JP2017175173A - Information processing device and parameter extraction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform optimization processing of an operation fluctuation of a device, and enable the estimation of the fluctuation factor.SOLUTION: An optimization part 13 calculates a plurality of parameters for causing an optical transmission module whose operation is controlled by a plurality of parameters to operate at a target value. A factor analysis part 15 extracts a factor parameter for contributing to the optical transmission module when the optical transmission module operates at the target value among the plurality of parameters calculated by the optimization part 13. An analysis result generation part 16 generates data showing a factor which causes the operation of the optical transmission module to deviate from the target value on the basis of the factor parameter extracted by the factor analysis part 15.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an information processing apparatus and a parameter extraction method.

Patent Document 1 includes a step of biasing a vertical cavity surface emitting laser (VCSEL) coupled to an optical medium at a transmitter, and the bias outputs optical power output to the optical medium by the VCSEL. Determining at least in part, modulating the VCSEL with the data at the transmitter to optically transmit data to the receiver via the optical medium, and transmitting At the receiver via a feedback channel from the receiver: an error vector representing the performance degradation of the VCSEL detected by the receiver, or an instruction vector having one or more coefficients for use in biasing the VCSEL Receiving the instruction vector, wherein the coefficients are derived from the error vector; and the transmitter And adjusting the bias of the VCSEL based on the error vector or the command vector to stabilize the optical power output to the optical medium by the VCSEL. Yes.

JP 2012-249284 A

  By the way, in an apparatus such as an optical transmission apparatus, the operation varies depending on various factors. When the apparatus fluctuates from the target operation, the apparatus controls to return to the target operation.

  However, there is a problem that if the device performs control without knowing the operating variation factor, the device may continue to operate in an unexpected state.

  For example, in an optical transmission device, when an optical element deteriorates or dust adheres to a connection part of an optical transmission path, the optical transmission power decreases. Further, in the optical transmission device, the optical transmission power is reduced due to a temperature change. In this case, the optical transmission device performs control to increase the decreased optical transmission power in order to return to the optical transmission power of the target value.

  However, when the optical transmission device is subject to fluctuations in the optical element or dust adhering, the optical transmission power can be reduced even though the target operation can be performed by replacing the optical element or removing the dust. It will be controlled to increase, and it will continue to operate with increased power consumption.

  In other words, if it is known that the fluctuation factor of the operation is, for example, deterioration of the optical element or dust adhesion, the optical transmission device can operate in an expected state by replacing the optical element or removing the dust. If the fluctuation factor is not known, the optical element cannot be replaced or the dust cannot be removed, and the operation continues in an unexpected state (in a state where the power consumption is increased).

  SUMMARY OF THE INVENTION An object of the present invention is to perform an optimization process with respect to apparatus operation fluctuations and to estimate the fluctuation factors.

  The present application includes a plurality of means for solving at least a part of the above-described problems. Examples of the means are as follows. In order to solve the above problem, an information processing apparatus according to the present invention is an apparatus whose operation is controlled by a plurality of parameters, and a calculation unit that calculates the plurality of parameters for the apparatus to operate at a target value; A factor parameter extracting unit that extracts a factor parameter that has contributed to the operation of the device at the target value among the plurality of parameters calculated by the calculating unit.

  According to the present invention, optimization processing can be performed with respect to fluctuations in the operation of the apparatus, and the fluctuation factors can be estimated.

  Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments.

It is the figure which showed the structural example of the optical transmission module to which the optimization assistance apparatus which concerns on 1st Embodiment is applied. It is the figure which showed the block structural example of the optimization assistance apparatus of FIG. It is the figure which showed the block structural example of the optimization part of FIG. It is a figure explaining particle | grains and a particle group. It is the figure which showed the block structural example of the factor analysis part of FIG. It is a figure explaining extraction of a parameter. It is the 1 of the figure explaining number calculation with respect to a parameter value. It is the 2 of the figure explaining the number calculation with respect to a parameter value. It is the 3 of the figure explaining number calculation with respect to a parameter value. It is the flowchart which showed the operation example of the optimization assistance apparatus. It is the flowchart which showed the process example of the factor analysis part. It is the figure which showed the block structural example of the analysis result production | generation part which concerns on 2nd Embodiment. It is the figure which showed the data structural example of the memory | storage part. It is a figure explaining the example of calculation of the contribution value of a factor parameter. It is the figure which showed the block structural example of the optimization assistance apparatus which concerns on 3rd Embodiment. It is the figure which showed the example of the optical transmission system to which the optimization assistance apparatus which concerns on 4th Embodiment is applied. It is the figure which showed the example of application of the optimization assistance apparatus which concerns on 5th Embodiment.

  Hereinafter, the case where the information processing apparatus of the present invention is applied to an optimization support apparatus will be described.

[First Embodiment]
FIG. 1 is a diagram illustrating a configuration example of an optical transmission module to which the optimization support apparatus according to the first embodiment is applied. An optical transmission module 1 shown in FIG. 1 converts an input signal, which is an electrical signal, into an optical signal, and transmits the optical signal to, for example, a measurement device (not shown) connected by an optical fiber. As shown in FIG. 1, the optical transmission module 1 includes an optimization support device 3, a driver circuit 4, and a laser diode 5.

  A measurement device (not shown) measures the input optical signal and outputs measurement data of the measured optical signal to the optimization support device 3. Measurement data measured by a measurement device (not shown) is a characteristic of the optical signal output from the laser diode 5, and is, for example, optical transmission power, jitter, extinction ratio, or amplitude of the optical signal.

  The optimization support apparatus 3 outputs control data for outputting a desired optical signal from the laser diode 5 based on optical signal measurement data measured by a measurement apparatus (not shown). For example, the optimization support apparatus 3 outputs control data for outputting an optical signal having desired optical transmission power, jitter, extinction ratio, and amplitude from the laser diode 5.

