Goods storage yard safety inspection system

Abstract

Relates to the technical field of goods inspection, in particular to a goods storage yard safety inspection system, which comprises a goods analysis module, a goods storage yard safety inspection module, a goods storage yard safety inspection module and a goods storage yard safety inspection module, the inspection analysis module is used for generating an inspection path based on the cargo stacking area and matching inspection parameters corresponding to cargo information in a preset inspection database; the inspection control module is used for indicating a preset inspection robot to inspect based on the inspection parameters and the inspection path, and performing temperature detection data interaction with an area temperature sensor in the stacking area so as to determine a detection temperature deviation value; and the inspection adjustment module is used for adjusting the detection frequency according to the detection temperature deviation value and a preset detection frequency adjustment strategy. The low-temperature cold-chain cargo inspection system has the advantages that the low-temperature cold-chain cargo inspection efficiency is improved, and the cargo damage probability caused by inspection lag is reduced.

Description

Technical Field

[0001] This application relates to the technical field of goods inspection, and in particular to a safety inspection system for a goods yard. Background Art

[0002] The inspection of goods yards is a safety inspection work for inspecting a large number of goods piles, and different inspection requirements are imposed on different goods during inspection.

[0003] In the related art, when some medicines or cold-chain goods with relatively high temperature requirements are stored in a temporary warehouse, it is necessary to regularly inspect the goods to detect whether the goods in different stacking areas are within the specified temperature range, and reduce the probability of goods deterioration caused by changes in the regional temperature. Usually, temperature sensors are installed in the warehouse to measure the temperature of the warehouse, and an alarm is given when the temperature change exceeds the set standard range, so that the warehouse management personnel can take temperature adjustment measures in time.

[0004] In the above related art, when some goods are stored in a relatively low-temperature warehouse, it is difficult to ensure that a single temperature sensor remains stable for a long time. At the same time, it is inconvenient for personnel to enter the inspection frequently, which is likely to cause a lag in hidden danger detection of the goods and affect the quality of the goods. Summary of the Invention

[0005] In order to solve the above technical problems, this application provides a safety inspection system for a goods yard.

[0006] In a first aspect, this application provides a safety inspection system for a goods yard, adopting the following technical solution:

[0007] A safety inspection system for a goods yard includes:

[0008] A goods analysis module, which collects the goods stacking information in the yard for analysis to obtain the goods information and the goods storage temperature information in different stacking areas;

[0009] An inspection analysis module, which generates an inspection path based on the goods stacking area and matches the inspection parameters corresponding to the goods information in the preset inspection database;

[0010] An inspection control module, which based on the inspection parameters and the inspection path, instructs the preset inspection robot to conduct inspections, and interacts with the regional temperature sensors in the stacking area for temperature detection data to determine the detected temperature deviation value;

[0011] An inspection adjustment module, which adjusts the inspection frequency according to the detected temperature deviation value and the preset inspection frequency adjustment strategy.

[0012] Optionally, the inspection frequency adjustment strategy includes:

[0013] Detect the inspection speed of the preset inspection robot and calculate the optimized inspection frequency of the inspection robot according to the detected temperature deviation value;

[0014] The formula for calculating the optimized inspection frequency is as follows:

[0015] f = α·β·f 0 ,

[0016]

[0017] where f represents the optimized inspection frequency, f 0 represents the inspection frequency during standard speed inspection, ΔT represents the calculated detected temperature deviation value, T th represents the set temperature deviation safety threshold, k represents the temperature adjustment coefficient, v represents the actual inspection speed of the inspection robot, v 0 represents the preset reference inspection speed of the inspection robot, m represents the speed adjustment coefficient of the inspection robot, and α represents the temperature adjustment factor.

[0018] Optionally, when calculating the optimized inspection frequency, it further includes:

[0019] Analyze the cargo information to determine pharmaceutical goods and food goods;

[0020] Match the pharmaceutical goods and food goods with the pharmaceutical priority weight factor and food priority weight factor in the preset weight database;

[0021] Calculate the secondary optimized detection frequency based on the pharmaceutical priority weight factor and food priority weight factor;

[0022] Update the current detection frequency of the inspection robot based on the secondary optimized detection frequency..

