Computer data transmission management system based on data processing

Abstract

The invention discloses a computer data transmission management system based on data processing, which relates to the technical field of data processing and comprises an electromagnetic induction coil structure module and a distributed self-adaptive data caching and conversion module. According to the invention, through the electromagnetic induction coil structure module, electromagnetic harmonic waves are converted into available resources by using an electromagnetic induction principle, winding parameters are accurately controlled, and the problems of electromagnetic harmonic wave accumulation interference and signal interference in network topology conflicts are effectively solved; through the distributed adaptive data caching and conversion module, adaptive data storage format adjustment and intelligent data sending rhythm control are realized, the problem of abnormal data transmission caused by an information island derivation phenomenon and a network topology conflict is successfully solved, the stability and high efficiency of the system in a complex dynamic network environment are remarkably improved, and the data transmission efficiency is improved. Accurate and timely transmission of data is guaranteed, the overall efficiency and management level of related operations are improved, and remarkable economic benefits and social benefits are brought.

Description

Technical Field

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

[0002] Under the current trend of multi-device collaboration, many fields, such as smart logistics and industrial automation, rely on the connection between a large number of communication terminals, backpack wireless terminals and other devices and data transmission management systems. However, this process has exposed many key issues that need to be solved urgently.

[0003] Dynamic topology conflicts in multi-device collaboration: With the rapid development of IoT technology, the number of devices has exploded, and the mobility of devices has become the norm. When multiple devices are connected to the data transmission management system at the same time to work together, the network topology is constantly changing. For example, in a large logistics park, many forklift drivers' handheld devices, cargo sorters' handheld devices, and backpack wireless terminals on transport vehicles interact with the base station at the same time. According to the research in [Reference 1: "Research on Network Topology Management in the Internet of Things Environment"], in such a complex environment, when two devices with similar distances try to connect to the same base station at the same time, due to the similar signal strength, the traditional data transmission management system faces 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 dynamically changing network topologies, resulting in data transmission delays or even interruptions, which seriously affects the stability and efficiency of data transmission.

[0004] The derivative phenomenon of "information islands" between devices: In a dynamic network with multiple devices working together, network topology conflicts are the main cause of the derivative phenomenon of "information islands". When the data transmission of devices is delayed or interrupted due to network topology conflicts, some devices will lose effective connection with other devices due to movement and network instability, forming an isolated small network. Taking the 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 device continues to generate and accumulate data. According to [Document 2: "Problems and Solutions of "Information Islands" in Industrial Internet of Things"], when the network topology returns to normal and these devices reconnect to the main network, a large amount of backlog data will flow in at the same time. In addition, the temporary data storage and processing methods used by the devices in the "information islands" are inconsistent with the main network, which will inevitably lead to problems such as incompatible data formats and data conflicts, seriously affecting the normal operation of the data transmission management system. However, the existing technology has obvious shortcomings in solving this complex problem.

[0005] Cumulative interference of electromagnetic harmonics between devices: When multiple devices work together, the circuit operation of each device will generate electromagnetic harmonics. Under normal circumstances, its intensity is low and has little impact on data transmission. However, as the number of devices increases, the operating time increases, and the network topology changes dynamically, electromagnetic harmonics will accumulate and resonate under certain conditions. For example, when the electromagnetic harmonic frequencies of multiple devices in a certain frequency band are close, they will superimpose on each other to form a high-intensity interference signal. According to [Document 3: "Principles and Applications of Electromagnetic Compatibility", Electronic Industry Press], this interference signal is coupled into the data transmission line, which will cause serious distortion of the data signal, resulting in a large number of data errors or transmission interruptions. Because the generation and propagation of electromagnetic harmonics are complex and difficult to predict, existing network anti-interference technologies, such as traditional shielding and filtering technologies, are difficult to effectively deal with.