  In addition, although only four characteristics of transmission power, jitter, extinction ratio, and amplitude are shown as a desired optical signal, it is not restricted to this. For example, the desired optical signal may include characteristics such as the frequency and waveform of the optical signal.

  When the operation of the optical transmission module 1 fluctuates (when the optical signal output from the laser diode 5 fluctuates), the optimization support apparatus 3 generates analysis data indicating the fluctuation factor. For example, when the fluctuation factor of the optical signal output from the laser diode 5 is deterioration of the laser diode 5, the optimization support apparatus 3 generates analysis data to that effect. The optimization support apparatus 3 stores the generated analysis data in a storage device such as an internal or external rewritable ROM (Read Only Memory) not shown.

  The analysis data stored in the storage device is read by a terminal device such as a personal computer, for example. Thereby, the user can know what factors caused the operation of the optical transmission module 1 to fluctuate.

  The driver circuit 4 amplifies an input signal, which is an electrical signal, in accordance with the control data output from the optimization support apparatus 3 and outputs the amplified signal to the laser diode 5. For example, the driver circuit 4 amplifies and outputs the input signal so that a desired optical signal is output from the laser diode 5 in accordance with the control data output from the optimization support apparatus 3.

  The laser diode 5 converts the electrical signal output from the driver circuit 4 into an optical signal and outputs the optical signal.

  FIG. 2 is a block diagram showing an example of the block configuration of the optimization support apparatus shown in FIG. As shown in FIG. 2, the optimization support apparatus 3 includes a receiving unit 11, a holding unit 12, an optimization unit 13, a control data output unit 14, a factor analysis unit 15, and an analysis result generation unit 16. Have.

  The receiving unit 11 receives measurement data output from a measurement device (not shown). The receiving unit 11 outputs the received measurement data to the holding unit 12.

  The holding unit 12 stores measurement data output from the receiving unit 11. The measurement data stored in the holding unit 12 is read by the optimization unit 13 and the factor analysis unit 15.

  The optimization unit 13 calculates a plurality of parameters based on the measurement data stored in the holding unit 12 so that the operation of the optical transmission module 1 operates at the target value. For example, when the optical transmission power, jitter, and amplitude are reduced due to deterioration of the laser diode 5, the optimization unit 13 calculates a plurality of parameters so that the optical transmission power, jitter, and amplitude satisfy target values. .

  There are a plurality of elements that define the operation of the optical transmission module 1. For example, the elements that define the operation of the optical transmission module 1 include the optical transmission power, jitter, extinction ratio, and amplitude of the optical signal output from the laser diode 5 as described above. For example, the optimization unit 13 calculates one index obtained from the plurality of elements (measurement data), and calculates a plurality of parameters so that the calculated index becomes a target value.

  The optimization unit 13 calculates a plurality of parameters by, for example, a particle group optimization algorithm. The optimization unit 13 outputs the calculated plurality of parameters to the control data output unit 14. The optimization unit 13 may calculate a plurality of parameters by an algorithm other than particle group optimization. For example, a plurality of parameters may be calculated using a genetic algorithm or the like. In the following description, the optimization unit 13 is described as calculating a plurality of parameters by the particle swarm optimization algorithm.

  The control data output unit 14 generates control data based on the plurality of parameters calculated by the optimization unit 13. For example, the control data output unit 14 converts a plurality of parameters into control data that allows the driver circuit 4 to control an input signal. When the driver circuit 4 can directly receive a plurality of parameters calculated by the optimization unit 13 and control the input signal, the control data output unit 14 is not necessary.

  The factor analysis unit 15 extracts parameters (factor parameters) that contribute to the operation of the optical transmission module 1 at the target value from among the plurality of parameters calculated by the optimization unit 13. For example, when the optical transmission power of the optical signal output from the laser diode 5 is reduced, the optimization unit 13 changes a certain parameter A related to the control of the optical transmission power, and reduces the optical transmission power to the target value. Suppose you raise it. In this case, the factor analysis unit 15 extracts the parameter A as a factor parameter.

  Based on the factor parameter extracted by the factor analysis unit 15, the analysis result generation unit 16 generates analysis data indicating a factor in which the operation of the optical transmission module 1 deviates from the target value. The generated analysis data is stored in the storage device as described above, for example, and is read out by the user information processing device.

  The functions shown in FIG. 2 can be realized by, for example, an application specific integrated circuit (ASIC) or a field-programmable gate array (FPGA). Or each part shown in FIG. 2 temporarily stores, for example, a CPU (Central Processing Unit) that executes a program for realizing the function of the optimization support apparatus 3, a ROM that stores the program, and a part of the program. The function can be realized by a RAM (Random Access Memory) that temporarily stores calculation data.

  FIG. 3 is a diagram illustrating a block configuration example of the optimization unit in FIG. 2. As illustrated in FIG. 3, the optimization unit 13 includes random number generation units 21 and 22, calculation units 23 to 25, and a movement amount calculation unit 26. As described above, the optimization unit 13 calculates a plurality of parameters by the particle swarm optimization algorithm.

  The particle swarm optimization algorithm is a kind of swarm intelligence, and is modeled by a particle swarm having position and velocity in a multidimensional space. The movement amount v ′ of the particle swarm optimization algorithm is expressed by the following equation (1).

  Here, w is an inertia constant, v is a current movement amount of particles, c1 and c2 are fixed values, xp is a particle best value, x is a current position of particles, xg is a group best value, and r1 and r2 are random numbers.

  The optimization unit 13 calculates the movement amount v ′ of each of the plurality of parameters according to the equation (1), and updates the values of the plurality of parameters. Then, the optimization unit 13 calculates a plurality of parameters for the optical transmission module 1 to operate at the target value.

