A logistics state monitoring system and method based on offline detection

Abstract

The application discloses a logistics state monitoring system and method based on offline detection, and relates to the technical field of logistics state monitoring.The method comprises the following steps: calculating the liquid level height through the pressure sensor of the intelligent core barrel, obtaining vibration data, establishing a digital file to record liquid information; calculating the liquid quality, comparing the deviation with the measured quality obtained by weighing to judge the liquid level, when the deviation exceeds the threshold, combining historical data and liquid density to record and analyze abnormal reasons; calculating the pressure deviation of the current and historical liquid under the same liquid level, marking the sensor as abnormal when the deviation exceeds the historical maximum fluctuation range, and vice versa, preferentially checking the density assumption; dividing the intelligent core barrel transportation scene into a first transportation scene and a second transportation scene according to the demand, monitoring the transportation inclination, liquid replacement and liquid level based on the actual liquid level height in the first transportation scene, detecting the internal pressure and the environmental temperature in the second transportation scene, and triggering diagnosis after temperature compensation and overpressure, so that the application realizes logistics state monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics state monitoring, in particular to a logistics state monitoring system and method based on offline detection. BACKGROUND

[0002] With the intelligent upgrading of the logistics industry and the increasing demand for dangerous goods and special liquid transportation, the state monitoring of the intelligent chip barrel during transportation is crucial to ensure transportation safety and reduce risks, as the intelligent chip barrel is a key carrier for liquid transportation and storage.

[0003] However, the traditional intelligent chip barrel state monitoring method often faces the following problems when dealing with complex transportation scenarios, multi-dimensional data processing and abnormal early warning: first, the monitoring accuracy is insufficient, relying on manual inspection or single sensor data, it is difficult to cross-verify the liquid level accuracy combined with pressure, weight and other multi-dimensional data, and it is easy to misjudge due to sensor errors or density preset errors; second, the scene adaptability is poor, and no differentiated monitoring solution is designed for different transportation scenarios such as user reuse and empty barrel recycling, and the unified monitoring logic is difficult to cope with diversified risk points such as liquid replacement, inclination and empty barrel pressure abnormalities; third, the abnormal analysis lacks intelligence, when facing data deviation, it is difficult to distinguish between sensor failure, density error or road conditions through historical data tracing and pattern recognition, and relies on manual troubleshooting, which is inefficient; in addition, there is a lack of complete digital archives in offline state, it is difficult to realize historical data tracing and risk prediction, and it is easy to miss key abnormalities when communication is interrupted. SUMMARY

[0004] The present application aims to provide a logistics state monitoring system and method based on offline detection to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a logistics state monitoring method based on offline detection, the method comprising the following steps:

[0006] The pressure sensor installed on the intelligent chip barrel calculates the liquid level height, the vibration sensor obtains vibration sensing data, and then a digital archive is established to record liquid related information;

[0007] The liquid quality is calculated according to the bottom area of the intelligent chip barrel and the calculated liquid level height, the measured liquid quality is obtained by weighing, and the deviation between the measured liquid quality and the calculated liquid quality is compared to judge the liquid level, when the deviation exceeds the set threshold, the abnormal reason is analyzed combined with historical data and liquid density preset record;

[0008] The deviation of the current pressure from the historical normal pressure under the same liquid and the same liquid level is calculated. If the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal. If it is within the historical maximum fluctuation range, the density assumption is checked first. The deviation, operation log and loading record are combined to determine whether the density assumption is incorrect due to human input error or residual liquid.

[0009] According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. In the first transportation scene, the actual liquid level height is monitored to monitor the transportation inclination, liquid replacement and liquid level. In the second transportation scene, the internal pressure and the ambient temperature are detected, and the diagnosis is triggered after temperature compensation and overpressure. The abnormal reason is identified through pressure fluctuation rules. If there is no rule, the sensor or road conditions are analyzed. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without loading liquid is transported to the base for reloading of liquid.

[0010] The liquid level height is calculated by the pressure sensor installed on the smart barrel, and the vibration sensing data is obtained by the vibration sensor, and then a digital file is established to record the liquid related information. The specific steps include:

[0011] The pressure of the liquid on the barrel bottom is collected by the pressure sensor installed at the bottom of the smart barrel. Under the premise of pre-setting the type of liquid in the smart barrel, the pre-set density of the liquid is read. The liquid level height is calculated by the formula h=P / (p*g), wherein h represents the liquid level height, P represents the pressure of the liquid on the barrel bottom, p represents the pre-set density of the liquid, and g represents the acceleration of gravity.

[0012] The vibration sensing data of the smart barrel is obtained by the vibration sensor installed on the smart barrel.

[0013] A digital history file of the smart barrel is constructed. The mass of the smart barrel when there is no loading liquid is obtained. The historical loading liquid information of the smart barrel is recorded, including the type of liquid, the density parameter, the start and end time of each loading, the empty state and the residual condition.

[0014] The liquid mass is calculated according to the bottom area of the smart barrel and the calculated liquid surface height. The measured liquid mass is obtained by weighing. The deviation of the measured liquid mass and the calculated liquid mass is compared to judge the liquid level. When the deviation exceeds the set threshold, the abnormal reason is analyzed in combination with the historical data and the pre-set record of the liquid density. The specific steps include:

[0015] According to the bottom area of the smart barrel and the calculated liquid surface height, the liquid mass is obtained according to the formula m=p*S*h; wherein m represents the liquid mass, S represents the bottom area of the smart barrel, the total mass of the smart barrel is measured by the weighing sensor, and the measured liquid mass is calculated by subtracting the mass when there is no loading from the total mass of the smart barrel.