[0006] To sum up, in order to ensure the stable and efficient operation of the data transmission management system in a multi-device collaborative working environment, there is an urgent need for a 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 object 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] In order to solve the above technical problems, the present invention provides a computer data transmission management system based on data processing, comprising: Electromagnetic induction coil structure module: An electromagnetic induction coil is installed in the data transmission line of the equipment. It uses ferrite magnetic material as the core and adopts a multi-layer nested winding structure with dynamically adjustable spacing. The inner thin wire senses high-frequency electromagnetic harmonics, and the outer thick wire senses low-frequency electromagnetic harmonics. The winding spacing is adjusted according to the electromagnetic harmonic frequency through the MEMS drive mechanism; Equipped with a low-power microprocessor to monitor the frequency and intensity of electromagnetic harmonics, with a built-in control program based on fuzzy logic algorithm, which comprehensively includes but is not limited to the change rate of electromagnetic harmonic frequency and the change trend of intensity, controls the number of winding turns and current; adjusts the winding structure to change the number of turns by including but not limited to DAC circuits and MEMS drive mechanisms, and changes the current by adjusting the PWM signal; Distributed adaptive data caching and conversion module: A distributed adaptive data cache module composed of high-speed phase change memory and programmable logic devices is set inside each device; Data processing mechanism: When the network is connected normally, the data is cached and processed according to the TCP / IP protocol of the main network and the specific binary encoding format; when the information island state is entered and the CPLD detects an abnormality, the cache module switches to the adaptive mode and uses the data entropy value analysis algorithm to dynamically adjust the storage format according to the data characteristics. For data with low entropy values, a custom format combining prefix coding and hash table is used to store and record the conversion rules to generate a mapping table; when the network returns to normal, the inverse conversion algorithm is used according to the rules to convert the data back to the main network compatible format; in the data sending stage, a network congestion window prediction algorithm is used to monitor the RTT and packet loss rate, and the network real-time weight is introduced and bandwidth change rate , according to the formula:

[0009] Predicted congestion window size.

[0010] Furthermore, the data entropy value analysis algorithm is: Assume data set ,Include data elements, each of which The probability of occurrence is , considering the update frequency of data and data size Impact on entropy value, introduction of correction factor and , data set The entropy value The calculation formula is:

[0011] This formula accurately converts the data back to a main network compatible format.

[0012] Furthermore, the built-in inverse conversion algorithm is: Let the forward transfer function be , the original data Convert to custom format ,Right now , the conversion mapping table is ,in , update the operation record as ,in Represents data elements Update operation; The inverse transformation formula is:

[0013] This formula accurately converts the data back to a main network compatible format.

[0014] Furthermore, in the fuzzification process of the fuzzy logic algorithm, a Gaussian membership function is used for the frequency change rate:

[0015] in is a fuzzy subset, and It is a parameter determined through a large number of experiments based on the actual electromagnetic harmonic variation range and control requirements.

[0016] Furthermore, in the fuzzy reasoning process of the fuzzy logic algorithm, the fuzzy rule base consists of a series of if-then rules. Let the fuzzy relationship matrix , the fuzzy vector of the input is and , the fuzzy output is obtained through fuzzy relation synthesis operation , the formula is:

[0017] in, represents the fuzzy Cartesian product operation, Represents a fuzzy composition operation.

[0018] Furthermore, in the defuzzification process of the fuzzy logic algorithm, the centroid method is used for defuzzification, and the formula is:

[0019] in, is the exact output value after defuzzification, is an element in the fuzzy output set, is its corresponding degree of membership.

[0020] Furthermore, in microprocessor control, when harmonic frequency and intensity changes are detected, the microprocessor adjusts the winding structure through the digital-to-analog conversion DAC circuit and the MEMS drive mechanism, and uses it to change the equivalent number of turns, and adjusts the pulse width modulation signal of the drive circuit to adjust the current size.

