A computer data transmission management system based on data processing

Abstract

The present invention discloses a computer data transmission management system based on data processing, which relates to the technical field of data processing and includes an electromagnetic induction coil structure module and a distributed adaptive data caching and conversion module. In the present invention, through the electromagnetic induction coil structure module, electromagnetic harmonics are converted into utilizable resources by using the principle of electromagnetic induction, and the winding parameters are accurately controlled, effectively solving the problems of signal interference in electromagnetic harmonic accumulation interference and network topology conflicts. Through the distributed adaptive data caching and conversion module, adaptive adjustment of data storage formats and intelligent control of data sending rhythms are realized, successfully solving the problems of data transmission anomalies caused by the derived phenomenon of information islands and network topology conflicts, significantly improving the stability and efficiency of the system in a complex dynamic network environment, ensuring the accurate and timely transmission of data, improving the overall efficiency and management level of related operations, and bringing significant economic and social benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a computer data transmission management system based on data processing. Background Art

[0002] Under the current trend of multi-device collaborative work, in many fields such as intelligent logistics and industrial automation, a large number of devices such as communication terminals and backpack wireless terminals rely on connection with the data transmission management system. However, many key problems that need to be solved urgently have emerged in this process.

[0003] Dynamic topology conflict in multi-device collaboration: With the rapid development of Internet of Things technology, the number of devices has increased explosively, and the mobility of devices has become the norm. When multiple devices are simultaneously connected to the data transmission management system for collaborative work, the network topology is constantly changing. For example, in a large logistics park, a large number of forklift drivers' handheld devices, goods sorters' handheld devices, and backpack wireless terminals on transport vehicles simultaneously interact with the base station. According to [Document 1: Research on Network Topology Management in the Internet of Things Environment], in such a complex environment, when two devices close to each other simultaneously attempt to connect to the same base station, due to similar signal strengths, traditional data transmission management systems face huge challenges in allocating communication resources. Traditional network topology management and conflict resolution algorithms, such as resource allocation algorithms based on fixed rules, cannot quickly and accurately allocate communication resources in the face of dynamic network topologies, resulting in data transmission delays or even interruptions, seriously affecting the stability and efficiency of data transmission.

[0004] Derivative phenomenon of "information island" between devices: In a dynamic network of multi-device collaboration, network topology conflict is the main cause of the derivative phenomenon of "information island". When devices experience data transmission delays or interruptions due to network topology conflicts, some devices will lose effective connection with other devices due to movement and network instability, forming isolated small networks. Taking a logistics park as an example, when a vehicle enters a weak signal area, it will be temporarily disconnected from the main network. During the isolation period, the devices still continuously generate and accumulate data. According to [Document 2: Problems and Solutions of "Information Island" in Industrial Internet of Things], when the network topology returns to normal and these devices reconnect to the main network, a large amount of backlogged data floods in simultaneously. Coupled with the inconsistent temporary data storage and processing methods used by the devices within the "information island" and the main network, it will inevitably lead to problems such as data format incompatibility and data conflicts, seriously affecting the normal operation of the data transmission management system. And the existing technologies have obvious deficiencies in solving this complex problem.

[0005] Electromagnetic harmonic cumulative interference in the equipment room: When multiple devices work together, the circuit operation of each device generates electromagnetic harmonics. Under normal circumstances, their intensity is low and has little impact on data transmission. However, with the increase in the number of devices, the growth of operation time, and the dynamic change of the network topology, electromagnetic harmonics will accumulate and resonate under specific conditions. For example, when the electromagnetic harmonic frequencies of multiple devices are close in a certain frequency band, they will be superimposed on each other to form a high-intensity interference signal. According to [Literature 3: "Principles and Applications of Electromagnetic Compatibility", Electronic Industry Press], when this interference signal is coupled into the data transmission line, it will cause the data signal to be severely distorted, resulting in a large number of data errors or transmission interruptions. Due to the complex and unpredictable generation and propagation laws of electromagnetic harmonics, existing network anti-interference technologies, such as traditional shielding and filtering technologies, are difficult to effectively cope with.

[0006] In summary, to ensure the stable and efficient operation of the data transmission management system in the multi-device collaborative working environment, there is an urgent need for a brand-new computer data transmission management system based on data processing to solve the many related problems existing in the above-mentioned existing technologies and meet the needs of practical applications. Summary of the Invention

[0007] The purpose of the present invention is to provide a computer data transmission management system based on data processing to solve the problems raised in the above background technology.

