Data synchronous-transmission optimization method and system based on analog optical fiber communications

Abstract

The present invention belongs to the technical field of communication optimization, and in particular relates to a data synchronous-transmission optimization method and system based on analog optical fiber communications. In the present invention, after redundant data is generated in a communication device, a temporary transient cycle can be constructed, such that whether the redundant data is instantaneous data is determined, thereby avoiding erroneous construction of a monitoring cycle. In addition, after the monitoring cycle is constructed, trend analysis and prediction analysis are performed on parameters to be assessed, which are within the monitoring cycle, and further verification is performed on a trend analysis result, such that the monitoring cycle is adjusted within a suitable amount of time, thereby ensuring the accuracy of a finally obtained estimated redundant volume. Then, the estimated redundant volume is taken as a basis, and an output time sequence of pieces of communication data is adjusted in view of an assessment model and an adaptive optimization model, thereby ensuring the timeliness and validity of data outputted by the communication device.

Description

A data synchronization transmission optimization method and system based on analog optical fiber communication Technical Field

[0001] The present invention belongs to the technical field of communication optimization, and in particular relates to a data synchronization transmission optimization method and system based on analog optical fiber communication. Background Art

[0002] The communication mode of distribution network automation terminal adopts optical fiber communication and wireless communication. There are problems such as high cost of laying optical cables, susceptibility to damage by external forces, and unstable wireless signals, which affect the practical level of some distribution automation terminals. At the same time, there are problems such as empty packet transmission, which will result in invalid transmission. It will also occupy the transmission capacity of communication equipment, causing delays in the transmission of other necessary communication data. It is one of the main reasons for the low carrier communication rate. In order to ensure the timeliness of data output, it is essential to perform reasonable optimization when empty packet data or redundant data is generated inside it.

[0003] In the existing technology, when optimizing optical fiber communication data, most of the time, empty packet data is simply filtered out to reduce the load on the communication equipment. However, the accumulation of other redundant data will cause the delay of some data to increase. Even if it can be output later, it will not be able to meet the current demand due to the excessive delay, which will undoubtedly make the data invalid. Based on this, this solution provides a data transmission optimization method that can adjust the output timing of each communication data after redundant data is generated in the communication equipment.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a data synchronization transmission optimization method and system based on analog optical fiber communication, which can adjust the output timing of each communication data after redundant data is generated in the communication equipment to ensure the validity of the output data of the communication equipment.

[0006] The technical solutions adopted by the present invention are as follows:

[0007] A data synchronization transmission optimization method based on simulated optical fiber communication, comprising:

[0008] Acquire data transmission information of communication data in the communication device, wherein the data transmission information includes a front-end input data volume and a back-end output data volume;

[0009] Comparing the front-end data input amount with the back-end data output amount;

[0010] If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device is transmitting data normally;

[0011] If the front-end data input amount is greater than the back-end data output amount, redundant data is generated in the communication device and communication data transmission is delayed;

[0012] Constructing a monitoring cycle, setting multiple sampling nodes within the monitoring cycle, and obtaining the amount of redundant data under each sampling node in real time, and then calibrating it as a parameter to be evaluated;

[0013] Inputting the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data amount;

[0014] Obtaining the current amount of redundant data and inputting it into the prediction model together with the change trend value of the redundant data amount to obtain an estimated amount of redundant data;

[0015] Obtaining an allowable redundancy threshold and comparing it with the estimated redundancy, and issuing an alarm signal when the estimated redundancy is higher than or equal to the allowable redundancy threshold, and inputting the estimated redundancy into an evaluation model when the estimated redundancy is lower than the allowable redundancy threshold to obtain an output delay of the communication data;

[0016] Obtain the communication data delay requirements and then input them into the adaptive optimization model to obtain the output timing of each communication data.

[0017] In a preferred embodiment, the step of establishing a monitoring cycle includes:

[0018] Obtain the node where the front-end data input volume is greater than the back-end data output volume, and mark it as the node to be evaluated;

[0019] Taking the node to be evaluated as the starting node, constructing a transient cycle;

[0020] Obtaining the difference between all front-end data input and back-end data output during the transient period, and then calibrating the difference as a deviation difference;

[0021] Counting all deviation differences within the transient period, and inputting the deviation differences into a regulation model to determine whether the deviation differences within the transient period continuously decrease;

[0022] If yes, obtain the monitoring duration and construct a monitoring cycle with the node to be evaluated as the starting node;

[0023] If not, it indicates that the deviation difference is instantaneous data, and the monitoring cycle is no longer constructed.

