Woody oil transportation safety tracking management method and system based on Internet of Things, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of logistics management, and discloses a woody oil transportation safety tracking management method and system based on the Internet of Things, electronic equipment and a storage medium, and the method comprises the steps: deploying sensor equipment in a woody oil transportation container, and collecting transportation environment parameters; uploading the collected transportation environment parameters to a cloud server; in the cloud service, a pre-trained safety detection model is utilized to judge the transportation environment parameters; and if the judgment result exceeds the preset safety range, giving an alarm to a worker. According to the invention, the dependence on manual inspection is reduced, the management efficiency is improved, the labor cost is reduced, and an efficient, intelligent and reliable solution is provided for the transportation management of woody oil plants.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a woody oilseed transportation safety tracking management method, system, electronic device and storage medium based on the Internet of Things. Background Art

[0002] Woody oilseeds, due to their high nutritional and economic value, are widely used in food processing, medicine, and other fields. However, woody oilseeds are susceptible to environmental factors during transportation, leading to quality degradation or damage. Furthermore, traditional transportation safety management methods often rely on manual inspection and record-keeping, which is subject to problems such as low efficiency, poor real-time performance, and unreliable data.

[0003] With the development of IoT technology, more and more companies are applying it to logistics management, enabling real-time monitoring and efficient management of cargo status. However, existing IoT solutions are mostly targeted at general cargo, and there are no dedicated safety tracking and management methods for woody oilseeds, which require special protection. Therefore, there is an urgent need for an IoT-based safety tracking and management method for woody oilseed transportation that can monitor environmental parameters in real time during transportation and provide intelligent early warning and management capabilities to ensure the quality and safety of woody oilseeds during transportation. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a woody oilseed transportation safety tracking and management method based on the Internet of Things, the method comprising:

[0005] Deploy sensor equipment inside woody oil transport containers to collect transport environment parameters;

[0006] Uploading the collected transportation environment parameters to a cloud server;

[0007] In the cloud service, the transportation environment parameters are determined using a pre-trained safety detection model;

[0008] If the judgment result exceeds the preset safety range, an alarm will be issued to the staff.

[0009] Preferably, the deployed sensor devices include: temperature sensors, humidity sensors, vibration sensors and light sensors;

[0010] The temperature sensor is used to monitor temperature changes during transportation to prevent excessively high or low temperatures from affecting the quality of woody oil.

[0011] The humidity sensor is used to ensure that the humidity of the transportation environment is within an appropriate range to prevent the oil from getting damp or mildewed due to excessive humidity;

[0012] The vibration sensor is used to detect the vibration intensity caused by vehicle bumps or collisions during transportation to prevent damage to the oil container or physical impact on the contents;

[0013] The light sensor is used to monitor light intensity to prevent the influence of direct strong light on certain light-sensitive woody oil plants.

[0014] Preferably, after the transportation environment parameters are uploaded to the cloud server, the server will preprocess the received data, including the following steps: first, decrypt and decompress the data to restore the original data; second, clean the data to remove invalid or abnormal data points; then, convert the data format to unify the format.

[0015] Preferably, the safety detection model uses an ant colony algorithm to construct an environmental parameter state transition network and dynamically identifies abnormal patterns based on pheromone concentrations:

[0016]

[0017] Among them, τ ij represents the pheromone concentration; η ij represents the heuristic information, α and β represent the weight parameters controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

[0018] Preferably, the method for iteratively optimizing the security detection model includes:

[0019] Ant colony simulation: Release a number of ants, each starting from a random initial state and generating a complete path based on the path selection probability;

[0020] Path evaluation: Calculate the anomaly score of each path. If the parameter change of a state transition in the path exceeds the preset threshold, it is marked as a potential anomaly.

[0021] Dynamic adjustment of pheromones: based on the path evaluation results, normal paths are strengthened and abnormal paths are weakened;

[0022] Convergence judgment: Repeat the iteration until the pheromone distribution is stable.

