Logistics state monitoring system and method based on offline detection

Abstract

The invention discloses a logistics state monitoring system and method based on offline detection, and relates to the technical field of logistics state monitoring, and the method comprises the following steps: calculating the liquid level height through a pressure sensor of an intelligent core barrel, obtaining vibration data, and building a digital file to record liquid information; calculating the liquid mass, comparing the deviation with the actually measured mass obtained by weighing to judge the liquid level, and when the deviation exceeds a threshold value, analyzing an abnormal reason in combination with historical data and a liquid density preset record; calculating the current and historical pressure deviation under the same liquid and liquid level, marking that the sensor is abnormal if the pressure deviation exceeds the historical maximum fluctuation range, and preferentially checking the density hypothesis if the pressure deviation exceeds the historical maximum fluctuation range; the intelligent core barrel conveying scene is divided into a first conveying scene and a second conveying scene according to requirements, and the first conveying scene monitors conveying inclination, liquid replacement and the liquid level based on the actual liquid level height; the second conveying scene detects the internal pressure intensity and the environment temperature, diagnosis is triggered after temperature compensation overpressure, and logistics state monitoring is achieved.

Description

Technical Field

[0001] This invention relates to the field of logistics status monitoring technology, specifically a logistics status monitoring system and method based on offline detection. Background Technology

[0002] With the intelligent upgrading of the logistics industry and the increasing demand for the transportation of hazardous materials and special liquids, the smart container, as a key carrier for liquid transportation and storage, plays a crucial role in ensuring transportation safety and reducing risks through status monitoring during transportation.

[0003] However, traditional smart bucket status monitoring methods often face the following problems when dealing with complex transportation scenarios, multi-dimensional data processing, and anomaly warnings: First, the monitoring accuracy is insufficient, relying on manual inspections or single sensor data, making it difficult to cross-verify the accuracy of liquid levels by combining multi-dimensional data such as pressure and weight, and easily leading to misjudgments due to sensor errors or density preset errors; Second, the scenario adaptability is poor, failing to design differentiated monitoring solutions for different transportation scenarios such as user reuse and empty bucket recycling, and the unified monitoring logic is difficult to cope with diverse risk points such as liquid replacement, tilting, and abnormal empty bucket pressure; Third, the anomaly analysis lacks intelligence, and when faced with data deviations, it is unable to distinguish sensor failures, density errors, or road condition impacts through historical data tracing and pattern recognition, relying on manual troubleshooting with low efficiency; In addition, in offline states, there is a lack of complete digital archive support, making it difficult to achieve historical data backtracking and risk prediction, and key anomalies are easily missed when communication is interrupted. Summary of the Invention

[0004] The purpose of this invention is to provide a logistics status monitoring system and method based on offline detection to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a logistics status monitoring method based on offline detection, the method comprising the following steps:

[0006] The liquid level is calculated by a pressure sensor installed in the smart tank, and vibration data is obtained by a vibration sensor. A digital archive is then established to record liquid-related information.

[0007] The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is obtained by weighing. The liquid level is determined by comparing the actual liquid mass with the calculated liquid mass and the deviation. When the deviation exceeds the set threshold, the cause of the abnormality is analyzed by combining historical data and preset liquid density records.

[0008] Calculate the deviation between the current pressure and the normal pressure under the same liquid and liquid level in history. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal. If it is within the historical maximum fluctuation range, check the density assumption first. Combine the historical mass calculation deviation, operation log and loading record to determine whether the density assumption is wrong due to human input error or residual liquid.

[0009] Based on the requirements, the smart tank transportation scenario is divided into a first transportation scenario and a second transportation scenario. In the first transportation scenario, the actual liquid level is monitored to detect the transportation tilt, liquid replacement, and liquid level. In the second transportation scenario, the internal pressure and ambient temperature are detected. After temperature compensation for overpressure, a diagnosis is triggered. The cause of the abnormality is identified by the pressure fluctuation pattern. If there is no pattern, the sensor or road conditions are analyzed. In the first transportation scenario, the smart tank is rented to the user and then reused by the user. In the second transportation scenario, the user purchases liquid and rents the smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling.

[0010] The liquid level is calculated using a pressure sensor installed in the smart tank, and vibration data is obtained using a vibration sensor. A digital archive is then created to record relevant liquid information. Specific steps include:

[0011] The pressure sensor installed at the bottom of the smart bucket collects the pressure of the liquid on the bottom of the bucket. Under the premise of the preset liquid type in the smart bucket, the preset density of the liquid is read, and the liquid level height is calculated using the formula h=P / (ρ*g), where h represents the liquid level height, P represents the pressure of the liquid on the bottom of the bucket, ρ represents the preset liquid density, and g represents the acceleration due to gravity.

[0012] Vibration sensor installed in the smart battery is used to obtain its vibration sensing data;

[0013] Construct a digital historical archive for the smart chip bucket, obtain the mass of the smart chip bucket when it was empty, and record its historical liquid information, including liquid type, density parameters, start and end time of each loading, emptying status and residual status.

[0014] The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is then weighed. The difference between the actual liquid mass and the calculated liquid mass is used to determine the liquid level. When the deviation exceeds a set threshold, the cause of the anomaly is analyzed by combining historical data and preset liquid density records. The specific steps include:

[0015] Based on the bottom area of ​​the smart chip tank and the calculated liquid level, the liquid mass is obtained according to the formula m=ρ*S*h; where m represents the liquid mass and S represents the bottom area of ​​the smart chip tank. The total mass of the smart chip tank is measured by a weighing sensor, and the actual liquid mass is calculated by subtracting the mass when unloaded from the total mass of the smart chip tank.

