrected vibration parameter, which is expressed by the formula: In the formula, is the initial material density, is the material density after transportation, is the corrected vibration parameter.

[0063] The flowability of the liquid material is analyzed in combination with the vibration influence data and the pressure distribution data, and the critical flow rate is adjusted in combination with the transportation time to obtain an adjusted flow rate, which is expressed by a formula as follows: In the formula, is the adjusted flow rate, is a time attenuation coefficient, is a critical time, is a critical flow rate, is a flowability comprehensive index, is a time.

[0064] The steps of generating the unloading strategy include: mapping the adjusted air pressure, the corrected vibration parameter and the adjusted flow rate to a unified scale to remove dimensional differences to obtain a parameter combination.

[0065] using K-means clustering to classify the parameter combination to obtain a plurality of category combinations, and constructing a strategy template library based on the material type data and the vehicle operation data.

[0066] selecting an optimal strategy template in the strategy template library as the unloading strategy according to the parameter characteristics of different category combinations through fuzzy logic rules.

[0067] extracting the air pressure adjustment amount, the vibration correction parameter and the flow rate adjustment value as key parameter characteristics from the plurality of category combinations, and defining a fuzzy language variable for each key parameter characteristic, which can include: air pressure state: {extremely low, low, medium, high, extremely high}; vibration intensity: {weak, medium, strong, intense}; flow rate level: {slow, moderate, fast, extremely fast}.

[0068] adopting a triangular or trapezoidal membership function to quantify the degree to which different key parameter characteristics belong to the fuzzy language variable to obtain a parameter characteristic fuzzy set, and formulating rules based on the material type data and the vehicle operation data and in combination with industrial experience. The rules can include: rule 1: if the air pressure is high, the vibration is medium, the flow rate is moderate, and the material is viscous particles, then the “segmented pressure reduction + intermittent vibration” strategy template is preferentially selected; rule 2: if the air pressure is extremely low, the flow rate is slow, and the vehicle load is large, then the “low pressure continuous + strong vibration” strategy template is selected to prevent blockage.

[0069] in the form of “IF condition THEN conclusion”, the parameter characteristic fuzzy set is associated with the strategy template library, and for the key parameter characteristics of different category combinations, the membership degrees in the parameter characteristic fuzzy set are calculated.

[0070] Match the rules that meet the preset conditions to calculate the triggering strength of each rule, and generate the corresponding fuzzy subset based on the membership degree.

[0071] The fuzzy subset output is converted into a definite value as the priority score of each strategy template in the strategy template library, and the optimal strategy template with the highest priority score is selected as the unloading strategy.

[0072] The steps to obtain multiple category combinations include: The elbow rule is used to calculate the intra-cluster sum of squares under different k values, and the k value corresponding to the descending inflection point is selected as the number of clusters.

[0073] The first centroid is randomly selected, and subsequent centroids are repeatedly selected from the data points farthest from the selected centroid until the preset number of centroids is selected.

[0074] Calculate the Euclidean distance of the parameter combination to the preset number of centroids and assign them to the nearest cluster. The formula is: Where, is the parameter combination vector, It is centroids, is the parameter combination vector To The Euclidean distance between the centroids, is the parameter combination vector eigenvalues, It is The centroid eigenvalues.

[0075] Recalculate the mean of each cluster parameter as the new centroid, the formula is expressed as: Where, It is The number of data points in the cluster, It is Cluster data collection.

[0076] When the centroid position change is less than the preset threshold, the allocation is stopped and the silhouette coefficient is calculated. The formula is: Where, Yes The average distance to other points in the same cluster, Yes The average distance to the nearest different cluster, It is The silhouette coefficient of the data points, Yes and Take the maximum value.

[0077] According to the profile coefficient, the parameter mean value and distribution of each cluster are analyzed, and the parameter combination is classified to obtain multiple classified combinations.

[0078] A curve generation module is configured to extract micro-particle motion data, tank pressure and material flow rate data from the tank internal data, and generate a dynamic unloading curve using a multi-parameter dynamic curve method.

[0079] The step of extracting the micro-particle motion data, the tank pressure and the material flow rate data comprises: The particle sensor data, the pressure sensor data and the flow rate sensor data are obtained through the tank internal data.

[0080] The particle sensor data is analyzed by time series to extract the motion trajectory and velocity distribution of the powder particles in the tank, analyze the flow pattern and motion characteristics of the particles, and determine the micro-particle motion data in combination with the aggregation area and distribution rule of the particles.