[0021] Furthermore, in the data format conversion mechanism, for data with low entropy values, a custom format based on prefix encoding and hash table is used for storage.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. Solve the problem of electromagnetic harmonic cumulative interference and network topology conflicts Utilizing electromagnetic harmonics: Existing technologies for solving electromagnetic interference mostly use conventional means such as shielding or filtering, which are not effective in complex dynamic network environments. The present invention takes the opposite approach and uses the electromagnetic induction principle through an electromagnetic induction coil structure module to convert electromagnetic harmonics into usable resources. Ferrite magnetic materials are selected as the core to construct the electromagnetic induction coil to enhance the sensing ability of electromagnetic harmonics. In a logistics park, where equipment is dense and the electromagnetic environment is complex, when multiple devices work simultaneously to generate electromagnetic harmonics of similar frequencies, traditional shielding or filtering technologies are difficult to cope with. The induction coil of the present invention can generate a reverse magnetic field to offset the 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 stable transmission of data signals.

[0023] Precise control of winding parameters: Traditional electromagnetic interference control methods mostly use fixed threshold judgments, which have poor adaptability to complex electromagnetic harmonic environments. The present invention is equipped with a low-power microprocessor and adopts dynamic voltage and frequency adjustment (DVFS) technology to reduce equipment energy consumption. At the same time, a control program based on a fuzzy logic algorithm is built in. The algorithm can comprehensively consider multiple parameters such as the frequency change rate and intensity change trend of electromagnetic harmonics, and accurately control the number of turns and current of the winding through fuzzification, fuzzy reasoning and defuzzification processes. When changes in harmonic frequency and intensity are detected, the microprocessor can quickly adjust the winding structure to change the number of turns through circuits such as digital analog conversion (DAC) and the MEMS drive mechanism, and change the current by adjusting the pulse width modulation (PWM) signal of the drive circuit to ensure that the induction coil is always in the best working state. Compared with traditional control methods, the present invention can more flexibly and accurately respond to complex changes in electromagnetic harmonics, improve the system's anti-interference ability and control accuracy, effectively solve the signal interference problem in network topology conflicts, and ensure the timeliness of data transmission. When two devices at close distances are connected to the same base station at the same time, the induction coils cancel out the electromagnetic interference generated by each device, making the base station receive signals clearer. The system can quickly and accurately allocate communication resources based on factors such as signal strength and device priority.

[0024] 2. Data processing to solve the phenomenon of "information islands" and network topology conflicts Adaptive data storage format adjustment: Existing data caching and format conversion technologies lack the ability to adapt and intelligently adjust when faced with complex network changes. The distributed adaptive data caching and conversion module of the present invention uses high-speed phase change memory and programmable logic devices in hardware to form an intelligent control circuit. 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; programmable logic devices can be programmed and adjusted on-site according to different data characteristics and network status. When the device enters the "information island" state, an algorithm based on data entropy analysis is used to dynamically adjust the data storage format according to data characteristics. For data with low entropy values, a custom format storage based on prefix coding and hash tables is used to reduce storage occupancy and improve 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 the logistics park, data format incompatibility and data conflicts caused by "information islands" are effectively avoided.

[0025] Intelligent data transmission 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 transmission link, the present invention adopts an algorithm based on network congestion window prediction. By real-time monitoring of the network's round-trip time and packet loss rate, it introduces network real-time weights and bandwidth change rates to predict the size of the network congestion window. When the network is congested, the cache module temporarily stores data according to the prediction results to avoid a large amount of data influx and aggravate congestion; when the network is unobstructed, the data is sent out at a reasonable rate. In a dynamic network with multiple devices working together, the data transmission rhythm can be intelligently adjusted according to the real-time status of the network to ensure the stability and efficiency of data transmission, reduce data transmission delays or interruptions, and solve data transmission abnormalities caused by network topology conflicts.