[0008] To solve the above technical problems, a computer data transmission management system based on data processing provided by the present invention includes:

[0009] Electromagnetic induction coil structure module:

[0010] An electromagnetic induction coil is arranged on the device data transmission line, with a ferrite magnetic material as the core, and a multi-layer nested winding structure with a dynamically adjustable spacing is adopted. The inner layer of thin wire senses high-frequency electromagnetic harmonics, and the outer layer of thick wire senses low-frequency electromagnetic harmonics. The MEMS drive mechanism adjusts the winding spacing according to the electromagnetic harmonic frequency;

[0011] A low-power microprocessor is equipped to monitor the electromagnetic harmonic frequency and intensity, and a control program based on the fuzzy logic algorithm is built-in. It comprehensively includes but is not limited to the change rate of the electromagnetic harmonic frequency and the intensity change trend, and controls the number of winding turns and the current magnitude; through including but not limited to the DAC circuit, and the MEMS drive mechanism adjusts the winding structure to change the number of turns, and changes the current magnitude by adjusting the PWM signal;

[0012] Distributed adaptive data caching and conversion module:

[0013] A distributed adaptive data caching module composed of a high-speed phase change memory and a programmable logic device is set inside each device;

[0014] Data Processing Mechanism: When connected to the network normally, data is cached and processed according to the main network TCP / IP protocol and a specific binary encoding format; when entering the information island state, after the CPLD detects an anomaly, the caching module switches to the adaptive mode. Using an algorithm based on data entropy value analysis, the storage format is dynamically adjusted according to the data characteristics. For data with a low entropy value, a custom format combining prefix coding and a hash table is used for storage, and the conversion rules are recorded to generate a mapping table; when the network returns to normal, the data is converted back to the main network compatible format according to the rules; in the data sending link, an algorithm based on network congestion window prediction is adopted to monitor the RTT and the packet loss rate, and the network real-time weight and the bandwidth change rate , according to the formula:

[0015]

[0016] Predict the size of the congestion window.

[0017] Furthermore, the algorithm based on data entropy value analysis is:

[0018] Let the data set , contain data elements, and the probability of each element appearing is . Considering the influence of the data update frequency and the data volume size on the entropy value, correction factors and are introduced. The entropy value of the data set is calculated by the formula:

[0019]

[0020] The data is accurately converted back to the main network compatible format through this formula.

[0021] Furthermore, the built-in inverse conversion algorithm is:

[0022] Let the forward conversion function be , and convert the original data to the custom format , that is , and the conversion mapping table is , where , and the update operation record is , where represents the update operation on the data element ;

[0023] The inverse conversion formula is:

[0024]

[0025] The data is accurately converted back to the main network compatible format through this formula.

[0026] Furthermore, in the fuzzification process of the fuzzy logic algorithm, for the rate of change of frequency, a Gaussian membership function is adopted:

[0027]

[0028] where is the fuzzy subset, and are parameters determined through a large number of experiments according to the actual electromagnetic harmonic change range and control requirements.

[0029] Furthermore, in the fuzzy inference process of the fuzzy logic algorithm, the fuzzy rule base consists of a series of if-then rules. Let the fuzzy relation matrix be , the fuzzy vector of the input quantity be and , and the fuzzy output is obtained through the fuzzy relation composition operation. The formula is:

[0030]

[0031] where, represents the fuzzy Cartesian product operation, represents the fuzzy composition operation.

[0032] Furthermore, in the defuzzification process of the fuzzy logic algorithm, the centroid method is used for defuzzification. The formula is:

[0033]

[0034] where, is the exact output value after defuzzification, is the element in the fuzzy output set, is its corresponding membership degree.

[0035] Furthermore, in the microprocessor control, when the changes in harmonic frequency and intensity are detected, the microprocessor adjusts the winding structure through the digital-to-analog conversion DAC circuit and the MEMS drive mechanism, and is used for the change in equivalent turns. By adjusting the pulse width modulation signal of the drive circuit, it is used for the adjustment of the current magnitude.