[0023] Optionally, the following formula is configured when calculating the secondary optimized detection frequency:

[0024] F i = f·(I med (i)·W med + I food (i)·W food ),

[0025] where F i represents the secondary optimized detection frequency of the inspection robot, f represents the optimized inspection frequency, W med represents the pharmaceutical goods priority weight factor, W food represents the food goods priority weight factor, I med represents when the goods are pharmaceuticals, I food represents the goods are food.

[0026] When generating an inspection path based on the goods stacking area, it includes:

[0027] Analyze the inspection area and goods information to obtain the coordinates of the goods inspection points;

[0028] Analyze the coordinates of the goods inspection points and the preset target inspection path function to obtain the minimum inspection path;

[0029] Send an inspection instruction based on the minimum inspection path to instruct the preset inspection robot to conduct goods inspection.

[0030] Optionally, it further includes:

[0031] Conduct an analysis of the goods inventory and environment in the yard stack area to obtain the goods inventory elements and environmental impact elements;

[0032] Calculate the inspection frequency based on the inventory elements and environmental impact elements, and correct the secondary optimized detection frequency.

[0033] Optionally, when calculating the inspection frequency based on the inventory elements and environmental impact elements, configure the following formula:

[0034] F i =f·(I med (i)·W med +I food (i)·W food )·C env ·C stock ,

[0035] where C env represents the weight coefficient of the preset environmental impact element, and C stock represents the weight coefficient of the preset inventory quantity element.

[0036] Optionally, when generating an inspection path based on the goods stacking area, it includes:

[0037] Analyze the inspection area and goods information to obtain the coordinates of the goods inspection points;

[0038] Analyze the coordinates of the goods inspection points and the preset target inspection path function to obtain the minimum inspection path;

[0039] Send an inspection instruction based on the minimum inspection path to instruct the preset inspection robot to conduct goods inspection.

[0040] Optionally, when collecting and analyzing the goods stacking information in the yard to obtain the goods storage temperature information, it further includes:

[0041] Establish a coupling model of environmental temperature and humidity and collect the environmental humidity in the goods stacking area;

[0042] Analyze based on environmental humidity and coupling model to obtain the temperature acquisition deviation amount;

[0043] Calculate the actual area temperature based on the temperature acquisition deviation amount.

[0044] Optionally, when calculating the actual area temperature based on the temperature acquisition deviation amount, the following formula is used:

[0045]

[0046] T true = T meas -ΔThum·[1 + α(T meas - T cal )],

[0047] where, ΔThum represents the temperature change amount related to humidity, k 1 represents the preset proportional constant, RH represents the relative humidity of the current environment, RH 0 represents the reference relative humidity, n represents the indication parameter for adjusting the influence degree and change trend of relative humidity on the result, k 2 represents the constant for the action intensity of the preset environmental temperature on the result, T env represents the measured environmental temperature, e represents the natural constant, T true represents the corrected actual area temperature, T meas represents the measured temperature obtained by direct measurement, and α represents the set calibration coefficient.

[0048] In summary, this application includes at least one of the following beneficial technical effects:

[0049] 1. While the temperature sensor in the stacking area monitors the temperature, the temperature detection interaction is carried out through the patrol robot and the temperature sensor, so as to improve the comprehensiveness of temperature detection, reduce the temperature detection error, improve the patrol efficiency at the same time, and reduce the trouble of inconvenient personnel patrol in the cold storage of cold regions;

[0050] 2. By optimizing the patrol frequency of the patrol robot in the stacking area sensitive to temperature changes, the cold chain goods sensitive to temperature can be patrolled in time, so as to reduce the probability that the goods cannot be informed in time due to temperature changes during the patrol interval, and help to keep the quality of the goods in a stable state;

[0051] 3. According to the different weight ratios of goods in food and medicine, optimize the patrol frequency of the patrol robot, so that different types of goods can be optimized for the patrol frequency, and further improve the comprehensiveness of patrol. Description of the Drawings

[0052] Figure 1It is the flowchart of the method for steps S100 to S103 in this application.

[0053] Figure 2 It is the flowchart of the method for steps S1031 to S1034 in this application.

[0054] Figure 3 It is the flowchart of the method for steps SS1035 to S1036 in this application.

[0055] Figure 4 It is the flowchart of the method for steps S1011 to S1013 in this application.