[0024] In a preferred embodiment, the step of inputting the deviation difference into the regulation model and determining whether the deviation difference continuously decreases during the transient period includes:

[0025] Obtain all deviation differences within the transient period and arrange them in order of occurrence time;

[0026] Obtaining the deviation difference at the end node of the transient period and calibrating it as the reference data;

[0027] Taking the time node corresponding to the benchmark data as the starting point and the center node of the transient period as the ending node, multiple deviation differences are collected and calibrated as parameters to be compared, and all parameters to be compared with values ​​greater than zero are screened out and their number is counted;

[0028] Obtaining an evaluation threshold, and comparing the threshold with the number of parameters to be compared whose values ​​are greater than zero;

[0029] If the evaluation threshold is greater than the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period is continuously decreasing;

[0030] If the evaluation threshold is less than or equal to the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period is instantaneous data.

[0031] In a preferred embodiment, the step of inputting the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data volume includes:

[0032] Obtain the parameters to be evaluated within the monitoring period;

[0033] Calling a trend analysis function from the trend analysis model;

[0034] All parameters to be evaluated within the monitoring period are input into a trend analysis function, and the output result is calibrated as a change trend value of the redundant data amount.

[0035] In a preferred solution, after the change trend value of the redundant data amount is determined, it is determined whether it can be input into the prediction model. The specific process is as follows:

[0036] Obtain adjacent parameters to be evaluated and calibrate them as verification parameters;

[0037] Calling a verification function, inputting the verification parameter and the change trend value of the redundant data amount into the verification function, and calculating the accuracy of the verification result;

[0038] Obtaining an evaluation threshold and comparing it with the accuracy of the verification result;

[0039] If the evaluation threshold is higher than the accuracy of the verification result, it indicates that the change trend value of the redundant data amount cannot be input into the prediction model, and the change trend value of the redundant data amount is recalculated;

[0040] If the evaluation threshold is lower than or equal to the accuracy of the verification result, it indicates that the change trend value of the redundant data amount can be input into the prediction model.

[0041] In a preferred embodiment, the step of obtaining the current amount of redundant data and inputting it into a prediction model together with a change trend value of the redundant data amount to obtain an estimated amount of redundant data includes:

[0042] Obtaining the current amount of redundant data, the predicted interval, and the changing trend value of the redundant data amount in the communication device;

[0043] Calling a prediction function from the prediction model;

[0044] The current redundant data amount, the prediction interval, and the changing trend value of the redundant data amount are input into the prediction function, and the output result is calibrated as the estimated redundancy amount.

[0045] In a preferred embodiment, when the estimated redundancy is lower than the allowable redundancy threshold, the step of inputting it into an evaluation model to obtain the output delay of the communication data includes:

[0046] Obtaining a difference between the estimated redundancy and an allowable redundancy threshold, and marking the difference as a redundancy difference;

[0047] Calling an evaluation function from the evaluation model;

[0048] The redundancy difference is input into an evaluation function, and the output result is calibrated as the output delay of the communication data.

[0049] In a preferred embodiment, the step of obtaining the communication data delay requirement and then inputting it into the adaptive optimization model to obtain the output timing of each communication data includes:

[0050] Obtain the output delay of all communication data and the allowable delay of all communication data;

[0051] Calling a standard function from the adaptive optimization model, inputting the output delay of all communication data and its allowable delay into the standard function, and calibrating the output result as the delay risk of the communication data;

[0052] The optimization function is called from the adaptive optimization model, and the redundant difference and delay risk are input into the optimization function together, and the optimization results are arranged in order from large to small, and then the arrangement order of the optimization results is determined as the output timing of each communication data.

