A Wireless Network-Based Intelligent Seal Terminal Control System and Method

Abstract

This invention relates to a control system and method for smart lead-sealed terminals based on a wireless network. The system includes: an identification acquisition mechanism, located at a big data server managing smart lead-sealed terminals of the same model, used to acquire multiple smart lead-sealed terminals whose steel wire ropes were recently cut as multiple damaged terminals; and a probability prediction mechanism, used to intelligently predict the probability of damage to each undamaged smart lead-sealed terminal of the same model based on various damage data of the multiple damaged terminals and various terminal parameters of the same model of smart lead-sealed terminals. This invention also relates to a control method for smart lead-sealed terminals based on a wireless network. Through this invention, the probability of damage to each undamaged smart lead-sealed terminal of the same model in different positions and under vibration states can be predicted, and damage warnings can be issued only for smart lead-sealed terminals under high damage risk, thereby improving the targeting of the security monitoring mechanism.

Description

Technical Field

[0001] This invention relates to the field of intelligent lead seal terminals, and more particularly to an intelligent lead seal terminal control system and method based on a wireless network. Background Technology

[0002] In the past, lead seals were typically marked with text or patterns printed on a wire seal. Given current manufacturing capabilities, copying one is extremely easy, making ordinary lead seals vulnerable to fraud. Currently, illegal activities such as electricity theft, water theft, gas theft, and oil theft are rampant due to the unauthorized replacement of lead seals, causing significant economic losses to supply companies. The old-fashioned wire seal is no longer a mainstream product and is teetering on the brink of obsolescence.

[0003] The smart seal terminal establishes a connection with the big data-based system software platform via GPRS network and MQTT. After being powered on, the smart seal terminal should establish a connection with the platform and immediately send the platform with information such as the smart seal terminal's location data, status information, battery level, and current time. Before use, the smart seal terminal should have its International Mobile Equipment Identity (IMEI) and other information pre-entered into the system. The smart seal terminal sends its unique IMEI and various status information to the platform so that the platform can save it in its database for subsequent historical tracing and processing.

[0004] For example, Chinese invention patent publication CN108646263 A discloses an intelligent lead seal device. The device includes an intelligent lead seal body, a GPS locator, a signal transmitter, a first terminal block, a second terminal block, a first connecting piece, a second connecting piece, a metal wire, and a battery. The signal transmitter is positioned above the GPS locator. A baffle is connected to the bottom of the first terminal block. The first connecting piece is positioned inside a groove. A through hole is formed in the center of the top surface of the end cap. A lead ring is sleeved in the middle of the metal wire. This invention incorporates a GPS locator and a signal transmitter. The GPS locator is used for positioning, and the signal transmitter simultaneously transmits location information, allowing professionals to promptly obtain location information. When the metal wire is cut, the signal transmitter simultaneously sends different information, enabling timely repair. By opening the end cap, removing the first connecting piece, and installing another first connecting piece in the groove, the seal can be reused. The lead seal is reusable, preventing resource waste.

[0005] For example, Chinese invention patent publication CN112085874 A proposes a secure passive dynamic signature lock system. The system includes a signature lock body and a verification terminal. The signature lock body contains a signature lock chip, which includes a key generation module, a key writing module, and an RFID storage module. The key generation module is configured with a key generation strategy, which generates a first dynamic key based on the UID of the signature lock chip R. The key writing module receives the first dynamic key and generates dynamic RFID data based on RFID data, storing it in the RFID storage module. Through this configuration, the randomness of the dynamic key prevents the replication of identical signature locks even if actual RFID transmission data is obtained, thus preventing illegal activities.

[0006] However, existing smart seal terminals typically determine the lock's closure status by detecting whether its steel wire rope is caught in its locking mechanism, and can also detect whether the steel wire rope has been cut to determine if the lock has been damaged. Clearly, with existing technology, by the time damage to the smart seal is detected, the lock has already been compromised, leaving no opportunity for on-site salvage.

[0007] It is evident that existing technologies lack a necessary and reliable early warning mechanism for damage, which is used to immediately rush to the scene to salvage the situation when a significant risk of damage to the smart seal terminal is detected and analyzed. This would reduce the damage to smart seal terminals, improve the security monitoring mechanism for smart seal terminals, and avoid issuing early warnings and on-site salvage for all smart seal terminals, thus saving the power consumption of the entire security monitoring mechanism. Summary of the Invention

[0008] To address the technical deficiencies in related fields, this invention provides a smart seal terminal control system and method based on a wireless network. This system uses an artificial intelligence model to predict the probability of damage to each smart seal terminal of the same model at different locations and under different vibration states, based on recorded information from damaged smart seal terminals of the same model and various terminal data. It only provides damage warnings to smart seal terminals at high risk of damage, achieving targeted and selective anti-damage processing. This improves the security monitoring mechanism for smart seal terminals while also ensuring energy efficiency.

[0009] According to a first aspect of the present invention, a smart lead seal terminal control system based on a wireless network is provided, the system comprising:

[0010] The identification acquisition mechanism is set up at the big data server that manages various smart lead-sealed terminals of the same model based on a wireless network. It is used to acquire multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest time as multiple damaged terminals, and to acquire multiple International Mobile Equipment Identity (IMEI) corresponding to the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0011] The jitter collection mechanism, connected to the identification acquisition mechanism, is used to collect the instantaneous jitter amplitude of the corresponding damaged terminal from the information database of the big data server based on each IMEI, when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively.