[0026] It has significantly improved the stability and efficiency of the computer data transmission management system based on data processing in complex dynamic network environments, overcome the shortcomings of existing technologies in dealing with multi-device collaboration problems, not only ensured the accurate and timely transmission of data, but also improved the overall efficiency and management level of related operations, bringing significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a schematic diagram of a computer data transmission management system based on data processing. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 , the present invention provides a technical solution: See also Figure 1 As shown, an embodiment of a computer data transmission management system based on data processing: 1. Implementation scenario In large logistics parks, cargo transportation and warehouse management are highly dependent on efficient data transmission. There are many base stations distributed in 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 characteristics of logistics operations, equipment continues to move with personnel and vehicles, and equipment in different areas frequently connects to or leaves 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 channel, the network connection changes continuously during the vehicle's driving, receiving dispatch instructions and road condition information.

[0030] 2. Solve electromagnetic harmonic cumulative interference and network topology conflicts In the prior art, conventional ideas of shielding or filtering are often used to solve electromagnetic interference, but the effect is not good in the complex and dynamic network environment of logistics parks. The present invention takes the opposite approach and uses the principle of electromagnetic induction to convert electromagnetic harmonics, an interference source, into a usable resource.

[0031] Electromagnetic induction coil structure module: Material selection: Ferrite magnetic material is used. Although this material is used in common electronic devices, it is usually used for simple electromagnetic shielding. This design uses its high magnetic permeability characteristics and uses it as the core material to build an electromagnetic induction coil. Different from the traditional shielding method, this is to enhance the induction ability of electromagnetic harmonics.

[0032] Winding structure: A multi-layer nested winding structure with dynamically adjustable spacing is adopted. Conventional electromagnetic induction coil windings have fixed spacing and are difficult to adapt to complex and changeable electromagnetic harmonics. In this design, the inner winding uses a thinner wire to sense high-frequency electromagnetic harmonics; the outer winding uses a thicker wire to sense low-frequency electromagnetic harmonics. Through the built-in micro-electromechanical system (MEMS) drive mechanism, the spacing between each layer of windings can be dynamically adjusted according to the detected electromagnetic harmonic frequency. For example, when the high-frequency harmonics of a certain frequency band are detected to be enhanced, the MEMS drive mechanism automatically reduces the spacing of the inner winding to enhance the induction and reverse magnetic field generation capabilities of the harmonics in that frequency band.

[0033] Microprocessor Control: 1. Equipped with a low-power microprocessor: Using dynamic voltage and frequency scaling (DVFS) technology, the operating voltage and frequency can be dynamically adjusted according to the task load, significantly reducing energy consumption while ensuring performance.

[0034] Reduce equipment energy consumption, extend battery life, reduce the number of times to charge or replace batteries, improve equipment availability and logistics operation efficiency, and ensure stable operation of logistics data transmission equipment.

[0035] (II) Real-time monitoring of the frequency and intensity of electromagnetic harmonics generated by the equipment In electromagnetic induction, the frequency of the induced magnetic field and electromagnetic harmonics and strength According to Faraday's law of electromagnetic induction, the induced electromotive force It is related to the rate of change of magnetic flux, which in turn is related to the frequency and intensity of electromagnetic harmonics. In the complex electromagnetic environment of the logistics park, the equipment operation status is changeable and the electromagnetic harmonics change dynamically. Only by real-time monitoring of these two key parameters can accurate data be provided for subsequent control.

[0036] It provides a data basis for precise control of the induction coil, so that the induction coil can be adjusted in time according to the actual situation of electromagnetic harmonics, effectively offset electromagnetic interference, and ensure stable data transmission.

[0037] (III) Built-in control program based on fuzzy logic algorithm The traditional fixed threshold judgment control method is simple and direct, but has poor adaptability to complex electromagnetic harmonic environments. , intensity change trend Multiple parameters such as fuzzy logic are interrelated and affect each other. Fuzzy logic algorithm can simulate human fuzzy thinking, take these parameters as input, and make comprehensive judgments on complex situations and make reasonable control decisions through the process of fuzzification, fuzzy reasoning and defuzzification.