[0053] The present invention further provides a data synchronization transmission optimization system based on simulated optical fiber communication, which is applied to the above-mentioned data synchronization transmission optimization method based on simulated optical fiber communication, comprising:

[0054] A data acquisition module, the data acquisition module is used to obtain data transmission information of communication data in the communication device, wherein the data transmission information includes the front-end input data volume and the back-end output data volume;

[0055] A comparison module, configured to compare the front-end data input amount with the back-end data output amount;

[0056] If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device is transmitting data normally;

[0057] If the front-end data input amount is greater than the back-end data output amount, redundant data is generated in the communication device and communication data transmission is delayed;

[0058] A monitoring module, wherein the monitoring module is used to construct a monitoring cycle, set multiple sampling nodes within the monitoring cycle, and obtain the amount of redundant data under each sampling node in real time, and then calibrate it as a parameter to be evaluated;

[0059] A trend analysis module, wherein the trend analysis module is used to input the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data amount;

[0060] A prediction module is used to obtain the current amount of redundant data and input it into a prediction model together with a change trend value of the redundant data amount to obtain an estimated amount of redundant data;

[0061] an evaluation module configured to obtain a permissible redundancy threshold, compare the obtained value with the estimated redundancy, and issue an alarm signal when the estimated redundancy is higher than or equal to the permissible redundancy threshold; and input the obtained value into an evaluation model to obtain an output delay of the communication data when the estimated redundancy is lower than the permissible redundancy threshold;

[0062] The optimization module is used to obtain the communication data delay requirement and then input it into the adaptive optimization model to obtain the output timing of each communication data.

[0063] And, a data synchronous transmission optimization terminal based on analog optical fiber communication, comprising:

[0064] at least one processor;

[0065] and a memory communicatively coupled to the at least one processor;

[0066] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned data synchronization transmission optimization method based on simulated optical fiber communication.

[0067] The technical effects achieved by the present invention are:

[0068] The present invention can construct a temporary transient period after redundant data is generated in the communication device to determine whether it is instantaneous data, thereby avoiding the incorrect construction of the monitoring period. At the same time, after the monitoring period is constructed, trend analysis and predictive analysis will be performed on the parameters to be evaluated therein, and the trend analysis results will be further verified, and the monitoring period will be adjusted in time to ensure the accuracy of the final estimated redundancy. Then, based on the estimated redundancy, the evaluation model and the adaptive optimization model are combined to adjust the output timing of each communication data to ensure the timeliness and effectiveness of the output data of the communication device. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] FIG1 is a flow chart of the method provided by the present invention;

[0070] FIG2 is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive of other embodiments.

[0074] 1 and 2 , the present invention provides a method for optimizing data synchronous transmission based on simulated optical fiber communication, comprising:

[0075] S1. Acquire data transmission information of communication data in a communication device, wherein the data transmission information includes a front-end input data volume and a back-end output data volume;

[0076] S2. Compare the front-end data input amount with the back-end data output amount;

[0077] If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device is transmitting data normally;

[0078] If the amount of data input to the front end is greater than the amount of data output to the back end, redundant data will be generated in the communication device and communication data transmission will be delayed;

[0079] S3. Construct a monitoring cycle, set multiple sampling nodes within the monitoring cycle, obtain the redundant data under each sampling node in real time, and then calibrate it as the parameter to be evaluated;

[0080] S4. Input the parameter to be evaluated into the trend analysis model to obtain the change trend value of the redundant data amount;

[0081] S5. Obtain the current amount of redundant data and input it into the prediction model together with the trend value of the redundant data amount to obtain the estimated redundancy amount;

[0082] S6. Obtaining an allowable redundancy threshold and comparing it with the estimated redundancy. When the estimated redundancy is higher than or equal to the allowable redundancy threshold, issuing an alarm signal. When the estimated redundancy is lower than the allowable redundancy threshold, inputting the estimated redundancy into an evaluation model to obtain the output delay of the communication data.

[0083] S7. Obtain the communication data delay requirement, and then input it into the adaptive optimization model to obtain the output timing of each communication data.