[0016] Compare the measured liquid mass with the calculated liquid mass. When the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to the set deviation threshold, the calculated liquid level height is determined to be the actual liquid level height of the smart tank.

[0017] When the deviation between the measured liquid mass and the calculated liquid mass exceeds the set deviation threshold, the cause of the anomaly is analyzed by combining historical data from the pressure sensor and preset records of liquid density.

[0018] Calculate the deviation between the current pressure and the historical normal pressure under the same liquid and level. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal; if it is within the historical maximum fluctuation range, prioritize checking the density assumption. Combine historical mass calculation deviations, operation logs, and loading records to determine if the density assumption is incorrect due to human input errors or residual liquid. Specific steps include:

[0019] Determining whether a pressure sensor poses an anomaly risk based on historical pressure sensor data involves the following steps:

[0020] The deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history is calculated as follows: Bc,h=|Pc-Ph| / Ph*100%; where Bc,h represents the deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history, Pc represents the current pressure data, and Ph represents the normal pressure value under the same liquid and liquid level in history;

[0021] If the historical data of the pressure sensor is stable, and the current pressure data deviates from the normal pressure value under the same historical conditions by more than the historical maximum fluctuation range, then the sensor is marked as abnormal. Here, stable historical data means that under the same liquid and liquid level in the past, the historical fluctuation range is within the historical maximum fluctuation range.

[0022] If the historical data of the pressure sensor is stable, and the current pressure data is within the range of the historical maximum fluctuation range relative to the normal pressure value under the same historical conditions, then first check whether the abnormal measured liquid mass and the calculated liquid mass are greater than the set deviation threshold due to an incorrect assumption of liquid density.

[0023] To analyze the consistency of the pre-recorded liquid density data and determine whether the density assumption is incorrect, the specific steps include:

[0024] If the current preset density is consistent with the density record of the same type of liquid in the past, and the mass calculated by the pressure sensor data, combined with the liquid density and the bottom area of ​​the bucket, is less than or equal to the actual weighed mass of the liquid in the past when the same type of liquid was loaded, then the deviation between the current preset density and the density record of the liquid in the past and the actual weighed mass is less than or equal to the set deviation threshold, then the deviation is first checked to see if it is caused by the sensor.

[0025] If the current preset density is inconsistent with the historical density records of the same type of liquid, check the operation log of the density preset to determine whether the incorrect liquid density assumption was caused by human input error.

[0026] By combining the historical loading records of the smart bucket with the current preset density, it is determined whether the actual average density is inconsistent with the preset due to residual liquid, which indicates an error in the liquid density assumption.

[0027] Based on demand, the smart tank transportation scenario is divided into two scenarios: Scenario 1 and Scenario 2. Scenario 1 monitors transport tilt, liquid replacement, and liquid level based on actual liquid level. Scenario 2 detects internal pressure and ambient temperature. Overpressure is compensated for by temperature, triggering diagnostics. Abnormal causes are identified through pressure fluctuation patterns; if no pattern is observed, sensor readings or road conditions are analyzed. Scenario 1 refers to the smart tank being rented to a user for repeated use. Scenario 2 refers to a user purchasing liquid and renting a smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling. Specific steps include:

[0028] The delivery scenario for the Smart Chip Bucket is divided into a first delivery scenario and a second delivery scenario. The first delivery scenario refers to the Smart Chip Bucket being rented to a user and then reused by the user. The second delivery scenario refers to the user purchasing liquid and renting a Smart Chip Bucket. After the liquid is used up, the empty Smart Chip Bucket is transported to the base to be refilled with liquid.

[0029] For the first transportation scenario, the actual liquid level of the smart chip tank is calculated, and the tilting, liquid replacement, and liquid level are monitored during transportation based on the actual liquid level of the smart chip tank.

[0030] For the second transportation scenario, the internal pressure and ambient temperature of the smart tank are detected. Temperature compensation is used to eliminate the interference of temperature on pressure detection. If the pressure after compensation exceeds the set pressure threshold, diagnosis is triggered and pattern recognition is performed based on the pressure fluctuation over a period of time: if the fluctuation is regular, the cause of the anomaly is determined based on historical known patterns; if the fluctuation is irregular, it is analyzed whether it is caused by sensor malfunction or transportation road conditions.

[0031] The process for replacing and testing the liquid in the smart tank includes:

[0032] Monitor whether there is any residue in the smart chip tank. If there is residue, clean the smart chip tank. If there is no residue, perform liquid replacement according to the established rules.

[0033] The process for detecting accidental spillage of liquids in the smart liquid container includes:

[0034] The initial angle when the Smart Chip Bucket is placed horizontally is used as the reference value for calculating the tilt angle, and is set to 0 degrees.

[0035] The system collects tilt angle data along both the X and Y axes, angle change rate data calculated based on high-frequency sampling, vibration data including peak vibration acceleration and duration, and records the time sequence information of each data point.

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

[0037] For the initial warning scenario, further verification was conducted by combining the angle change rate and vibration data:

[0038] If the rate of change of angle is greater than the preset threshold for the rate of change of angle, and instantaneous impact characteristics appear in the vibration data (such as acceleration peak > 5g), it is determined that "there is a risk of sudden tipping over".