[0016] The relationship between the measured liquid mass and the calculated liquid mass is compared, and when the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to the set deviation threshold, it is determined that the calculated liquid level height is the actual liquid level height of the smart barrel;

[0017] When the deviation between the measured liquid mass and the calculated liquid mass is greater than the set deviation threshold, the historical data of the pressure sensor and the preset record of the liquid density are combined to analyze the cause of the abnormality.

[0018] The deviation of the current pressure from the historical normal pressure under the same liquid and liquid level is calculated, and if the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, and if it is within the historical maximum fluctuation range, the density assumption is checked first; combined with the historical mass calculation deviation, the operation log and the loading record, it is judged whether the density assumption error is caused by human input error or residual liquid, and the specific steps include:

[0019] Based on the historical data of the pressure sensor, it is judged whether the sensor has abnormal risk, and the specific steps include:

[0020] The deviation of the current pressure data from the historical normal pressure value under the same liquid and liquid level is calculated, and is defined as follows: Bc,h = |Pc-Ph| / Ph*100%; wherein Bc,h represents the deviation of the current pressure data from the historical normal pressure value under the same liquid and liquid level, Pc represents the current pressure data, and Ph represents the historical normal pressure value under the same liquid and liquid level;

[0021] If the historical data of the pressure sensor is stable, and the deviation of the current pressure data from the historical normal pressure value under the same condition exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, wherein the historical data stability means that in the past same liquid and same liquid level scene, the historical fluctuation range is within the historical maximum fluctuation range;

[0022] If the historical data of the pressure sensor is stable, and the deviation of the current pressure data from the historical normal pressure value under the same condition is within the historical maximum fluctuation range, it is checked first whether the abnormal measured liquid mass and the calculated liquid mass deviation greater than the set deviation threshold is caused by the error of the liquid density assumption;

[0023] The consistency of the preset record of the liquid density is analyzed to determine whether the density assumption is incorrect, and the specific steps include:

[0024] If the preset density is consistent with the historical density record of the same kind of liquid, and the deviation between the mass calculated by the liquid level, the liquid density and the area of the barrel bottom through the pressure sensor data and the actual mass of the liquid obtained by weighing is less than or equal to the set deviation threshold, the deviation caused by the sensor is checked first;

[0025] If the preset density is inconsistent with the historical density record of the same kind of liquid, the operation log of the density preset is checked to determine whether the human input error causes the liquid density assumption error;

[0026] The historical loading record of the smart barrel and the current preset density are combined to determine whether the actual average density is inconsistent with the preset due to residual liquid, which belongs to the liquid density assumption error.

[0027] According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. In the first transportation scene, the transportation inclination, liquid replacement and liquid level are monitored based on the actual liquid level. In the second transportation scene, the internal pressure and the ambient temperature are detected, and the diagnosis is triggered after temperature compensation overpressure. The abnormal reason is identified by pressure fluctuation rule, and the sensor or road condition is analyzed if there is no rule. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without loading liquid is transported to the base for reloading liquid. The specific steps include:

[0028] The smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without loading liquid is transported to the base for reloading liquid.

[0029] For the first transportation scene, the actual liquid level of the smart barrel is calculated. According to the actual liquid level of the smart barrel, the inclination during transportation, the replacement of the liquid in the smart barrel and the liquid level are monitored.

[0030] For the second transportation scene, the internal pressure and the ambient temperature of the smart barrel are detected. The temperature compensation is used to eliminate the interference of temperature on pressure detection. If the compensated pressure exceeds the set pressure threshold, the diagnosis is triggered and the pattern recognition is performed based on the pressure fluctuation in a period of time. If the fluctuation has a rule, the abnormal reason is determined according to the historical known rule. If the fluctuation has no rule, it is analyzed whether the sensor is abnormal or the transportation road condition causes it.

[0031] The replacement detection of the liquid of the smart barrel includes the following specific process:

[0032] Monitoring whether there is residual in the smart chip barrel, when there is residual, the smart chip barrel is cleaned, when there is no residual, liquid replacement operation is performed according to the established rules;

[0033] Monitoring of the smart chip barrel liquid, the specific process includes:

[0034] According to the initial angle of the smart chip barrel when normally placed horizontally as the reference value of the inclination angle calculation, it is set to 0 degrees;

[0035] Collecting the inclination angle data of X-axis and Y-axis, the angle change rate data calculated based on high-frequency sampling, and the vibration data containing vibration acceleration peak value and duration, and recording the time sequence information of each data;

[0036] When the angle of any axis in the inclination angle data is greater than or equal to the preset dumping threshold, a preliminary warning is triggered, and the next step is verified; if the angle is always less than the preset dumping threshold, it is determined as "normal state";

[0037] For the preliminary warning scene, further verification is combined with the angle change rate and vibration data:

[0038] If the angle change rate is greater than the preset angle change rate threshold, and the instantaneous impact feature (such as acceleration peak value > 5g) appears in the vibration data, it is determined as "there is a sudden dumping risk";

[0039] If the angle change rate is less than or equal to the preset angle change rate threshold, it is determined as "normal inclination (such as carrying operation)", and the preliminary warning is removed;

[0040] For the scene of "there is a sudden dumping risk", the continuous duration of the inclination angle greater than or equal to the preset dumping threshold is counted:

[0041] If the duration is greater than or equal to the set duration threshold (such as 3s), it is finally determined as "dumping event", and the alarm (such as sound and light prompt or data upload) is triggered;

[0042] If the duration is less than the set duration threshold, it is determined as "instantaneous inclination", and the warning is removed;

[0043] Liquid level monitoring of the smart chip barrel liquid, the specific process includes:

[0044] Setting low liquid level threshold and high liquid level threshold, based on the preset low liquid level threshold and high liquid level threshold, continuously monitoring the liquid level in the barrel: if the liquid level is lower than the low threshold, triggering low liquid level warning and corresponding protection measures (such as prompting to add liquid); if the liquid level is higher than the high threshold, triggering high liquid level warning and corresponding protection measures (such as stopping adding liquid or alarming); if the liquid level is between the high and low thresholds, it is determined that the liquid level is normal, and the current operation is maintained.

[0045] A logistics state monitoring system based on offline detection, comprising a data acquisition module, an abnormality analysis module, a scenario monitoring module, and a diagnosis and decision module, the data acquisition module is used to calculate the liquid level height through the pressure sensor of the smart barrel, obtain the vibration data, and establish a digital archive to record the liquid information; the abnormality analysis module is used to calculate the liquid quality, compare the deviation with the measured quality obtained by weighing to judge the liquid level, and combine the historical data and the liquid density preset record to analyze the abnormal reason when the deviation exceeds the threshold; the scenario monitoring module is used to divide the smart barrel transportation scenario into a first transportation scenario and a second transportation scenario according to the demand; the diagnosis and decision module is used to monitor the transportation tilt, liquid replacement and liquid level based on the actual liquid level height in the first transportation scenario, and detect the internal pressure and the environmental temperature in the second transportation scenario, and trigger the diagnosis after temperature compensation and overpressure.

[0046] The data acquisition module comprises a pressure sensing unit, a vibration sensing unit, a weighing sensing unit and a temperature sensing unit, the pressure sensing unit collects the pressure data of the liquid on the bottom of the smart barrel through the pressure sensor installed on the bottom of the smart barrel; the vibration sensing unit obtains the vibration sensing data in the transportation and use process through the vibration sensor installed on the main body of the smart barrel; the weighing sensing unit is used to measure the total mass of the smart barrel; the temperature sensing unit is used to collect the temperature data of the environment where the smart barrel is located.

[0047] The abnormality analysis module comprises a liquid level and quality calculation unit, a deviation analysis unit, a sensor abnormality judgment unit and a density assumption verification unit, the liquid level and quality calculation unit is used to calculate the liquid level height based on the pressure data collected by the pressure sensing unit, combined with the preset liquid density, through the formula, combined with the bottom area of the smart barrel, to calculate the theoretical mass of the liquid; the deviation analysis unit is used to compare the measured liquid mass obtained by the weighing sensing unit with the theoretical mass, and calculate the deviation value; the sensor abnormality judgment unit is used to retrieve the historical data of the pressure sensor, calculate the deviation of the current pressure from the normal pressure under the same liquid and liquid level, judge whether it exceeds the historical maximum fluctuation range, and determine whether the sensor is abnormal; the density assumption verification unit is used to verify whether the density preset is wrong by combining the liquid density preset record, the operation log and the historical loading record.

[0048] The scene monitoring module comprises a first scene monitoring unit and a second scene monitoring unit, the first scene monitoring unit is used for monitoring the inclination in the transportation process, the liquid replacement in the smart chip barrel and the liquid level change in the first transportation scene based on the actual liquid level height output by the liquid level and mass calculation unit, and the second scene monitoring unit is used for monitoring the internal pressure of the smart chip barrel in the second transportation scene in combination with the temperature compensation of the temperature sensing unit data; when the pressure exceeds the threshold value, the pressure fluctuation analysis is triggered, wherein the first transportation scene represents that the smart chip barrel is rented to the user for repeated use by the user, and the second transportation scene represents that the user purchases the liquid and rents the smart chip barrel, and after the liquid is used up, the smart chip barrel without the loaded liquid is transported to the base for reloading of the liquid.

[0049] The diagnostic and decision module comprises a pattern recognition unit and an abnormal reason decision unit, the pattern recognition unit is used for pattern recognition on the pressure fluctuation data in a period of time to judge whether the fluctuation has a rule for the second transportation scene, and the abnormal reason decision unit is used for combining the pattern recognition result and the analysis of the sensor abnormality judgment unit, if the fluctuation has a rule, the abnormal reason is determined according to the historical known rule, and if the fluctuation has no rule, whether the sensor is abnormal or the transportation road condition is analyzed.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] 1. The present application improves the monitoring precision through multi-sensor data fusion and cross verification, combines the pressure sensor to calculate the liquid level, and the weighing sensor to obtain the measured mass, and identifies the abnormality through deviation analysis and correlation of historical data and density preset records, which is different from the single sensor data dependence in the prior art, can capture the coupling relationship among the pressure, mass and density, and realizes the liquid level and abnormal source;

[0052] 2. The present application adopts a scene-based dynamic monitoring mechanism, designs differential monitoring logic for two scenes of "user self-reuse" and "empty barrel returning to base", the first scene monitors the inclination, liquid replacement and liquid level change based on the liquid level, the second scene analyzes the abnormality in combination with temperature compensation and pressure fluctuation rule, which is different from the unified monitoring mode in the prior art, can adapt to diversified transportation demands, and distinguishes the sensor abnormality, density assumption error or road condition influence through historical data tracing and pattern recognition. BRIEF DESCRIPTION OF DRAWINGS

[0053] Fig. 1 It is a smart chip barrel liquid level monitoring and abnormal diagnosis flowchart based on mass deviation of the logistics state monitoring method based on offline detection of the present application;

[0054] Fig. 2 It is a smart chip barrel transportation scene differential monitoring flowchart of the logistics state monitoring method based on offline detection of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0056] In embodiments, as shown in the accompanying drawings, the present application provides a technical solution, a logistics state detection method based on offline detection, which comprises the following steps: Figs. 1-2

[0057] The liquid level height is calculated by the pressure sensor installed on the smart barrel, the vibration sensing data is obtained by the vibration sensor, and then a digital file is established to record the liquid related information.