[0084] As described in the above steps S1-S7, the communication mode of the distribution network automation terminal adopts optical fiber communication and wireless communication mode, which has problems such as high cost of laying optical cables, easy damage by external forces and unstable wireless signals, which affect the practical level of some distribution automation terminals. At the same time, there are problems such as empty packet transmission, which will result in invalid transmission, and it will also occupy the transmission capacity of the communication equipment, causing delays in the transmission of other necessary communication data, which is one of the main reasons for the low carrier communication rate. In this embodiment, the transmission signal capability of the communication equipment is first obtained, that is, whether the front-end input data volume and the back-end data output volume in the transmission data can be equal, or whether the back-end data output volume can be greater than the front-end data input volume. When the back-end data output volume is lower than the front-end data input volume, redundant data will be generated in the communication equipment. When too much redundant data accumulates, This will slow down the response speed of the communication device and cause delays in the data transmission process. However, this is an inevitable phenomenon during the operation of the communication device. Based on this, after the redundant data is generated, this embodiment constructs a monitoring cycle, counts the amount of redundant data at each sampling node within the monitoring cycle, and then inputs it into the trend analysis model to calculate the changing trend value of the redundant data amount. Subsequently, combined with the current redundant data amount, the estimated redundancy at the next node or the user-set node is calculated through the prediction model, and compared with the allowable redundancy threshold before it occurs. The output delay of the communication data is obtained by combining the evaluation model, and finally the output timing of each communication data is determined according to the adaptive optimization model, which meets the actual needs of users and reduces the delay in data transmission. At the same time, empty packet data can be screened out to ensure the stability of data transmission of the communication device.

[0085] In a preferred embodiment, the step of establishing a monitoring cycle includes:

[0086] S301, obtaining a node where the front-end data input volume is greater than the back-end data output volume, and marking it as a node to be evaluated;

[0087] S302: Taking the node to be evaluated as the starting node, construct a transient cycle;

[0088] S303, obtaining the difference between all front-end data input and back-end data output in the transient period, and then calibrating it as the deviation difference;

[0089] S304: Count all deviation differences within the transient period, input the deviation differences into the regulation model, and determine whether the deviation differences within the transient period continuously decrease;

[0090] If yes, obtain the monitoring duration and construct a monitoring cycle with the node to be evaluated as the starting node;

[0091] If not, it indicates that the deviation difference is instantaneous data and the monitoring cycle is no longer constructed.

[0092] As described in the above steps S301-S304, when constructing a monitoring cycle, the first thing that needs to be determined is the node where the front-end data input is greater than the back-end data output. This embodiment calibrates it as the node to be evaluated, and then constructs a transient cycle with it as the starting node. The purpose of constructing the transient cycle is to determine whether the front-end data input is greater than the back-end data output is a transient phenomenon. Specifically, the difference between the front-end data input and the back-end data output is calibrated as the deviation difference, and then the deviation difference is input into the adjustment model to determine whether the deviation difference continues to decrease. Only when it continues to decrease will the monitoring cycle be constructed. The starting node of the monitoring cycle is the node to be evaluated, and the end node of the monitoring cycle needs to be set according to the preset monitoring time. The setting of the monitoring time should be set according to the use environment of the communication equipment, and no specific restrictions are imposed on it in this article.

[0093] In a preferred embodiment, the step of inputting the deviation difference into the regulation model and determining whether the deviation difference continuously decreases during the transient period includes:

[0094] Stp1. Obtain all deviation differences within the transient period and arrange them in the order of occurrence time;

[0095] Stp2, obtain the deviation difference at the end node of the transient period and calibrate it as the benchmark data;

[0096] Stp3, starting from the time node corresponding to the benchmark data and ending at the center node of the transient period, collects multiple deviation differences and calibrates them as parameters to be compared. All parameters to be compared whose values ​​are greater than zero are screened out and their number is counted.

[0097] Step 4: Get the evaluation threshold and compare it with the number of parameters to be compared whose values ​​are greater than zero;

[0098] If the evaluation threshold is higher than the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period has continuously decreased;

[0099] If the evaluation threshold is less than or equal to the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period is instantaneous data.

[0100] As described in the above steps Stp1-Stp4, after the transient period is determined, all deviation differences within the transient period are counted and arranged according to the time of occurrence. Then, the deviation difference at the end node of the transient period is determined as the benchmark data, and reverse sampling is performed. The end node of the sampling process is the central node of the transient period. Then, a screening operation is performed on it to screen out all parameters to be compared whose values ​​are greater than zero. After counting their number, they are compared with a preset evaluation threshold. Only when the evaluation threshold is higher than the number of parameters to be compared whose values ​​are greater than zero, will it be determined that the deviation difference within the transient period has continuously decreased. Otherwise, the deviation difference within the transient period is deemed to be instantaneous data, and the construction of the monitoring period is stopped.