  The random number generator 21 generates a random number r1. The random number generator 22 generates a random number r2. The random number r1 generated by the random number generation unit 21 is output to the calculation unit 24, and the random number r2 generated by the random number generation unit 22 is output to the calculation unit 25.

  The calculation unit 23 receives the inertia constant w and the current movement amount v. The calculation unit 23 multiplies the input inertia constant w and the current movement amount v, and outputs the result to the movement amount calculation unit 26.

  The calculation unit 24 receives the fixed value c1, the current position x, the particle best value xp, and the random number r1. The calculation unit 24 multiplies the value obtained by subtracting the current position x from the particle best value xp by the fixed value c1 and the random number r1, and outputs the result to the movement amount calculation unit 26.

  A fixed value c2, a current position x, a group best value xg, and a random number r2 are input to the calculation unit 25. The calculation unit 25 multiplies the value obtained by subtracting the current position x from the group best value xg by the fixed value c2 and the random number r2, and outputs the result to the movement amount calculation unit 26.

  The movement amount calculation unit 26 adds the values output from the calculation units 23 to 25. As a result, the movement amount calculation unit 26 obtains the movement amount v ′ shown in Expression (1).

  FIG. 4 is a diagram for explaining particles and particle groups. FIG. 4 shows an example in which there are three parameters.

  A to C shown in FIG. 4 indicate types of parameters. For example, the parameter A is a parameter for controlling the optical transmission power of the optical signal output from the laser diode 5, the parameter B is a parameter for controlling the jitter of the optical signal, and the parameter C is the extinction ratio of the optical signal. It is a parameter that controls

  The maximum and minimum shown in FIG. 4 indicate the maximum and minimum values that the parameters A to C can take. For example, the parameter A indicates that the maximum value is “6” and the minimum value is “0”.

  S1 to S4 shown in FIG. 4 indicate particles. FIG. 4 shows an example of four particles. Each of the four particles S1 to S4 has parameters A to C as elements. For example, in the example of FIG. 4, the particle S <b> 1 has a parameter A value “0”, a parameter B value “0”, and a parameter C value “1”.

  A dotted frame A1 shown in FIG. 4 indicates a particle group. In the example of FIG. 4, the particle group is composed of four particles S1 to S4.

  For example, the optimization unit 13 updates the values of the particles S1 to S4 shown in FIG. 4 based on the formula (1), and calculates the optimum values of the parameters A to C.

  The functions shown in FIG. 3 can be realized by, for example, an ASIC or FPGA. Alternatively, each unit illustrated in FIG. 3 includes, for example, a CPU that executes a program for realizing the function of the optimization unit 13, a ROM that stores the program, a part of the program, and temporarily stores operation data. The function can be realized by a RAM or the like for storing.

  Hereinafter, in order to simplify the description, the plurality of parameters will be described as three parameters A to C. Parameters A to C take integer values and take the maximum and minimum ranges shown in FIG.

  FIG. 5 is a diagram illustrating a block configuration example of the factor analysis unit of FIG. As shown in FIG. 5, the factor analysis unit 15 includes a parameter extraction unit 31, a number acquisition unit 32, a variance value calculation unit 33, and a factor parameter extraction unit 34.

  The parameter extraction unit 31 extracts parameters A to C when the operation of the optical transmission module 1 is within a predetermined range with respect to the target value. For example, the parameter extraction unit 31 extracts the parameters A to C when the measurement data of the optical signal measured by a measurement device (not shown) is within a predetermined range with respect to the target value. More specifically, the parameter extraction unit 31 calculates an index of measurement data output from a measurement device (not shown) (a value for comparison with the target value described above), and the calculated index becomes the target value. On the other hand, parameters A to C when it is within a predetermined range are extracted.

  FIG. 6 is a diagram for explaining parameter extraction. The horizontal axis shown in FIG. 6 indicates the number of measurements of a measurement device (not shown), and the vertical axis indicates the measurement value (measurement data index) of the measurement device (not shown).

  In FIG. 6, particles S1 to S4 are shown. The optimization unit 13 calculates the movement amount v ′ of the particles S1 to S4 based on the measurement data of a measuring device (not shown), and updates the particles S1 to S4. In the example of FIG. 6, the particles S <b> 1 to S <b> 4 and their particle groups are approaching the target value as the number of measurements increases (some particles S <b> 1 to S <b> 4 may be updated so as not to approach the target value). is there).

  When one particle among the particles S1 to S4 reaches the target value, the optimum parameters A to C of the optical transmission module 1 are obtained. For example, if the particle indicated by the arrow A <b> 11 in FIG. 6 is obtained by the optimization unit 13, the parameters A to C included in the particle are the optimum parameters of the optical transmission module 1.

  As described above, the parameter extraction unit 31 extracts the parameters A to C when the index calculated based on the measurement data is within a predetermined range with respect to the target value. For example, the parameter extraction unit 31 extracts a plurality of parameters A to C when the calculated index is within “X” with respect to the target value. More specifically, the particles within the frame indicated by the one-dot chain line A12 in FIG. 6 are extracted. Each of the particles S1 to S4 has values of parameters A to C as described with reference to FIG. Therefore, the parameter extraction unit 31 can extract a plurality of parameters A to C by extracting particles within the frame of the alternate long and short dash line A12.

  Returning to the description of FIG. The number acquisition unit 32 acquires the extracted number for the parameter value in each of the parameters A to C extracted by the parameter extraction unit 31.

  FIG. 7 is a first diagram illustrating the calculation of the number of parameter values. A to C shown in FIG. 7 indicate parameters A to C. The numerical values shown in FIG. 7 indicate the parameter values of the parameters A to C extracted by the parameter extraction unit 31.