[0012] A data downloading mechanism, connected to the identification acquisition mechanism, is used to download the current location data of the corresponding damaged terminal from the information database of the big data server based on each IMEI, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0013] The parameter extraction mechanism is used to obtain the weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server and output them as various parameters of the smart lead-sealed terminal managed by the big data server.

[0014] The probability prediction mechanism is connected to the jitter acquisition mechanism, the data download mechanism, and the parameter extraction mechanism, respectively. It is used to use a deep neural network that has been trained multiple times to intelligently predict the probability value of the smart seal terminal currently managed by the big data server being damaged, based on various parameters of the smart seal terminal managed by the big data server, multiple instant jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0015] The status judgment mechanism, connected to the probability prediction mechanism, is used to determine that the smart seal terminal is a high-risk damage terminal when the probability value of the smart seal terminal being damaged under the current positioning data and the current jitter amplitude is greater than or equal to a set probability limit, so as to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal and the monitoring and control is terminated.

[0016] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0017] The number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0018] According to a second aspect of the present invention, a smart lead-sealed terminal control system based on a wireless network is provided, the system comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the following steps:

[0019] The identification acquisition mechanism is set up at the big data server that manages various smart lead-sealed terminals of the same model based on a wireless network. It is used to acquire multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest time as multiple damaged terminals, and to acquire multiple International Mobile Equipment Identity (IMEI) corresponding to the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0020] The jitter collection mechanism, connected to the identification acquisition mechanism, is used to collect the instantaneous jitter amplitude of the corresponding damaged terminal from the information database of the big data server based on each IMEI, when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively.

[0021] A data downloading mechanism, connected to the identification acquisition mechanism, is used to download the current location data of the corresponding damaged terminal from the information database of the big data server based on each IMEI, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0022] The parameter extraction mechanism is used to obtain the weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server and output them as various parameters of the smart lead-sealed terminal managed by the big data server.

[0023] The probability prediction mechanism is connected to the jitter acquisition mechanism, the data download mechanism, and the parameter extraction mechanism, respectively. It is used to use a deep neural network that has been trained multiple times to intelligently predict the probability value of the smart seal terminal currently managed by the big data server being damaged, based on various parameters of the smart seal terminal managed by the big data server, multiple instant jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0024] The status judgment mechanism, connected to the probability prediction mechanism, is used to determine that the smart seal terminal is a high-risk damage terminal when the probability value of the smart seal terminal being damaged under the current positioning data and the current jitter amplitude is greater than or equal to a set probability limit, so as to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal and the monitoring and control is terminated.

[0025] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0026] The number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0027] According to a third aspect of the present invention, a method for controlling a smart lead-sealed terminal based on a wireless network is provided, the method comprising:

[0028] The identification acquisition mechanism is set up at the big data server that manages various smart lead-sealed terminals of the same model based on a wireless network. It is used to acquire multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest time as multiple damaged terminals, and to acquire multiple International Mobile Equipment Identity (IMEI) corresponding to the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0029] The jitter collection mechanism, connected to the identification acquisition mechanism, is used to collect the instantaneous jitter amplitude of the corresponding damaged terminal from the information database of the big data server based on each IMEI, when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively.

[0030] A data downloading mechanism, connected to the identification acquisition mechanism, is used to download the current location data of the corresponding damaged terminal from the information database of the big data server based on each IMEI, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0031] The parameter extraction mechanism is used to obtain the weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server and output them as various parameters of the smart lead-sealed terminal managed by the big data server.

[0032] The probability prediction mechanism is connected to the jitter acquisition mechanism, the data download mechanism, and the parameter extraction mechanism, respectively. It is used to use a deep neural network that has been trained multiple times to intelligently predict the probability value of the smart seal terminal currently managed by the big data server being damaged, based on various parameters of the smart seal terminal managed by the big data server, multiple instant jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0033] The status judgment mechanism, connected to the probability prediction mechanism, is used to determine that the smart seal terminal is a high-risk damage terminal when the probability value of the smart seal terminal being damaged under the current positioning data and the current jitter amplitude is greater than or equal to a set probability limit, so as to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal and the monitoring and control is terminated.

[0034] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0035] The number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0036] Compared with the prior art, the present invention has at least the following three key inventive points:

[0037] Firstly, for any smart seal terminal, based on the jitter data and positioning data of multiple recently damaged smart seal terminals of the same brand at the time of damage, as well as multiple terminal parameters of the smart seal terminals of the aforementioned brand, an artificial intelligence model is used to predict the probability of any smart seal terminal being damaged at its current location and current jitter amplitude. Based on the magnitude of the predicted probability, it is determined whether necessary damage warning operations need to be triggered. Thus, while improving the security management mechanism of smart seal terminals, the security management cost is effectively reduced because it is not necessary to issue damage warnings for all smart seal terminals.