[0056] Figure 5 It is the flowchart of the method for steps S1001 to S1004 in this application. Detailed implementation manners

[0057] In order to make the purpose, technical solutions and advantages of this application clearer and more understandable, the following will further elaborate on this application in combination with the attached Figures 1-5 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0058] The embodiments of the present invention will be further described in detail below in conjunction with the drawings of the specification.

[0059] The embodiments of this application disclose a safety inspection system for a cargo yard. While the temperature sensors in the stacking area monitor the temperature, temperature detection interaction is carried out through inspection robots and temperature sensors to improve the comprehensiveness of temperature detection, reduce temperature detection errors, improve the inspection efficiency, and reduce the inconvenience of personnel inspection in the cold area warehouses.

[0060] Referring to Figure 1 , the method process of the safety inspection system for the cargo yard includes the following steps:

[0061] Step S100: The cargo analysis module collects the cargo stacking information in the yard, analyzes it, and obtains the cargo information and the cargo storage temperature information in different stacking areas;

[0062] The cargo stacking information includes the specific name, category of the cargo, and the requirement of the storage temperature. By parsing the information set, the cargo information, that is, the cargo name and category, can be obtained, and at the same time, the storage temperature of the cargo can also be known. This storage temperature requirement is defined as the cargo storage temperature information for subsequent further analysis and call.

[0063] Step S101: The inspection analysis module generates an inspection path based on the cargo stacking area and matches the inspection parameters corresponding to the cargo information in the preset inspection database;

[0064] The goods stacking area is a field stacking area for stacking goods, which is set by the actual stacking warehouse or factory building. The inspection path is the moving path for the inspection robot to inspect all goods stacking areas. It can be automatically planned and generated by inputting the coordinates of the stacking area into the set path generation model to obtain a path that meets the requirements. The inspection database is a pre-established inspection parameter database, which stores different inspection parameters and the corresponding goods information. When the goods information is input, the corresponding inspection parameters are automatically searched and output. It should be further noted that the inspection parameters include temperature detection and appearance stacking detection of the goods in the goods stacking area, which are used to ensure that the stacked goods will not fall during the temporary placement period or the storage temperature exceeds the restricted range, thus helping to ensure that the quality of the goods is not easily affected by temperature.

[0065] Step S102: The inspection control module, based on the inspection parameters and the inspection path, instructs the preset inspection robot to conduct inspections, and conducts temperature detection data interaction with the area temperature sensors in the stacking area to determine the detected temperature deviation value.

[0066] The inspection robot is a mobile inspection robot with temperature detection and image detection and recognition capabilities. By loading a temperature measuring instrument and a high-definition recognition camera, corresponding temperature and image detection operations can be carried out.

[0067] In the stacking area of the field stack, multiple temperature sensors are also set up to measure the ambient temperature in different areas and store and update the ambient temperature in real time. When the inspection robot moves to a position near the temperature sensor according to the inspection path, by comparing the temperature detected by the inspection robot with the temperature sensor installed in the area, the temperature difference measured between the two can be obtained and defined as the detected temperature deviation value for subsequent further analysis.

[0068] Step S103: The inspection adjustment module adjusts the inspection frequency according to the detected temperature deviation value and the preset inspection frequency adjustment strategy.

[0069] Under normal circumstances, the detected temperature deviation value has an allowable deviation range. When the temperature difference detected by the inspection robot and the temperature sensor in the stacking area exceeds the allowable deviation range, it indicates that the temperature in the goods stacking area changes frequently at this time, and a warning prompt signal is issued. At the same time, in order to reduce the untimely prediction caused by problems with the temperature sensor, the inspection frequency of the inspection robot is adjusted through the preset inspection frequency adjustment strategy, so that the inspection robot can frequently inspect this area as needed, and the inspection timeliness is further improved.