[0036] Furthermore, in the data format conversion mechanism, for the data with a low entropy value, a custom format based on the combination of prefix coding and hash table is adopted for storage.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. In terms of solving the cumulative interference of electromagnetic harmonics and network topology conflicts

[0039] Utilizing electromagnetic harmonics: In the prior art, conventional means such as shielding or filtering are mostly used to solve electromagnetic interference, but the effect is not good in a complex dynamic network environment. This invention does the opposite. Through the electromagnetic induction coil structure module, using the principle of electromagnetic induction, electromagnetic harmonics are converted into available resources. Ferrite magnetic material is selected as the core to construct the electromagnetic induction coil, strengthening the induction ability of electromagnetic harmonics. In a logistics park where there are intensive devices and a complex electromagnetic environment, when multiple devices work simultaneously and generate electromagnetic harmonics with similar frequencies, traditional shielding or filtering technologies are difficult to handle. However, the induction coil of this invention can generate a reverse magnetic field to cancel electromagnetic harmonics in time, effectively avoiding the accumulation and resonance of electromagnetic harmonics, greatly reducing data errors and transmission interruptions caused by electromagnetic interference, and ensuring the stable transmission of data signals.

[0040] Precisely controlling winding parameters: Traditional electromagnetic interference control methods mostly use fixed threshold judgment, which has poor adaptability to complex electromagnetic harmonic environments. This invention is equipped with a low-power microprocessor and adopts dynamic voltage and frequency scaling (DVFS) technology to reduce device power consumption. At the same time, a control program based on fuzzy logic algorithm is built in. This algorithm can comprehensively consider multiple parameters such as the change rate of electromagnetic harmonic frequency and the change trend of intensity. Through the processes of fuzzification, fuzzy inference, and defuzzification, it precisely controls the number of turns and current magnitude of the winding. When detecting changes in harmonic frequency and intensity, the microprocessor can quickly adjust the winding structure to change the number of turns through circuits such as digital-to-analog conversion (DAC) and adjust the current magnitude by changing the pulse width modulation (PWM) signal of the drive circuit with the help of a MEMS drive mechanism, ensuring that the induction coil is always in the best working state. Compared with traditional control methods, this invention can respond more flexibly and accurately to the complex changes of electromagnetic harmonics, improve the anti-interference ability and control accuracy of the system, effectively solve the signal interference problem in network topology conflicts, and ensure the timeliness of data transmission. When two devices with close distances are connected to the same base station simultaneously, the induction coil cancels the electromagnetic interference generated by each other, making the signal received by the base station clearer. The system can quickly and accurately allocate communication resources according to factors such as signal strength and device priority.

[0041] 2. In terms of data processing for solving the derivative phenomena of "information islands" and network topology conflicts

[0042] Adaptive Data Storage Format Adjustment: Existing data caching and format conversion technologies lack the ability of self - adaptation and intelligent adjustment when facing complex network changes. The distributed adaptive data caching and conversion module of the present invention uses a high - speed phase - change memory and a programmable logic device to form an intelligent control circuit in terms of hardware. The high - speed phase - change memory has the characteristics of high - speed reading and writing, low energy consumption and non - volatility. Compared with traditional static random - access memory or dynamic random - access memory, it is more suitable for data caching in complex environments; the programmable logic device can be field - programmed and adjusted according to different data characteristics and network states. When the device enters the "information island" state, an algorithm based on data entropy value analysis is used to dynamically adjust the data storage format according to the data characteristics. For data with a low entropy value, a custom format combining prefix coding and hash table is used for storage, reducing storage occupancy and improving processing efficiency. At the same time, the format conversion rules are recorded in detail to generate a conversion mapping table. In the complex network environment of a logistics park, data format incompatibility and data conflicts caused by the "information island" are effectively avoided.

[0043] Intelligent Data Sending Rhythm Control: Traditional network congestion control algorithms are mostly based on fixed window - size adjustment strategies and cannot accurately adapt to complex and changeable network environments. In the data - sending link of the present invention, an algorithm based on network congestion window prediction is adopted. By real - time monitoring the round - trip time and packet loss rate of the network, introducing network real - time weight and bandwidth change rate, the size of the network congestion window is predicted. When the network is congested, the caching module temporarily stores data according to the prediction result to avoid a large amount of data pouring in and aggravating the congestion; when the network is unobstructed, the data is sent out at a reasonable rate. In a dynamic network with multi - device collaboration, it can intelligently adjust the data - sending rhythm according to the real - time network state, ensure the stability and efficiency of data transmission, reduce data - transmission delay or interruption, and solve the problem of abnormal data transmission caused by network topology conflicts.