[0101] In a preferred embodiment, the step of inputting the parameter to be evaluated into the trend analysis model to obtain the change trend value of the redundant data volume includes:

[0102] S401, obtaining parameters to be evaluated within a monitoring period;

[0103] S402, calling a trend analysis function from a trend analysis model;

[0104] S403: Input all parameters to be evaluated within the monitoring period into the trend analysis function, and calibrate the output result as a change trend value of the redundant data amount.

[0105] As described in steps S401-S403 above, when the monitoring cycle is established, it indicates that persistent redundant data appears in the communication device. Then, the trend analysis function is called from the trend analysis model, and the parameters to be evaluated are input into the trend analysis function, wherein the trend analysis function is: In the formula, Q represents the trend value of the amount of redundant data, n represents the number of parameters to be evaluated, i represents the number of parameters to be evaluated and does not participate in the actual calculation, R i and R i-1 It represents the adjacent parameters to be evaluated. Based on this, the changing trend value of the redundant data volume can be calculated, providing corresponding data support for the subsequent prediction of the redundant data volume in the communication device.

[0106] In a preferred embodiment, after the trend value of the redundant data amount is determined, it is determined whether it can be input into the prediction model. The specific process is as follows:

[0107] Obtain adjacent parameters to be evaluated and calibrate them as verification parameters;

[0108] Call the verification function, input the verification parameters and the trend value of the redundant data volume into the verification function, and calculate the accuracy of the verification result;

[0109] Obtain the evaluation threshold and compare it with the accuracy of the verification result;

[0110] If the evaluation threshold is higher than the accuracy of the verification result, it indicates that the change trend value of the redundant data volume cannot be input into the prediction model, and the change trend value of the redundant data volume is recalculated;

[0111] If the evaluation threshold is lower than or equal to the accuracy of the verification result, it indicates that the changing trend value of the redundant data amount can be input into the prediction model.

[0112] As mentioned above, after the trend value of the redundant data volume is determined, it needs to be verified first to ensure the accuracy of the subsequent prediction model output results. Specifically, the adjacent parameters to be evaluated are determined as verification parameters, and the verification is performed in combination with the obtained trend value of the redundant data volume. The verification function is: R h =R q +Qt, where R h Indicates the parameters to be evaluated at the lower position, R q Represents the parameter to be evaluated that ranks high, and t represents the time length between adjacent parameters to be evaluated. Based on the above formula, for the verification parameters that meet this formula, the verification result is judged to be accurate, otherwise it is judged to be wrong. Of course, in order to avoid the influence of small deviations, an allowable deviation interval can be preset. When the verification result is within the allowable deviation interval, it can be judged to be correct. Finally, it is only necessary to count its correctness (accuracy). Only when the accuracy of the verification result is higher than or equal to the evaluation threshold can the changing trend value of the redundant data volume be input into the prediction model. Otherwise, it is necessary to extend the duration of the monitoring period to increase the amount of data input into the trend analysis model to ensure the accuracy of the changing trend value of the redundant data volume.

[0113] In a preferred embodiment, the step of obtaining the current amount of redundant data and inputting it into a prediction model together with a trend value of the amount of redundant data change to obtain an estimated amount of redundant data includes:

[0114] S501, obtaining the current redundant data volume, prediction interval, and redundant data volume change trend value in the communication device;

[0115] S502, calling a prediction function from a prediction model;

[0116] S503: Input the current redundant data amount, the prediction interval, and the change trend value of the redundant data amount into the prediction function, and mark the output result as the estimated redundant amount.

[0117] As described in steps S501-S503 above, when calculating the estimated redundancy, the prediction function is first called from the prediction model, where the prediction function is: R y =R d +QT, where R y Represents the estimated redundancy, Rd Represents the current amount of redundant data, and T represents the prediction interval. Based on the above formula, the estimated redundancy under user demand or system default node can be calculated, and an advance assessment can be performed before it occurs to ensure the timeliness of communication data optimization in the communication equipment.