  The number of numerical columns shown in FIG. 7 is equal to the number of particles extracted by the parameter extraction unit 31. For example, when 20 particles are contained in the frame of the alternate long and short dash line A12 in FIG. 6, the parameter extraction unit 31 extracts 20 particles (parameters A to C). In this case, the number of numerical columns in FIG.

  FIG. 8 is a second diagram illustrating the calculation of the number of parameter values. FIG. 8 shows the relationship of the number extracted by the parameter extraction unit 31 with respect to the parameter value of the parameter A. The horizontal axis indicates the parameter value of parameter A, and the vertical axis indicates the number of extracted values for the parameter value.

  The parameter A takes a value of 0 to 6, as described with reference to FIG. The number acquisition unit 32 acquires the number extracted by the parameter extraction unit 31 for the parameter values 0 to 6 of the parameter A.

  For example, in the parameter value of parameter A shown in FIG. 7, it is assumed that the number of parameter values 0 to 6 is 1, 1, 3, 10, 3, 2, 0. In this case, the relationship between the parameter values 0 to 6 of the parameter A and the number extracted by the parameter extraction unit 31 is as shown in FIG.

  The total number of parameter values 0 to 6 is “20”, which is the number extracted by the parameter extraction unit 31.

  FIG. 9 is a third diagram illustrating the calculation of the number of parameter values. FIG. 9 shows the relationship of the number extracted by the parameter extraction unit 31 with respect to the parameter value of the parameter B. The horizontal axis indicates the parameter value of parameter B, and the vertical axis indicates the number of extracted parameters.

  The parameter B takes a value of 0 to 4 as described in FIG. The number acquisition unit 32 acquires the number extracted by the parameter extraction unit 31 for the parameter values 0 to 4 of the parameter B.

  For example, it is assumed that the number of parameter values 0 to 4 in the parameter value of parameter B shown in FIG. In this case, the relationship between the parameter values 0 to 4 of the parameter B and the number extracted by the parameter extraction unit 31 is as shown in FIG.

  The total number of parameter values 0 to 4 is “20”, which is the number extracted by the parameter extraction unit 31.

  In the parameter C, the number acquisition unit 32 acquires the number extracted by the parameter extraction unit 31 for the parameter value in the same manner as described with reference to FIGS.

  Returning to the description of FIG. The variance value calculation unit 33 calculates the variance value of the parameter value in each of the parameters A to C based on the number of parameter values acquired by the number acquisition unit 32. For example, the variance value calculation unit 33 calculates the variance value of the parameter value of the parameter A shown in FIG. Further, the variance value calculation unit 33 calculates the variance value of the parameter value of the parameter B shown in FIG. Further, the variance value calculation unit 33 calculates the variance value of the parameter value of the parameter C. The variance value in FIG. 8 is smaller than the variance value in FIG.

  The factor parameter extraction unit 34 contributes to the operation of the optical transmission module 1 at the target value based on the dispersion value in each of the plurality of parameters A to C calculated by the dispersion value calculation unit 33 (optical transmission module 1 Extract the factor parameters that contributed to the movement of For example, the factor parameter extraction unit 34 compares the variance value of each parameter value of the parameters A to C with a predetermined threshold value, and determines that the parameter is a factor parameter if the variance value is smaller than the predetermined threshold value. More specifically, if the variance value of the parameter value of the parameter A shown in FIG. 8 is smaller than a predetermined threshold, the factor parameter extraction unit 34 determines that the parameter A is a factor parameter. If the variance of the parameter values of the parameter B shown in FIG. 9 is not smaller than the predetermined threshold, the factor parameter extraction unit 34 determines that the parameter B is not a factor parameter (non-factor parameter). The factor parameter extraction unit 34 determines the parameter C in the same manner.

  The factor parameters extracted by the factor parameter extraction unit 34 are output to the analysis result generation unit 16. Based on the factor parameter extracted by the factor parameter extraction unit 34, the analysis result generation unit 16 generates analysis data indicating a factor in which the operation of the optical transmission module 1 deviates from the target value. For example, when the parameter A is extracted as a factor parameter, the analysis result generation unit 16 generates analysis data that indicates degradation of the optical signal due to dust. Further, when the parameters A to C are extracted as factor parameters, the analysis result generation unit 16 generates analysis data indicating deterioration of the laser diode 5.

  The functions shown in FIG. 5 can be realized by, for example, ASIC or FPGA. Alternatively, each unit illustrated in FIG. 5 includes, for example, a CPU that executes a program for realizing the function of the factor analysis unit 15, a ROM that stores the program, a part of the program, and temporarily stores operation data. The function can be realized by a RAM or the like for storing.

  As described with reference to FIG. 6, the measured value of the optical signal of the optical transmission module 1 approaches the target value as the number of measurements increases. The parameter value of the factor parameter (a parameter highly correlated with the target value) that contributes to the measurement value approaching the target value is set within the frame of the one-dot chain line A12 in FIG. Once entered, it will be updated around a certain value. For example, the parameter A shown in FIG. 7 is a factor parameter and changes near the parameter value “3”. Accordingly, the number of factor parameters with respect to the parameter values is concentrated on a certain parameter value. For example, as shown in FIG. 8, the number of parameters A with respect to the parameter value is concentrated on the parameter value “3”.

  On the other hand, parameter values of non-factor parameters (parameters having a low correlation with the target value) that do not contribute to the measurement value approaching the target value are updated with random values. For example, the parameter B shown in FIG. 7 is a non-factor parameter and changes with various values in the range of 0 to 4. Therefore, the number of non-factor parameters with respect to parameter values is uniformly distributed. For example, as shown in FIG. 9, the number of parameter B with respect to the parameter value is uniformly distributed.

  Therefore, as described above, the optimization support apparatus 3 extracts the parameters A to C when the operation of the optical transmission module 1 is close to the target value, and in each of the extracted parameters A to C, By calculating the extracted number of variance values for the parameter value, it is possible to determine whether or not the plurality of parameters A to C are factor parameters.