[0039] If the rate of angle change is less than or equal to the preset rate of angle change threshold, it is determined to be "normal tilt (such as handling operation)" and the initial warning is lifted;

[0040] For scenarios with a "risk of sudden tipping over", the duration of consecutive periods when the tilt angle is greater than or equal to a preset tipping threshold is recorded:

[0041] If the duration is greater than or equal to the set duration threshold (e.g., 3 seconds), it will be determined as a "tilting event" and trigger an alarm (e.g., audible and visual alerts or data upload).

[0042] If the duration is less than the set duration threshold, it is determined to be "instantaneous tilt" and the warning is lifted;

[0043] The liquid level monitoring process in the smart tank includes:

[0044] Set low and high liquid level thresholds. Based on the preset low and high liquid level thresholds, continuously monitor the liquid level in the tank: if the liquid level is detected to be lower than the low threshold, a low liquid level warning and corresponding protection measures (such as prompting to add liquid) are triggered; if the liquid level is detected to be higher than the high threshold, a high liquid level warning and corresponding protection measures (such as stopping liquid addition or alarm) are triggered; if the liquid level is between the high and low thresholds, the liquid level is determined to be normal, and the current operation is maintained.

[0045] An offline detection-based logistics status monitoring system includes: a data acquisition module, an anomaly analysis module, a scenario-based monitoring module, and a diagnosis and decision-making module. The data acquisition module calculates the liquid level and acquires vibration data using a pressure sensor in the smart container, establishing a digital archive to record liquid information. The anomaly analysis module calculates the liquid mass and compares the deviation with the measured mass obtained by weighing to determine the liquid level. When the deviation exceeds a threshold, it combines historical data and preset records of liquid density to analyze the cause of the anomaly. The scenario-based monitoring module divides the smart container's transportation scenario into a first transportation scenario and a second transportation scenario according to requirements. The diagnosis and decision-making module monitors transportation tilt, liquid replacement, and liquid level based on the actual liquid level in the first transportation scenario. In the second transportation scenario, it detects internal pressure and ambient temperature, triggering diagnosis after temperature compensation for overpressure.

[0046] 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 chip bucket through a pressure sensor installed at the bottom of the smart chip bucket. The vibration sensing unit acquires vibration data during transportation and use through a vibration sensor installed on the main body of the smart chip bucket. The weighing sensing unit is used to measure the total mass of the smart chip bucket. The temperature sensing unit is used to collect the temperature data of the environment in which the smart chip bucket is located.

[0047] The anomaly analysis module includes a liquid level and mass calculation unit, a deviation analysis unit, a sensor anomaly judgment unit, and a density hypothesis verification unit. The liquid level and mass calculation unit calculates the liquid level height based on pressure data collected by the pressure sensor unit, combined with a preset liquid density, using a formula. It then calculates the theoretical mass of the liquid based on the bottom area of ​​the smart tank. The deviation analysis unit compares the measured liquid mass obtained by the weighing sensor unit with the theoretical mass to calculate the deviation value. The sensor anomaly judgment unit retrieves historical data from the pressure sensor, calculates the deviation between the current pressure and the normal pressure under the same liquid and level in the past, determines whether it exceeds the historical maximum fluctuation range, and identifies whether the sensor is malfunctioning. The density hypothesis verification unit verifies whether there are errors in the preset density based on the preset liquid density record, operation log, and historical loading record.

[0048] The scenario-based monitoring module includes a first scenario monitoring unit and a second scenario monitoring unit. The first scenario monitoring unit is used in the first transportation scenario to monitor the tilting, liquid replacement, and liquid level changes during transportation based on the actual liquid level height output by the liquid level and mass calculation unit. The second scenario monitoring unit is used in the second transportation scenario to perform temperature compensation by combining data from the temperature sensing unit and to monitor the internal pressure of the smart chip bucket. When the pressure exceeds the threshold, pressure fluctuation analysis is triggered. The first transportation scenario indicates that the smart chip bucket is rented to the user and then reused by the user. The second transportation scenario indicates that the user purchases liquid and rents the smart chip bucket. After the liquid is used up, the empty smart chip bucket is transported to the base for refilling.

[0049] The diagnosis and decision-making module includes a pattern recognition unit and an anomaly cause decision-making unit. The pattern recognition unit is used to perform pattern recognition on pressure fluctuation data over a period of time for the second transportation scenario to determine whether the fluctuation has a regularity. The anomaly cause decision-making unit is used to combine the pattern recognition results with the analysis of the sensor anomaly judgment unit. If the fluctuation has a regularity, the anomaly cause is determined based on historical known patterns. If the fluctuation has no regularity, the anomaly cause is analyzed to determine whether it is caused by sensor anomaly or transportation road conditions.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. This invention improves monitoring accuracy through multi-sensor data fusion and cross-validation. It combines pressure sensors to calculate liquid level and weighing sensors to obtain measured mass. By analyzing deviations and associating them with historical data and preset density records, it identifies anomalies. Unlike existing technologies that rely on single sensor data, this invention can capture the coupling relationship between pressure, mass, and density, and achieve the detection of liquid level and the source of anomalies.