[0118] In a preferred embodiment, when the estimated redundancy is lower than the allowable redundancy threshold, the step of inputting it into the evaluation model to obtain the output delay of the communication data includes:

[0119] S601, obtaining a difference between the estimated redundancy and the allowable redundancy threshold, and marking it as a redundancy difference;

[0120] S602, calling the evaluation function from the evaluation model;

[0121] S603: Input the redundancy difference into the evaluation function, and calibrate the output result as the output delay of the communication data.

[0122] As described in steps S601-S603 above, when redundant data exists in the communication device and the estimated redundancy is lower than the allowable redundancy threshold, the difference between the estimated redundancy and the allowable redundancy threshold is first calculated and calibrated as the redundancy difference. Then, an evaluation function is called from the evaluation model, and the redundancy difference is input into the evaluation function. The evaluation function is: S = aR c , where S represents the delay of communication data, a represents the delay coefficient, which can be determined by experiment before the communication equipment is put into use and is a fixed constant value, R c Represents the redundancy difference. Based on the above formula, it can reflect the delay after the redundant data is generated in the communication device. The user can view it intuitively. At the same time, the adaptive optimization model can also perform optimization operations based on the delay and adjust the data timing of subsequent transmission of the communication device.

[0123] In a preferred embodiment, the step of obtaining the communication data delay requirement and then inputting it into the adaptive optimization model to obtain the output timing of each communication data includes:

[0124] S701, obtaining the output delay of all communication data and the allowable delay of all communication data;

[0125] S702: Calling a standard function from the adaptive optimization model, inputting the output delay of all communication data and its allowable delay into the standard function, and calibrating the output result as the delay risk of the communication data;

[0126] S703. Call the optimization function from the adaptive optimization model, input the redundancy difference and delay risk into the optimization function, and arrange the optimization results in descending order, and then determine the order of arrangement of the optimization results as the output timing of each communication data.

[0127] As described in steps S701-S703 above, after the delay of the communication data is determined, it is input into the adaptive optimization model. First, the standard function f=S / S is called. r , where f represents the delay risk of communication data, S r Indicates the allowable delay, and then calls the optimization function from the self-use optimization model, where the optimization function is: Y = k1f + k2R c , where Y represents the optimization result, k1 represents the delay risk weight coefficient of the communication data, and k2 represents the weight coefficient of the redundant difference, where k1+k2=1. The optimization results are then arranged in descending order, and the arrangement results are used as the output timing priority of the data to ensure that all communication data can still be output in a timely manner when there is redundant data in the communication equipment.

[0128] The present invention further provides a data synchronization transmission optimization system based on simulated optical fiber communication, which is applied to the above-mentioned data synchronization transmission optimization method based on simulated optical fiber communication, comprising:

[0129] The data acquisition module is used to obtain data transmission information of communication data in the communication device, wherein the data transmission information includes the front-end input data volume and the back-end output data volume;

[0130] Comparison module, which is used to compare the front-end data input and back-end data output;

[0131] If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device is transmitting data normally;

[0132] If the amount of data input to the front end is greater than the amount of data output to the back end, redundant data will be generated in the communication device and communication data transmission will be delayed;

[0133] The monitoring module is used to build a monitoring cycle, set multiple sampling nodes within the monitoring cycle, and obtain the redundant data under each sampling node in real time, and then calibrate it as the parameter to be evaluated;

[0134] The trend analysis module is used to input the parameters to be evaluated into the trend analysis model to obtain the changing trend value of the redundant data volume;

[0135] The prediction module is used to obtain the current amount of redundant data and input it into the prediction model together with the change trend value of the redundant data amount to obtain the estimated redundancy;

[0136] An evaluation module is used to obtain an allowable redundancy threshold and compare it with the estimated redundancy. When the estimated redundancy is higher than or equal to the allowable redundancy threshold, an alarm signal is issued. When the estimated redundancy is lower than the allowable redundancy threshold, the estimated redundancy is input into an evaluation model to obtain the output delay of the communication data.

[0137] Optimization module,The optimization module is used to obtain the communication data delay requirements, and then input them into the adaptive optimization model to obtain the output timing of each communication data.