[0070] The inspection frequency adjustment strategy includes:

[0071] Detect the inspection speed of the preset inspection robot and calculate the optimized inspection frequency of the inspection robot according to the detected temperature deviation value;

[0072] The formula for calculating the optimized inspection frequency is as follows:

[0073] f = α·β·f 0 ,

[0074]

[0075] Among them, f represents the optimized inspection frequency, f 0 represents the inspection frequency during standard speed inspection, ΔT represents the calculated detected temperature deviation value, T th represents the set temperature deviation safety threshold, k represents the temperature adjustment coefficient, v represents the actual inspection speed of the inspection robot, v 0 represents the preset reference inspection speed of the inspection robot, m represents the speed adjustment coefficient of the inspection robot, and α represents the temperature adjustment factor. It should be noted that the increase in the inspection frequency is positively correlated with the detected temperature deviation value. When the temperature deviation value is larger, the inspection frequency is correspondingly increased. Conversely, the inspection frequency is reduced, which plays a role in saving inspection resources and helps reduce the consumption of inspection resources.

[0076] Refer to Figure 2 , where when calculating the optimized inspection frequency, it also includes:

[0077] Step S1031: Analyze the cargo information to determine pharmaceutical goods and food goods;

[0078] By screening the two key cargo categories in the cargo information, when the stacked cargo belongs to some food goods and pharmaceutical goods that are sensitive to temperature, different inspection frequencies need to be set to correct the inspection times of the inspection robot, so as to improve the monitoring reliability of the cargo.

[0079] Step S1032: Match the pharmaceutical priority weight factor and the food priority weight factor in the preset weight database based on the pharmaceutical goods and food goods;

[0080] Corresponding priority weight factors are set for different pharmaceutical goods and food goods. By pre-constructing a weight database, different pharmaceutical goods and food goods are stored in the weight database, and the corresponding pharmaceutical priority weight factors and food priority weight factors are stored. When the corresponding names of pharmaceutical goods or food goods are input, the corresponding weight factors are automatically searched for and output.

[0081] Step S1033: Calculate the secondary optimized detection frequency based on the pharmaceutical priority weight factor and the food priority weight factor;

[0082] Step S1034: Update the current detection frequency of the inspection robot based on the secondary optimized detection frequency.

[0083] By taking the drug weight factor and the video weight factor as adjustment factors, the detection frequency of the inspection robot is further calculated and defined as the secondary optimized detection frequency. The secondary optimized frequency is updated as the current detection frequency of the inspection robot, enabling the inspection robot to make corresponding inspection frequency adjustments for relatively special food and drug goods, and improving the effectiveness of cargo monitoring.

[0084] Furthermore, when calculating the secondary optimized detection frequency, the following formula is configured:

[0085] F i =f·(I med (i)·W med +I food (i)·W food ),

[0086] where, F i represents the secondary optimized detection frequency of the inspection robot, f represents the optimized inspection frequency, W med represents the priority weight factor of drug goods, W food represents the priority weight factor of food goods, I med represents when the goods are drugs, I food represents the goods are food.

[0087] Furthermore, referring to Figure 3 , when calculating the optimized inspection frequency, it also includes:

[0088] Step S1035: Conduct a cargo inventory and environmental analysis of the yard stack area to obtain cargo inventory elements and environmental impact elements;

[0089] Step S1036: Calculate the inspection frequency based on the inventory elements and environmental impact elements, and correct the secondary optimized detection frequency.

[0090] When calculating the inspection frequency based on the inventory elements and environmental impact elements, the following formula is configured:

[0091] F i =f·(I med (i)·W med +I food (i)·W food )·C env ·C stock ,

[0092] where, C env represents the weight coefficient of the pre-set environmental impact element, C stockRepresents the weight coefficient of the pre-set inventory elements.

[0093] It should be noted that, represents the weight coefficient of the pre-set environmental impact elements, and represents the weight coefficient of the pre-set inventory elements.

[0094] In the goods yard safety inspection system, the adjustment of the inspection frequency is an important aspect. By combining the inventory elements and environmental impact elements, the inspection frequency can be determined more accurately. Specifically, the inventory elements refer to the quantity and types of goods in the stacking area, and these factors directly affect the inspection frequency and key points. The environmental impact elements include environmental parameters such as temperature and humidity, and the changes of these parameters will affect the storage conditions of the goods and need special attention.

[0095] In specific implementation, first, the weight coefficients of the environmental impact elements and the inventory elements need to be pre-set. These weight coefficients can be set according to the actual yard situation and the characteristics of the goods. For example, for temperature-sensitive goods, a higher weight coefficient of the environmental impact elements can be set, and for areas with a large inventory, a higher weight coefficient of the inventory elements can be set.