  FIG. 10 is a flowchart illustrating an operation example of the optimization support apparatus. The flowchart shown in FIG. 10 is periodically executed, for example, when the optical transmission module 1 is powered on or when the optical transmission module 1 is operating.

  First, the optimization unit 13 sets initial values for the parameters A to C (step S1).

  Next, the optimization unit 13 determines whether or not the parameter A to C optimization processing (parameter A to C calculation processing) has been performed n times (step S2). If the optimization unit 13 determines that the optimization process for the parameters A to C has been performed n times (“Yes” in step S2), the optimization unit 13 proceeds to the process in step S5. If the optimization unit 13 determines that the optimization process for the parameters A to C is not performed n times (“No” in step S2), the optimization unit 13 proceeds to the process in step S3.

  If the optimization unit 13 determines in step S2 that the optimization process for parameters A to C has not been performed n times (“No” in step S2), the optimization unit 13 performs the optimization process for parameters A to C. Perform (step S3).

  Next, the optimization unit 13 determines whether or not the operation of the optical transmission module 1 has reached the target value based on the parameters A to C calculated in Step S3 (Step S4). For example, as indicated by an arrow A11 in FIG. 6, the optimization unit 13 determines whether the particles have reached a target value. When the optimization unit 13 determines that the operation of the optical transmission module 1 has reached the target value (“Yes” in step S4), the optimization unit 13 ends the process of the flowchart. If the optimization unit 13 determines that the operation of the optical transmission module 1 has not reached the target value (“No” in step S4), the optimization unit 13 proceeds to the process of step S1.

  If the factor analysis unit 15 determines in step S2 that the optimization process of the parameters A to C has been performed n times (“Yes” in step S2), the factor analysis process is performed (step S5). . In addition, when the optimization process of the parameters A to C is performed n times, the factor analysis process is performed by a sample for performing the factor analysis (for example, particles within the frame of the alternate long and short dash line A12 illustrated in FIG. This is for collecting a predetermined number. After finishing the process of step S5, the factor analysis unit 15 proceeds to the process of step S3.

  In addition to the condition in step S2, the condition for performing factor analysis may be a condition in which the measured value shown in FIG. 6 exceeds a certain value. Alternatively, the condition may be the number of particles in the frame of the alternate long and short dash line A12 shown in FIG.

  FIG. 11 is a flowchart illustrating a processing example of the factor analysis unit. The flowchart shown in FIG. 11 shows detailed processing in step S5 of FIG. The flowchart shown in FIG. 11 is executed when it is determined in step S2 of FIG. 10 that the optimization process of parameters A to C has been performed n times (in the case of “Yes” in step S2). Is done.

  First, the parameter extraction unit 31 extracts parameters A to C when the operation of the optical transmission module 1 is within a predetermined range with respect to the target value. For example, the parameter extraction unit 31 extracts the particles S1 to S4 within the frame of the alternate long and short dash line A12 illustrated in FIG. 6 (Step S11).

  Next, the number acquisition unit 32 acquires the number extracted for the parameter value in each of the parameters A to C extracted by the parameter extraction unit 31 (step S12). For example, as shown in FIGS. 8 and 9, the number acquisition unit 32 acquires the extracted number for the parameter value in each of the parameters A and B. The number acquisition unit 32 also acquires the number of parameter values for the parameter C in the same manner.

  Next, the variance value calculation unit 33 calculates the variance value of the parameter value in each of the parameters A to C based on the number acquired by the number acquisition unit 32 (step S13). For example, the variance value calculation unit 33 calculates the variance value of the parameter value of the parameter A shown in FIG. 8, the variance value of the parameter value of the parameter B shown in FIG. 9, and the variance value of the parameter value of the parameter C.

  Next, the factor parameter extraction unit 34 extracts factor parameters based on the variance values of the parameter values of the parameters A to C calculated by the variance value calculation unit 33 (step S14). For example, the factor parameter extraction unit 34 compares the variance value of each parameter value of the parameters A to C with a predetermined threshold value, and extracts the factor parameter.

  As described above, the optimization unit 13 of the optimization support apparatus 3 calculates the plurality of parameters A to C for the optical transmission module 1 whose operation is controlled by the plurality of parameters A to C to operate at the target value. . Then, the factor analysis unit 15 extracts a factor parameter that contributes to the operation of the optical transmission module 1 at the target value among the plurality of parameters A to C calculated by the optimization unit 13. Thereby, the optimization support apparatus 3 can perform the optimization process with respect to the operation variation of the optical transmission module 1 and can estimate the factor of the operation variation based on the extracted factor parameter.

  Further, based on the analysis result of the factor of the operation variation of the optical transmission module 1, the optical transmission module 1 can operate in the originally expected state. For example, if the analysis result of the fluctuation factor of the operation is dust adhering at the connection part of the optical transmission line, the optical transmission module 1 can operate in the originally expected state by removing the dust. it can.

  5 does not contribute to the operation of the optical transmission module 1 at the target value (the operation of the optical transmission module 1 did not contribute to approach the target value). Factor parameters may be extracted. For example, the factor parameter extraction unit 34 may extract a parameter having a variance value larger than a predetermined threshold as a non-factor parameter.

  The factor parameter extraction unit 34 outputs the extracted non-factor parameters to the optimization unit 13. The optimization unit 13 calculates optimal values for a plurality of parameters other than the non-factor parameters. For example, if the parameter B is a non-factor parameter, the optimization unit 13 does not calculate the optimum value of the parameter B that has a low correlation with the target value, and does not calculate the parameters A and C that have a high correlation with the target value. Calculate the optimum value. Thereby, the optimization part 13 can shorten the calculation time of the optimal value of a parameter.