[0038] Compared with traditional control, fuzzy logic algorithms can respond to complex changes in electromagnetic harmonics more flexibly and accurately, improve the system's anti-interference ability and control accuracy, and reduce data errors and transmission interruptions.

[0039] (IV) Fuzzy logic algorithm makes comprehensive judgment based on multiple parameters A single parameter cannot fully reflect the characteristics of electromagnetic harmonics. For example, judging only by the intensity of electromagnetic harmonics will ignore the impact of frequency changes on the induction effect of the induction coil; focusing only on the frequency will not be able to adjust the induction coil in time according to the intensity changes. Comprehensively considering parameters such as the frequency change rate and the intensity change trend can comprehensively analyze the electromagnetic harmonic state and provide richer information for control decisions.

[0040] The microprocessor can 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.

[0041] 5. More accurate control of winding turns and current According to the law of electromagnetic induction , induced electromotive force Number of winding turns and the rate of change of magnetic flux The rate of change of magnetic flux is proportional to the current. and the frequency of electromagnetic harmonics ,strength In this embodiment, the appropriate number of winding turns and current are calculated by a fuzzy logic algorithm, and the reverse magnetic field strength and phase generated by the induction coil can be accurately adjusted in consideration of the dynamic changes of electromagnetic harmonics, so as to better offset the magnetic field generated by the electromagnetic harmonics.

[0042] Effectively offset electromagnetic harmonics, reduce the impact of electromagnetic interference on data transmission, ensure stable transmission of data signals, and improve the accuracy and timeliness of data transmission.

[0043] (VI) When changes in harmonic frequency and intensity are detected, the microprocessor quickly calculates and adjusts the winding parameters The frequency and intensity changes of electromagnetic harmonics will change the magnetic field characteristics they generate. In order to maintain the offset effect between the reverse magnetic field of the induction coil and the electromagnetic harmonic magnetic field, 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 calculate the results based on the fuzzy logic algorithm and adjust the number of turns of the winding (adjusting the winding structure through the MEMS drive mechanism to achieve the equivalent number of turns) and the current size (achieved by adjusting the pulse width modulation (PWM) signal of the drive circuit) through circuits such as digital analog conversion (DAC) to ensure that the induction coil is always in the best working state.

[0044] Rapidly respond to changes in electromagnetic harmonics, allowing the induction coil to continuously and effectively offset electromagnetic harmonics, avoiding data transmission problems caused by changes in electromagnetic harmonics, and ensuring data transmission stability and continuity.

[0045] Formula derivation based on fuzzy logic algorithm Fuzzy logic algorithm mainly includes three processes: fuzzification, fuzzy reasoning and defuzzification.

[0046] Fuzzification: The precise quantity of input (such as harmonic frequency change rate) , intensity change trend ) is converted into a fuzzy quantity. Let the fuzzy subset of the frequency change rate be (representing negative large, negative small, zero, positive small, positive large), the fuzzy subset of the intensity change trend is also . Define the membership function To represent the input Belongs to fuzzy subset For example, for the frequency change rate , using Gaussian membership function:

[0047]

[0048]

[0049] in, and It is a parameter determined through a large number of experiments based on the actual electromagnetic harmonic variation range and control requirements.

[0050] Reasoning is performed based on the fuzzy rule base. The fuzzy rule base consists of a series of "if-then" rules, for example: "if the frequency change rate isNB and the intensity change trend isNS, then the winding turns adjustment issmalldecrease and the current adjustment ismediumdecrease". Through the fuzzy relationship synthesis operation, the fuzzy output is obtained. Let the fuzzy relationship matrix , the fuzzy vector of the input is and , then the fuzzy output for:

[0051] in, represents the fuzzy Cartesian product operation, Represents a fuzzy composition operation.