[0096] In the calculation formula of the inspection frequency, represents the weight coefficient of the pre-set environmental impact elements, and represents the weight coefficient of the pre-set inventory elements. Through this formula, the environmental and inventory factors can be comprehensively considered to calculate a more reasonable inspection frequency to ensure the safe storage of the goods. It can be seen that this application provides a more accurate inspection frequency calculation method by introducing the weight coefficients of the inventory elements and the environmental impact elements. Compared with the prior art, this method can consider various factors affecting the safety of the goods more comprehensively, improve the effectiveness and timeliness of the inspection, and reduce the quality problems of the goods caused by environmental changes.

[0097] Refer to Figure 4 , when generating the inspection path based on the goods stacking area, it includes:

[0098] Step S1011: Analyze according to the inspection area and goods information to obtain the coordinates of the goods inspection points;

[0099] Step S1012: Analyze based on the coordinates of the goods inspection points and the pre-set target inspection path function to obtain the minimum inspection path;

[0100] Step S1013: Issue an inspection instruction based on the minimum inspection path to instruct the pre-set inspection robot to conduct goods inspection.

[0101] It should be noted that the coordinates of the cargo inspection points are obtained by analyzing the inspection area and the cargo information; based on the coordinates of the cargo inspection points and the preset target inspection path function, the minimum inspection path is obtained; based on the minimum inspection path, an inspection instruction is issued to instruct the preset inspection robot to perform cargo inspection.

[0102] Technical understanding: The inspection path generation method proposed in this application aims to determine the coordinates of the cargo inspection points by analyzing the cargo information and the inspection area, and further calculate the minimum inspection path through the target inspection path function. Through the generation of the minimum inspection path, the inspection robot can efficiently complete the inspection task and ensure the safe storage of the cargo.

[0103] Detailed explanation of technical features: In the process of generating the inspection path, first, the inspection area and the cargo information need to be analyzed in detail to determine the specific coordinates of each cargo inspection point. These coordinates can be obtained through sensors or other detection devices. Next, through the preset target inspection path function, these coordinates are processed and analyzed to obtain the minimum inspection path of the inspection robot. The minimum inspection path can be implemented through optimization algorithms such as the Dijkstra algorithm to ensure that the inspection robot can complete the inspection task with the shortest path. Finally, based on the generated minimum inspection path, an inspection instruction is issued to guide the inspection robot to perform the inspection according to the planned path. This method not only improves the inspection efficiency but also effectively reduces the energy consumption and working time of the inspection robot.

[0104] By analyzing the cargo information and the inspection area, the minimum inspection path is generated to guide the inspection robot to perform efficient cargo inspection. Compared with the prior art, the method of this application can more accurately determine the coordinates of the inspection points and generate the shortest inspection path through the optimization algorithm, thereby improving the inspection efficiency, reducing the energy consumption and working time, and ensuring the safe storage of the cargo.

[0105] Refer to Figure 5 , when collecting the cargo stacking information in the yard and analyzing to obtain the cargo storage temperature information, it further includes:

[0106] Step S1001: Establish a coupling model of the environmental temperature and humidity and collect the environmental humidity in the cargo stacking area;

[0107] Step S1002: Analyze based on the environmental humidity and the coupling model to obtain the temperature acquisition deviation;

[0108] Step S1003: Calculate the actual area temperature based on the temperature acquisition deviation.

[0109] When calculating the actual area temperature based on the temperature acquisition deviation, the following formula is used:

[0110]

[0111] T true = T meas - ΔThum·[1 + α(T meas - T cal )],

[0112] where ΔThum represents the temperature change amount related to humidity, k 1 represents a preset proportional constant, RH represents the relative humidity of the current environment, RH 0 represents the reference relative humidity, n represents an indication parameter for adjusting the influence degree and change trend of relative humidity on the result, k 2 represents a constant for the action intensity of the preset ambient temperature on the result, T env represents the measured ambient temperature, e represents the natural constant, T true represents the corrected actual area temperature, T meas represents the measured temperature obtained by direct measurement, and α represents a set calibration coefficient.