[0058] The liquid quality is calculated according to the bottom area of the smart barrel and the calculated liquid level height, the measured liquid quality is obtained by weighing, and the liquid level is judged by comparing the deviation of the measured liquid quality and the calculated liquid quality, when the deviation exceeds the set threshold, the historical data and the liquid density are combined to record and analyze the abnormal reasons.

[0059] The deviation of the current pressure and the normal pressure under the same liquid and the same liquid level is calculated, if the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, if it is within the historical maximum fluctuation range, the density assumption is checked first, the deviation is calculated combined with the historical quality, the operation log and the loading record, whether the density assumption error is caused by human input error or residual liquid is judged.

[0060] According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene, under the first transportation scene, the actual liquid level height is monitored to monitor the transportation inclination, liquid replacement and liquid level, under the second transportation scene, the internal pressure and the environment temperature are detected, after temperature compensation overpressure, diagnosis is triggered, the abnormal reasons are identified by pressure fluctuation rule, and if there is no rule, the sensor or road condition is analyzed, wherein the first transportation scene means that the smart barrel is rented to the user, and the user repeatedly uses it by himself / herself, the second transportation scene means that the user purchases the liquid, and rents the smart barrel, after the liquid is used up, the smart barrel without loading liquid is transported to the base to reload the liquid.

[0061] The liquid level height is calculated by the pressure sensor installed on the smart barrel, the vibration sensing data is obtained by the vibration sensor, and then a digital file is established to record the liquid related information, and the specific steps include:

[0062] ​The pressure sensor installed at the bottom of the smart barrel collects the pressure of the liquid on the barrel bottom. Under the premise of the preset liquid type in the smart barrel, the preset density of the liquid is read, and the liquid level height is calculated by the formula h=P / (p*g), wherein h represents the liquid level height, P represents the pressure of the liquid on the barrel bottom, p represents the preset liquid density, and g represents the acceleration of gravity.

[0063] The vibration sensor installed on the smart barrel obtains vibration sensing data.

[0064] The digital history file of the smart barrel is constructed, the mass of the smart barrel when there is no loaded liquid is obtained, and the historical loaded liquid information is recorded, including the liquid type, the density parameter, the start and end time of each loading, the empty state and the residual condition.

[0065] Specifically, taking the transportation of diesel oil (preset density p=850 kg / m 3 ) in the smart barrel as an example, the smart barrel parameters are: bottom area S=0.5 m 2 , empty barrel mass 50 kg, historical maximum pressure fluctuation range ±5%, mass deviation threshold 5%, and second scene pressure threshold 100 Pa.

[0066] The pressure sensor is installed at the bottom of the barrel, and the pressure of the diesel oil on the barrel bottom is 4900 Pa.

[0067] The vibration sensor records the vibration data during transportation.

[0068] The weighing sensor measures the total mass of the smart barrel as 305 kg.

[0069] According to the formula h=P / (p*g), and g=9.8 N / kg, h=0.6 m is obtained.

[0070] According to the bottom area of the smart barrel and the calculated liquid surface height, the liquid mass is calculated, the measured liquid mass is obtained by weighing, and the liquid level is judged by comparing the deviation between the measured liquid mass and the calculated liquid mass. When the deviation exceeds the set threshold, the abnormal reason is analyzed in combination with the historical data and the liquid density preset record. The specific steps include:

[0071] According to the bottom area of the smart barrel and the calculated liquid surface height, the liquid mass is calculated according to the formula m=p*S*h; wherein m represents the liquid mass, S represents the bottom area of the smart barrel, the total mass of the smart barrel is measured by the weighing sensor, and the measured liquid mass is calculated by subtracting the mass without loading from the total mass of the smart barrel.

[0072] The relationship between the measured liquid mass and the calculated liquid mass is compared, and when the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to the set deviation threshold, it is judged that the calculated liquid level height is the actual liquid level height of the smart barrel.

[0073] When the deviation between the measured liquid mass and the calculated liquid mass is greater than the set deviation threshold, the cause of the abnormality is analyzed in combination with the pressure sensor historical data and the liquid density preset record.

[0074] Specifically, the liquid mass is calculated: m = p * S * h = 255 kg;

[0075] The measured liquid mass is: total mass - empty barrel mass = 305 - 50 = 255 kg;

[0076] The deviation between the calculated mass and the measured mass is 0% (≤5% threshold), and it is confirmed that the liquid level 0.6 m is the actual liquid level, and the digital file record is recorded as "diesel, liquid level 0.6 m, mass 255 kg, pressure 4900 Pa".

