Electric power communication optical cable operation state monitoring operation and maintenance system and method

Abstract

The invention discloses an electric power communication optical cable operation state monitoring operation and maintenance method and system, and relates to the technical field of efficiency evaluation, and the method comprises the steps: collecting the monitoring data of an electric power communication optical cable in each installation region in real time; the monitoring data, collected in real time, of all the installation areas are optimized; carrying out a power communication optical cable communication state experiment according to the operation data log of the power communication optical cable, and establishing a power communication optical cable communication state data list; establishing a communication state evaluation model; and based on the monitoring data of the electric power communication optical cable in each installation area, combining the communication state evaluation model to monitor the operation state of the electric power communication optical cable. The method has the advantages that the monitoring data of the monitoring equipment is optimized by dynamically optimizing and predicting the state of the monitoring data, the operation state of the electric power communication optical cable is monitored and maintained, and the high-precision monitoring analysis of the electric power communication optical cable is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of efficiency evaluation, and in particular to a method and system for monitoring and operating the operating status of a power communication optical cable. Background Art

[0002] Power communication optical cables are various types of composite cables and special optical cables used in power systems to take into account both power transmission and information communication. It combines optical fibers in the structural layer of power cables, making it have the functions of power transmission and optical fiber communication at the same time. It plays a vital role in the power system and is mainly used for monitoring, control, and protection of the power system.

[0003] The communication performance of power communication optical cables involves the influence of multiple factors. Therefore, it is necessary to comprehensively consider multiple factors when conducting monitoring and evaluation of the operating status of power communication optical cables. The existing monitoring and evaluation technology lacks optimized prediction of environmental monitoring data, resulting in the actual evaluation results being based on past environmental monitoring data, causing deviations in the evaluation and affecting the communication status evaluation results of power communication optical cables. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, a method and system for monitoring and maintaining the operating status of a power communication optical cable is provided. This technical solution solves the problem that the existing monitoring and evaluation technology mentioned above lacks optimized prediction of environmental monitoring data, resulting in actual evaluation results being based on past environmental monitoring data, causing deviations in the evaluation and affecting the communication status evaluation results of the power communication optical cable.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for monitoring and maintaining the operation status of a power communication optical cable, comprising:

[0007] According to the installation environment of the power communication optical cable, monitoring equipment is set up to collect real-time monitoring data of the power communication optical cable in each installation area;

[0008] Optimize the monitoring data collected in real time from each installation area;

[0009] Conduct communication status experiments on power communication optical cables according to the operation data log of power communication optical cables, and establish a communication status data list for power communication optical cables;

[0010] According to the communication status data list of power communication optical cables, a communication status assessment model is established based on machine learning algorithms;

[0011] Based on the monitoring data of the power communication optical cable in each installation area, the communication status of the power communication optical cable is predicted in combination with the communication status evaluation model, and the real-time communication status of the power communication optical cable is collected. The deviation value is calculated from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output and the power communication optical cable operation and maintenance is performed. Otherwise, the power communication optical cable is judged to be in normal status.

[0012] Preferably, the optimizing processing of the monitoring data of each installation area collected in real time specifically includes:

[0013] Construct state equations and observation equations for monitoring data;

[0014] According to the state equation and observation equation of the monitoring data, the error covariance of the next state and the state is calculated;

[0015] According to the state equation and observation equation, update the state and update the error covariance of the state;

[0016] Based on the actual monitored data and the model calculation results, the relevant parameters are adjusted, wherein the relevant parameters include: the state transfer matrix H, the observation matrix C, the process noise vector ω, the measurement noise vector v, the covariance matrix Q of the process noise ω, and the covariance matrix R of the measurement noise v;

[0017] Receive monitoring data in real time, and display the change process and comparison results of monitoring data and predicted data in real time through visual linear charts.