  In the above description, the optical transmission module 1 includes the optimization support device 3. However, the optimization support device 3 may be provided outside the optical transmission module 1.

  The optimization support apparatus 3 can also be applied to an optical reception module that receives an optical signal. For example, the measurement apparatus inputs a part of the optical signal received by the optical reception module and measures the input optical signal. Based on the optical signal measured by the measurement device, the optimization support device 3 controls the electrical signal that has been subjected to O / E conversion to be a desired electrical signal, and analyzes the variation factors of the received optical signal. To do.

  Moreover, although the example which applied the optimization assistance apparatus 3 to the optical transmission module 1 was demonstrated above, it is not restricted to this. For example, the optimization support device 3 can be applied to a device whose parameters are controlled. For example, the present invention can be applied to a module that outputs a signal such as an electric signal or an infrared signal that is parameter-controlled.

In the above description, the optimization unit 13 calculates one parameter obtained from a plurality of elements (measurement data), and calculates a plurality of parameters so that the calculated index becomes a target value. However, a plurality of parameters may be calculated such that a plurality of target values are obtained. For example, the optimization unit 13 has measured data of optical transmission power, jitter, extinction ratio, and amplitude,
A plurality of parameters may be calculated so as to be target values set for the optical transmission power, jitter, extinction ratio, and amplitude.

[Second Embodiment]
Next, a second embodiment will be described. There is one parameter that controls the operation of the apparatus, and there are cases where it is related to a plurality of elements that define the operation of the apparatus. For example, the parameter A may be related not only to the optical transmission power of the optical transmission module 1 but also to jitter control. In this case, when the parameter A is extracted as a factor parameter, it is difficult to determine whether the variation factor is based on the optical transmission power or the jitter. Therefore, in the second embodiment, the variation factor can be estimated even when one parameter is related to a plurality of elements that define the operation of the apparatus.

  FIG. 12 is a diagram illustrating a block configuration example of the analysis result generation unit according to the second embodiment. The analysis result generation unit 40 illustrated in FIG. 12 corresponds to the analysis result generation unit 16 of the optimization support apparatus 3 illustrated in FIG. That is, in the second embodiment, the analysis result generation unit 16 is different from the optimization support apparatus 3 shown in FIG. 2, and the other parts are the same as those in the first embodiment. Therefore, the following description will be made using the reference numerals of the first embodiment except for the analysis result generation unit 40.

  As illustrated in FIG. 12, the analysis result generation unit 40 includes a storage unit 41, a contribution value calculation unit 42, and an analysis unit 43.

  The storage unit 41 stores a contribution rate indicating how much each of the plurality of parameters contributes to the plurality of elements that define the operation of the optical transmission module 1.

  FIG. 13 is a diagram illustrating a data configuration example of the storage unit. As illustrated in FIG. 13, the storage unit 41 stores contribution rates for a plurality of elements that define the operation of the optical transmission module 1 in each of the parameters A to C. The contribution rate is stored in the storage unit 41 in advance by the user, for example.

  For example, in the example of FIG. 13, the parameter A contributes to the control of the optical transmission power of the optical signal of the optical transmission module 1 at a rate of “0.5”. The parameter A contributes to the control of the jitter of the optical signal of the optical transmission module 1 at a rate of “0.4”. The parameter A contributes to the control of the extinction ratio of the optical signal of the optical transmission module 1 at a rate of “0” (that is, the parameter A does not contribute to the control of the extinction ratio).

  Returning to the description of FIG. The factor parameter extracted by the factor parameter extraction unit 34 is input to the contribution value calculation unit 42. Further, the contribution value calculation unit 42 receives the parameter value before the optimization process from the optimization unit 13.

  The contribution value calculation unit 42 refers to the contribution rate of the storage unit 41, and how much the factor parameter extracted by the factor parameter extraction unit 34 contributes to a plurality of elements that define the operation of the optical transmission module 1. The contribution value of is calculated. The contribution value calculation unit 42 prescribes the operation of the optical transmission module 1 for the factor parameter by multiplying the change in the parameter value before and after the optimization by the contribution rate stored in the storage unit 41. The contribution value for a plurality of elements is calculated.

  FIG. 14 is a diagram for explaining a calculation example of the contribution value of the factor parameter. FIG. 14 shows an example of calculating the contribution values of the parameters A and B when the parameters A and B are extracted as the factor parameters.

  For example, it is assumed that the parameter A is updated from “1” to “6” by the optimization process. In this case, the change amount V of the parameter A is “5”.

  The contribution value calculation unit 42 multiplies the change amount “5” of the parameter A by the contribution rate of the parameter A stored in the storage unit 41 to define a plurality of parameters A that define the operation of the optical transmission module 1. Calculate the contribution value for the element. In the example of FIG. 13, the contribution ratio of the parameter A stored in the storage unit 41 is the optical transmission power “0.7”, the jitter “0.4”, and the extinction ratio “0”. The contribution values for the transmission power, jitter, and extinction ratio are “3.5”, “2.0”, and “0” as shown in FIG.

  The reason why the contribution rate of the storage unit 41 is multiplied by the change amount V of the parameter A is that the factor parameter having a larger change in the parameter value is more involved in the control of the optical transmission module 1. That is, the contribution value calculation unit 42 calculates the contribution value so that the factor parameter greatly related to the control of the optical transmission module 1 increases.

  The contribution value calculation unit 42 similarly calculates a contribution value for the parameter B that is a factor parameter. In the case of the example of FIG. 14, the contribution values of the parameter B to the optical transmission power, jitter, and extinction ratio are “0.6”, “1.2”, and “0.2”.

  Returning to the description of FIG. Based on the contribution value calculated by the contribution value calculation unit 42, the analysis unit 43 analyzes a factor that causes the operation of the optical transmission module 1 to deviate from the target value. For example, the analysis unit 43 adds the respective contribution values of each factor parameter calculated by the contribution value calculation unit 42, and performs an operation factor analysis based on the ratio of the added contribution values.