[0044] It significantly improves the stability and efficiency of a computer data - transmission management system based on data processing in a complex dynamic network environment, overcomes the deficiencies of existing technologies in dealing with multi - device collaboration problems, not only ensures the accurate and timely transmission of data, but also improves the overall efficiency and management level of related operations, bringing significant economic and social benefits. Brief Description of the Drawings

[0045] Figure 1 It is a schematic diagram of a computer data - transmission management system based on data processing of the present invention. Detailed Embodiment

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figure 1 , the present invention provides a technical solution:

[0048] Refer to Figure 1 As shown, an embodiment of a computer data transmission management system based on data processing:

[0049] I. Embodiment scenario

[0050] In a large logistics park, cargo transportation and warehousing management highly rely on efficient data transmission. There are numerous base stations distributed within the park, providing network connections for a large number of communication terminals (such as handheld devices of forklift drivers and cargo sorters) and backpack wireless terminals on transport vehicles. Due to the dynamic nature of logistics operations, the devices continuously move with personnel and vehicles, and devices in different areas frequently access or leave the network, forming a complex dynamic network topology. For example, in the cargo loading and unloading area, multiple forklifts simultaneously interact with nearby base stations to report information such as cargo location and loading and unloading progress; on the transportation route, the network connection of vehicles continuously changes during driving to receive dispatching instructions and road condition information.

[0051] II. Solving electromagnetic harmonic cumulative interference and network topology conflicts

[0052] In the prior art, the conventional ideas of shielding or filtering are mostly adopted to solve electromagnetic interference, but the effect is not good in the complex and dynamic network environment of the logistics park. The present invention goes against the conventional way and uses the principle of electromagnetic induction to convert the electromagnetic harmonic, which is an interference source, into a resource that can be utilized.

[0053] Electromagnetic induction coil structure module:

[0054] Material selection: Ferrite magnetic material is selected. Although this material is used in common electronic devices, it is usually used for simple electromagnetic shielding. In this design, its high magnetic permeability characteristic is utilized to construct an electromagnetic induction coil with it as the core material. Different from the traditional shielding method, here it is to enhance the induction ability of electromagnetic harmonics.

[0055] Winding Structure: A multi-layer nested winding structure with dynamically adjustable spacing is adopted. In conventional electromagnetic induction coils, the winding spacing is fixed, making it difficult to adapt to complex and variable electromagnetic harmonics. In this design, the inner layer of the winding uses thinner wires to sense high-frequency electromagnetic harmonics, while the outer layer uses thicker wires to be responsible for sensing low-frequency electromagnetic harmonics. Through the built-in microelectromechanical system (MEMS) drive mechanism, the spacing between the layers of the winding can be dynamically adjusted according to the detected frequency of electromagnetic harmonics. For example, when a certain frequency band of high-frequency harmonics is detected to increase, the MEMS drive mechanism automatically reduces the spacing of the inner layer winding to enhance the induction of harmonics in this frequency band and the ability to generate reverse magnetic fields.

[0056] Microprocessor Control:

[0057] (I). Equipped with a low-power microprocessor: The dynamic voltage and frequency scaling (DVFS) technology is adopted, which can dynamically adjust the working voltage and frequency according to the task load, significantly reducing energy consumption while ensuring performance.

[0058] Reduce the energy consumption of the device, extend the battery usage time, reduce the number of battery charges or replacements, improve the availability of the device and the efficiency of logistics operations, and ensure the stable operation of the logistics data transmission device.

[0059] (II). Real-time monitor the frequency and intensity of the electromagnetic harmonics generated by the device

[0060] In electromagnetic induction, the induced magnetic field is closely related to the frequency and intensity of electromagnetic harmonics. According to Faraday's law of electromagnetic induction, the induced electromotive force is related to the rate of change of magnetic flux, and the change of magnetic flux is related to the frequency and intensity of electromagnetic harmonics. In the complex electromagnetic environment of a logistics park, the operating state of the device is variable and the electromagnetic harmonics change dynamically. Only by real-time monitoring these two key parameters can accurate data be provided for subsequent control.

[0061] Provide a data basis for precise control of the induction coil, enabling the induction coil to adjust in a timely manner according to the actual situation of electromagnetic harmonics, effectively canceling electromagnetic interference and ensuring stable data transmission.

[0062] (III). Built-in control program based on fuzzy logic algorithm

[0063] The traditional fixed-threshold judgment control method is simple and direct, but has poor adaptability to complex electromagnetic harmonic environments. The rate of change of the frequency and the changing trend of the intensity of electromagnetic harmonics and other multiple parameters are interrelated and affect each other. The fuzzy logic algorithm can simulate human fuzzy thinking, take these parameters as inputs, and through the processes of fuzzification, fuzzy inference, and defuzzification, comprehensively judge complex situations and make reasonable control decisions.

[0064] Compared with traditional control, the fuzzy logic algorithm can respond more flexibly and accurately to the complex changes of electromagnetic harmonics, improve the anti-interference ability and control accuracy of the system, and reduce data errors and transmission interruptions.