[0081] The pressure sensor data is analyzed in time domain to extract the change trend and fluctuation of the internal pressure of the tank, and analyze the distribution rule and difference of the pressure in the tank to obtain the tank pressure.

[0082] The flow rate of the liquid material in the unloading pipeline is calculated according to the flow rate sensor data, and the stability and uniformity of the flow rate are analyzed by calculating the fluctuation range and change amount of the flow rate to obtain the material flow rate data.

[0083] The step of generating the dynamic unloading curve comprises: The liquid state index and collision energy are obtained as motion characteristics by analyzing the micro-particle motion data from the particle velocity distribution and particle trajectory distribution.

[0084] The pressure fluctuation coefficient and pressure gradient are obtained as pressure field characteristic parameters by analyzing the tank pressure from the pressure change trend and distribution.

[0085] The time-averaged flow rate and flow rate attenuation rate are obtained as flow rate characteristic parameters by analyzing the material flow rate data from the flow rate change trend and flow rate distribution.

[0086] The mutual relationship among the motion characteristics, the pressure field characteristic parameters and the flow rate characteristic parameters is analyzed, and the curve parameters are determined in combination with the dynamic characteristics of the unloading process.

[0087] According to the curve parameters, the dynamic unloading curve is drawn with the unloading time as the horizontal axis and the category combination as the vertical axis.

[0088] A scheme adjustment module is configured to perform abnormality identification analysis on the dynamic unloading curve using a curve abnormality diagnosis method to obtain an abnormality reason, adjust the unloading strategy according to the abnormality reason, and obtain an optimized unloading scheme.

[0089] The step of analyzing the cause of the anomaly includes: The real-time data is obtained by feature extraction on the dynamic unloading curve, and the real-time data is monitored to determine whether the data point exceeds the preset threshold. If yes, an abnormal alarm is triggered to output the abnormal point.

[0090] The abnormal point is analyzed to obtain the abnormal characteristics by observing the change of the abnormal point at the curve and the influence on the curve shape. Observe whether the flow rate curve at the abnormal point has sudden change, continuous decline or rise, etc. Whether the pressure curve has abnormal fluctuation, sudden rise or sudden drop. Whether the particle motion curve has abnormal speed, trajectory disorder, etc.

[0091] Based on the experience of the operator and combined with the abnormal characteristics, a preliminary judgment is made, and the cause of the anomaly is determined according to the running state of the unloading equipment and the characteristics of the material. According to the experience and field knowledge of the operator, a preliminary judgment is made combined with the abnormal characteristics. For example, the flow rate suddenly decreases and the pressure rises at the same time, which may be caused by pipe blockage. The particle motion trajectory is disordered and the flow rate fluctuates greatly, which may be caused by improper vibration parameter setting. Compare the current abnormal situation with similar abnormal cases in history to find similar abnormal characteristics and reasons, which can provide reference for the diagnosis of the current anomaly. Check the running state of the unloading equipment, including whether the air pressure system, vibration equipment, unloading valve and pipeline are working normally, whether there is failure or wear and tear. For example, check whether the air pressure pump is insufficient, whether the vibration motor is malfunctioning, whether the pipeline is leaking or blocked, etc. Check whether the characteristics of the material have changed, such as humidity, particle size distribution, flowability, etc. For example, material clumping due to moisture may cause flow rate to decrease and pressure to be abnormal. Material particles that are too large may cause pipe blockage, etc.

[0092] The step of adjusting to obtain the optimized strategy scheme includes: The abnormal type identification is performed on the abnormal causes to obtain a plurality of abnormal category causes, and the influence range of local anomaly and systematic anomaly is evaluated on the plurality of abnormal category causes.

[0093] According to the abnormal causes, the category combination that needs to be modified is determined, and the adjustment range is determined according to the influence range.

[0094] The optimal strategy template is determined according to the category combination that needs to be modified, and the unloading strategy is adjusted according to the adjustment range to obtain the optimized strategy scheme.