[0052] Defuzzification: Convert the fuzzy output into an accurate value to obtain the actual control winding turns and current adjustment value. The centroid method is used for defuzzification, and the formula is:

[0053] in, is the exact output value after defuzzification, is an element in the fuzzy output set, is its corresponding degree of membership.

[0054] Through the above-mentioned fuzzy logic algorithm, multiple parameters are comprehensively considered to achieve precise control of the number of winding turns and current size, effectively solving the problems of electromagnetic harmonic cumulative interference and signal interference in network topology conflicts.

[0055] Solve the cumulative interference of electromagnetic harmonics: In the environment of logistics parks with dense equipment and long operation time, the accumulation and resonance of electromagnetic harmonics are effectively avoided. When multiple devices work at the same time and generate electromagnetic harmonics of similar frequencies, the reverse magnetic field generated by the induction coil can be offset in time to ensure stable data signal transmission and greatly reduce data errors and transmission interruptions caused by electromagnetic interference.

[0056] Solve signal interference in network topology conflicts: When two devices with close distances (such as adjacent forklifts) are connected to the same base station at the same time, the induction coils cancel out the electromagnetic interference generated by each other, making the base station receive clearer signals. The system can quickly and accurately allocate communication resources based on factors such as signal strength and device priority, avoiding confusion in resource allocation caused by signal interference and ensuring timely data transmission.

[0057] 3. Data processing technology to solve the phenomenon of "information islands" and network topology conflicts Existing data caching and format conversion technologies lack the ability to adapt and intelligently adjust when facing complex network changes. The present invention breaks the convention and starts with the dynamic adjustment of data storage format and the intelligent control of data transmission rhythm.

[0058] Distributed adaptive data caching module and conversion module: Hardware composition: It is composed of intelligent control circuits composed of high-speed phase change memory (PCM) and programmable logic devices (CPLD). Traditional data cache mostly uses static random access memory (SRAM) or dynamic random access memory (DRAM), which has limitations in read and write speed and energy consumption. PCM has the characteristics of high-speed read and 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 (ASICs), it can be programmed and adjusted on-site according to different data characteristics and network status.

[0059] Data format conversion mechanism: When the device is connected to the network normally, the cache module caches and processes data according to the TCP / IP protocol and specific binary encoding format of the main network. Once the device enters the "information island" state due to a network topology conflict (such as a vehicle entering a weak signal area), the CPLD detects the network anomaly and starts the adaptive mode. According to the characteristics of the data currently generated by the device (such as the size and update frequency of the cargo transportation record data), an algorithm based on data entropy analysis is used to dynamically adjust the data storage format. For example, for cargo location information with a small amount of data but frequent updates, it is converted into a custom format based on a combination of prefix coding and hash tables to reduce storage occupancy and improve processing efficiency. At the same time, the format conversion rules are recorded in detail to generate a conversion mapping table.

[0060] Algorithm based on data entropy analysis: Data entropy is an indicator of data uncertainty in information theory. In logistics data, different types of data (such as cargo location information, transportation records, etc.) have different information characteristics. By calculating data entropy, the complexity and redundancy of data can be evaluated, thus providing a basis for data format conversion. Traditional data format conversion often lacks consideration of the intrinsic information characteristics of the data, while algorithms based on data entropy value analysis can dynamically adjust the storage format according to the actual information content of the data to achieve optimal storage and processing effects.

[0061] Formula derivation: Assume data set ,Include data elements, each of which The probability of occurrence is , then the data set The entropy value The calculation formula is:

[0062] In practical applications, in order to more accurately reflect the characteristics of logistics data, the frequency of data update should be considered. and data size Impact on entropy value, introduction of correction factor and , and get the improved formula:

[0063] in, and Through the analysis and experimentation of a large amount of logistics data, it is used to balance the impact of data update frequency and data size on entropy. For example, for cargo location information that is updated frequently but has a small amount of data, it is appropriate to increase The value of is adjusted to highlight the impact of its update frequency on data processing; for transportation record data with large data volume but low update frequency, The value of can reasonably reflect the data size in the entropy calculation.