[0077] The deviation between the current pressure and the historical normal pressure under the same liquid and the same liquid level is calculated, and if the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, and if it is within the historical maximum fluctuation range, the density assumption is checked first; in combination with the historical mass calculation deviation, the operation log and the loading record, it is judged whether the density assumption error is caused by human input error or residual liquid, and the specific steps include:

[0078] Based on the pressure sensor historical data, it is judged whether the sensor has abnormal risk, and the specific steps include:

[0079] The deviation between the current pressure data and the historical normal pressure value under the same liquid and the same liquid level is calculated, and is defined as shown below: Bc,h = |Pc-Ph| / Ph*100%; wherein, Bc,h represents the deviation between the current pressure data and the historical normal pressure value under the same liquid and the same liquid level, Pc represents the current pressure data, and Ph represents the historical normal pressure value under the same liquid and the same liquid level;

[0080] If the pressure sensor historical data is stable, and the deviation of the current pressure data relative to the historical normal pressure value under the same condition exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, wherein the historical data stable means that in the past same liquid and same liquid level scene, the historical fluctuation range is within the historical maximum fluctuation range;

[0081] If the pressure sensor historical data is stable, and the deviation of the current pressure data relative to the historical normal pressure value under the same condition is within the historical maximum fluctuation range, the measured liquid mass and the calculated liquid mass are checked first whether the deviation caused by the error of the liquid density assumption is greater than the set deviation threshold;

[0082] The consistency of the liquid density preset record is analyzed to determine whether the density assumption is wrong, and the specific steps include:

[0083] If the preset density is consistent with the historical density record of the same kind of liquid, and the deviation between the mass calculated by the pressure sensor data, combined with the liquid density and the area of the barrel bottom, and the actual mass obtained by weighing is less than or equal to the set deviation threshold, then the deviation is checked whether it is caused by the sensor;

[0084] If the preset density is not consistent with the historical density record of the same kind of liquid, the operation log of the density preset is checked to determine whether the human input error causes the liquid density assumption error;

[0085] The historical loading record of the smart barrel is combined with the current preset density to determine whether the actual average density is inconsistent with the preset due to residual liquid, which is a liquid density assumption error.

[0086] Specifically, if the weighing sensor measures the total mass of 292.25 kg, the actual measured mass is 292.25-50=242.25 kg, and the deviation from the calculated mass of 255 kg is 5% (reaching the threshold);

[0087] The pressure deviation is calculated: the current pressure P=4655 Pa (corresponding to the actual measured mass), the historical normal pressure Ph of the same liquid level and the same liquid is 4900 Pa, and the deviation Bc,h is 5% (within the historical fluctuation range);

[0088] The density assumption verification: check the historical record, the preset density of diesel is 850 kg / m 3 , and the operation log has no input error, but the historical loading record shows that "the last loading of gasoline (ρ=750 kg / m 3 ) was not completely emptied", which is judged to be caused by residual gasoline, which is a density assumption error.

[0089] According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. In the first transportation scene, the transportation inclination, liquid replacement and liquid level are monitored based on the actual liquid level height. In the second transportation scene, the internal pressure and the environment temperature are detected, and the diagnosis is triggered after temperature compensation overpressure. The abnormal reason is identified by pressure fluctuation rule, and the sensor or road condition is analyzed if there is no rule. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without loading liquid is transported to the base for reloading liquid. The specific steps include:

[0090] The smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. The first transportation scene represents that the smart barrel is rented to a user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without the liquid is transported to the base for reloading of the liquid.

[0091] For the first transportation scene, the actual liquid level height of the smart barrel is calculated. According to the actual liquid level height of the smart barrel, the inclination during transportation, the replacement of the liquid in the smart barrel, and the liquid level height are monitored.

[0092] For the second transportation scene, the internal pressure and the ambient temperature of the smart barrel are detected. The interference of the temperature on the pressure detection is eliminated through temperature compensation. If the compensated pressure exceeds the set pressure threshold, diagnosis is triggered and pattern recognition is performed based on the pressure fluctuation in a period of time. If the fluctuation has a rule, the abnormal reason is determined according to the historical known rule. If the fluctuation has no rule, it is analyzed whether the sensor is abnormal or the transportation road condition causes it.

[0093] The replacement detection of the liquid in the smart barrel includes the following specific processes:

[0094] The presence of residues in the smart barrel is monitored. When the residues exist, the smart barrel is cleaned. When the residues do not exist, the liquid replacement operation is performed according to the predetermined rule.

[0095] The mis-pouring monitoring of the liquid in the smart barrel includes the following specific processes:

[0096] The initial angle of the smart barrel when normally placed horizontally is set as the reference value of the inclination angle calculation, which is 0 degrees.

[0097] The inclination angle data of the X-axis and Y-axis, the angle change rate data calculated based on high-frequency sampling, and the vibration data containing the vibration acceleration peak value and the duration are collected, and the time sequence information of each data is recorded.

[0098] When the angle of any axis in the inclination angle data is greater than or equal to the preset pouring threshold, the preliminary warning is triggered, and the next verification is entered. If the angle is always less than the preset pouring threshold, it is determined as “normal state”.

[0099] For the preliminary warning scene, the angle change rate and the vibration data are further verified.

[0100] If the angle change rate is greater than the preset angle change rate threshold, and the instantaneous impact feature (such as acceleration peak value > 5g) appears in the vibration data, it is determined as “there is a sudden pouring risk”.

[0101] If the angle change rate is less than or equal to the preset angle change rate threshold, it is determined as “normal inclination (such as carrying operation)”, and the preliminary warning is released.