  For example, in the case of the example in FIG. 14, the contribution values of the optical transmission power of the parameters A and B that are the factor parameters are the contribution value “3.5” of the parameter A and the contribution value “0.6” of the parameter B. The sum is "4.1". Similarly, the contribution values of the jitter and extinction ratio of the parameters A and B are “3.2” and “0.2”.

  In this case, it can be seen that the optimization support apparatus 3 is involved in the control of the optical transmission power and the jitter, and not so much in the control of the extinction ratio. Therefore, for example, the analysis unit 43 analyzes that the connection part of the optical transmission path is not sufficiently connected, and generates analysis data to that effect.

  As described above, the storage unit 41 of the analysis result generation unit 40 stores the contribution rates for the plurality of elements that define the operation of the optical transmission module 1 in each of the plurality of parameters, and the contribution value calculation unit 42 includes the storage unit. 41, the contribution values of the factor parameters for the plurality of elements that define the operation of the optical transmission module 1 are calculated. Then, the analysis unit 43 analyzes a factor that causes the operation of the optical transmission module 1 to deviate from the target value based on the contribution value calculated by the contribution value calculation unit 42. Thereby, the optimization support apparatus 3 can estimate the factor of the operation fluctuation even when one parameter is related to a plurality of elements that define the operation of the optical transmission module 1.

[Third Embodiment]
Next, a third embodiment will be described. In the first embodiment, the optimization support apparatus 3 stores the analysis data in the storage device. In the third embodiment, the optimization support apparatus 3 causes a light emitting element such as an LED (Light Emitting Diode) to emit light based on the analysis data and informs the user of the analysis result.

  FIG. 15 is a diagram illustrating a block configuration example of the optimization support apparatus according to the third embodiment. 15 that are the same as those in FIG. 2 are given the same reference numerals, and descriptions thereof are omitted.

  As illustrated in FIG. 15, the optimization support apparatus 50 includes a notification unit 51. The notification unit 51 includes, for example, a plurality of light emitting elements such as LEDs.

  The notification unit 51 causes one of a plurality of light emitting elements to emit light based on the analysis data generated by the analysis result generation unit 16. For example, when the analysis data generated by the analysis result generation unit 16 is the analysis data A, the notification unit 51 causes the light emitting element A to emit light, and the analysis data generated by the analysis result generation unit 16 is the analysis data B. In this case, the light emitting element B emits light.

  As described above, the notification unit 51 emits one of a plurality of light emitting elements based on the analysis data. Thereby, the user can know what causes the operation change of the optical transmission module 1.

  In the above description, the notification unit 51 emits one of a plurality of light-emitting elements. However, the notification unit 51 includes one light-emitting element and changes the color emitted by the light-emitting element based on analysis data. Good.

  The notification unit 51 may notify the user of the result of the analysis data by sound.

[Fourth Embodiment]
Next, a fourth embodiment will be described. In the fourth embodiment, the optical receiver module has a measuring device. The optimization support apparatus controls the optical signal transmitted from the optical transmission module so that the optical signal received by the optical reception module becomes a desired optical signal.

  FIG. 16 is a diagram illustrating an example of an optical transmission system to which the optimization support apparatus according to the fourth embodiment is applied. 16, the same components as those in FIG. 1 are denoted by the same reference numerals, and the description thereof is omitted.

  As illustrated in FIG. 16, the optical transmission system includes an optical transmission module 60 and an optical reception module 70. The optical transmission module 60 has an optimization support device 61.

  The optimization support device 61 receives measurement data output from the measurement device 73 of the optical reception module 70. Based on the received measurement data, the optimization support device 61 controls the driver circuit 4 so that the optical signal received by the optical reception module 70 becomes a desired optical signal. The optimization support device 61 is different from the optimization support device 3 shown in FIG. 2 in that the optical signal received by the optical reception module 70 is controlled to be a desired optical signal. Is the same as the optimization support apparatus 3.

  The optical receiving module 70 includes a photodiode 71, a receiving circuit 72, and a measuring device 73.

  The photodiode 71 receives the optical signal transmitted from the optical transmission module 60 and converts the received optical signal into an electrical signal. The photodiode 71 outputs the converted electric signal to the receiving circuit 72 and the measuring device 73.

  The receiving circuit 72 amplifies the electrical signal output from the photodiode 71, performs predetermined processing, and outputs the amplified signal to a subsequent circuit (not shown).

  The measuring device 73 measures the electrical signal output from the photodiode 71 and outputs measurement data of the measured electrical signal to the optimization support device 3 of the optical transmission module 60. The measurement data measured by the measurement device 73 is, for example, the characteristics of the optical signal received by the photodiode 71, and includes the optical reception power, jitter, extinction ratio, or amplitude of the optical signal.

  As described above, the optimization support device 61 can control the optical signal on the transmission side based on the optical signal on the reception side, and can estimate the cause of the operation fluctuation of the optical signal on the reception side.

  In the above description, the optical transmission module 60 includes the optimization support device 61. However, the optical reception module 70 may include the optimization support device 61.

  In the above description, the measurement device 73 measures the electrical signal output from the photodiode 71, but may measure the electrical signal output from the receiving circuit 72.

[Fifth Embodiment]
Next, a fifth embodiment will be described. In the fifth embodiment, a case where an optimization target device, a measurement device, and an optimization support device are independent from each other will be described.

  FIG. 17 is a diagram illustrating an application example of the optimization support apparatus according to the fifth embodiment. FIG. 17 shows a device 81, a measuring device 82, and an optimization support device 83.

  The device 81 is an optimization target device. The operation of the device 81 is controlled by a plurality of parameters. The apparatus 81 is, for example, an optical transmission module that does not include the optimization support apparatus 3 in FIG.