[0023] The present invention also provides a woody oilseed transportation safety tracking and management system based on the Internet of Things, wherein the system is used to implement the above method and comprises a collection module, a transmission module, a determination module and an alarm module;

[0024] The acquisition module is used to collect transportation environment parameters;

[0025] The transmission module is used to upload the collected transportation environment parameters to the cloud server;

[0026] The judgment module is used to judge the transportation environment parameters using a pre-trained safety detection model;

[0027] If the determination result exceeds the preset safety range, the alarm module will send an alarm to the staff.

[0028] Preferably, the safety detection model uses an ant colony algorithm to construct an environmental parameter state transition network and dynamically identifies abnormal patterns based on pheromone concentrations:

[0029]

[0030] Among them, τ ij represents the pheromone concentration; η ij represents the heuristic information, α and β represent the weight parameters controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

[0031] Preferably, the process of iteratively optimizing the security detection model includes:

[0032] Ant colony simulation: Release a number of ants, each starting from a random initial state and generating a complete path based on the path selection probability;

[0033] Path evaluation: Calculate the anomaly score of each path. If the parameter change of a state transition in the path exceeds the preset threshold, it is marked as a potential anomaly.

[0034] Dynamic adjustment of pheromones: based on the path evaluation results, normal paths are strengthened and abnormal paths are weakened;

[0035] Convergence judgment: Repeat the iteration until the pheromone distribution is stable.

[0036] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0037] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the above method is implemented.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] This invention leverages Internet of Things (IoT) technology to enable real-time monitoring and intelligent management of woody oilseed transportation. A safety detection model built using an ant colony algorithm can dynamically identify abnormal patterns, promptly detect potential risks, and issue alerts. This effectively prevents degradation or damage to woody oilseeds caused by environmental factors, improving the safety and reliability of transportation. Furthermore, this invention reduces reliance on manual inspections, improves management efficiency, and reduces labor costs, providing an efficient, intelligent, and reliable solution for woody oilseed transportation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0042] Figure 2 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Description of the drawings:

[0044] 1010 , processor; 1020 , memory; 1030 , input / output interface; 1040 , communication interface; 1050 , bus. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example 1

[0049] As we know from the background, woody oilseeds are easily affected by environmental factors during transportation, resulting in quality degradation or damage. In addition, traditional transportation safety management methods usually rely on manual inspection and record keeping, which has problems such as low efficiency, poor real-time performance, and unreliable data.

[0050] The embodiment of the present invention provides a woody oilseed transportation safety tracking and management method based on the Internet of Things, such as Figure 1 As shown, the steps include:

[0051] S1. Deploy sensor equipment inside woody oil transport containers to collect transportation environment parameters.

[0052] Various types of sensor devices are deployed in woody oil transport containers (such as containers, tanks, or packaging boxes) to collect key environmental parameters in real time during transportation. These sensors include but are not limited to:

[0053] Temperature sensors are used to monitor temperature changes during transportation to prevent excessively high or low temperatures from affecting the quality of woody oilseeds;

[0054] Humidity sensor: Ensures that the humidity of the transportation environment is within an appropriate range to prevent the oil from getting damp or mildewed due to excessive humidity;

[0055] Vibration sensor: detects the vibration intensity caused by vehicle bumps or collisions during transportation to prevent damage to oil containers or physical impact on contents;

[0056] Light sensor: monitors light intensity to prevent the effects of direct sunlight on certain light-sensitive woody oils.

[0057] The sensor's installation location must be optimized based on the shipping container's structure and the characteristics of the woody oilseed to ensure that the collected data fully reflects the actual conditions of the transportation environment. Furthermore, to ensure the stability and reliability of data collection, the sensor equipment must be calibrated and tested, and its battery life and anti-interference performance must be ensured to adapt to complex transportation scenarios.

[0058] S2. Upload the collected transportation environment parameters to the cloud server.