[0052] 2. This invention adopts a scenario-based dynamic monitoring mechanism, and designs differentiated monitoring logic for two scenarios: "users reuse the product themselves" and "empty barrels are transported back to the base". The first scenario is based on monitoring liquid level tilt, liquid replacement and liquid level changes. The second scenario combines temperature compensation and pressure fluctuation law to analyze anomalies. Unlike the unified monitoring mode in the existing technology, it can adapt to diverse transportation needs, and distinguish sensor anomalies, density assumption errors or road condition effects through historical data tracing and pattern recognition. Attached Figure Description

[0053] Figure 1 This is a flowchart of a smart tank liquid level monitoring and anomaly diagnosis method based on quality deviation, which is a logistics status monitoring method based on offline detection according to the present invention.

[0054] Figure 2 This is a flowchart illustrating the differentiated monitoring process for the delivery scenario of the smart bucket, a logistics status monitoring method based on offline detection, according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In the embodiment: such as Figures 1-2 As shown, the present invention provides a technical solution, a logistics status detection method based on offline detection, the method comprising the following steps:

[0057] The liquid level is calculated by a pressure sensor installed in the smart tank, and vibration data is obtained by a vibration sensor. A digital archive is then established to record liquid-related information.

[0058] The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is obtained by weighing. The liquid level is determined by comparing the actual liquid mass with the calculated liquid mass and the deviation. When the deviation exceeds the set threshold, the cause of the abnormality is analyzed by combining historical data and preset liquid density records.

[0059] Calculate the deviation between the current pressure and the normal pressure under the same liquid and liquid level in history. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal. If it is within the historical maximum fluctuation range, check the density assumption first. Combine the historical mass calculation deviation, operation log and loading record to determine whether the density assumption is wrong due to human input error or residual liquid.

[0060] Based on the requirements, the smart tank transportation scenario is divided into a first transportation scenario and a second transportation scenario. In the first transportation scenario, the actual liquid level is monitored to detect the transportation tilt, liquid replacement, and liquid level. In the second transportation scenario, the internal pressure and ambient temperature are detected. After temperature compensation for overpressure, a diagnosis is triggered. The cause of the abnormality is identified by the pressure fluctuation pattern. If there is no pattern, the sensor or road conditions are analyzed. In the first transportation scenario, the smart tank is rented to the user and then reused by the user. In the second transportation scenario, the user purchases liquid and rents the smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling.

[0061] The liquid level is calculated using a pressure sensor installed in the smart tank, and vibration data is obtained using a vibration sensor. A digital archive is then created to record relevant liquid information. Specific steps include:

[0062] The pressure sensor installed at the bottom of the smart bucket collects the pressure of the liquid on the bottom of the bucket. Under the premise of the preset liquid type in the smart bucket, the preset density of the liquid is read, and the liquid level height is calculated using the formula h=P / (ρ*g), where h represents the liquid level height, P represents the pressure of the liquid on the bottom of the bucket, ρ represents the preset liquid density, and g represents the acceleration due to gravity.

[0063] Vibration sensor installed in the smart battery is used to obtain its vibration sensing data;

[0064] Construct a digital historical archive for the smart chip bucket, obtain the mass of the smart chip bucket when it was empty, and record its historical liquid information, including liquid type, density parameters, start and end time of each loading, emptying status and residual status.

[0065] Specifically, diesel fuel (preset density ρ = 850 kg / m³) is transported using a smart cylinder. 3 For example, the parameters of the smart battery are: bottom area S = 0.5m². 2 The empty barrel weighs 50kg, the historical maximum pressure fluctuation range is ±5%, the mass deviation threshold is 5%, and the second scenario pressure threshold is 100Pa.

[0066] The pressure sensor is installed at the bottom of the barrel to collect the pressure of the diesel fuel on the bottom of the barrel, which is 4900 Pa.

[0067] Vibration sensors record vibration data during transportation;

[0068] The weighing sensor measured the total weight of the smart battery to be 305 kg.

[0069] According to the formula h=P / (ρ*g), substituting g=9.8N / kg, we get h=0.6m.

[0070] The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is then weighed. The difference between the actual liquid mass and the calculated liquid mass is used to determine the liquid level. When the deviation exceeds a set threshold, the cause of the anomaly is analyzed by combining historical data and preset liquid density records. The specific steps include:

[0071] Based on the bottom area of ​​the smart chip tank and the calculated liquid level, the liquid mass is obtained according to the formula m=ρ*S*h; where m represents the liquid mass and S represents the bottom area of ​​the smart chip tank. The total mass of the smart chip tank is measured by a weighing sensor, and the actual liquid mass is calculated by subtracting the mass when unloaded from the total mass of the smart chip tank.

[0072] Compare the measured liquid mass with the calculated liquid mass. When the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to the set deviation threshold, the calculated liquid level height is determined to be the actual liquid level height of the smart tank.

[0073] When the deviation between the measured liquid mass and the calculated liquid mass exceeds the set deviation threshold, the cause of the anomaly is analyzed by combining historical data from the pressure sensor and preset records of liquid density.

[0074] Specifically, calculate the liquid mass: m = ρ * S * h = 255 kg;

[0075] Actual liquid mass: Total mass - empty container mass = 305 - 50 = 255 kg;

[0076] If the calculated mass deviates from the measured mass by 0% (≤5% threshold), the liquid level of 0.6m is confirmed as the actual liquid level, and the digital record shows "Diesel, liquid level 0.6m, mass 255kg, pressure 4900Pa".