[0038] Secondly, the artificial intelligence model used is a deep neural network that has been trained multiple times. The number of training times is proportional to the damage response time of the brand's smart lead seal terminal. The damage response time of the brand's smart lead seal terminal is the time difference between the cutting time of the steel wire rope of the brand's smart lead seal terminal and the measurement time of the instantaneous shaking amplitude of the brand's smart lead seal terminal.

[0039] Thirdly, the network structure of the deep neural network after multiple training iterations is a customized structure. This is reflected in the positive correlation between the number of the latest damaged smart lead-sealed terminals of the same brand and the weight and volume of the smart lead-sealed terminals of that brand, thereby ensuring the stability and reliability of the prediction results. Attached Figure Description

[0040] The embodiments of the present invention will now be described with reference to the accompanying drawings, wherein:

[0041] Figure 1 This is a technical flowchart of the intelligent lead-seal terminal control system and method based on wireless network according to the present invention.

[0042] Figure 2 This is a schematic diagram illustrating the usage environment of a wireless network-based intelligent lead seal terminal control system according to various embodiments of the present invention.

[0043] Figure 3 This is a schematic diagram of the internal structure of an intelligent seal terminal used in a wireless network-based intelligent seal terminal control system according to various embodiments of the present invention.

[0044] Figure 4 The above are schematic diagrams of the external structure of the smart seal terminal used in the wireless network-based smart seal terminal control system according to various embodiments of the present invention, viewed from different angles.

[0045] Figure 5 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 1 of the present invention.

[0046] Figure 6 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 2 of the present invention.

[0047] Figure 7 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 3 of the present invention.

[0048] Figure 8 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 4 of the present invention.

[0049] Figure 9 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 5 of the present invention.

[0050] Figure 10 The following is a flowchart illustrating the steps of a smart lead-seal terminal control method based on a wireless network according to Embodiment 6 of the present invention. Detailed Implementation

[0051] like Figure 1 The diagram shows a technical flowchart of a smart lead-seal terminal control system and method based on a wireless network according to the present invention.

[0052] like Figure 1 As shown, the specific technical process of the present invention is as follows:

[0053] First, for the smart seal terminal whose probability of being damaged needs to be predicted, take it as the current smart seal terminal and obtain its current on-site information, including its current location data and real-time jitter amplitude.

[0054] In this way, for each smart lead seal terminal whose probability of being damaged needs to be predicted, its current on-site information can be obtained as the basis for subsequent probability prediction.

[0055] For example, the current positioning data may be GPS positioning data, BeiDou positioning data, or Galileo positioning data;

[0056] Secondly, obtain multiple jitter data and multiple location data of multiple smart seal terminals of the same brand that were recently damaged when they were damaged;

[0057] Next, obtain the various terminal parameters of the same brand of smart lead seal terminal, including the weight, volume, damage response time and wire rope length of the smart lead seal terminal.

[0058] Finally, an artificial intelligence model for predicting execution probability is established. This model takes the current on-site information of the smart seal terminal, multiple jitter data and multiple positioning data of multiple recently damaged smart seal terminals of the same brand at the time of damage, and various terminal parameters of smart seal terminals of the same brand as the input content of the artificial intelligence model. The model is then run to obtain the probability value of the current smart seal terminal being damaged under the on-site conditions of current positioning data and instantaneous jitter amplitude.

[0059] like Figure 1 As shown, when the probability of damage to an undamaged smart seal terminal exceeds the limit, a corresponding monitoring strategy is triggered, including determining that the smart seal terminal is a high-risk damage terminal to drive the big data server to perform monitoring and control. Conversely, when the probability of damage to an undamaged smart seal terminal does not exceed the limit, it is determined to be a low-risk damage terminal to exit monitoring and control.

[0060] like Figure 1 As shown, the artificial intelligence model is a deep neural network that has undergone multiple training iterations. The deep neural network that has undergone multiple training iterations includes a single input layer, a single output layer, and multiple hidden layers.

[0061] The plurality of hidden layers are located between the single input layer and the single output layer;

[0062] Therefore, obtaining the probability value of damage to each undamaged smart seal terminal creates an adjustment for applying damage monitoring only to smart seal terminals in high-risk damage states, thereby avoiding applying damage monitoring to all smart seal terminals and achieving an effective balance between energy conservation, environmental protection, and improved monitoring.

[0063] The key points of this invention are: the structural design of the artificial intelligence model for performing probability prediction, including the targeted selection of prediction base data, the proportionality between the number of model training sessions and the damage response time of the same brand of smart lead-sealed terminals, and the positive correlation between the number of damaged same brand smart lead-sealed terminals and their weight and volume, respectively, to ensure the accuracy of the prediction results; and the application of more stringent damage monitoring to multiple undamaged smart lead-sealed terminals with high probability values ​​based on the probability prediction data of each undamaged smart lead-sealed terminal, thereby achieving a technical effect that balances monitoring effectiveness and energy conservation and environmental protection.

[0064] Figure 2 This is a schematic diagram illustrating the usage environment of a wireless network-based intelligent lead seal terminal control system according to various embodiments of the present invention.

[0065] like Figure 2As shown, after the smart lead seal terminal is applied to the logistics equipment, it establishes information interaction with the remote big data server through the network base station via wireless network, including GPRS wireless network and 4G wireless network.