[0059] In step S2, the collected transport environment parameters are uploaded to the cloud server through the IoT device. Specifically, the sensor device transmits the collected environmental parameters such as temperature, humidity, vibration, and light to the cloud server in real time through wireless communication technology (such as Wi-Fi, 4G / 5G, etc.). In order to ensure the security and efficiency of the data, the upload process is encrypted and compressed. In terms of encryption, a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA) is used to encrypt the data collected by the sensor to ensure that the data is not stolen or tampered with during transmission. The encryption key can be managed through a secure key distribution mechanism (such as a pre-shared key or a key exchange based on a public key infrastructure). At the same time, a general compression algorithm (such as GZIP or LZ77) is used to compress the data to reduce the amount of transmitted data, thereby reducing bandwidth consumption and improving transmission efficiency.

[0060] After data is uploaded to the cloud server, it undergoes preprocessing. This preprocessing involves several steps: first, decrypting and decompressing the data to recover the original data; second, cleaning the data to remove invalid or abnormal data points (such as outliers caused by sensor failure); and finally, converting the data into a format suitable for subsequent processing (such as JSON or CSV).

[0061] S3. In the cloud service, use the pre-trained safety detection model to determine the transportation environment parameters.

[0062] This embodiment uses the ant colony algorithm to simulate the path selection behavior of ant colonies, build an environmental parameter state transition network, and dynamically identify abnormal patterns based on pheromone concentrations. The specific implementation is as follows:

[0063] 1. Feature extraction and state definition

[0064] The temperature, humidity, vibration intensity, light intensity and other parameters collected by the sensor are standardized to eliminate the dimension difference. Then each parameter is divided into discrete state intervals (such as temperature is divided into three levels: low temperature, normal temperature and high temperature), forming a state node set S = {s1, s2, s3, ..., s n}.

[0065] Use a time series sliding window (such as window length T = 10 minutes) to convert the continuous time series into a state transition sequence:

[0066] [s t-T , s t-T+1 ,...,s t ]

[0067] Among them S t Represents the value of a sequence at time point t.

[0068] 2. Path selection probability

[0069] When the ant moves between state nodes, it chooses the next state s j The probability is given by the pheromone concentration τ ij and heuristic information η ij Jointly decided:

[0070]

[0071] Among them, α and β represent the weight parameters of controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

[0072] The summation in the numerator is performed over all allowed nodes k to which the ant can move from node i.

[0073] 3. Pheromone update rules

[0074] After each ant completes a path, it updates the pheromone according to the "normality" of the path:

[0075] Normal path enhancement: If the environmental parameters corresponding to the path do not trigger an anomaly, pheromones are added according to the following rules:

[0076] τ ij ←(1-ρ)·τ ij +ρ·Δτ

[0077] ρ represents the evaporation rate of pheromone; Δτ represents the increase of pheromone.

[0078] Abnormal path suppression: If a path triggers an abnormality (such as a sudden temperature change), the pheromone of the path will be reduced:

[0079] τ ij ←τij (1-γ)

[0080] Here, γ represents the decay rate of pheromone.

[0081] In this embodiment, the security detection model and training process are as follows:

[0082] Load historical normal transportation data, divide the state intervals, and construct the initial state transition network. At the same time, initialize the pheromone concentration of all paths.

[0083] Then perform iterative optimization:

[0084] Ant colony simulation: Release multiple ants (e.g., 100 ants), each ant starts from a random initial state and generates a complete path based on the path selection probability.

[0085] Path evaluation: Calculate the anomaly score of each path. If the parameter change of a state transition in the path exceeds the preset threshold (such as the temperature change rate > 5°C / min), it is marked as a potential anomaly.

[0086] Dynamic adjustment of pheromones: Based on the path evaluation results, the pheromone concentration is updated according to the above rules to strengthen the normal path and weaken the abnormal path.

[0087] Convergence judgment: Repeat iterations until the pheromone distribution is stable (e.g., the pheromone change rate is <1% after 10 consecutive iterations).

[0088] Finally, we perform threshold calibration, statistically analyzing the distribution of anomaly scores for all paths in the training set, and taking the 99th percentile as the decision threshold. When this threshold is exceeded, it is considered an anomaly and an alarm is triggered.