[0065] (4). The fuzzy logic algorithm makes a comprehensive judgment based on multiple parameters

[0066] A single parameter cannot fully reflect the characteristics of electromagnetic harmonics. For example, if only the intensity of electromagnetic harmonics is judged, the influence of frequency changes on the induction effect of the induction coil will be ignored; if only the frequency is concerned, the induction coil cannot be adjusted in time according to the intensity changes. By comprehensively considering parameters such as the frequency change rate and the intensity change trend, the state of electromagnetic harmonics can be comprehensively analyzed, providing richer information for control decisions.

[0067] Enable the microprocessor to accurately control the induction coil according to the comprehensive situation of electromagnetic harmonics, improve the electromagnetic harmonic cancellation effect, and enhance the stability and reliability of data transmission.

[0068] (5). More precisely control the number of turns of the winding and the magnitude of the current

[0069] According to Faraday's law of electromagnetic induction , the induced electromotive force is proportional to the number of turns of the winding and the rate of change of magnetic flux . And the rate of change of magnetic flux is related to the magnitude of the current and the frequency and intensity of electromagnetic harmonics. In this embodiment, the appropriate number of turns of the winding and the magnitude of the current are calculated through the fuzzy logic algorithm. Considering the dynamic changes of electromagnetic harmonics, the intensity and phase of the reverse magnetic field generated by the induction coil can be accurately adjusted to better cancel the magnetic field generated by electromagnetic harmonics.

[0070] Effectively cancel electromagnetic harmonics, reduce the influence of electromagnetic interference on data transmission, ensure the stable transmission of data signals, and improve the accuracy and timeliness of data transmission.

[0071] (6). When the changes in harmonic frequency and intensity are detected, the microprocessor quickly calculates and adjusts the winding parameters

[0072] The changes in the frequency and intensity of electromagnetic harmonics will change the characteristics of the magnetic field they generate. To maintain the cancellation effect between the reverse magnetic field of the induction coil and the magnetic field of electromagnetic harmonics, the winding parameters need to be adjusted in time. The microprocessor has fast computing capabilities. After detecting the changes in harmonic frequency and intensity, it can quickly adjust the number of turns of the winding (realize the equivalent change of the number of turns by adjusting the winding structure through the MEMS drive mechanism) and the magnitude of the current (realize by adjusting the pulse width modulation (PWM) signal of the drive circuit) according to the calculation results of the fuzzy logic algorithm, ensuring that the induction coil is always in the best working state.

[0073] Respond quickly to electromagnetic harmonic changes, enabling the induction coil to continuously and effectively cancel electromagnetic harmonics, avoiding data transmission problems caused by electromagnetic harmonic changes, and ensuring the stability and continuity of data transmission.

[0074] Formula derivation based on fuzzy logic algorithm

[0075] The fuzzy logic algorithm mainly includes three processes: fuzzification, fuzzy inference, and defuzzification.

[0076] Fuzzification: Convert the input precise quantity (such as the harmonic frequency change rate , intensity change trend ) into a fuzzy quantity. Let the fuzzy subset of the frequency change rate be (representing negative large, negative small, zero, positive small, and positive large respectively), and the fuzzy subset of the intensity change trend is also . Define the membership function to represent the degree to which the input quantity belongs to the fuzzy subset . For example, for the frequency change rate , use the Gaussian membership function:

[0077]

[0078]

[0079]

[0080] Among them, and are parameters determined through a large number of experiments according to the actual electromagnetic harmonic change range and control requirements.

[0081] Inference is carried out according to the fuzzy rule base. The fuzzy rule base consists of a series of "if-then" rules. For example: "if the frequency change rate is NB and the intensity change trend is NS, then the adjustment amount of the number of winding turns is small decrease and the adjustment amount of the current is medium decrease". Through the fuzzy relation composition operation, a fuzzy output is obtained. Let the fuzzy relation matrix , the fuzzy vectors of the input quantities be and , then the fuzzy output is:

[0082]

[0083] Among them, represents the fuzzy Cartesian product operation, and represents the fuzzy composition operation.

[0084] Defuzzification: Convert the fuzzy output into an exact quantity to obtain the adjustment values for the actual number of turns of the control winding and the magnitude of the current. The centroid method is used for defuzzification, and the formula is:

[0085]

[0086] where is the exact output value after defuzzification, is an element in the fuzzy output set, is its corresponding membership degree.