[0018] Preferably, the state equation and observation equation of the monitoring data are expressed as:

[0019] x k =Hx k-1 +ω k-1

[0020] y k =Cx k +v k

[0021] Among them, x k is the state value of different monitoring types at the kth moment, H is the state transfer matrix, x k-1 is the state value of different monitoring types at the k-1th moment, ω k-1 is the process noise vector at the k-1th moment, y k is the monitoring value of different monitoring types at the kth moment, C is the observation matrix, v k is the noise vector measured at the kth moment;

[0022] The error covariance expression of the next state and the state is:

[0023]

[0024] in, is the prior estimate of the state at the kth moment, is the optimal state prior estimate at the k-1th moment, is the prior estimate of the noise covariance at the kth moment, P k-1 is the optimal state noise covariance prior estimate at the k-1th moment, Q is the covariance matrix of the process noise ω;

[0025] The update state and the error covariance expression of the update state are:

[0026]

[0027] in, is the posterior estimate of the state at the kth moment, K k is the Kalman gain at the kth moment, y k is the monitoring value of different monitoring types at the kth moment, P k is the posterior estimate of the optimal state noise covariance value at the kth moment, and E is a matrix whose elements are all 1.

[0028] Preferably, the conducting of the communication status experiment of the power communication optical cable according to the operation data log of the power communication optical cable and establishing the communication status data list of the power communication optical cable specifically includes:

[0029] According to the influencing factors of the communication status of the power communication optical cable and the environmental data of the installation area, the test range of each influencing factor is set, wherein the influencing factors at least include: temperature, humidity, and dust concentration;

[0030] Through the method of controlled variables and result inversion, the communication status experiment of power communication optical cable in each installation area is carried out, and the communication status data list of power communication optical cable is established.

[0031] Preferably, the establishing of a communication status evaluation model based on a machine learning algorithm according to the communication status data list of the power communication optical cable specifically includes:

[0032] According to the influencing factors of the communication status of the power communication optical cable, a regression equation of the communication status is established;

[0033] Based on the regression equation of the communication state, a loss function of the communication state is established;

[0034] Based on the mini-batch gradient descent method, the parameter update equation of the regression equation is established;

[0035] According to the actual test situation, adjust the value of the learning rate and the number of samples in each iteration.

[0036] Preferably, the regression equation of the communication state is:

[0037]

[0038] Among them, f(γ) is the predicted value of the communication state under different influencing factors of the power communication optical cable communication state, θ0 is the error constant term, θ g is the parameter value of the gth factor affecting the communication status of the power communication optical cable, γ g is the model coefficient of the gth factor affecting the communication status of the power communication optical cable, α g is the model index of the g-th factor affecting the communication status of the power communication optical cable;

[0039] The loss function of the communication state is:

[0040]

[0041] Among them, F θ is the relationship between the measured value and the actual value of the communication state of the power communication optical cable, m is the number of samples of the small batch sample data, is the communication status prediction value of the i-th data in the sample data, f i is the actual value of the communication status of the i-th data in the sample data;

[0042] The parameter update equation of the regression equation is:

[0043]

[0044] in, is the update parameter of the jth influencing factor of the i-th sample data, is the parameter of the jth influencing factor of the i-th sample data, α is the learning rate, is the value of the jth influencing factor of the i-th sample data.

[0045] Furthermore, a power communication optical cable operation status monitoring and maintenance system is proposed, which is used to implement the power communication optical cable operation status monitoring and maintenance method as described above, specifically including:

[0046] A monitoring module, which is used to set monitoring equipment according to the installation environment of the power communication optical cable and collect monitoring data of the power communication optical cable in various installation areas in real time;

[0047] A data processing module, the data processing module is electrically connected to the monitoring module, and the data processing module is used to optimize the monitoring data of each installation area collected in real time;

[0048] A model building module, wherein the model building module is used to carry out a communication state experiment of the power communication optical cable according to an operation data log of the power communication optical cable, and to establish a communication state data list of the power communication optical cable, and to establish a communication state evaluation model based on the communication state data list of the power communication optical cable and a machine learning algorithm;

[0049] An evaluation module is electrically connected to the data processing module and the model building module, and is used to predict the communication status of the power communication optical cable based on the monitoring data of the power communication optical cable in each installation area in combination with the communication status evaluation model, and collect the real-time communication status of the power communication optical cable, and calculate the deviation value from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output, and the power communication optical cable operation and maintenance is performed. Otherwise, it is determined that the power communication optical cable is in normal status.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention proposes a monitoring and operation and maintenance solution for the operation status of an electric power communication optical cable. By dynamically optimizing and predicting the status of monitoring data and optimizing the monitoring data of monitoring equipment, dynamic real-time updating of the partition status of the electric power communication optical cable is realized, thereby realizing instant and accurate prediction of the communication status of the electric power communication optical cable. Secondly, a communication status evaluation model is established through a machine learning algorithm, and the communication status of the electric power communication optical cable is predicted in combination with the dynamic real-time update data of the partition status of the electric power communication optical cable. By comparing the real-time dynamically estimated communication status of the electric power communication optical cable with the actual communication status of the electric power communication optical cable, the operation status of the electric power communication optical cable is monitored and operated, thereby realizing high-precision monitoring and analysis of the electric power communication optical cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the power communication optical cable operation status monitoring and maintenance method proposed by the present invention;