[0089] S4. If the determination result exceeds the preset safety range, an alarm is issued to the staff.

[0090] The technical solution of the present invention realizes real-time monitoring and intelligent management of the woody oilseed transportation process through Internet of Things technology. By deploying a variety of sensor devices in the transport container, key environmental parameters such as temperature, humidity, vibration and light can be accurately collected, and the data can be uploaded to the cloud server in real time for analysis and processing. The safety detection model constructed using the ant colony algorithm can dynamically identify abnormal patterns, promptly detect potential risks and issue alarms, effectively avoiding the quality degradation or damage of woody oilseeds caused by environmental factors, and improving the safety and reliability of the transportation process. At the same time, the present invention reduces reliance on manual inspections, improves management efficiency, reduces labor costs, and provides an efficient, intelligent and reliable solution for the transportation management of woody oilseeds.

[0091] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0092] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0093] Example 2

[0094] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a woody oil transportation safety tracking and management system based on the Internet of Things, including: a collection module, a transmission module, a judgment module and an alarm module; the collection module is used to collect transportation environment parameters; the transmission module is used to upload the collected transportation environment parameters to a cloud server; the judgment module is used to use a pre-trained safety detection model to judge the transportation environment parameters; if the judgment result exceeds the preset safety range, the alarm module will issue an alarm to the staff.

[0095] The following will describe in detail how the present invention solves technical problems in practical work in conjunction with this embodiment.

[0096] First, a collection module is deployed in the woody oil transport container to collect transportation environment parameters.

[0097] Specifically, in this embodiment, the acquisition module includes various types of sensor devices for real-time acquisition of key environmental parameters during transportation. These sensors include but are not limited to:

[0098] Temperature sensors are used to monitor temperature changes during transportation to prevent excessively high or low temperatures from affecting the quality of woody oilseeds;

[0099] Humidity sensor: Ensures that the humidity of the transportation environment is within an appropriate range to prevent the oil from getting damp or mildewed due to excessive humidity;

[0100] Vibration sensor: detects the vibration intensity caused by vehicle bumps or collisions during transportation to prevent damage to oil containers or physical impact on contents;

[0101] Light sensor: monitors light intensity to prevent the effects of direct sunlight on certain light-sensitive woody oils.

[0102] The sensor's installation location must be optimized based on the shipping container's structure and the characteristics of the woody oilseed to ensure that the collected data fully reflects the actual conditions of the transportation environment. Furthermore, to ensure the stability and reliability of data collection, the sensor equipment must be calibrated and tested, and its battery life and anti-interference performance must be ensured to adapt to complex transportation scenarios.

[0103] The transmission module is used to upload the collected transportation environment parameters to the cloud server.

[0104] In this embodiment, the collected transport environment parameters are uploaded to the cloud server through the Internet of Things device. Specifically, the sensor device transmits the collected environmental parameters such as temperature, humidity, vibration, and light to the cloud server in real time through wireless communication technology (such as Wi-Fi, 4G / 5G, etc.). In order to ensure the security and efficiency of the data, the upload process is encrypted and compressed. In terms of encryption, a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA) is used to encrypt the data collected by the sensor to ensure that the data is not stolen or tampered with during transmission. The encryption key can be managed through a secure key distribution mechanism (such as a pre-shared key or a key exchange based on a public key infrastructure). At the same time, a general compression algorithm (such as GZIP or LZ77) is used to compress the data to reduce the amount of data transmitted, thereby reducing bandwidth consumption and improving transmission efficiency.

[0105] After data is uploaded to the cloud server, it undergoes preprocessing. This preprocessing involves several steps: first, decrypting and decompressing the data to recover the original data; second, cleaning the data to remove invalid or abnormal data points (such as outliers caused by sensor failure); and finally, converting the data into a format suitable for subsequent processing (such as JSON or CSV).

[0106] In the cloud service, the construction module uses the pre-trained safety detection model to determine the transportation environment parameters.