[0077] Calculate the deviation between the current pressure and the historical normal pressure under the same liquid and level. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal; if it is within the historical maximum fluctuation range, prioritize checking the density assumption. Combine historical mass calculation deviations, operation logs, and loading records to determine if the density assumption is incorrect due to human input errors or residual liquid. Specific steps include:

[0078] Determining whether a pressure sensor poses an anomaly risk based on historical pressure sensor data involves the following steps:

[0079] The deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history is calculated as follows: Bc,h=|Pc-Ph| / Ph*100%; where Bc,h represents the deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history, Pc represents the current pressure data, and Ph represents the normal pressure value under the same liquid and liquid level in history;

[0080] If the historical data of the pressure sensor is stable, and the current pressure data deviates from the normal pressure value under the same historical conditions by more than the historical maximum fluctuation range, then the sensor is marked as abnormal. Here, stable historical data means that under the same liquid and liquid level in the past, the historical fluctuation range is within the historical maximum fluctuation range.

[0081] If the historical data of the pressure sensor is stable, and the current pressure data is within the range of the historical maximum fluctuation range relative to the normal pressure value under the same historical conditions, then first check whether the abnormal measured liquid mass and the calculated liquid mass are greater than the set deviation threshold due to an incorrect assumption of liquid density.

[0082] To analyze the consistency of the pre-recorded liquid density data and determine whether the density assumption is incorrect, the specific steps include:

[0083] If the current preset density is consistent with the density record of the same type of liquid in the past, and the mass calculated by the pressure sensor data, combined with the liquid density and the bottom area of ​​the bucket, is less than or equal to the actual weighed mass of the liquid in the past when the same type of liquid was loaded, then the deviation between the current preset density and the density record of the liquid in the past and the actual weighed mass is less than or equal to the set deviation threshold, then the deviation is first checked to see if it is caused by the sensor.

[0084] If the current preset density is inconsistent with the historical density records of the same type of liquid, check the operation log of the density preset to determine whether the incorrect liquid density assumption was caused by human input error.

[0085] By combining the historical loading records of the smart bucket with the current preset density, it is determined whether the actual average density is inconsistent with the preset due to residual liquid, which indicates an error in the liquid density assumption.

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

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

[0088] Density assumption verification: Historical records show that the preset density of diesel fuel is 850 kg / m³. 3 The operation log shows no input errors, but the historical loading record shows "Last loaded gasoline (ρ=750kg / m³)". 3 The statement that "the gas was not completely emptied" indicates that the actual density was reduced due to residual gasoline, which is an incorrect density assumption.

[0089] Based on demand, the smart tank transportation scenario is divided into two scenarios: Scenario 1 and Scenario 2. Scenario 1 monitors transport tilt, liquid replacement, and liquid level based on actual liquid level. Scenario 2 detects internal pressure and ambient temperature. Overpressure is compensated for by temperature, triggering diagnostics. Abnormal causes are identified through pressure fluctuation patterns; if no pattern is observed, sensor readings or road conditions are analyzed. Scenario 1 refers to the smart tank being rented to a user for repeated use. Scenario 2 refers to a user purchasing liquid and renting a smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling. Specific steps include:

[0090] The delivery scenario for the Smart Chip Bucket is divided into a first delivery scenario and a second delivery scenario. The first delivery scenario refers to the Smart Chip Bucket being rented to a user and then reused by the user. The second delivery scenario refers to the user purchasing liquid and renting a Smart Chip Bucket. After the liquid is used up, the empty Smart Chip Bucket is transported to the base to be refilled with liquid.

[0091] For the first transportation scenario, the actual liquid level of the smart chip tank is calculated, and the tilting, liquid replacement, and liquid level are monitored during transportation based on the actual liquid level of the smart chip tank.

[0092] For the second transportation scenario, the internal pressure and ambient temperature of the smart tank are detected. Temperature compensation is used to eliminate the interference of temperature on pressure detection. If the pressure after compensation exceeds the set pressure threshold, diagnosis is triggered and pattern recognition is performed based on the pressure fluctuation over a period of time: if the fluctuation is regular, the cause of the anomaly is determined based on historical known patterns; if the fluctuation is irregular, it is analyzed whether it is caused by sensor malfunction or transportation road conditions.

[0093] The process for replacing and testing the liquid in the smart tank includes:

[0094] Monitor whether there is any residue in the smart chip tank. If there is residue, clean the smart chip tank. If there is no residue, perform liquid replacement according to the established rules.

[0095] The process for detecting accidental spillage of liquids in the smart liquid container includes:

[0096] The initial angle when the Smart Chip Bucket is placed horizontally is used as the reference value for calculating the tilt angle, and is set to 0 degrees.

[0097] The system collects tilt angle data along both the X and Y axes, angle change rate data calculated based on high-frequency sampling, vibration data including peak vibration acceleration and duration, and records the time sequence information of each data point.

[0098] When the angle of any axis in the tilt angle data is greater than or equal to the preset tilt threshold, a preliminary warning is triggered, and the next step of verification is initiated; if the angle is always less than the preset tilt threshold, it is determined to be in a "normal state".

[0099] For the initial warning scenario, further verification was conducted by combining the angle change rate and vibration data:

[0100] If the rate of change of angle is greater than the preset threshold for the rate of change of angle, and instantaneous impact characteristics appear in the vibration data (such as acceleration peak > 5g), it is determined that "there is a risk of sudden tipping over".