[0053] Figure 2 A flow chart of a method for optimizing the monitoring data of each installation area collected in real time in the present invention;

[0054] Figure 3 A flow chart of a method for establishing a power communication optical cable communication status data list in the present invention;

[0055] Figure 4 A flow chart of a method for establishing a communication status evaluation model in the present invention;

[0056] Figure 5 A schematic diagram of the architecture of the electronic device proposed by the present invention;

[0057] Figure 6This is a schematic diagram of the computer-readable storage medium structure proposed by the present invention. DETAILED DESCRIPTION

[0058] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0059] Reference Figure 1 As shown, a method for monitoring and operating the operation status of a power communication optical cable comprises:

[0060] According to the installation environment of the power communication optical cable, monitoring equipment is set up to collect real-time monitoring data of the power communication optical cable in each installation area;

[0061] Optimize the monitoring data collected in real time from each installation area;

[0062] Conduct communication status experiments on power communication optical cables according to the operation data log of power communication optical cables, and establish a communication status data list for power communication optical cables;

[0063] According to the communication status data list of power communication optical cables, a communication status assessment model is established based on machine learning algorithms;

[0064] Based on the monitoring data of the power communication optical cable in each installation area, the communication status of the power communication optical cable is predicted in combination with the communication status evaluation model, and the real-time communication status of the power communication optical cable is collected. The deviation value is calculated from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output and the power communication optical cable operation and maintenance is performed. Otherwise, the power communication optical cable is judged to be in normal status.

[0065] By analyzing the monitoring data, the state data in the next state is dynamically optimized and predicted, thereby improving the data quality of the monitoring equipment. This enables the partition status information of the power communication optical cable to be updated in real time, thereby instantly and accurately predicting the communication status of the power communication optical cable. In addition, a communication status evaluation model is constructed using a machine learning algorithm. The model combines the real-time updated data of the partition status of the power communication optical cable to predict its communication status. By comparing the communication status predicted by the model with the actual communication status, the communication status of the power communication optical cable can be evaluated and analyzed with high precision.

[0066] Reference Figure 2 As shown, the optimization processing of the monitoring data of each installation area collected in real time specifically includes:

[0067] Construct state equations and observation equations for monitoring data;

[0068] According to the state equation and observation equation of the monitoring data, the error covariance of the next state and the state is calculated;

[0069] According to the state equation and observation equation, update the state and update the error covariance of the state;

[0070] Based on the actual monitored data and model calculation results, adjust the relevant parameters, where the relevant parameters include: state transfer matrix H, observation matrix C, process noise vector ω, measurement noise vector v, covariance matrix Q of process noise ω, and covariance matrix R of measurement noise v;

[0071] Receive monitoring data in real time, and display the change process and comparison results of monitoring data and predicted data in real time through visual linear charts.

[0072] The state equation and observation equation of the monitoring data are expressed as:

[0073] x k =Hx k-1 +ω k-1

[0074] y k =Cx k +v k

[0075] Among them, x k is the state value of different monitoring types at the kth moment, H is the state transfer matrix, x k-1 is the state value of different monitoring types at the k-1th moment, ω k-1 is the process noise vector at the k-1th moment, y k is the monitoring value of different monitoring types at the kth moment, C is the observation matrix, v k is the noise vector measured at the kth moment;

[0076] The error covariance expression of the next state and the state is:

[0077]

[0078] in, is the prior estimate of the state at the kth moment, is the optimal state prior estimate at the k-1th moment, is the prior estimate of the noise covariance at the kth moment, P k-1 is the optimal state noise covariance prior estimate at the k-1th moment, Q is the covariance matrix of the process noise ω;

[0079] The update state and the error covariance expression of the update state are:

[0080]

[0081] in, is the posterior estimate of the state at the kth moment, K k is the Kalman gain at the kth moment, y k is the monitoring value of different monitoring types at the kth moment, P k is the posterior estimate of the optimal state noise covariance value at the kth moment, and E is a matrix whose elements are all 1.