  A signal output from the device 81 is input to the measuring device 82. The measurement device 82 measures the input signal and outputs the measurement data to the optimization support device 83.

  The optimization support device 83 has the same function as the optimization support device 3 shown in FIG. 1 and outputs control data for controlling the device 81 based on the measurement data output from the measurement device 82. . In addition, when the operation of the device 81 varies (when the optical signal output from the device 81 varies), the optimization support device 83 generates analysis data indicating the variation factor.

  The optimization support device 83 is realized by a terminal device such as a personal computer, for example. For example, the optimization support device 83 can realize the same function as the optimization support device 3 by causing the terminal device to execute a program that realizes the function of the optimization support device 3 illustrated in FIG. 1. For example, the optimization support device 83 displays the generated analysis data on a display.

  As described above, even when the optimization target device, the measurement device, and the optimization support device are independent from each other, the optimization target device can be controlled and the cause of the operation variation can be estimated.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiment. In addition, it is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention. Moreover, it is also possible to combine each embodiment.

  In each of the above embodiments, the functional configuration of each unit is classified according to the main processing contents in order to facilitate understanding of the configuration. The present invention is not limited by the way of classification and names of the constituent elements. The configuration of each unit can be classified into more components according to the processing content. Moreover, it can also classify | categorize so that one component may perform more processes. Further, the processing of each component may be executed by one hardware or may be executed by a plurality of hardware.

  Further, the processing unit of the flowchart described above is divided according to the main processing contents in order to facilitate understanding of the processing of the optimization support apparatus and the factor analysis unit. The present invention is not limited by the way of dividing the processing unit or the name. The processing of the optimization support device and the factor analysis unit can be divided into more processing units according to the processing contents. Moreover, it can also divide | segment so that one process unit may contain many processes. Further, the processing order of the above flowchart is not limited to the illustrated example.

  In each of the above-described embodiments, the control lines and information lines indicate what is considered necessary for the description, and not all the control lines and information lines on the product are necessarily shown. In practice, it may be considered that almost all configurations are connected to each other.

  1, 60, 81: Optical transmission module, 73, 82: Measuring device, 3, 50, 83: Optimization support device, 4: Driver circuit, 5: Laser diode, 11: Receiving unit, 12: Holding unit, 13: Optimization unit, 14: control data output unit, 15: factor analysis unit, 16: analysis result generation unit, 21, 22: random number generation unit, 23-25: calculation unit, 26: movement amount calculation unit, 31: parameter extraction Unit: 32: number acquisition unit, 33: variance value calculation unit, 34: factor parameter extraction unit, 40: analysis result generation unit, 41: storage unit, 42: contribution value calculation unit, 43: analysis unit, 51: notification unit , 70: light receiving module, 71: photodiode, 72: receiving circuit.

Claims (11)

A device whose operation is controlled by a plurality of parameters, the calculation unit calculating the plurality of parameters for the device to operate at a target value;
Among the plurality of parameters calculated by the calculation unit, a factor parameter extraction unit that extracts a factor parameter that contributed to the operation of the device at the target value;
An information processing apparatus comprising: The information processing apparatus according to claim 1,
The information processing apparatus, wherein the factor parameter extraction unit extracts the factor parameter based on a variance value of parameter values in each parameter of the plurality of parameters. The information processing apparatus according to claim 1,
A parameter extraction unit for extracting the plurality of parameters when the operation of the apparatus is within a predetermined range with respect to the target value;
In each of the plurality of parameters extracted by the parameter extraction unit, a number acquisition unit that acquires the number extracted for the parameter value;
A variance value calculation unit that calculates a variance value of the parameter value based on the number acquired by the number acquisition unit;
Further comprising
The information processing apparatus, wherein the factor parameter extraction unit extracts the factor parameter based on the variance value calculated by the variance value calculation unit. The information processing apparatus according to claim 2 or 3,
The information processing apparatus according to claim 1, wherein the factor parameter extraction unit extracts the factor parameter when the variance value is smaller than a predetermined threshold value. The information processing apparatus according to claim 1,
The factor parameter extraction unit extracts a non-factor parameter that did not contribute to the operation of the device at the target value from the plurality of parameters calculated by the calculation unit. . The information processing apparatus according to claim 1,
The information processing apparatus, wherein the calculation unit calculates a parameter other than the non-factor parameters extracted by the extraction unit. The information processing apparatus according to claim 1,
Based on the factor parameter extracted by the extraction unit, a generation unit that generates data indicating a factor that the operation of the device deviates from a target value;
An information processing apparatus further comprising: The information processing apparatus according to claim 7,
The generator is
A storage unit storing contribution rates for a plurality of elements that define the operation of the device in each of the plurality of parameters;
A contribution value calculation unit that refers to the storage unit and calculates contribution values for a plurality of elements that define the operation of the device of the factor parameter;
Based on the contribution value calculated by the contribution value calculation unit, an analysis unit that analyzes a factor that the operation of the device deviates from a target value;
An information processing apparatus comprising: The information processing apparatus according to claim 1,
The information processing apparatus, wherein the calculation unit calculates the plurality of parameters so that a signal output from the apparatus becomes a desired signal. The information processing apparatus according to claim 1,
The apparatus is an optical transmission module,
The information processing apparatus, wherein the calculation unit calculates the plurality of parameters so that an optical signal output from the optical transmission module becomes a desired optical signal. A parameter extraction method for an information processing apparatus, comprising:
A device whose operation is controlled by a plurality of parameters, the calculating step for calculating the plurality of parameters for the device to operate at a target value;
An extraction step of extracting a factor parameter that contributed to the operation of the device at the target value among the plurality of parameters calculated by the calculation step;
A parameter extracting method characterized by comprising:

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