[0107] This embodiment uses the ant colony algorithm to simulate the path selection behavior of ant colonies, build an environmental parameter state transition network, and dynamically identify abnormal patterns based on pheromone concentrations. The specific implementation is as follows:

[0108] 1. Feature extraction and state definition

[0109] The temperature, humidity, vibration intensity, light intensity and other parameters collected by the sensor are standardized to eliminate the dimension difference. Then each parameter is divided into discrete state intervals (such as temperature is divided into three levels: low temperature, normal temperature and high temperature), forming a state node set S = {s1, s2, s3, ..., s n}.

[0110] Use a time series sliding window (such as window length T = 10 minutes) to convert the continuous time series into a state transition sequence:

[0111] [s t-T , s t-T+1 ,...,s t ]

[0112] Among them S t Represents the value of a sequence at time point t.

[0113] 2. Path selection probability

[0114] When the ant moves between state nodes, it chooses the next state s j The probability is given by the pheromone concentration τ ij and heuristic information η ij Jointly decided:

[0115]

[0116] Among them, α and β represent the weight parameters of controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

[0117] The summation in the numerator is performed over all allowed nodes k to which the ant can move from node i.

[0118] 3. Pheromone update rules

[0119] After each ant completes a path, it updates the pheromone according to the "normality" of the path:

[0120] Normal path enhancement: If the environmental parameters corresponding to the path do not trigger an anomaly, pheromones are added according to the following rules:

[0121] τ ij ←(1-ρ)·τ ij +ρ·Δτ

[0122] ρ represents the evaporation rate of pheromone; Δτ represents the increase of pheromone.

[0123] Abnormal path suppression: If a path triggers an abnormality (such as a sudden temperature change), the pheromone of the path will be reduced:

[0124] τij ←τ ij (1-γ)

[0125] Here, γ represents the decay rate of pheromone.

[0126] In this embodiment, the security detection model and training process are as follows:

[0127] Load historical normal transportation data, divide the state intervals, and construct the initial state transition network. At the same time, initialize the pheromone concentration of all paths.

[0128] Then perform iterative optimization:

[0129] Ant colony simulation: Release multiple ants (e.g., 100 ants), each ant starts from a random initial state and generates a complete path based on the path selection probability.

[0130] Path evaluation: Calculate the anomaly score for each path. If the parameter change of a state transition in the path exceeds the preset threshold (such as the temperature change rate > 5°C / min), it is marked as a potential anomaly.

[0131] Dynamic adjustment of pheromones: Based on the path evaluation results, the pheromone concentration is updated according to the above rules to strengthen the normal path and weaken the abnormal path.

[0132] Convergence judgment: Repeat iterations until the pheromone distribution is stable (e.g., the pheromone change rate is <1% after 10 consecutive iterations).

[0133] Finally, we perform threshold calibration, statistically analyzing the distribution of anomaly scores for all paths in the training set, and taking the 99th percentile as the decision threshold. When this threshold is exceeded, it is considered an anomaly and an alarm is triggered.

[0134] If the judgment result exceeds the preset safety range, the alarm module will alert the staff.

[0135] The system of the above embodiment is used to implement the corresponding woody oil transportation safety tracking and management method based on the Internet of Things in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0136] It should be noted that the above-mentioned woody oilseed transportation safety tracking and management system based on the Internet of Things is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0137] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the aforementioned functionality. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0138] Example 3

[0139] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the woody oil transportation safety tracking and management method based on the Internet of Things as described in any of the above embodiments is implemented.

[0140] Figure 2 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0141] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0142] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0143] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0144] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (e.g., USB (Universal Serial Bus), network cable, etc.) or a wireless method (e.g., mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0145] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0146] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0147] The system of the above embodiment is used to implement the corresponding woody oil transportation safety tracking and management method based on the Internet of Things in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0148] Example 4

[0149] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the Internet of Things-based woody oil transportation safety tracking and management method as described in any of the above embodiments.