[0066] exist Figure 2 In this process, the status data of each smart sealed terminal stored on the big data server can be transmitted to the Web management platform for data management, or transmitted to each client APP for data display.

[0067] Figure 3 This is a schematic diagram of the internal structure of an intelligent seal terminal used in a wireless network-based intelligent seal terminal control system according to various embodiments of the present invention.

[0068] like Figure 3 As shown, the internal structure of each smart lead seal terminal is given. Each smart lead seal terminal may include a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0069] Figure 4 The above are schematic diagrams of the external structure of the smart seal terminal used in the wireless network-based smart seal terminal control system according to various embodiments of the present invention, viewed from different angles.

[0070] like Figure 4 As shown, the shape and structure of the intelligent lead-sealed terminal from different perspectives include the shape and structure from the front, side, back, top, overall, and bottom. Figure 4 In this document, the unit for each dimension is centimeters.

[0071] The present invention will now be described in detail by way of embodiments of the intelligent lead seal terminal control system and method based on wireless network.

[0072] Example 1

[0073] Figure 5 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 1 of the present invention.

[0074] like Figure 5 As shown, the wireless network-based intelligent lead seal terminal control system includes the following components:

[0075] The identification acquisition mechanism is set up at the big data server that manages various smart lead-sealed terminals of the same model based on a wireless network. It is used to acquire multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest time as multiple damaged terminals, and to acquire multiple International Mobile Equipment Identity (IMEI) corresponding to the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0076] For example, the smart lead-sealed terminals of the same model managed by the big data server have the same structure and size, and the steel wire rope of each smart lead-sealed terminal is made of stainless steel.

[0077] The jitter collection mechanism, connected to the identification acquisition mechanism, is used to collect the instantaneous jitter amplitude of the corresponding damaged terminal from the information database of the big data server based on each IMEI, when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively.

[0078] A data downloading mechanism, connected to the identification acquisition mechanism, is used to download the current location data of the corresponding damaged terminal from the information database of the big data server based on each IMEI, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0079] The parameter extraction mechanism is used to obtain the weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server and output them as various parameters of the smart lead-sealed terminal managed by the big data server.

[0080] The probability prediction mechanism is connected to the jitter acquisition mechanism, the data download mechanism, and the parameter extraction mechanism, respectively. It is used to use a deep neural network that has been trained multiple times to intelligently predict the probability value of the smart seal terminal currently managed by the big data server being damaged, based on various parameters of the smart seal terminal managed by the big data server, multiple instant jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0081] The status judgment mechanism, connected to the probability prediction mechanism, is used to determine that the smart seal terminal is a high-risk damage terminal when the probability value of the smart seal terminal being damaged under the current positioning data and the current jitter amplitude is greater than or equal to a set probability limit, so as to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal and the monitoring and control is terminated.

[0082] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0083] Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0084] The method employs a deep neural network, after multiple training iterations, to intelligently predict the probability of damage to the smart sealed terminal currently managed by the big data server. This prediction is based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. The number of training iterations of the deep neural network is proportional to the damage response time of the smart sealed terminal managed by the big data server.

[0085] For example, the number of times the deep neural network is trained is proportional to the damage response time of the smart sealed terminal managed by the big data server, including: the damage response time of the smart sealed terminal managed by the big data server is 0.1 milliseconds, and the number of times the deep neural network is trained is 100; the damage response time of the smart sealed terminal managed by the big data server is 0.5 milliseconds, and the number of times the deep neural network is trained is 500; and the damage response time of the smart sealed terminal managed by the big data server is 1 millisecond, and the number of times the deep neural network is trained is 1000.

[0086] The positive correlation between the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment and the weight and volume of the smart lead-sealed terminals managed by the big data server includes: using a weighted calculation formula to express a two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment.

[0087] For example, the weighted calculation formula is used to express the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest time. This includes: using the MATLAB toolbox to simulate the weighted calculation formula.

[0088] The weighted calculation formula used to represent the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment includes the following: in the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminals managed by the big data server are equal.

[0089] Example 2

[0090] Figure 6 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 2 of the present invention.

[0091] like Figure 6 As shown, with Figure 5 Unlike the embodiments described above, the wireless network-based intelligent lead seal terminal control system further includes the following components:

[0092] The content storage mechanism is located at the big data server and is implemented using the big data nodes of the big data server. A relational database is used to store various terminal information of each smart lead-sealed terminal managed by the big data server.

[0093] For example, the content storage mechanism can be implemented using an MMC memory card, a FLASH memory chip, or an SD memory card;

[0094] Among them, the use of a relational database to store various terminal information of each smart seal terminal managed by the big data server includes: in the relational database, using IMEI as an index, recording various terminal information corresponding to each smart seal terminal at each moment;

[0095] In the relational database, using IMEI as an index, the terminal information corresponding to each smart seal terminal at each moment is recorded, including: the terminal information corresponding to each smart seal terminal includes the instantaneous shaking amplitude of the smart seal terminal when its steel wire rope is cut, the current positioning data when its steel wire rope is cut, the time data when its steel wire rope is cut, and whether it is a terminal in a high-risk damage state.