[0113] Regarding steps S1001 to S1004, it should be noted that in the cargo yard, the changes in environmental humidity and temperature have a significant impact on the storage conditions of the goods. In order to accurately reflect the actual temperature of the cargo stacking area, this application can more precisely capture and analyze the temperature acquisition deviation amount by establishing a coupling model of environmental temperature and humidity. Specifically, first, the environmental humidity data of the cargo stacking area is collected, and then based on this data and the coupling model, the temperature acquisition deviation amount is analyzed. Through this deviation amount, the actual area temperature can be calculated more accurately, so as to ensure that the goods are stored under the optimal temperature conditions.

[0114] The coupling model of environmental temperature and humidity proposed in this application can be implemented in various ways. For example, a linear regression model can be used to establish the relationship between temperature and humidity, or more complex machine learning algorithms such as neural networks or decision tree models can be adopted to capture more complex non-linear relationships. No matter which method is adopted, the key lies in training and validating through a large amount of historical data to ensure the accuracy and reliability of the model.

[0115] Compared with the prior art, by introducing the coupling model of environmental temperature and humidity, this application can more accurately reflect the actual area temperature, avoiding the stability problem caused by the long-term use of a single temperature sensor. Therefore, it can improve the accuracy and timeliness of cargo inspection, reduce the cargo quality problems caused by temperature deviation, and is especially suitable for the storage and inspection of medicines or cold chain goods with high temperature requirements.

[0116] Among them, $\Delta T$ represents the temperature change related to humidity, $k$ represents a preset proportionality constant, $RH$ represents the relative humidity of the current environment, $RH_{ref}$ represents the reference relative humidity, $\alpha$ represents an indication parameter for adjusting the influence degree and change trend of relative humidity on the result, $k_T$ represents a constant for the action intensity of the preset environmental temperature on the result, $T$ represents the measured environmental temperature, $e$ represents the natural constant, $T_{act}$ represents the corrected actual area temperature, $T_{meas}$ represents the measured temperature obtained by direct measurement, and $C$ represents the set calibration coefficient.

[0117] The solution of this application can more accurately reflect the actual area temperature by introducing the calculation formula of the temperature acquisition deviation. Specifically, based on the relative humidity and reference relative humidity of the current environment, the temperature change related to humidity is calculated, and then combined with the preset proportionality constant and the action intensity of the environmental temperature on the result. Finally, the actual area temperature is obtained through the correction formula. Thus, the temperature measurement error caused by the change of environmental humidity can be effectively reduced, and the accuracy of temperature detection can be improved.

[0118] Furthermore, the technical solution of this application can be implemented in various ways. For example, high-precision temperature and humidity sensors can be used to collect environmental humidity and temperature data in real time, and the temperature acquisition deviation can be calculated in real time through an embedded system. It is also possible to analyze and process the collected data through a cloud computing platform to obtain a more accurate actual area temperature. In addition, according to different application scenarios, the preset proportionality constant and calibration coefficient can be adjusted to meet the temperature detection requirements in different environments.

[0119] Compared with the prior art, the technical solution of this application has significant advantages. By introducing the calculation of the temperature change related to humidity, it can more accurately reflect the actual area temperature and reduce the temperature measurement error. Further, by adjusting the proportionality constant and calibration coefficient, it can flexibly adapt to different application scenarios and improve the adaptability and reliability of the system. These improvements effectively solve the problems of poor long-term stability of temperature sensors and inconvenient personnel patrol inspection in the prior art, and improve the efficiency and accuracy of the safety patrol inspection of the cargo yard.

[0120] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0121] The embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor for the cargo yard safety patrol inspection system.

[0122] Computer storage media include, for example: various media that can store program codes, such as USB flash drives, external hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0123] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor. A computer program capable of being loaded and executed by the processor for the cargo yard safety inspection system is stored on the memory.

[0124] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0125] The above are all preferred embodiments of the present application. The protection scope of the present application is not limited thereby. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A cargo yard safety inspection system, characterized in that: include: The cargo analysis module collects the cargo stacking information in the yard and analyzes it to obtain cargo information in different stacking areas and cargo storage temperature information; Inspection analysis module, which generates inspection paths based on the cargo storage area and matches inspection parameters corresponding to cargo information in a preset inspection database; The inspection control module instructs the preset inspection robot to perform inspection based on the inspection parameters and inspection path, and interacts with the regional temperature sensor in the stacking area to perform temperature detection data to determine the detection temperature deviation value; The inspection and adjustment module adjusts the detection frequency according to the detection temperature deviation value and the preset detection frequency adjustment strategy.