[0087] Through the above fuzzy logic algorithm, considering multiple parameters comprehensively, precise control of the number of turns of the winding and the magnitude of the current is achieved, effectively solving the problems of signal interference in electromagnetic harmonic cumulative interference and network topology conflicts.

[0088] Solving electromagnetic harmonic cumulative interference: In the environment of dense equipment and long operating hours in the logistics park, the cumulative and resonance of electromagnetic harmonics are effectively avoided. When multiple devices work simultaneously and generate electromagnetic harmonics with similar frequencies, the reverse magnetic field generated by the induction coil can be offset in time, ensuring stable data signal transmission and greatly reducing data errors and transmission interruptions caused by electromagnetic interference.

[0089] Solving signal interference in network topology conflicts: When two devices close to each other (such as adjacent forklifts) are connected to the same base station simultaneously, the induction coil cancels the electromagnetic interference generated by each other, making the signal received by the base station clearer. The system can quickly and accurately allocate communication resources according to factors such as signal strength and device priority, avoiding resource allocation chaos caused by signal interference and ensuring the timeliness of data transmission.

[0090] III. Data processing technology for solving the derivative phenomenon of "information island" and network topology conflicts

[0091] Existing data caching and format conversion technologies lack the ability of self - adaptation and intelligent adjustment in the face of complex network changes. This invention breaks the routine and starts from the dynamic adjustment of data storage format and the intelligent control of data sending rhythm.

[0092] Distributed adaptive data caching module and conversion module:

[0093] Hardware composition: It consists of an intelligent control circuit composed of a high - speed phase - change memory (PCM) and a complex programmable logic device (CPLD). Traditional data caching mostly uses static random - access memory (SRAM) or dynamic random - access memory (DRAM), which have limitations in read - write speed and energy consumption. PCM has the characteristics of high - speed read - write, low energy consumption and non - volatility, and is more suitable for data caching in complex environments. CPLD is used to achieve flexible logic control. Compared with traditional application - specific integrated circuits (ASIC), it can be field - programmed and adjusted according to different data characteristics and network states.

[0094] Data format conversion mechanism: When the device is normally connected to the network, the cache module caches and processes data according to the TCP / IP protocol of the main network and a specific binary coding format. Once the device enters the "information island" state due to network topology conflicts (such as the vehicle entering a weak signal area), after the CPLD detects the network anomaly, it starts the adaptive mode. According to the characteristics of the data currently generated by the device (such as the size and update frequency of cargo transportation record data), an algorithm based on data entropy value analysis is used to dynamically adjust the data storage format. For example, for the cargo location information with small data volume but high update frequency, it is converted into a custom format based on the combination of prefix coding and hash table, reducing storage occupancy and improving processing efficiency. At the same time, the format conversion rules are recorded in detail to generate a conversion mapping table.

[0095] Algorithm based on data entropy value analysis:

[0096] Data entropy is an index in information theory to measure the uncertainty of data. In logistics data, different types of data (such as cargo location information, transportation records, etc.) have different information characteristics. By calculating the data entropy, the complexity and redundancy of the data can be evaluated, providing a basis for data format conversion. Traditional data format conversion often lacks consideration of the inherent information characteristics of the data, while the algorithm based on data entropy value analysis can dynamically adjust the storage format according to the actual information content of the data to achieve the optimal storage and processing effect.

[0097] Formula derivation:

[0098] Let the data set , contain data elements, and the probability of each element appearing is , then the entropy value of the data set is calculated by the formula:

[0099]

[0100] In practical applications, in order to more accurately reflect the characteristics of logistics data, considering the influence of the data update frequency and the data volume size on the entropy value, correction factors and are introduced to obtain the improved formula:

[0101]

[0102] Among them, and Determined through the analysis and experimentation of a large amount of logistics data, it is used to balance the influence degrees of data update frequency and data volume size on the entropy value. For example, for the cargo location information with frequent updates but small data volume, appropriately increase the value to highlight the influence of its update frequency on data processing; for the transportation record data with large data volume but low update frequency, adjust the value so that the data volume size can be reasonably reflected in the entropy value calculation.

[0103] According to the calculated entropy value, select an appropriate data storage format. For example, for the data with a lower entropy value (higher data redundancy), adopt a custom format combining prefix coding and hash table, use prefix coding to reduce the storage of duplicate data, and use the hash table to improve the data lookup efficiency, thereby reducing storage occupancy and improving processing efficiency. At the same time, record the format conversion rules in detail and generate a conversion mapping table.