[0096] Among them, the terminal information of each IMEI-corded smart seal terminal includes the instantaneous jitter amplitude of the smart seal terminal when its steel wire rope is cut, the current positioning data when its steel wire rope is cut, the time data when its steel wire rope is cut, and whether it is a high-risk damage terminal. The terminal uses a judgment identifier to indicate whether the smart seal terminal corresponding to each IMEI is a high-risk damage terminal.

[0097] Example 3

[0098] Figure 7 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 3 of the present invention.

[0099] like Figure 7 As shown, with Figure 5 Unlike the embodiments described above, the wireless network-based intelligent lead seal terminal control system further includes the following components:

[0100] A timing service provider, connected to the identification acquisition provider, is used to provide timing service operations for the identification acquisition provider;

[0101] For example, the timing service mechanism includes a quartz oscillator and a pulse counter, the pulse counter being connected to the quartz oscillator and used to acquire current timing data using a pulse counting method.

[0102] Example 4

[0103] Figure 8 This is a schematic diagram of the structure of a wireless network-based intelligent lead seal terminal control system according to Embodiment 4 of the present invention.

[0104] like Figure 8 As shown, with Figure 5 Unlike the embodiments described above, the wireless network-based intelligent lead seal terminal control system further includes the following components:

[0105] A training execution mechanism is connected to the probability prediction mechanism and is used to train the deep neural network multiple times, and send the deep neural network after multiple trainings to the probability prediction mechanism for use by the probability prediction mechanism.

[0106] For example, training a deep neural network multiple times and sending the trained deep neural network to the probability prediction agency for its use includes: training the deep neural network multiple times using a numerical simulation mode and sending the trained deep neural network to the probability prediction agency for its use.

[0107] Next, the various embodiments of the present invention will be described in detail.

[0108] In any embodiment of the present invention, a wireless network-based intelligent lead-seal terminal control system:

[0109] The deep neural network, after multiple training iterations, intelligently predicts the probability of a smart sealed terminal currently managed by the big data server being damaged, based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. This includes using the various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes as the item-by-item input data of the deep neural network after multiple training iterations.

[0110] For example, before inputting the various parameters of the smart sealed terminal managed by the big data server, the multiple instantaneous jitter amplitudes corresponding to the multiple damaged IMEIs, the multiple current location data, real-time location data, and real-time jitter amplitudes corresponding to the multiple damaged IMEIs into the deep neural network after multiple training iterations, binary conversion processing is performed on the various parameters of the smart sealed terminal managed by the big data server, the multiple instantaneous jitter amplitudes corresponding to the multiple damaged IMEIs, the multiple current location data, real-time location data, and real-time jitter amplitudes corresponding to the multiple damaged IMEIs, respectively.

[0111] The method employs a deep neural network, trained multiple times, to intelligently predict the probability of damage to a smart sealed terminal currently managed by the big data server. This prediction is based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. This includes running the deep neural network, trained multiple times, to obtain the probability of damage to each smart sealed terminal currently managed by the big data server under the current location data and current jitter amplitude.

[0112] In any embodiment of the present invention, a wireless network-based intelligent lead-seal terminal control system:

[0113] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal, including: the cutting time of the steel wire rope of the intelligent lead seal terminal is earlier than the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0114] For example, the fact that the cutting time of the wire rope of the smart seal terminal is earlier than the measurement time of the instantaneous jitter amplitude of the smart seal terminal includes: the cutting time of the wire rope of the smart seal terminal and the measurement time of the instantaneous jitter amplitude of the smart seal terminal are both expressed with a timing accuracy of milliseconds.

[0115] And in any embodiment of the present invention, a wireless network-based intelligent lead-seal terminal control system:

[0116] The closure detection device is used to detect whether the wire rope is stuck in the locking iron piece, and the lock body detection device is used to detect whether the wire rope has been cut;

[0117] The smart seal terminal also includes a jitter measurement device and a wireless upload device. The jitter measurement device is used to measure the jitter amplitude of the smart seal terminal at each time. The wireless upload device is used to wirelessly upload the terminal information of the smart seal terminal at each time to a remote big data server.

[0118] The wireless upload device is used to wirelessly upload various terminal information corresponding to the smart seal terminal at various times to a remote big data server, including: the wireless upload device wirelessly uploads various terminal information corresponding to the smart seal terminal at various times to a remote big data server via a GPRS communication network or a 4G communication network.

[0119] Example 5

[0120] Figure 9 This is a structural block diagram of a wireless network-based intelligent lead seal terminal control system according to Embodiment 5 of the present invention.

[0121] like Figure 9 As shown, the wireless network-based intelligent lead-seal terminal control system includes a memory and one or more processors. The memory stores a computer program configured to be executed by the one or more processors to complete the following steps:

[0122] The system retrieves multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest moment from the big data server that manages various smart lead-sealed terminals of the same model as multiple damaged terminals, and retrieves multiple International Mobile Equipment Identity (IMEI) corresponding to each of the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron piece, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0123] For example, the smart lead-sealed terminals of the same model managed by the big data server have the same structure and size, and the steel wire rope of each smart lead-sealed terminal is made of stainless steel.