[0095] The embodiment provides a liquid tank truck intelligent monitoring unloading management system based on Internet of Things, comprehensively considers multiple parameters, processes data classification by using K-means clustering, fuzzy logic and other methods, and constructs a strategy template library. Based on real-time data, the optimal strategy template is matched, the accurate formulation and efficient execution of the unloading strategy are realized. And the unloading curve is monitored in real time, and the abnormality is quickly identified. Combined with operation experience and the state of equipment and materials, the abnormal reasons are analyzed in depth. The strategy is dynamically adjusted according to the influence range, and the efficient and stable unloading process is ensured. According to the transportation influence data and the tank body internal data, the intelligent algorithm is used to accurately adjust the unloading parameters. The resonance theory and fluid mechanics principle are used to optimize the vibration parameters and air pressure, adapt to the change of material characteristics and transportation conditions, and improve the unloading efficiency and stability. The application can dynamically adjust the unloading strategy according to the change of material characteristics, transportation influence and tank body internal data, quickly adapt to different working conditions and variable requirements, ensure the unloading effect, discover and handle the abnormality in the unloading process in time, prevent problems such as blockage and leakage, reduce the safety and environmental protection risk, ensure the safety of personnel and equipment, and reduce environmental pollution. The unloading parameters are accurately adjusted, the optimal unloading strategy is generated, the unloading process is more efficient and stable. The real-time monitoring and adjustment function can handle the abnormality in time, reduce interruption, and improve efficiency.

[0096] The device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. Those skilled in the art can understand and implement without creative labor.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some parts of the embodiment.

[0098] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things, characterized in that: include: a discharge strategy module, configured to collect material type data, transportation impact data, and tank internal data, set initial discharge parameters according to the material type data, adjust the initial discharge parameters according to the transportation impact data to obtain adjusted discharge parameters, and generate a discharge strategy according to the adjusted discharge parameters; a curve generation module for extracting microscopic particle motion data, tank pressure and material flow rate data from the internal data of the tank, and generating a dynamic unloading curve using a multi-parameter dynamic curve method; The scheme adjustment module is used to use the curve abnormality diagnosis method to perform abnormality identification and analysis on the dynamic unloading curve to obtain the abnormal cause, and adjust the unloading strategy according to the abnormal cause to obtain an optimized unloading scheme.

2. The liquid tank truck intelligent monitoring and unloading management system based on the Internet of Things according to claim 1 is characterized in that: The steps of setting the initial unloading parameters include: Determine characteristic information and recommended flow rate ranges of different materials based on the material type data, and determine optimal discharge pressure ranges of the materials under different environmental conditions to obtain basic parameters; Dynamically supplementing the basic parameters according to the behavioral characteristics of the material in the flow state to obtain basic characteristic parameters, and analyzing the basic characteristic parameters to obtain the material's natural frequency and initial discharge speed; Determine the minimum air pressure for fluidization of materials based on fluid mechanics; The minimum initial air pressure is corrected according to the moisture content of the material, the ambient temperature and the particle size of the material to obtain the minimum initial air pressure; Destroy material bridges based on resonance theory and calculate vibration parameters matching the material's natural frequency; The critical flow velocity is calculated from the initial discharge velocity and based on particle settling theory.

3. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 1 is characterized in that: The steps of obtaining the transport impact data include: Track the execution of transportation tasks in real time and obtain key cargo information and vehicle operation data; Extracting vehicle acceleration from the vehicle operation data and performing fast Fourier transform, analyzing power spectrum density, identifying the main vibration frequency and obtaining vibration impact data; Extract pressure data from the cargo key information, calculate the coefficient of variation through the pressure mean and standard deviation, and determine whether the coefficient of variation is greater than a preset value. If so, output pressure distribution data; otherwise, continue to collect the cargo key information.

4. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 2 is characterized in that: The step of adjusting the unloading parameters includes: Analyzing the influence of the vibration degree of the vehicle on the liquid material during the transportation process according to the vibration influence data, and adjusting the minimum initial air pressure to obtain an adjusted air pressure in combination with the change of the air pressure distribution on the flow of the liquid material according to the pressure distribution data; Analyzing the proximity between the vehicle vibration frequency and the natural vibration frequency of the liquid material during transportation according to the vibration impact data, and adjusting the vibration parameters to obtain corrected vibration parameters; The fluidity of the liquid material is analyzed in combination with the vibration impact data and the pressure distribution data, and the critical flow rate is adjusted in combination with the transportation time to obtain an adjusted flow rate.

5. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 4 is characterized in that: The steps of generating the unloading strategy include: Mapping the adjusted air pressure, the modified vibration parameter, and the adjusted flow rate to a unified scale, removing dimension differences to obtain a parameter combination; Using K-means clustering to classify the parameter combinations to obtain multiple category combinations, and building a strategy template library based on the material type data and the vehicle operation data; According to the parameter characteristics of different category combinations, the optimal strategy template in the strategy template library is selected as the unloading strategy through fuzzy logic rules.