[0104] Data sending rhythm control

[0105] When the network topology resumes normal and the device reconnects to the main network, the cache module, based on the recorded format conversion rules and using the built-in inverse conversion algorithm, quickly converts the data back to the main network compatible format.

[0106] Data sending rhythm control: When the network topology resumes normal and the device reconnects to the main network, the cache module, based on the recorded format conversion rules and using the built-in inverse conversion algorithm, quickly converts the data back to the main network compatible format.

[0107] Built-in inverse conversion algorithm:

[0108] The data format conversion process is a two-way operation. The forward conversion is to convert the data into a custom format according to the data entropy value analysis result to adapt to local storage and processing in the "information island" state. The inverse conversion is to restore the data to the format conforming to the main network protocol after the network is restored. Traditional inverse conversion algorithms are often simple reverse operations and lack consideration of the possible changes in the data in the "information island" state (such as data updates, partial data loss, etc.). The inverse conversion algorithm of the present invention combines the conversion mapping table and the operation records of the data in the "information island" state, and can more accurately convert the data back to the main network compatible format.

[0109] Formula derivation:

[0110] Let the forward conversion function be , and convert the original data into the custom format , that is, . The conversion mapping table records the corresponding relationships of each data element in the conversion process, which is set as , where Represents the original data element Converted to an element in the custom format .

[0111] The inverse conversion algorithm first based on the conversion mapping table , converts the custom format data The elements in Are mapped back to the possible set of original data elements . Considering the possible update operations of the data in the "information island" state, let the update operation record be , where Represents the update operation on the data element (such as modifying the value, adding new data, etc.).

[0112] The inverse conversion formula is:

[0113]

[0114] Among them, Represents reverse lookup according to the mapping table The corresponding original data element, Represents the inverse operation of the update operation , restores the updated data element to the original data element or updates it to a new data element that conforms to the main network format. In this way, the data can be accurately converted back to the main network compatible format, avoiding data format incompatibility and data conflict problems.

[0115]

[0115] In the data sending link, an algorithm based on network congestion window prediction is adopted. By real-time monitoring the round-trip time (RTT) of the network and the packet loss rate, the size of the network congestion window is predicted. When the network is congested (such as during peak hours in the logistics park), the cache module temporarily stores the data according to the prediction results to avoid a large amount of data flooding and aggravating the congestion; when the network is unobstructed, the data is sent out at a reasonable rate.

[0116] Algorithm based on network congestion window prediction:

[0117] Inference basis: In network communication, network congestion will cause data transmission delays or even interruptions. Traditional network congestion control algorithms are mostly based on fixed window size adjustment strategies and cannot accurately adapt to the complex and changeable network environment in the logistics park. This algorithm predicts the size of the network congestion window by real-time monitoring the round-trip time (RTT) of the network and the packet loss rate, and comprehensively considering the real-time requirements of logistics data and the dynamic changes of network bandwidth, so as to achieve more accurate data sending control.

[0118] Formula derivation: Let the current round-trip time of the network be , and the packet loss rate be , the congestion window size at the previous moment is . Introducing network real-time weight and bandwidth change rate , where the bandwidth change rate It is calculated by monitoring the changes in data transmission volume per unit time.

[0119] Predicted congestion window size The formula is:

[0120]

[0121] When the network is congested (such as during peak hours in logistics parks, Higher, increase), according to the predicted results Reduce, cache module temporarily stores data to avoid a large amount of data influx and aggravate congestion; when the network is unobstructed ( Lower, Stablize), Increase, send data at a reasonable rate, and ensure the stability and efficiency of data transmission.

[0122] Solve the problems derived from "information islands": In the complex network environment of the logistics park, it effectively avoids data format incompatibility and data conflicts caused by "information islands". When the device reconnects to the main network, it can quickly and accurately convert and transmit data, avoiding network congestion caused by a large amount of backlog data.

[0123] Resolve data transmission anomalies caused by network topology conflicts: In a dynamic network with multiple devices working together, the cache module intelligently adjusts the data transmission rhythm according to the real-time status of the network to ensure the stability and efficiency of data transmission and reduce data transmission delays or interruptions.