[0124] Based on each IMEI, the instantaneous jitter amplitude of the corresponding damaged terminal is collected from the information database of the big data server when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged |MEIs respectively.

[0125] Based on each IMEI, the current location data of the corresponding damaged terminal when its steel wire rope was cut is downloaded from the information database of the big data server, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0126] The weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server are obtained and output as various parameters of the smart lead-sealed terminal managed by the big data server.

[0127] The deep neural network, after multiple training iterations, intelligently predicts the probability of damage to the smart seal terminal currently managed by the big data server based on various parameters of the smart seal terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0128] If the probability of a smart seal terminal being damaged under the current location data and current jitter amplitude is greater than or equal to a set probability limit, the smart seal terminal is determined to be a high-risk damage terminal to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal to exit monitoring and control.

[0129] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0130] Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0131] The method employs a deep neural network, after multiple training iterations, to intelligently predict the probability of damage to the smart sealed terminal currently managed by the big data server. This prediction is based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. The number of training iterations of the deep neural network is proportional to the damage response time of the smart sealed terminal managed by the big data server.

[0132] For example, the number of times the deep neural network is trained is proportional to the damage response time of the smart sealed terminal managed by the big data server, including: the damage response time of the smart sealed terminal managed by the big data server is 0.1 milliseconds, and the number of times the deep neural network is trained is 100; the damage response time of the smart sealed terminal managed by the big data server is 0.5 milliseconds, and the number of times the deep neural network is trained is 500; and the damage response time of the smart sealed terminal managed by the big data server is 1 millisecond, and the number of times the deep neural network is trained is 1000.

[0133] The positive correlation between the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment and the weight and volume of the smart lead-sealed terminals managed by the big data server includes: using a weighted calculation formula to express a two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment.

[0134] For example, the weighted calculation formula is used to express the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest time. This includes: using the MATLAB toolbox to simulate the weighted calculation formula.

[0135] The weighted calculation formula used to express the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminal managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment includes: in the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminal managed by the big data server are equal.

[0136] like Figure 9 As shown, for example, N processors are given, where N is a natural number greater than or equal to 1.

[0137] Example 6

[0138] Figure 10 The following is a flowchart illustrating the steps of a smart lead-seal terminal control method based on a wireless network according to Embodiment 6 of the present invention.

[0139] like Figure 10 As shown, the intelligent lead-seal terminal control method based on wireless network includes the following steps:

[0140] The system retrieves multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest moment from the big data server that manages various smart lead-sealed terminals of the same model as multiple damaged terminals, and retrieves multiple International Mobile Equipment Identity (IMEI) corresponding to each of the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron piece, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device.

[0141] For example, the smart lead-sealed terminals of the same model managed by the big data server have the same structure and size, and the steel wire rope of each smart lead-sealed terminal is made of stainless steel.

[0142] Based on each IMEI, the instantaneous jitter amplitude of the corresponding damaged terminal is collected from the information database of the big data server when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged |MEIs respectively.

[0143] Based on each IMEI, the current location data of the corresponding damaged terminal when its steel wire rope was cut is downloaded from the information database of the big data server, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively.

[0144] The weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server are obtained and output as various parameters of the smart lead-sealed terminal managed by the big data server.

[0145] The deep neural network, after multiple training iterations, intelligently predicts the probability of damage to the smart seal terminal currently managed by the big data server based on various parameters of the smart seal terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes.

[0146] If the probability of a smart seal terminal being damaged under the current location data and current jitter amplitude is greater than or equal to a set probability limit, the smart seal terminal is determined to be a high-risk damage terminal to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal to exit monitoring and control.

[0147] The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

[0148] Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server.

[0149] The method employs a deep neural network, after multiple training iterations, to intelligently predict the probability of damage to the smart sealed terminal currently managed by the big data server. This prediction is based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. The number of training iterations of the deep neural network is proportional to the damage response time of the smart sealed terminal managed by the big data server.

[0150] For example, the number of times the deep neural network is trained is proportional to the damage response time of the smart sealed terminal managed by the big data server, including: the damage response time of the smart sealed terminal managed by the big data server is 0.1 milliseconds, and the number of times the deep neural network is trained is 100; the damage response time of the smart sealed terminal managed by the big data server is 0.5 milliseconds, and the number of times the deep neural network is trained is 500; and the damage response time of the smart sealed terminal managed by the big data server is 1 millisecond, and the number of times the deep neural network is trained is 1000.

[0151] The positive correlation between the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment and the weight and volume of the smart lead-sealed terminals managed by the big data server includes: using a weighted calculation formula to express a two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment.

[0152] For example, the weighted calculation formula is used to express the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest time. This includes: using the MATLAB toolbox to simulate the weighted calculation formula.

[0153] The weighted calculation formula used to represent the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment includes the following: in the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminals managed by the big data server are equal.