6. The liquid tank truck intelligent monitoring and unloading management system based on the Internet of Things according to claim 5 is characterized in that: The steps to obtain multiple category combinations include: The intra-cluster sum of squares under different k values ​​is calculated by the elbow rule, and the k value corresponding to the descending inflection point is selected as the number of clusters; The first centroid is randomly selected, and subsequent centroids are repeatedly selected with the data point farthest from the selected centroid until the preset number of centroids is selected; Calculate the Euclidean distance of the parameter combination to a preset number of centroids and assign them to the nearest cluster. The formula is: Where, is the parameter combination vector, It is centroids, is the parameter combination vector To The Euclidean distance between the centroids, is the parameter combination vector eigenvalues, It is The centroid eigenvalues; Recalculate the mean of each cluster parameter as the new centroid, the formula is expressed as: Where, It is The number of data points in the cluster, It is Cluster data set; When the centroid position change is less than the preset threshold, the allocation is stopped and the silhouette coefficient is calculated. The formula is: Where, Yes The average distance to other points in the same cluster, Yes The average distance to the nearest different cluster, It is The silhouette coefficient of the data points, Yes and Take the maximum value; The mean and distribution of each cluster parameter are analyzed according to the silhouette coefficient, and the parameter combinations are classified to obtain a plurality of classification combinations.

7. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 1 is characterized in that: The step of extracting the microscopic particle motion data, the tank pressure and the material flow rate data comprises: Acquiring particle sensor data, pressure sensor data, and flow rate sensor data through the internal data of the tank; By analyzing the particle sensor data in a time series manner, the movement trajectory and velocity distribution of the powder particles in the tank are extracted, the flow pattern and movement characteristics of the particles are analyzed, and the microscopic particle movement data is determined in combination with the particle aggregation area and distribution pattern; Performing time domain analysis on the pressure sensor data to extract the changing trend and fluctuation of the internal pressure of the tank, and analyzing the distribution pattern and difference of the pressure in the tank to obtain the tank pressure; The flow rate of the liquid material in the discharge pipe is calculated based on the flow rate sensor data, and the material flow rate data is obtained by analyzing the stability and uniformity of the flow rate by calculating the fluctuation range and change amount of the flow rate.

8. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 1 is characterized in that: The steps of generating the dynamic unloading curve include: Analyzing the microscopic particle motion data from particle velocity distribution and particle trajectory distribution to obtain a liquefaction index and collision energy as motion characteristics; Analyzing the tank pressure based on the pressure variation trend and distribution to obtain the pressure fluctuation coefficient and pressure gradient as pressure field characteristic parameters; Analyzing the material flow rate data based on the flow rate change trend and flow rate distribution to obtain the time-averaged flow rate and flow rate attenuation rate as flow rate characteristic parameters; Analyzing the relationship between the motion characteristics, the pressure field characteristic parameters, and the flow velocity characteristic parameters, and determining curve parameters based on the dynamic characteristics of the unloading process; The dynamic unloading curve is drawn based on the curve parameters, with the unloading time as the horizontal axis and the category combination as the vertical axis.

9. The intelligent monitoring and unloading management system for liquid tank trucks based on the Internet of Things according to claim 1 is characterized in that: The steps of analyzing and obtaining the cause of the abnormality include: Extracting features from the dynamic unloading curve to obtain real-time data, and monitoring the real-time data to determine whether a data point exceeds a preset threshold, and if so, triggering an abnormal alarm to output an abnormal point; Analyze the abnormal point and obtain abnormal characteristics by observing the change of the abnormal point on the curve and the influence on the curve shape; A preliminary judgment is made based on the operator's experience and in combination with the abnormal characteristics, and the cause of the abnormality is determined in combination with the operating status of the unloading equipment and the material characteristics.

10. The liquid tank truck intelligent monitoring and unloading management system based on the Internet of Things according to claim 5 is characterized in that: The steps to adjust the optimized strategy include: Identifying the abnormality type of the abnormality cause to obtain multiple abnormality category causes, and evaluating the impact range of local abnormalities and systemic abnormalities on the multiple abnormality category causes; Determine the category combination that needs to be modified based on the cause of the abnormality, and determine the adjustment range based on the scope of impact; The optimal strategy template is modified according to the category combination that needs to be modified, and the unloading strategy is adjusted according to the adjustment range to obtain the optimized strategy solution.

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