[0064] According to the calculated entropy value, select the appropriate data storage format. For example, for data with low entropy value (high data redundancy), use a custom format based on prefix coding and hash table. Prefix coding is used to reduce duplicate data storage, and hash table improves data search efficiency, thereby reducing storage usage and improving processing efficiency. At the same time, record the format conversion rules in detail and generate a conversion mapping table.

[0065] Data sending rhythm control When the network topology returns to normal and the device reconnects to the main network, the cache module uses the built-in inverse conversion algorithm based on the recorded format conversion rules to quickly convert the data back to a format compatible with the main network.

[0066] Data sending rhythm control: When the network topology returns to normal and the device reconnects to the main network, the cache module uses the built-in inverse conversion algorithm based on the recorded format conversion rules to quickly convert the data back to the main network compatible format.

[0067] Built-in inverse conversion algorithm: The data format conversion process is a two-way operation. The forward conversion is to convert the data into a custom format based on the data entropy analysis results to adapt to local storage and processing in the "information island" state. The reverse conversion is to restore the data to a format that complies with the main network protocol after the network is restored. Traditional reverse conversion algorithms are often simple reverse operations, lacking consideration of possible changes in the data in the "information island" state (such as data updates, partial data loss, etc.). The reverse conversion algorithm of the present invention combines the conversion mapping table and the operation records of the data in the "information island" state to more accurately convert the data back to the main network compatible format.

[0068] Formula derivation: Let the forward transfer function be , the original data Convert to custom format ,Right now The conversion mapping table records the corresponding relationship of each data element in the conversion process, set as ,in Represents the original data element Convert to custom format elements .

[0069] The inverse conversion algorithm first converts the , the custom format data Elements in Mapping back to the possible set of original data elements Considering the possible update operations that may occur in the "information island" state, let the update operation record be ,in Represents data elements Update operations (such as modifying values, adding new data, etc.).

[0070] The inverse transformation formula is:

[0071] in, According to the mapping table Reverse Lookup The corresponding raw data element, Indicates update operation The inverse operation is to restore the updated data elements to the original data elements or update them to new data elements that conform to the main network format. In this way, the data can be accurately converted back to the main network compatible format to avoid data format incompatibility and data conflict issues.

[0072] In the data transmission link, an algorithm based on network congestion window prediction is adopted. The network congestion window size is predicted by real-time monitoring of the network's round-trip time (RTT) and packet loss rate. When the network is congested (such as during peak hours in logistics parks), the cache module temporarily stores data based on the prediction results to avoid a large influx of data that aggravates congestion; when the network is unblocked, the data is sent out at a reasonable rate.

[0073] Algorithm based on network congestion window prediction: Reasoning: In network communications, network congestion can cause data transmission delays or even interruptions. Traditional network congestion control algorithms are mostly based on fixed window size adjustment strategies, which cannot accurately adapt to the complex and changing network environment of logistics parks. This algorithm monitors the network's round-trip time (RTT) and packet loss rate in real time, comprehensively considers the real-time requirements of logistics data and the dynamic changes of network bandwidth, and predicts the network congestion window size, thereby achieving more accurate data transmission control.

[0074] Formula derivation: Assume the round trip time of the current network is , the packet loss rate is , 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.

[0075] Predicted congestion window size The formula is:

[0076] 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.

[0077] 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.

[0078] 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.