[0154] In addition, the present invention may also refer to the following technical contents to highlight the significant technical progress of the present invention:

[0155] Based on each IMEI, the current location data of the corresponding damaged terminal when its steel wire rope was cut is downloaded from the information database of the big data server, so as to obtain multiple current location data corresponding to multiple damaged IMEIs, including: the current location data corresponding to each damaged IMEI includes the current latitude data and current accuracy data of the damaged terminal corresponding to each damaged IMEI when its steel wire rope was cut;

[0156] The method of using a judgment identifier to indicate whether the smart seal terminal corresponding to each IMEI is a high-risk damage terminal includes: when the judgment identifier corresponding to a smart seal terminal for a certain IMEI is OB01, it indicates that the smart seal terminal is a high-risk damage terminal; when the judgment identifier corresponding to a smart seal terminal for a certain IMEI is 0B00, it indicates that the smart seal terminal is a low-risk damage terminal.

[0157] The present invention has been described with reference to its exemplary embodiments; however, those skilled in the art will be able to make various modifications to the described embodiments without departing from its spirit and scope. The terminology and descriptions used herein are set forth by way of example only and are not intended to be limiting. In particular, although the methods have been described by way of example, the steps of the methods may be performed in a different order or simultaneously than those exemplified. Those skilled in the art will recognize that these and other changes may be made within the spirit and scope defined by the following claims and their equivalents.

Claims

1. A smart lead seal terminal control system based on a wireless network, characterized in that, The system includes: The identification acquisition mechanism is set up at the big data server that manages various smart lead-sealed terminals of the same model based on a wireless network. It is used to acquire multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest time as multiple damaged terminals, and to acquire multiple International Mobile Equipment Identity (IMEI) corresponding to the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron part, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device. The jitter collection mechanism, connected to the identification acquisition mechanism, is used to collect the instantaneous jitter amplitude of the corresponding damaged terminal from the information database of the big data server based on each IMEI, when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively. A data downloading mechanism, connected to the identification acquisition mechanism, is used to download the current location data of the corresponding damaged terminal from the information database of the big data server based on each IMEI, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively. The parameter extraction mechanism is used to obtain the weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server and output them as various parameters of the smart lead-sealed terminal managed by the big data server. The probability prediction mechanism is connected to the jitter acquisition mechanism, the data download mechanism, and the parameter extraction mechanism, respectively. It is used to use a deep neural network that has been trained multiple times to intelligently predict the probability value of the smart seal terminal currently managed by the big data server being damaged, based on various parameters of the smart seal terminal managed by the big data server, multiple instant jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The status judgment mechanism, connected to the probability prediction mechanism, is used to determine that the smart seal terminal is a high-risk damage terminal when the probability value of the smart seal terminal being damaged under the current positioning data and the current jitter amplitude is greater than or equal to a set probability limit, so as to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal and the monitoring and control is terminated. The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal. Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server. The weighted calculation formula represents the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment. In the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminals managed by the big data server are equal.

2. The intelligent lead seal terminal control system based on wireless network as described in claim 1, characterized in that: The deep neural network, after multiple training iterations, intelligently predicts the probability of damage to the smart sealed terminal currently managed by the big data server based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes of the smart sealed terminal to be predicted include the following: the number of training iterations of the deep neural network is proportional to the damage response time of the smart sealed terminal managed by the big data server.

3. The intelligent lead seal terminal control system based on wireless network as described in claim 2, characterized in that, The system also includes: The content storage mechanism is located at the big data server and is implemented using the big data nodes of the big data server. A relational database is used to store various terminal information of each smart lead-sealed terminal managed by the big data server. Among them, the use of a relational database to store various terminal information of each smart seal terminal managed by the big data server includes: in the relational database, using IMEI as an index, recording various terminal information corresponding to each smart seal terminal at each moment; In the relational database, using IMEI as an index, the terminal information corresponding to each smart seal terminal at each moment is recorded, including: the terminal information corresponding to each smart seal terminal includes the instantaneous shaking amplitude of the smart seal terminal when its steel wire rope is cut, the current positioning data when its steel wire rope is cut, the time data when its steel wire rope is cut, and whether it is a terminal in a high-risk damage state. Among them, the terminal information of each IMEI-corded smart seal terminal includes the instantaneous jitter amplitude of the smart seal terminal when its steel wire rope is cut, the current positioning data when its steel wire rope is cut, the time data when its steel wire rope is cut, and whether it is a high-risk damage terminal. The terminal uses a judgment identifier to indicate whether the smart seal terminal corresponding to each IMEI is a high-risk damage terminal.

4. The intelligent lead seal terminal control system based on wireless network as described in claim 2, characterized in that, The system also includes: A timing service provider, connected to the identification acquisition provider, is used to provide timing service operations for the identification acquisition provider.

5. The intelligent lead seal terminal control system based on wireless network as described in claim 2, characterized in that, The system also includes: The training execution mechanism is connected to the probability prediction mechanism and is used to train the deep neural network multiple times, and send the deep neural network after multiple trainings to the probability prediction mechanism for its use.

6. The intelligent lead seal terminal control system based on wireless network as described in any one of claims 2-5, characterized in that: The deep neural network, after multiple training iterations, intelligently predicts the probability of a smart sealed terminal currently managed by the big data server being damaged, based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. This includes using the various parameters of the smart sealed terminal managed by the big data server, the multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, and the multiple current location data corresponding to multiple damaged IMEIs as the item-by-item input data of the deep neural network after multiple training iterations. The method employs a deep neural network, trained multiple times, to intelligently predict the probability of damage to a smart sealed terminal currently managed by the big data server. This prediction is based on various parameters of the smart sealed terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. The real-time location data and real-time jitter amplitudes are the current location data and instantaneous jitter amplitudes of the smart sealed terminal to be predicted. This includes running the deep neural network, trained multiple times, to obtain the probability of damage to each smart sealed terminal currently managed by the big data server under the current location data and current jitter amplitude.