[0101] If the rate of angle change is less than or equal to the preset rate of angle change threshold, it is determined to be "normal tilt (such as handling operation)" and the initial warning is lifted;

[0102] For scenarios with a "risk of sudden tipping over", the duration of consecutive periods when the tilt angle is greater than or equal to a preset tipping threshold is recorded:

[0103] If the duration is greater than or equal to the set duration threshold (e.g., 3 seconds), it will be determined as a "tilting event" and trigger an alarm (e.g., audible and visual alerts or data upload).

[0104] If the duration is less than the set duration threshold, it is determined to be "instantaneous tilt" and the warning is lifted;

[0105] The liquid level monitoring process in the smart tank includes:

[0106] Set low and high liquid level thresholds. Based on the preset low and high liquid level thresholds, continuously monitor the liquid level in the tank: if the liquid level is detected to be lower than the low threshold, a low liquid level warning and corresponding protection measures (such as prompting to add liquid) are triggered; if the liquid level is detected to be higher than the high threshold, a high liquid level warning and corresponding protection measures (such as stopping liquid addition or alarm) are triggered; if the liquid level is between the high and low thresholds, the liquid level is determined to be normal, and the current operation is maintained.

[0107] An offline detection-based logistics status monitoring system includes: a data acquisition module, an anomaly analysis module, a scenario-based monitoring module, and a diagnosis and decision-making module. The data acquisition module calculates the liquid level and acquires vibration data using a pressure sensor in the smart container, establishing a digital archive to record liquid information. The anomaly analysis module calculates the liquid mass and compares the deviation with the measured mass obtained by weighing to determine the liquid level. When the deviation exceeds a threshold, it combines historical data and preset records of liquid density to analyze the cause of the anomaly. The scenario-based monitoring module divides the smart container's transportation scenario into a first transportation scenario and a second transportation scenario according to requirements. The diagnosis and decision-making module monitors transportation tilt, liquid replacement, and liquid level based on the actual liquid level in the first transportation scenario. In the second transportation scenario, it detects internal pressure and ambient temperature, triggering diagnosis after temperature compensation for overpressure.

[0108] 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 chip bucket through a pressure sensor installed at the bottom of the smart chip bucket. The vibration sensing unit acquires vibration data during transportation and use through a vibration sensor installed on the main body of the smart chip bucket. The weighing sensing unit is used to measure the total mass of the smart chip bucket. The temperature sensing unit is used to collect the temperature data of the environment in which the smart chip bucket is located.

[0109] The anomaly analysis module includes a liquid level and mass calculation unit, a deviation analysis unit, a sensor anomaly judgment unit, and a density hypothesis verification unit. The liquid level and mass calculation unit calculates the liquid level height based on pressure data collected by the pressure sensor unit, combined with a preset liquid density, using a formula. It then calculates the theoretical mass of the liquid based on the bottom area of ​​the smart tank. The deviation analysis unit compares the measured liquid mass obtained by the weighing sensor unit with the theoretical mass to calculate the deviation value. The sensor anomaly judgment unit retrieves historical data from the pressure sensor, calculates the deviation between the current pressure and the normal pressure under the same liquid and level in the past, determines whether it exceeds the historical maximum fluctuation range, and identifies whether the sensor is malfunctioning. The density hypothesis verification unit verifies whether there are errors in the preset density based on the preset liquid density record, operation log, and historical loading record.

[0110] The scenario-based monitoring module includes a first scenario monitoring unit and a second scenario monitoring unit. The first scenario monitoring unit is used in the first transportation scenario to monitor the tilting, liquid replacement, and liquid level changes during transportation based on the actual liquid level height output by the liquid level and mass calculation unit. The second scenario monitoring unit is used in the second transportation scenario to perform temperature compensation by combining data from the temperature sensing unit and to monitor the internal pressure of the smart chip bucket. When the pressure exceeds the threshold, pressure fluctuation analysis is triggered. The first transportation scenario indicates that the smart chip bucket is rented to the user and then reused by the user. The second transportation scenario indicates that the user purchases liquid and rents the smart chip bucket. After the liquid is used up, the empty smart chip bucket is transported to the base for refilling.

[0111] The diagnosis and decision-making module includes a pattern recognition unit and an anomaly cause decision-making unit. The pattern recognition unit is used to perform pattern recognition on pressure fluctuation data over a period of time for the second transportation scenario to determine whether the fluctuation has a regularity. The anomaly cause decision-making unit is used to combine the pattern recognition results with the analysis of the sensor anomaly judgment unit. If the fluctuation has a regularity, the anomaly cause is determined based on historical known patterns. If the fluctuation has no regularity, the anomaly cause is analyzed to determine whether it is caused by sensor anomaly or transportation road conditions.