[0079] IV. Conclusion In the implementation example of a large logistics park, the electromagnetic induction coil structure with a unique design and the principle of electromagnetic harmonic self-cancellation are used to solve the resource allocation problem caused by electromagnetic harmonic cumulative interference and signal interference in network topology conflicts; the distributed adaptive data cache and conversion module based on phase change memory and programmable logic devices are used to solve the "information island" derivative phenomenon and data transmission anomaly caused by network topology conflicts. These two technical means have outstanding substantive characteristics. Compared with the existing technology, they adopt unconventional design ideas and technical means, produce unexpected technical effects, significantly improve the stability and efficiency of computer data transmission management systems based on data processing in complex dynamic network environments, overcome the shortcomings of existing technologies in dealing with multi-device collaboration problems, and are creative. They not only ensure the accurate and timely transmission of data in the logistics park, but also improve 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: include: Electromagnetic induction coil structure module: An electromagnetic induction coil is installed in the data transmission line of the equipment. It uses ferrite magnetic material as the core and adopts a multi-layer nested winding structure with dynamically adjustable spacing. The inner thin wire senses high-frequency electromagnetic harmonics, and the outer thick wire senses low-frequency electromagnetic harmonics. The winding spacing is adjusted according to the electromagnetic harmonic frequency through the MEMS drive mechanism; Equipped with a low-power microprocessor to monitor the frequency and intensity of electromagnetic harmonics, with a built-in control program based on fuzzy logic algorithm, which comprehensively includes but is not limited to the change rate of electromagnetic harmonic frequency and the change trend of intensity, controls the number of winding turns and current; adjusts the winding structure to change the number of turns by including but not limited to DAC circuits and MEMS drive mechanisms, and changes the current by adjusting the PWM signal; Distributed adaptive data caching and conversion module: A distributed adaptive data cache module composed of high-speed phase change memory and programmable logic devices is set inside each device; Data processing mechanism: When connected to the Internet normally, data is cached and processed according to the main network TCP / IP protocol and specific binary encoding format; When entering the information island state and the CPLD detects an abnormality, the cache module switches to the adaptive mode and uses a data entropy analysis algorithm to dynamically adjust the storage format according to the data characteristics. For data with low entropy values, a custom format combining prefix coding and hash tables is used to store and record conversion rules to generate a mapping table. When the network returns to normal, the data is converted back to a format compatible with the main network using the reverse conversion algorithm according to the rules; In the data transmission process, a network congestion window prediction algorithm is used to monitor RTT and packet loss rate, and network real-time weight is introduced. and bandwidth change rate , according to the formula: Predicted congestion window size.

2. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: The data entropy analysis algorithm is: Assume data set ,Include data elements, each of which The probability of occurrence is , considering the update frequency of data and data size Impact on entropy value, introduction of correction factor and , data set The entropy value The calculation formula is: This formula accurately converts the data back to a main network compatible format.

3. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: The built-in inverse conversion algorithms are: Let the forward transfer function be , the original data Convert to custom format ,Right now , the conversion mapping table is ,in , update the operation record as ,in Represents data elements Update operation; The inverse transformation formula is: This formula accurately converts the data back to a main network compatible format.

4. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: In the fuzzification process of the fuzzy logic algorithm, a Gaussian membership function is used for the frequency change rate: in is a fuzzy subset, and It is a parameter determined through a large number of experiments based on the actual electromagnetic harmonic variation range and control requirements.

5. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: In the fuzzy reasoning process of fuzzy logic algorithm, the fuzzy rule base consists of a series of if-then rules. Let the fuzzy relationship matrix , the fuzzy vector of the input is and , the fuzzy output is obtained through fuzzy relation synthesis operation , the formula is: in, represents the fuzzy Cartesian product operation, Represents a fuzzy composite operation.

6. A computer data transmission management system based on data processing as claimed in 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: in, is the exact output value after defuzzification, is an element in the fuzzy output set, is its corresponding degree of membership.

7. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: In microprocessor control, when 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 uses it to change the equivalent number of turns, and adjusts the pulse width modulation signal of the drive circuit to adjust the current size.

8. A computer data transmission management system based on data processing as claimed in claim 1, characterized in that: In the data format conversion mechanism, for data with low entropy values, a custom format based on prefix encoding and hash table is used for storage.

Images