[0150] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0151] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the woody oil transportation safety tracking and management method based on the Internet of Things as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0152] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0153] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0154] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0155] Therefore, the units of each example described in the embodiments of this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0156] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A woody oilseed transportation safety tracking and management method based on the Internet of Things, characterized in that: The method comprises: Deploy sensor equipment inside woody oil transport containers to collect transport environment parameters; Uploading the collected transportation environment parameters to a cloud server; In the cloud service, the transportation environment parameters are determined using a pre-trained safety detection model; If the judgment result exceeds the preset safety range, an alarm will be issued to the staff.

2. The woody oilseed transportation safety tracking and management method based on the Internet of Things according to claim 1 is characterized in that: The deployed sensor devices include: temperature sensor, humidity sensor, vibration sensor and light sensor; The temperature sensor is used to monitor temperature changes during transportation to prevent excessively high or low temperatures from affecting the quality of woody oil. The humidity sensor is used to ensure that the humidity of the transportation environment is within an appropriate range to prevent the oil from getting damp or mildewed due to excessive humidity; The vibration sensor is used to detect the vibration intensity caused by vehicle bumps or collisions during transportation to prevent damage to the oil container or physical impact on the contents; The light sensor is used to monitor light intensity to prevent the influence of direct strong light on certain light-sensitive woody oil plants.

3. The woody oilseed transportation safety tracking and management method based on the Internet of Things according to claim 1 is characterized in that: After the transportation environment parameters are uploaded to the cloud server, the server will preprocess the received data, including the following steps: first, decrypt and decompress the data to restore the original data; second, clean the data to remove invalid or abnormal data points; then, convert the data format to unify the format.

4. The woody oilseed transportation safety tracking and management method based on the Internet of Things according to claim 1 is characterized in that: The safety detection model uses an ant colony algorithm to construct an environmental parameter state transition network and dynamically identifies abnormal patterns based on pheromone concentrations: Among them, τ ij represents the pheromone concentration; η ij represents the heuristic information, α and β represent the weight parameters controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

5. The woody oilseed transportation safety tracking and management method based on the Internet of Things according to claim 4 is characterized in that: The method for iteratively optimizing the security detection model includes: Ant colony simulation: Release a number of ants, each starting from a random initial state and generating a complete path based on the path selection probability; Path evaluation: Calculate the anomaly score of each path. If the parameter change of a state transition in the path exceeds the preset threshold, it is marked as a potential anomaly. Dynamic adjustment of pheromones: based on the path evaluation results, normal paths are strengthened and abnormal paths are weakened; Convergence judgment: Repeat the iteration until the pheromone distribution is stable.

6. A woody oilseed transportation safety tracking and management system based on the Internet of Things, the system being used to implement the method according to any one of claims 1 to 5, characterized in that: include: Acquisition module, transmission module, judgment module and alarm module; The acquisition module is used to collect transportation environment parameters; The transmission module is used to upload the collected transportation environment parameters to the cloud server; The judgment module is used to judge the transportation environment parameters using a pre-trained safety detection model; If the determination result exceeds the preset safety range, the alarm module will send an alarm to the staff.

7. The woody oil transportation safety tracking and management system based on the Internet of Things according to claim 6 is characterized in that: The safety detection model uses an ant colony algorithm to construct an environmental parameter state transition network and dynamically identifies abnormal patterns based on pheromone concentrations: Among them, τ ij represents the pheromone concentration; η ij represents the heuristic information, α and β represent the weight parameters controlling pheromone and heuristic information; τ ik represents the pheromone level of nodes i to k.

8. The woody oil transportation safety tracking and management system based on the Internet of Things according to claim 7 is characterized in that: The process of iteratively optimizing the security detection model includes: Ant colony simulation: Release a number of ants, each starting from a random initial state and generating a complete path based on the path selection probability; Path evaluation: Calculate the anomaly score of each path. If the parameter change of a state transition in the path exceeds the preset threshold, it is marked as a potential anomaly. Dynamic adjustment of pheromones: based on the path evaluation results, normal paths are strengthened and abnormal paths are weakened; Convergence judgment: Repeat the iteration until the pheromone distribution is stable.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 5 is implemented.

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