[0138] As mentioned above, when optimizing the communication data, the front-end input data volume and the back-end output data volume of the communication device are first collected through the data acquisition module, and then the comparison module is used to determine whether redundant data will be generated in the communication device. After the redundant data is generated, a monitoring cycle is constructed through the monitoring module, and the redundant data volume within the monitoring cycle is collected in real time, and then input into the trend analysis module and the prediction module to obtain the estimated redundancy of the next node. Based on the estimated redundancy, the evaluation module is used to calculate the output delay of the communication data, and finally it is combined with the optimization module to calculate the output timing of all communication data, so that the communication device can still ensure the timeliness of data output under the premise of generating redundant data.

[0139] And, a data synchronous transmission optimization terminal based on analog optical fiber communication, comprising:

[0140] at least one processor;

[0141] and a memory communicatively coupled to the at least one processor;

[0142] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned data synchronous transmission optimization method based on simulated optical fiber communication.

[0143] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0144] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A data synchronization transmission optimization method based on analog optical fiber communication, characterized in that: include: Acquire data transmission information of communication data in the communication device, wherein the data transmission information includes a front-end input data volume and a back-end output data volume; Comparing the front-end data input amount with the back-end data output amount; If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device transmits data normally; If the front-end data input amount is greater than the back-end data output amount, redundant data is generated in the communication device and communication data transmission is delayed; Constructing a monitoring cycle, setting a plurality of sampling nodes within the monitoring cycle, and acquiring the amount of redundant data under each sampling node in real time, and then calibrating it as a parameter to be evaluated; Inputting the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data amount; Obtaining the current amount of redundant data and inputting it into the prediction model together with the change trend value of the redundant data amount to obtain an estimated amount of redundant data; Obtaining an allowable redundancy threshold and comparing it with the estimated redundancy, and issuing an alarm signal when the estimated redundancy is higher than or equal to the allowable redundancy threshold, and inputting it into an evaluation model when the estimated redundancy is lower than the allowable redundancy threshold to obtain an output delay of the communication data; The communication data delay requirements are obtained and then input into the adaptive optimization model to obtain the output timing of each communication data.

2. The method for optimizing data synchronous transmission based on analog optical fiber communication according to claim 1, characterized in that: The steps of constructing a monitoring cycle include: Obtain the node where the front-end data input amount is greater than the back-end data output amount, and mark it as a node to be evaluated; Taking the node to be evaluated as the starting node, constructing a transient cycle; Obtaining the difference between all front-end data input and back-end data output in the transient period, and then calibrating it as a deviation difference; Counting all deviation differences within the transient period, and inputting the deviation differences into the regulation model to determine whether the deviation differences within the transient period continuously decrease; If yes, the monitoring duration is obtained, and a monitoring cycle is constructed with the node to be evaluated as the starting node; If not, it indicates that the deviation difference is instantaneous data, and the monitoring cycle is no longer constructed.

3. The method for optimizing data synchronous transmission based on simulated optical fiber communication according to claim 2, characterized in that: The step of inputting the deviation difference into the regulation model to determine whether the deviation difference decreases continuously during the transient period includes: Obtaining all deviation differences within the transient period and arranging them in order of occurrence time; Obtaining the deviation difference at the end node of the transient period and calibrating it as the reference data; Taking the time node corresponding to the benchmark data as the starting point and the central node of the transient period as the ending node, multiple deviation differences are collected and calibrated as parameters to be compared, and all parameters to be compared whose values ​​are greater than zero are screened out and their number is counted; Obtaining an evaluation threshold, and comparing the threshold with the number of parameters to be compared whose values ​​are greater than zero; If the evaluation threshold is higher than the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period is continuously decreasing; If the evaluation threshold is less than or equal to the number of parameters to be compared that is greater than zero, it indicates that the deviation difference within the transient period is instantaneous data.

4. The method for optimizing data synchronous transmission based on analog optical fiber communication according to claim 1, characterized in that: The step of inputting the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data volume comprises: Obtain the parameters to be evaluated within the monitoring period; Calling a trend analysis function from the trend analysis model; All parameters to be evaluated within the monitoring period are input into the trend analysis function, and the output result is calibrated as the change trend value of the redundant data amount.