[0082] In this scheme, the current state is used to predict the next state, and the state estimate is updated through new observation data. The dynamic characteristics and noise characteristics of the monitoring system are fully considered, and the influence of abnormal peaks in the monitoring data caused by electromagnetic and jitter of the monitoring equipment is effectively filtered out, and the monitoring data will not deviate too much. At the same time, accurate and stable data is provided for subsequent communication status evaluation to avoid inaccurate evaluation results and misjudgment due to the influence of abnormal peaks. Among them, the state transfer matrix H and the observation matrix C are matrices with all elements of 1. The remaining matrices and vectors can be optimized and determined through the actual monitoring data and model calculation results.

[0083] Reference Figure 3 As shown, according to the operation data log of the power communication optical cable, the power communication optical cable communication status experiment is carried out, and the power communication optical cable communication status data list is established, which specifically includes:

[0084] According to the influencing factors of the communication status of the power communication optical cable and the environmental data of the installation area, the test range of each influencing factor is set, wherein the influencing factors at least include: temperature, humidity, and dust concentration;

[0085] Through the method of controlled variables and result inversion, the communication status experiment of power communication optical cable in each installation area is carried out, and the communication status data list of power communication optical cable is established.

[0086] When determining the unit sample interval of the variable, it can be set based on literature and experimental test results of professional institutions, so as to generate as few sample experimental factor groups as possible, reduce the number of power communication optical cable communication status experiments, and improve efficiency.

[0087] Reference Figure 4 As shown in the figure, according to the communication status data list of power communication optical cable, based on the machine learning algorithm, the communication status evaluation model is established, which specifically includes:

[0088] According to the influencing factors of the communication status of the power communication optical cable, a regression equation of the communication status is established;

[0089] Based on the regression equation of the communication state, a loss function of the communication state is established;

[0090] Based on the mini-batch gradient descent method, the parameter update equation of the regression equation is established;

[0091] According to the actual test situation, adjust the value of the learning rate and the number of samples in each iteration.

[0092] The regression equation of the communication state is:

[0093]

[0094] Among them, f(γ) is the predicted value of the communication state under different influencing factors of the power communication optical cable communication state, θ0 is the error constant term, θ g is the parameter value of the gth factor affecting the communication status of the power communication optical cable, γ g is the model coefficient of the gth factor affecting the communication status of the power communication optical cable, α g is the model index of the g-th factor affecting the communication status of the power communication optical cable;

[0095] The loss function of the communication state is:

[0096]

[0097] Among them, F θ is the relationship between the measured value and the actual value of the communication state of the power communication optical cable, m is the number of samples of the small batch sample data, is the communication status prediction value of the i-th data in the sample data, f i is the actual value of the communication status of the i-th data in the sample data;

[0098] The parameter update equation of the regression equation is:

[0099]

[0100] in, is the update parameter of the jth influencing factor of the i-th sample data, is the parameter of the jth influencing factor of the i-th sample data, α is the learning rate, is the value of the jth influencing factor of the i-th sample data.

[0101] Among them, α gThe value range of is any value among 1, 2, and 3. In some embodiments, a combined fitting method is adopted to determine the model index, that is, a value is randomly selected from 1, 2, and 3 as the model index of the influencing factor affecting the communication state of the power communication optical cable, and an index parameter set is generated. All non-repeated index parameter sets are traversed to generate, and the regression equation of the communication state is obtained by training based on each index parameter set, and the regression equation with the smallest loss value is selected as the regression equation of the communication state finally determined. In some embodiments, α can also be g All values ​​are 1;

[0102] The learning rate and the number of samples in each iteration are key parameters that affect the accuracy of the communication state and the training time of the power communication optical cable. The smaller the learning rate, the higher the accuracy of the communication state and the longer the training time; the more samples in each iteration, the higher the accuracy of the communication state and the longer the training time. Therefore, it is necessary to make adjustments based on the actual test results. If the test results are significantly different from the predicted values, it is necessary to prioritize reducing the learning rate and then adjust by increasing the number of samples in each iteration. If the test results are not much different from the predicted values ​​and the training time is long, it is necessary to prioritize reducing the number of samples in each iteration and then adjust by increasing the learning rate when the results are not much different.