[0112] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A logistics status monitoring method based on offline detection, characterized in that: The method includes the following steps: The liquid level is calculated by a pressure sensor installed in the smart tank, and vibration data is obtained by a vibration sensor. A digital archive is then established to record liquid-related information. The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is obtained by weighing. The liquid level is determined by comparing the actual liquid mass with the calculated liquid mass and the deviation. When the deviation exceeds the set threshold, the cause of the abnormality is analyzed by combining historical data and preset liquid density records. Calculate the deviation between the current pressure and the normal pressure under the same liquid and liquid level in history. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal. If it is within the historical maximum fluctuation range, check the density assumption first. Combine the historical mass calculation deviation, operation log and loading record to determine whether the density assumption is wrong due to human input error or residual liquid. Based on the requirements, the smart tank transportation scenario is divided into a first transportation scenario and a second transportation scenario. In the first transportation scenario, the actual liquid level is monitored to detect the transportation tilt, liquid replacement, and liquid level. In the second transportation scenario, the internal pressure and ambient temperature are detected. After temperature compensation for overpressure, a diagnosis is triggered. The cause of the abnormality is identified by the pressure fluctuation pattern. If there is no pattern, the sensor or road conditions are analyzed. In the first transportation scenario, the smart tank is rented to the user and then reused by the user. In the second transportation scenario, the user purchases liquid and rents the smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling.

2. The logistics status monitoring method based on offline detection according to claim 1, characterized in that: The liquid level is calculated using a pressure sensor installed in the smart tank, and vibration data is obtained using a vibration sensor. A digital archive is then created to record relevant liquid information. Specific steps include: The pressure sensor installed at the bottom of the smart bucket collects the pressure of the liquid on the bottom of the bucket. Under the premise of the preset liquid type in the smart bucket, the preset density of the liquid is read, and the liquid level height is calculated using the formula h=P / (ρ*g), where h represents the liquid level height, P represents the pressure of the liquid on the bottom of the bucket, ρ represents the preset liquid density, and g represents the acceleration due to gravity. Vibration sensor installed in the smart battery is used to obtain its vibration sensing data; Construct a digital historical archive for the smart chip bucket, obtain the mass of the smart chip bucket when it was empty, and record its historical liquid information, including liquid type, density parameters, start and end time of each loading, emptying status and residual status.

3. The logistics status monitoring method based on offline detection according to claim 2, characterized in that: The liquid mass is calculated based on the bottom area of ​​the smart tank and the calculated liquid level. The actual liquid mass is then weighed. The difference between the actual liquid mass and the calculated liquid mass is used to determine the liquid level. When the deviation exceeds a set threshold, the cause of the anomaly is analyzed by combining historical data and preset liquid density records. The specific steps include: Based on the bottom area of ​​the smart chip tank and the calculated liquid level, the liquid mass is obtained according to the formula m=ρ*S*h; where m represents the liquid mass and S represents the bottom area of ​​the smart chip tank. The total mass of the smart chip tank is measured by a weighing sensor, and the actual liquid mass is calculated by subtracting the mass when unloaded from the total mass of the smart chip tank. Compare the measured liquid mass with the calculated liquid mass. When the deviation between the measured liquid mass and the calculated liquid mass is less than or equal to the set deviation threshold, the calculated liquid level height is determined to be the actual liquid level height of the smart tank. When the deviation between the measured liquid mass and the calculated liquid mass exceeds the set deviation threshold, the cause of the anomaly is analyzed by combining historical data from the pressure sensor and preset records of liquid density.

4. The logistics status monitoring method based on offline detection according to claim 3, characterized in that: Calculate the deviation between the current pressure and the normal pressure under the same liquid and liquid level in history. If the deviation exceeds the historical maximum fluctuation range, mark the sensor as abnormal. If it is within the historical maximum fluctuation range, check the density assumption first. By combining historical mass calculation deviations, operation logs, and loading records, it is determined whether the density assumption was incorrect due to human input errors or residual liquid. Specific steps include: Determining whether a pressure sensor poses an anomaly risk based on historical pressure sensor data involves the following steps: The deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history is calculated as follows: Bc,h=|Pc-Ph| / Ph*100%; where Bc,h represents the deviation between the current pressure data and the normal pressure value under the same liquid and liquid level in history, Pc represents the current pressure data, and Ph represents the normal pressure value under the same liquid and liquid level in history; If the historical data of the pressure sensor is stable, and the current pressure data deviates from the normal pressure value under the same historical conditions by more than the historical maximum fluctuation range, then the sensor is marked as abnormal. Here, stable historical data means that under the same liquid and liquid level in the past, the historical fluctuation range is within the historical maximum fluctuation range. If the historical data of the pressure sensor is stable, and the current pressure data is within the range of the historical maximum fluctuation range relative to the normal pressure value under the same historical conditions, then first check whether the abnormal measured liquid mass and the calculated liquid mass are greater than the set deviation threshold due to an incorrect assumption of liquid density. To analyze the consistency of the pre-recorded liquid density data and determine whether the density assumption is incorrect, the specific steps include: If the current preset density is consistent with the density record of the same type of liquid in the past, and the mass calculated by the pressure sensor data, combined with the liquid density and the bottom area of ​​the bucket, is less than or equal to the actual weighed mass of the liquid in the past when the same type of liquid was loaded, then the deviation between the current preset density and the density record of the liquid in the past and the actual weighed mass is less than or equal to the set deviation threshold, then the deviation is first checked to see if it is caused by the sensor. If the current preset density is inconsistent with the historical density records of the same type of liquid, check the operation log of the density preset to determine whether the incorrect liquid density assumption was caused by human input error. By combining the historical loading records of the smart bucket with the current preset density, it is determined whether the actual average density is inconsistent with the preset due to residual liquid, which indicates an error in the liquid density assumption.