[0102] For the scenario of "existence of sudden dumping risk", the continuous duration of the inclination angle greater than or equal to the preset dumping threshold is counted:

[0103] If the duration is greater than or equal to the set duration threshold (such as 3s), it is finally determined as "dumping event", triggering the alarm (such as sound and light prompt or data uploading);

[0104] If the duration is less than the set duration threshold, it is determined as "instantaneous inclination", and the warning is removed;

[0105] The specific process of liquid level monitoring of the smart barrel includes:

[0106] The low liquid level threshold and the high liquid level threshold are set, and the liquid level in the barrel is continuously monitored based on the preset low liquid level threshold and the high liquid level threshold: if the monitored liquid level is lower than the low threshold, a low liquid level warning and corresponding protection measures (such as prompting to add liquid) are triggered; if the monitored liquid level is higher than the high threshold, a high liquid level warning and corresponding protection measures (such as stopping adding liquid or alarming) are triggered; if the liquid level is between the high and low thresholds, it is determined that the liquid level is normal, and the current operation is maintained.

[0107] A logistics state monitoring system based on offline detection, the system comprises: a data acquisition module, an abnormality analysis module, a scenario monitoring module and a diagnosis and decision module, the data acquisition module is used for calculating liquid level height through pressure sensor of smart barrel, obtaining vibration data, establishing digital file record liquid information; the abnormality analysis module is used for calculating liquid mass, comparing deviation with measured mass obtained by weighing to judge liquid level, when deviation is greater than threshold, combining historical data and liquid density preset record to analyze abnormal reason; the scenario monitoring module is used for dividing smart barrel transportation scene into first transportation scene and second transportation scene according to demand; the diagnosis and decision module is used for monitoring transportation inclination, liquid replacement and liquid level based on actual liquid level height in first transportation scene; detecting internal pressure and environmental temperature in second transportation scene, triggering diagnosis after temperature compensation overpressure.

[0108] The data acquisition module comprises pressure sensing unit, vibration sensing unit, weighing sensing unit and temperature sensing unit, the pressure sensing unit acquires pressure data of liquid to the bottom of smart barrel through pressure sensor installed at the bottom of smart barrel; the vibration sensing unit acquires vibration sensing data in transportation and use process through vibration sensor installed at the main body of smart barrel; the weighing sensing unit is used for measuring total mass of smart barrel; the temperature sensing unit is used for acquiring temperature data of environment where smart barrel is located.

[0109] The abnormality analysis module comprises a liquid level and mass calculation unit, a deviation analysis unit, a sensor abnormality judgment unit and a density assumption verification unit, the liquid level and mass calculation unit is used for calculating the liquid level height by formula based on the pressure data collected by the pressure sensing unit, combining the preset liquid density, and calculating the theoretical mass of the liquid combining the area of the smart bucket bottom; the deviation analysis unit is used for comparing the measured liquid mass obtained by the weighing sensing unit with the theoretical mass to calculate the deviation value; the sensor abnormality judgment unit is used for retrieving the historical data of the pressure sensor, calculating the deviation of the current pressure from the normal pressure under the same liquid and liquid level, judging whether it exceeds the historical maximum fluctuation range, and determining whether the sensor is abnormal; the density assumption verification unit is used for verifying whether the density preset is wrong by combining the liquid density preset record, operation log and historical loading record.

[0110] The scenario-based monitoring module comprises a first scenario monitoring unit and a second scenario monitoring unit, the first scenario monitoring unit is used for monitoring the inclination in the transportation process, the liquid replacement in the smart bucket and the liquid level change based on the actual liquid level height output by the liquid level and mass calculation unit in the first delivery scenario; the second scenario monitoring unit is used for monitoring the internal pressure of the smart bucket by combining the temperature compensation data of the temperature sensing unit in the second delivery scenario; when the pressure exceeds the threshold value, the pressure fluctuation analysis is triggered, wherein the first delivery scenario represents that the smart bucket is rented to the user for repeated use by the user; the second delivery scenario represents that the user purchases the liquid and rents the smart bucket, and after the liquid is used up, the smart bucket without the loaded liquid is transported to the base for reloading of the liquid.

[0111] The diagnosis and decision module comprises a pattern recognition unit and an abnormality reason decision unit, the pattern recognition unit is used for pattern recognition on the pressure fluctuation data in a period of time in the second delivery scenario to judge whether the fluctuation has a rule; the abnormality reason decision unit is used for combining the pattern recognition result and the analysis of the sensor abnormality judgment unit, determining the abnormality reason according to the known historical rule if the fluctuation has a rule, and analyzing whether the sensor is abnormal or the transportation road condition causes the fluctuation if the fluctuation has no rule.

[0112] It is apparent for those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all aspects as illustrative and not restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and range of the equivalent elements of the claims are intended to be embraced in the present application. Any reference signs in the claims should not be considered as limiting the claims involved.