7. The intelligent lead seal terminal control system based on wireless network as described in any one of claims 2-5, characterized in that: The destructive response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal, including: the cutting time of the steel wire rope of the intelligent lead seal terminal is earlier than the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal.

8. The intelligent lead seal terminal control system based on wireless network as described in any one of claims 2-5, characterized in that: The closure detection device is used to detect whether the wire rope is stuck in the locking iron piece, and the lock body detection device is used to detect whether the wire rope has been cut; The smart seal terminal also includes a jitter measurement device and a wireless upload device. The jitter measurement device is used to measure the jitter amplitude of the smart seal terminal at each time. The wireless upload device is used to wirelessly upload the terminal information of the smart seal terminal at each time to a remote big data server. The wireless upload device is used to wirelessly upload various terminal information corresponding to the smart seal terminal at various times to a remote big data server, including: the wireless upload device wirelessly uploads various terminal information corresponding to the smart seal terminal at various times to a remote big data server via a GPRS communication network or a 4G communication network.

9. A smart lead seal terminal control system based on a wireless network, characterized in that, The system includes a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the following steps: The system retrieves multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest moment from the big data server that manages various smart lead-sealed terminals of the same model as multiple damaged terminals, and retrieves multiple International Mobile Equipment Identity (IMEI) corresponding to each of the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron piece, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device. Based on each IMEI, the instantaneous jitter amplitude of the corresponding damaged terminal is collected from the information database of the big data server when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively. Based on each IMEI, the current location data of the corresponding damaged terminal when its steel wire rope was cut is downloaded from the information database of the big data server, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively. The weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server are obtained and output as various parameters of the smart lead-sealed terminal managed by the big data server. The deep neural network, after multiple training iterations, intelligently predicts the probability of damage to the smart seal terminal currently managed by the big data server based on various parameters of the smart seal terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. If the probability of a smart seal terminal being damaged under the current location data and current jitter amplitude is greater than or equal to a set probability limit, the smart seal terminal is determined to be a high-risk damage terminal to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal to exit monitoring and control. The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal. Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server. The weighted calculation formula represents the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment. In the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminals managed by the big data server are equal.

10. A method for controlling an intelligent lead-seal terminal based on a wireless network, characterized in that, The method includes: The system retrieves multiple smart lead-sealed terminals whose steel wire ropes were cut at the latest moment from the big data server that manages various smart lead-sealed terminals of the same model as multiple damaged terminals, and retrieves multiple International Mobile Equipment Identity (IMEI) corresponding to each of the multiple damaged terminals as multiple damaged IMEI outputs. Each smart lead-sealed terminal includes a rechargeable lithium battery, a TYPE C charging port, a locking iron piece, a steel wire rope, an IoT card, a closure detection device, and a lock body detection device. Based on each IMEI, the instantaneous jitter amplitude of the corresponding damaged terminal is collected from the information database of the big data server when the steel wire rope is cut, so as to obtain multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs respectively. Based on each IMEI, the current location data of the corresponding damaged terminal when its steel wire rope was cut is downloaded from the information database of the big data server, so as to obtain multiple current location data corresponding to multiple damaged IMEIs respectively. The weight, volume, damage response time, and wire rope length of the smart lead-sealed terminal managed by the big data server are obtained and output as various parameters of the smart lead-sealed terminal managed by the big data server. The deep neural network, after multiple training iterations, intelligently predicts the probability of damage to the smart seal terminal currently managed by the big data server based on various parameters of the smart seal terminal managed by the big data server, multiple instantaneous jitter amplitudes corresponding to multiple damaged IMEIs, multiple current location data corresponding to multiple damaged IMEIs, real-time location data, and real-time jitter amplitudes. If the probability of a smart seal terminal being damaged under the current location data and current jitter amplitude is greater than or equal to a set probability limit, the smart seal terminal is determined to be a high-risk damage terminal to drive the big data server to perform monitoring and control; otherwise, it is determined to be a low-risk damage terminal to exit monitoring and control. The damage response time of the intelligent lead seal terminal is the time difference between the cutting time of the steel wire rope of the intelligent lead seal terminal and the measurement time of the instantaneous vibration amplitude of the intelligent lead seal terminal. Among them, the number of smart lead-sealed terminals whose steel wire ropes were cut most recently before the current moment is positively correlated with the weight and volume of the smart lead-sealed terminals managed by the big data server. The weighted calculation formula represents the two-to-one numerical correspondence between the weight and volume of the smart lead-sealed terminals managed by the big data server and the number of smart lead-sealed terminals whose steel wire ropes were cut at the latest moment. In the weighted calculation formula, the two weight values ​​corresponding to the weight and volume of the smart lead-sealed terminals managed by the big data server are equal.

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