[0124] IV. Conclusion

[0125] In the embodiment of a large logistics park, through a uniquely designed electromagnetic induction coil structure and using the principle of electromagnetic harmonic self-cancellation, the problem of resource allocation caused by the cumulative interference of electromagnetic harmonics and signal interference in network topology conflicts is solved; through a distributed adaptive data caching and conversion module based on phase change memory and programmable logic devices, the problems of data transmission anomalies caused by the derivative phenomenon of "information islands" and network topology conflicts are solved. These two technical means have prominent substantive features. Compared with the prior art, unconventional design ideas and technical means are adopted, producing unexpected technical effects, significantly improving the stability and efficiency of the computer data transmission management system based on data processing in a complex dynamic network environment, overcoming the deficiencies of the prior art in dealing with multi-device collaboration problems, and possessing creativity. It not only ensures the accurate and timely transmission of data within the logistics park, but also improves the overall efficiency and management level of logistics operations, bringing significant economic and social benefits.

Claims

1. A computer data transmission management system based on data processing, characterized in that, Including: Electromagnetic induction coil structure module: An electromagnetic induction coil is provided on the device data transmission line. With a ferrite magnetic material as the core, a multi-layer nested winding structure with dynamically adjustable spacing is adopted. The inner layer of thin wire senses high-frequency electromagnetic harmonics, and the outer layer of thick wire senses low-frequency electromagnetic harmonics. The MEMS drive mechanism adjusts the winding spacing according to the frequency of the electromagnetic harmonics; Equipped with a low-power microprocessor to monitor the frequency and intensity of electromagnetic harmonics. A control program based on a fuzzy logic algorithm is built-in, comprehensively including the rate of change of electromagnetic harmonic frequency and the trend of intensity change, to control the number of winding turns and the magnitude of the current; By including a DAC circuit, and the MEMS drive mechanism adjusts the winding structure to change the number of turns, and changes the magnitude of the current by adjusting the PWM signal; Distributed adaptive data caching and conversion module: A distributed adaptive data caching module composed of a high-speed phase change memory and a programmable logic device is set inside each device; Data processing mechanism: When normally connected to the network, cache and process data according to the main network TCP / IP protocol and a specific binary coding format; Entering the information island state, after the CPLD detects an abnormality, the cache module switches to the adaptive mode, uses an algorithm based on data entropy value analysis, dynamically adjusts the storage format according to the data characteristics, and stores the data with low entropy value in a custom format combined with prefix coding and a hash table and records the conversion rules to generate a mapping table; When the network returns to normal, the data is converted back to the main network compatible format according to the data format conversion rules using the inverse conversion algorithm; In the data sending process, a network congestion window prediction algorithm is adopted to monitor the RTT and packet loss rate, and a network real-time weight and bandwidth change rate are introduced. According to the formula: ； Predict the congestion window size, where is the network round-trip time; is the packet loss rate; is the congestion window size at the current moment; is the predicted congestion window size at the next moment.

2. The computer data transmission management system based on data processing according to claim 1, characterized in that: The algorithm based on data entropy value analysis is: Let the data set , contain data elements, and the probability of each element appearing is . Considering the influence of the data update frequency and the data volume size on the entropy value, introduce correction coefficients and . The entropy value of the data set is calculated by the formula: ； Dynamically adjust the data storage format through this formula to generate a conversion mapping table.

3. A computer data transmission management system based on data processing according to claim 1, characterized in that: In the fuzzification process of the fuzzy logic algorithm, for the rate of change of frequency, a Gaussian membership function is adopted: Among them, represents the input quantity, the rate of change of frequency belongs to the degree of the fuzzy subset .

4. A computer data transmission management system based on data processing according to claim 1, characterized in that: During the fuzzy inference process of the fuzzy logic algorithm, the fuzzy rule base consists of a series of if-then rules. Let the fuzzy relation matrix , and the fuzzy vectors of the input quantities be and . The fuzzy output is obtained through the fuzzy relation composition operation. The formula is: Among them, represents the fuzzy Cartesian product operation, represents the fuzzy composition operation.

5. A computer data transmission management system based on data processing according to claim 1, characterized in that: In the defuzzification process of the fuzzy logic algorithm, the centroid method is used for defuzzification, and the formula is: Among them, is the precise output value after defuzzification, is an element in the fuzzy output set, is its corresponding membership degree.

6. A computer data transmission management system based on data processing as described in claim 1, characterized in that: In the microprocessor control, when detecting changes in harmonic frequency and intensity, the microprocessor adjusts the winding structure through a digital-to-analog conversion DAC circuit and the MEMS drive mechanism, and is used for equivalent changes in the number of turns. By adjusting the pulse width modulation signal of the drive circuit, it is used for adjusting the magnitude of the current.

7. A computer data transmission management system based on data processing according to claim 1, characterized in that: In the data format conversion mechanism, for data with a lower entropy value, a custom format combined with prefix coding and a hash table is used for storage.

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