Claims

1. A method for monitoring the state of logistics based on offline detection, characterized in that: The method comprises the following steps: The liquid level height is calculated by the pressure sensor installed on the smart barrel, the vibration sensing data is obtained by the vibration sensor, and then a digital file is established to record the liquid related information; The pressure of the liquid on the bottom of the barrel is collected by the pressure sensor installed on the bottom of the smart barrel; The liquid quality is calculated according to the bottom area of the smart barrel and the calculated liquid level height, the measured liquid quality is obtained by weighing, and the liquid level is judged by comparing the deviation of the measured liquid quality and the calculated liquid quality, when the deviation exceeds the set threshold, the historical data and the preset record of the liquid density are combined to analyze the abnormal reasons; The deviation of the current pressure and the normal pressure under the same liquid and the same liquid level in history is calculated, if the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, if it is within the historical maximum fluctuation range, the density assumption is checked first; combined with the historical quality calculation deviation, the operation log and the loading record, whether the density assumption error is caused by human input error or residual liquid is judged; According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene, under the first transportation scene, the actual liquid level height is monitored to monitor the transportation inclination, liquid replacement and liquid level; under the second transportation scene, the internal pressure and the environment temperature are detected, the diagnosis is triggered after temperature compensation overpressure, the abnormal reasons are identified through pressure fluctuation rule, and the sensor or road condition is analyzed without rule, wherein the first transportation scene represents that the smart barrel is rented to the user for repeated use by the user; the second transportation scene represents that the user purchases the liquid, rents the smart barrel, and transports the smart barrel without loading liquid to the base for reloading liquid after the liquid is used up.

2. The method of claim 1, wherein: The liquid level height is calculated by the pressure sensor installed on the smart barrel, the vibration sensing data is obtained by the vibration sensor, and then a digital file is established to record the liquid related information, and the specific steps comprise: Under the premise that the type of the liquid in the preset smart barrel is known, the preset density of the liquid is read, and the liquid level height is calculated by the formula h=P / (p*g), wherein h represents the liquid level height, P represents the pressure of the liquid on the bottom of the barrel, p represents the preset density of the liquid, and g represents the acceleration of gravity; The vibration sensing data of the smart barrel is obtained by the vibration sensor installed on the smart barrel; The digital historical file of the smart barrel is constructed, the mass of the smart barrel without loading liquid is obtained, and the historical loading liquid information of the smart barrel is recorded, including the type of the liquid, the density parameter, the start and end time of each loading, the empty state and the residual condition.

3. The method of claim 2, wherein: The liquid quality is calculated according to the bottom area of the smart barrel and the calculated liquid level height, the measured liquid quality is obtained by weighing, and the liquid level is judged by comparing the deviation of the measured liquid quality and the calculated liquid quality, when the deviation exceeds the set threshold, the historical data and the preset record of the liquid density are combined to analyze the abnormal reasons, and the specific steps comprise: The liquid quality is calculated according to the bottom area of the smart barrel and the calculated liquid level height, and the formula m=p*S*h is used to obtain the liquid quality; wherein m represents the liquid quality, S represents the bottom area of the smart barrel, the total mass of the smart barrel is measured by the weighing sensor, and the measured liquid quality is calculated by subtracting the mass without loading from the total mass of the smart barrel. The relationship between the measured liquid mass and the calculated liquid mass is compared, and when the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to a set deviation threshold, it is determined that the calculated liquid level height is the actual liquid level height of the smart barrel; When the deviation between the measured liquid mass and the calculated liquid mass is greater than the set deviation threshold, the causes of the abnormality are analyzed in combination with historical pressure sensor data and liquid density preset records.

4. The method of claim 3, wherein: The deviation of the current pressure from the historical normal pressure under the same liquid and liquid level is calculated, and if the deviation exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, and if it is within the historical maximum fluctuation range, the density assumption is checked first; In combination with the historical mass calculation deviation, the operation log and the loading record, it is judged whether the density assumption error is caused by human input error or residual liquid, and the specific steps include: Based on the historical data of the pressure sensor, it is judged whether the sensor has abnormal risks, and the specific steps include: The deviation of the current pressure data from the historical normal pressure value under the same liquid and liquid level is calculated, and is defined as follows: Bc,h=|Pc-Ph| / Ph*100%; wherein Bc,h represents the deviation of the current pressure data from the historical normal pressure value under the same liquid and liquid level, Pc represents the current pressure data, and Ph represents the historical normal pressure value under the same liquid and liquid level; If the pressure sensor historical data is stable, and the deviation of the current pressure data from the historical normal pressure value under the same condition exceeds the historical maximum fluctuation range, the sensor is marked as abnormal, wherein the historical data stability means that in the past same liquid and same liquid level scene, the historical fluctuation range is within the historical maximum fluctuation range; If the pressure sensor historical data is stable, and the deviation of the current pressure data from the historical normal pressure value under the same condition is within the historical maximum fluctuation range, it is checked first whether the abnormal deviation between the measured liquid mass and the calculated liquid mass caused by the liquid density assumption error is greater than the set deviation threshold; The consistency of the liquid density preset record is analyzed to determine whether the density assumption is incorrect, and the specific steps include: The preset density is compared with the historical density record of the same kind of liquid, and if the current preset density is consistent with the historical density record of the same kind of liquid when the same kind of liquid is loaded, and the deviation between the mass calculated by combining the liquid density and the barrel bottom area and the actual liquid mass obtained by weighing is less than or equal to the set deviation threshold when the same kind of liquid is loaded, it is checked first whether the deviation is caused by the sensor; If the current preset density is inconsistent with the historical density record of the same kind of liquid, the operation log of the density preset is checked to determine whether the human input error causes the liquid density assumption error; In combination with the historical loading record of the smart barrel and the current preset density, it is judged whether the actual average density is inconsistent with the preset due to residual liquid, which belongs to the liquid density assumption error.