5. The method for optimizing data synchronous transmission based on analog optical fiber communication according to claim 4, characterized in that: After the change trend value of the redundant data volume is determined, it is determined whether it can be input into the prediction model. The specific process is as follows: Obtain adjacent parameters to be evaluated and calibrate them as verification parameters; Calling a verification function, inputting the verification parameter and the change trend value of the redundant data amount into the verification function, and calculating the accuracy of the verification result; Obtaining an evaluation threshold and comparing it with the accuracy of the verification result; If the evaluation threshold is higher than the accuracy of the verification result, it indicates that the change trend value of the redundant data volume cannot be input into the prediction model, and the change trend value of the redundant data volume is recalculated; If the evaluation threshold is lower than or equal to the accuracy of the verification result, it indicates that the change trend value of the redundant data volume can be input into the prediction model.

6. The method for optimizing data synchronous transmission based on simulated optical fiber communication according to claim 1, characterized in that: The step of obtaining the current amount of redundant data and inputting it into the prediction model together with the change trend value of the amount of redundant data to obtain the estimated amount of redundant data includes: Obtaining the current amount of redundant data, the prediction interval, and the change trend value of the redundant data amount in the communication device; Calling a prediction function from the prediction model; The current redundant data volume, the prediction interval and the change trend value of the redundant data volume are input into the prediction function together. The output result is marked as the estimated redundancy.

7. The method for optimizing data synchronous transmission based on analog optical fiber communication according to claim 1, characterized in that: When the estimated redundancy is lower than the allowable redundancy threshold, the estimated redundancy is input into the evaluation model to obtain the output delay of the communication data, comprising: Obtaining a difference between the estimated redundancy and an allowable redundancy threshold, and marking it as a redundancy difference; Calling an evaluation function from the evaluation model; The redundant difference is input into an evaluation function, and the output result is calibrated as the output delay of the communication data.

8. The method for optimizing data synchronous transmission based on simulated optical fiber communication according to claim 7, characterized in that: The step of obtaining the communication data delay requirement and then inputting it into the adaptive optimization model to obtain the output timing of each communication data includes: Obtain the output delay of all communication data and the allowable delay of all communication data; Calling a standard function from the adaptive optimization model, inputting the output delay of all communication data and its allowable delay into the standard function, and marking the output result as the delay risk of the communication data; The optimization function is called from the adaptive optimization model, and the redundant difference and the delay risk are input into the optimization function together, and the optimization results are arranged in order from large to small, and then the arrangement order of the optimization results is determined as the output timing of each communication data.

9. A data synchronization transmission optimization system based on simulated optical fiber communication, applied to the data synchronization transmission optimization method based on simulated optical fiber communication according to any one of claims 1 to 8, characterized in that: include: A data acquisition module, the data acquisition module is used to obtain data transmission information of communication data in the communication device, wherein the data transmission information includes the front-end input data volume and the back-end output data volume; A comparison module, the comparison module is used to compare the front-end data input amount with the back-end data output amount; If the front-end data input amount is less than or equal to the back-end data output amount, it indicates that the communication device transmits data normally; If the front-end data input amount is greater than the back-end data output amount, redundant data is generated in the communication device and communication data transmission is delayed; A monitoring module, wherein the monitoring module is used to construct a monitoring cycle, set a plurality of sampling nodes within the monitoring cycle, and obtain the amount of redundant data under each sampling node in real time, and then calibrate it as a parameter to be evaluated; A trend analysis module, wherein the trend analysis module is used to input the parameter to be evaluated into a trend analysis model to obtain a change trend value of the redundant data amount; A prediction module, the prediction module is used to obtain the current redundant data volume and input it into the prediction model together with the change trend value of the redundant data volume to obtain the estimated redundant volume; An evaluation module is used to obtain an allowable redundancy threshold and compare it with the estimated redundancy. When the estimated redundancy is higher than or equal to the allowable redundancy threshold, an alarm signal is issued, and when the estimated redundancy is lower than the allowable redundancy threshold, it is input into the evaluation model to obtain the output delay of the communication data; The optimization module is used to obtain the communication data delay requirement, and then input it into the adaptive optimization model to obtain the output timing of each communication data.

10. A data synchronous transmission optimization terminal based on analog optical fiber communication, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the data synchronization transmission optimization method based on simulated optical fiber communication as described in any one of claims 1 to 8.

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