5. The logistics status monitoring method based on offline detection according to claim 4, characterized in that: Based on demand, the smart tank transportation scenario is divided into two scenarios: Scenario 1 and Scenario 2. Scenario 1 monitors transport tilt, liquid replacement, and liquid level based on actual liquid level. Scenario 2 detects internal pressure and ambient temperature. Overpressure is compensated for by temperature, triggering diagnostics. Abnormal causes are identified through pressure fluctuation patterns; if no pattern is observed, sensor readings or road conditions are analyzed. Scenario 1 refers to the smart tank being rented to a user for repeated use. Scenario 2 refers to a user purchasing liquid and renting a smart tank. After the liquid is used up, the empty smart tank is transported to the base for refilling. Specific steps include: The delivery scenario for the Smart Chip Bucket is divided into a first delivery scenario and a second delivery scenario. The first delivery scenario refers to the Smart Chip Bucket being rented to a user and then reused by the user. The second delivery scenario refers to the user purchasing liquid and renting a Smart Chip Bucket. After the liquid is used up, the empty Smart Chip Bucket is transported to the base to be refilled with liquid. For the first transportation scenario, the actual liquid level of the smart chip tank is calculated, and the tilting, liquid replacement, and liquid level are monitored during transportation based on the actual liquid level of the smart chip tank. For the second transportation scenario, the internal pressure and ambient temperature of the smart tank are detected. Temperature compensation is used to eliminate the interference of temperature on pressure detection. If the pressure after compensation exceeds the set pressure threshold, diagnosis is triggered and pattern recognition is performed based on the pressure fluctuation over a period of time: if the fluctuation is regular, the cause of the anomaly is determined based on historical known patterns; if the fluctuation is irregular, it is analyzed whether it is caused by sensor malfunction or transportation road conditions.

6. A logistics status monitoring system based on offline detection, applied to the logistics status monitoring method based on offline detection as described in any one of claims 1-5, characterized in that: The system includes a data acquisition module, an anomaly analysis module, a scenario-based monitoring module, and a diagnosis and decision-making module. The data acquisition module calculates the liquid level and acquires vibration data using the pressure sensor of the smart tank, establishing a digital archive to record liquid information. The anomaly analysis module calculates the liquid mass and compares the deviation with the measured mass obtained by weighing to determine the liquid level. When the deviation exceeds a threshold, it combines historical data and preset records of liquid density to analyze the cause of the anomaly. The scenario-based monitoring module divides the smart tank transportation scenario into a first transportation scenario and a second transportation scenario according to needs. The diagnosis and decision-making module monitors transportation tilt, liquid replacement, and liquid level based on the actual liquid level in the first transportation scenario. In the second transportation scenario, it detects internal pressure and ambient temperature, and triggers diagnosis after temperature compensation for overpressure.

7. The logistics status 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 chip bucket through a pressure sensor installed at the bottom of the smart chip bucket. The vibration sensing unit acquires vibration data during transportation and use through a vibration sensor installed on the main body of the smart chip bucket. The weighing sensing unit is used to measure the total mass of the smart chip bucket. The temperature sensing unit is used to collect the temperature data of the environment in which the smart chip bucket is located.

8. The logistics status monitoring system based on offline detection according to claim 7, characterized in that: The anomaly analysis module includes a liquid level and mass calculation unit, a deviation analysis unit, a sensor anomaly judgment unit, and a density hypothesis verification unit. The liquid level and mass 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, and to calculate the theoretical mass of the liquid based on the bottom area of ​​the smart tank. The deviation analysis unit is used to compare the measured liquid mass obtained by the weighing sensor unit with the theoretical mass and calculate the deviation value; the sensor anomaly judgment unit is used to retrieve historical data of the pressure sensor, calculate the deviation between the current pressure and the historical normal pressure under the same liquid and liquid level, determine 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 there are errors in the density preset by combining the liquid density preset record, operation log and historical loading record.

9. A logistics status monitoring system based on offline detection according to claim 8, characterized in that: The scenario-based monitoring module includes a first scenario monitoring unit and a second scenario monitoring unit. The first scenario monitoring unit is used in the first transportation scenario to monitor the tilting, liquid replacement, and liquid level changes during transportation based on the actual liquid level height output by the liquid level and mass calculation unit. The second scenario monitoring unit is used in the second transportation scenario to perform temperature compensation by combining data from the temperature sensing unit and to monitor the internal pressure of the smart chip bucket. When the pressure exceeds the threshold, pressure fluctuation analysis is triggered. The first transportation scenario indicates that the smart chip bucket is rented to the user and then reused by the user. The second transportation scenario indicates that the user purchases liquid and rents the smart chip bucket. After the liquid is used up, the empty smart chip bucket is transported to the base for refilling.

10. A logistics status monitoring system based on offline detection according to claim 9, characterized in that: The diagnosis and decision-making module includes a pattern recognition unit and an anomaly cause decision-making unit. The pattern recognition unit is used to perform pattern recognition on pressure fluctuation data over a period of time for the second transportation scenario to determine whether the fluctuation has a regularity. The anomaly cause decision-making unit is used to combine the pattern recognition results with the analysis of the sensor anomaly judgment unit. If the fluctuation has a regularity, the anomaly cause is determined based on historical known patterns. If the fluctuation has no regularity, the anomaly cause is analyzed to determine whether it is caused by sensor anomaly or transportation road conditions.

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