[0103] Further, based on the same inventive concept as the above-mentioned power communication optical cable operation status monitoring and operation and maintenance method, this solution also proposes a power communication optical cable operation status monitoring and operation and maintenance system, including:

[0104] Monitoring module, the monitoring module is used to set monitoring equipment according to the installation environment of the power communication optical cable, and collect monitoring data of the power communication optical cable in each installation area in real time;

[0105] A data processing module, the data processing module is electrically connected to the monitoring module, and the data processing module is used to optimize the monitoring data of each installation area collected in real time;

[0106] A model building module, the model building module is used to carry out a communication state experiment of the power communication optical cable according to the operation data log of the power communication optical cable, and to establish a communication state data list of the power communication optical cable, and to establish a communication state evaluation model based on the communication state data list of the power communication optical cable and a machine learning algorithm;

[0107] An evaluation module is electrically connected to the data processing module and the model building module. The evaluation module is used to predict the communication status of the power communication optical cable based on the monitoring data of the power communication optical cable in each installation area in combination with the communication status evaluation model, and collect the real-time communication status of the power communication optical cable, and calculate the deviation value from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output, and the power communication optical cable operation and maintenance is performed. Otherwise, the power communication optical cable is judged to be in normal status.

[0108] The specific working steps of the power communication optical cable operation status monitoring and maintenance system are as follows:

[0109] Step 1: The model building module conducts a communication status experiment of the power communication optical cable according to the operation data log of the power communication optical cable, and establishes a communication status data list of the power communication optical cable, and establishes a communication status evaluation model based on the machine learning algorithm according to the communication status data list of the power communication optical cable;

[0110] Step 2: The monitoring module sets up monitoring equipment according to the installation environment of the power communication optical cable, and collects monitoring data of the power communication optical cable in each installation area in real time;

[0111] Step 3: The data processing module optimizes the monitoring data of each installation area collected in real time;

[0112] Step 4: The evaluation module predicts the communication status of the power communication optical cable based on the monitoring data of the power communication optical cable in each installation area and combines the communication status evaluation model, collects the real-time communication status of the power communication optical cable, and calculates the deviation value with the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, the power communication optical cable fault signal is output and the power communication optical cable operation and maintenance is performed. Otherwise, the power communication optical cable is judged to be in normal status.

[0113] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 5 The electronic device architecture shown in FIG. Figure 5 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method for monitoring and operating the operation status of a power communication optical cable provided in the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 5 One or more components of an electronic device are shown.

[0114] Figure 6 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Figure 6 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the power communication optical cable operation status monitoring and operation and maintenance method according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0115] In summary, the advantages of the present invention are: by dynamically optimizing and predicting the status of monitoring data, optimizing the monitoring data of monitoring equipment, and performing operation and maintenance of the operating status of power communication optical cables, high-precision monitoring and analysis of power communication optical cables can be achieved.

[0116] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for monitoring and maintaining the operation status of a power communication optical cable, characterized in that: include: According to the installation environment of the power communication optical cable, monitoring equipment is set up to collect real-time monitoring data of the power communication optical cable in each installation area; Optimize the monitoring data collected in real time from each installation area; Conduct communication status experiments on power communication optical cables according to the operation data log of power communication optical cables, and establish a communication status data list for power communication optical cables; According to the communication status data list of power communication optical cables, a communication status assessment model is established based on machine learning algorithms; Based on the monitoring data of the power communication optical cable in each installation area, the communication status of the power communication optical cable is predicted in combination with the communication status evaluation model, and the real-time communication status of the power communication optical cable is collected. The deviation value is calculated from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output and the power communication optical cable operation and maintenance is performed. Otherwise, the power communication optical cable is judged to be in normal status.

2. A method for monitoring and operating the operation status of a power communication optical cable according to claim 1, characterized in that: The optimization processing of the monitoring data of each installation area collected in real time specifically includes: Construct state equations and observation equations for monitoring data; According to the state equation and observation equation of the monitoring data, the error covariance of the next state and the state is calculated; According to the state equation and observation equation, update the state and update the error covariance of the state; Based on the actual monitored data and the model calculation results, the relevant parameters are adjusted, wherein the relevant parameters include: the state transfer matrix H, the observation matrix C, the process noise vector ω, the measurement noise vector v, the covariance matrix Q of the process noise ω, and the covariance matrix R of the measurement noise v; Receive monitoring data in real time, and display the change process and comparison results of monitoring data and predicted data in real time through visual linear charts.