2. A cargo yard safety inspection system according to claim 1, characterized in that: The detection frequency adjustment strategy includes: Detect the inspection speed of the preset inspection robot and calculate the optimized inspection frequency of the inspection robot according to the detected temperature deviation value; The formula for calculating the optimized inspection frequency is as follows: f=α·β·f0, Where f represents the optimized inspection frequency, f0 represents the inspection frequency at standard speed inspection, ΔT represents the calculated detection temperature deviation value, T th represents the set temperature deviation safety threshold, k represents the temperature adjustment coefficient, v represents the actual inspection speed of the inspection robot, v0 represents the preset benchmark inspection speed of the inspection robot, m represents the speed adjustment coefficient of the inspection robot, and α represents the temperature adjustment factor.

3. A cargo yard safety inspection system according to claim 2, characterized in that: When calculating the optimized inspection frequency, it also includes: Analyze cargo information to identify pharmaceutical and food cargo; Based on matching the drug cargo and the food cargo with the drug priority weight factor and the food priority weight factor in the preset weight database; The secondary optimized detection frequency is calculated based on the drug priority weight factor and the food priority weight factor; The current detection frequency of the inspection robot is updated based on the secondary optimization detection frequency.

4. A cargo yard safety inspection system according to claim 3, characterized in that: The following formula is configured when calculating the secondary optimization detection frequency: F i =f·(I med (i)·W med +I food (i)·W food ), Among them, F i represents the secondary optimization detection frequency of the inspection robot, f represents the optimized inspection frequency, W med represents the priority weight factor of pharmaceutical goods, W food represents the food cargo priority weight factor, I med Indicates that when the goods are medicines, I food Indicates that the goods are food.

5. A cargo yard safety inspection system according to claim 4, characterized in that: Also includes: Cargo inventory and environmental analysis of the yard area are conducted to obtain cargo inventory factors and environmental impact factors; The inspection frequency is calculated based on inventory factors and environmental impact factors, and the secondary optimization inspection frequency is corrected.

6. A cargo yard safety inspection system according to claim 5, characterized in that: The following formula is configured when calculating the inspection frequency based on inventory factors and environmental impact factors: F i =f·(I med (i)·W med +I food (i)·W food )·C env ·C stock , Among them, C env Represents the weight coefficient of the pre-set environmental impact factor, C stock Indicates the weight coefficient of the preset inventory quantity factor.

7. A cargo yard safety inspection system according to claim 1, characterized in that: When generating an inspection route based on the cargo storage area, it includes: Analyze the inspection area and cargo information to obtain the coordinates of the cargo inspection points; The minimum inspection path is obtained by analyzing the coordinates of the cargo inspection points and the preset target inspection path function; Issue inspection instructions based on the minimum inspection path to instruct the preset inspection robot to conduct cargo inspection.

8. A cargo yard safety inspection system according to claim 1, characterized in that: When collecting the cargo stacking information in the yard and analyzing the cargo storage temperature information, it also includes: Establish a coupled model of ambient temperature and humidity and collect ambient humidity in the cargo storage area; The temperature acquisition deviation is obtained by analyzing the ambient humidity and coupling model; The actual area temperature is calculated based on the temperature acquisition deviation.

9. A cargo yard safety inspection system according to claim 8, characterized in that: The following formula is used to calculate the actual area temperature based on the temperature acquisition deviation: T true =T meas -ΔThum·[1+α(T meas -T cal )], Where ΔThum represents the temperature change related to humidity, k1 represents the preset proportional constant, RH represents the relative humidity of the current environment, RH0 represents the reference relative humidity, n represents the indicator parameter of the influence degree and change trend of adjusting the relative humidity on the result, k2 represents the constant of the effect intensity of the preset ambient temperature on the result, T env represents the measured ambient temperature, e represents the natural constant, T true Indicates the actual area temperature after correction, T meas It represents the measured temperature obtained by direct measurement, and α represents the set calibration coefficient.

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