5. The method of claim 4, wherein: According to the demand, the smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. In the first transportation scene, the actual liquid level height is monitored based on the actual liquid level height to monitor the transportation inclination, liquid replacement, and liquid level. In the second transportation scene, the internal pressure and the ambient temperature are detected, and after temperature compensation, the overpressure triggers diagnosis, and the abnormal reason is identified based on the pressure fluctuation rule. If there is no rule, the sensor or the road condition is analyzed. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without the loaded liquid is transported to the base to reload the liquid. The specific steps include: The smart barrel transportation scene is divided into a first transportation scene and a second transportation scene. The first transportation scene represents that the smart barrel is rented to the user for repeated use by the user. The second transportation scene represents that the user purchases liquid and rents the smart barrel. After the liquid is used up, the smart barrel without the loaded liquid is transported to the base to reload the liquid. For the first transportation scene, the actual liquid level height of the smart barrel is calculated. Based on the actual liquid level height of the smart barrel, the inclination during transportation, the replacement of the liquid in the smart barrel, and the liquid level are monitored. For the second transportation scene, the internal pressure and the ambient temperature of the smart barrel are detected. The interference of temperature on the pressure detection is eliminated through temperature compensation. If the compensated pressure exceeds the set pressure threshold, diagnosis is triggered and pattern recognition is performed based on the pressure fluctuation in a period of time. If the fluctuation has a rule, the abnormal reason is determined according to the historical known rule. If the fluctuation has no rule, it is analyzed whether the sensor is abnormal or the transportation road condition causes it.

6. A logistics state monitoring system based on offline detection, applied to the logistics state monitoring method based on offline detection in any one of claims 1-5, characterized in that: The system includes a data acquisition module, an abnormal analysis module, a scenario monitoring module, and a diagnosis and decision module. The data acquisition module is used to calculate the liquid level height through the pressure sensor of the smart barrel, obtain vibration data, and establish a digital file to record liquid information. The abnormal analysis module is used to calculate the liquid quality, compare the deviation with the measured quality obtained by weighing to judge the liquid level, and analyze the abnormal reason when the deviation exceeds the threshold combined with historical data and liquid density preset records. The scenario monitoring module is used to divide the smart barrel transportation scene into a first transportation scene and a second transportation scene according to the demand. The diagnosis and decision module is used to monitor the transportation inclination, liquid replacement, and liquid level based on the actual liquid level height in the first transportation scene. In the second transportation scene, the internal pressure and the ambient temperature are detected, and after temperature compensation, the overpressure triggers diagnosis.

7. The logistics condition monitoring system based on offline detection according to claim 6, characterized in that: The data acquisition module includes a pressure sensing unit, a vibration sensing unit, a weighing sensing unit, and a temperature sensing unit. The pressure sensing unit collects the pressure data of the liquid on the bottom of the smart barrel through the pressure sensor installed on the bottom of the smart barrel. The vibration sensing unit obtains the vibration sensing data in the transportation and use process through the vibration sensor installed on the main body of the smart barrel. The weighing sensing unit is used to measure the total mass of the smart barrel. The temperature sensing unit is used to collect the temperature data of the environment where the smart barrel is located.

8. The logistics state monitoring system based on offline detection according to claim 7, characterized in that: The abnormality analysis module comprises a liquid level and mass calculation unit, a deviation analysis unit, a sensor abnormality judgment unit and a density assumption verification unit, the liquid level and mass calculation unit is configured to calculate the liquid level height by formula based on the pressure data collected by the pressure sensor unit and in combination with the preset liquid density, and calculate the theoretical mass of the liquid in combination with the area of the barrel bottom of the smart chip; The deviation analysis unit is configured to compare the measured liquid mass obtained by the weighing sensor unit with the theoretical mass to calculate the deviation value; the sensor abnormality judgment unit is configured to retrieve the historical data of the pressure sensor, calculate the deviation of the current pressure from the normal pressure under the same liquid and liquid level, determine whether the deviation exceeds the historical maximum fluctuation range, and determine whether the sensor is abnormal; The density assumption verification unit is configured to verify whether the density preset is incorrect in combination with the liquid density preset record, the operation log and the historical loading record.

9. The logistics condition monitoring system based on offline detection according to claim 8, characterized in that: The scenario-based monitoring module comprises a first scenario monitoring unit and a second scenario monitoring unit, the first scenario monitoring unit is configured to monitor the inclination during transportation, the replacement of the liquid in the barrel of the smart chip and the change of the liquid level based on the actual liquid level height output by the liquid level and mass calculation unit in the first delivery scenario; the second scenario monitoring unit is configured to monitor the pressure inside the barrel of the smart chip in combination with the temperature compensation data of the temperature sensor unit in the second delivery scenario; when the pressure exceeds the threshold value, the pressure fluctuation analysis is triggered, wherein the first delivery scenario represents that the barrel of the smart chip is rented to the user for repeated use by the user; the second delivery scenario represents that the user purchases the liquid and rents the barrel of the smart chip, and after the liquid is used up, the barrel of the smart chip without the liquid is transported to the base for reloading of the liquid.

10. The logistics condition monitoring system based on offline detection according to claim 9, characterized in that: The diagnosis and decision module comprises a pattern recognition unit and an abnormality cause decision unit, the pattern recognition unit is configured to perform pattern recognition on the pressure fluctuation data in a period of time for the second delivery scenario to determine whether the fluctuation has a rule; the abnormality cause decision unit is configured to determine the abnormality cause according to the historical known rule if the fluctuation has a rule in combination with the pattern recognition result and the analysis of the sensor abnormality judgment unit; if the fluctuation has no rule, it is analyzed whether the sensor is abnormal or the transportation road condition causes the fluctuation.

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