3. A method for monitoring and operating the operation status of a power communication optical cable according to claim 2, characterized in that: The state equation and observation equation expressions of the monitoring data are: x k =Hx k-1 +oh k-1 y k =Cx k +v k Among them, x k is the state value of different monitoring types at the kth moment, H is the state transfer matrix, x k-1 is the state value of different monitoring types at the k-1th moment, ω k-1 is the process noise vector at the k-1th moment, y k is the monitoring value of different monitoring types at the kth moment, C is the observation matrix, v k is the noise vector measured at the kth moment; The error covariance expression of the next state and the state is: in, is the prior estimate of the state at the kth moment, is the optimal state prior estimate at the k-1th moment, is the prior estimate of the noise covariance at the kth moment, P k-1 is the optimal state noise covariance prior estimate at the k-1th moment, Q is the covariance matrix of the process noise ω; The update state and the error covariance expression of the update state are: in, is the posterior estimate of the state at the kth moment, K k is the Kalman gain at the kth moment, y k is the monitoring value of different monitoring types at the kth moment, P k is the posterior estimate of the optimal state noise covariance value at the kth moment, and E is a matrix whose elements are all 1.

4. A method for monitoring and operating the operation status of a power communication optical cable according to claim 3, characterized in that: The above-mentioned carrying out the communication status experiment of the power communication optical cable according to the operation data log of the power communication optical cable and establishing the communication status data list of the power communication optical cable specifically includes: According to the influencing factors of the communication status of the power communication optical cable and the environmental data of the installation area, the test range of each influencing factor is set, wherein the influencing factors at least include: temperature, humidity, and dust concentration; Through the method of controlled variables and result inversion, the communication status experiment of power communication optical cable in each installation area is carried out, and the communication status data list of power communication optical cable is established.

5. A method for monitoring and operating the operation status of a power communication optical cable according to claim 4, characterized in that: The establishing of a communication status evaluation model based on the power communication optical cable communication status data list and the machine learning algorithm specifically includes: According to the influencing factors of the communication status of the power communication optical cable, a regression equation of the communication status is established; Based on the regression equation of the communication state, a loss function of the communication state is established; Based on the mini-batch gradient descent method, the parameter update equation of the regression equation is established; According to the actual test situation, adjust the value of the learning rate and the number of samples in each iteration.

6. A method for monitoring and operating the operation status of a power communication optical cable according to claim 5, characterized in that: The regression equation of the communication state is: Among them, f(γ) is the predicted value of the communication state under different influencing factors of the power communication optical cable communication state, θ0 is the error constant term, and θ g is the parameter value of the gth factor affecting the communication status of the power communication optical cable, γ g is the model coefficient of the gth factor affecting the communication status of the power communication optical cable, α g is the model index of the g-th factor affecting the communication status of the power communication optical cable; The loss function of the communication state is: Among them, F θ is the relationship between the measured value and the actual value of the communication state of the power communication optical cable, m is the number of samples of the small batch sample data, is the communication status prediction value of the i-th data in the sample data, f i is the actual value of the communication status of the i-th data in the sample data; The parameter update equation of the regression equation is: in, is the update parameter of the jth influencing factor of the i-th sample data, is the parameter of the jth influencing factor of the i-th sample data, α is the learning rate, is the value of the jth influencing factor of the i-th sample data.

7. A power communication optical cable operation status monitoring and maintenance system, characterized in that: The method for monitoring and operating the power communication optical cable operating status according to any one of claims 1 to 6 specifically comprises: A monitoring module, which is used to set monitoring equipment according to the installation environment of the power communication optical cable and collect monitoring data of the power communication optical cable in various installation areas in real time; A data processing module, the data processing module is electrically connected to the monitoring module, and the data processing module is used to optimize the monitoring data of each installation area collected in real time; A model building module, wherein the model building module is used to carry out a communication state experiment of the power communication optical cable according to an operation data log of the power communication optical cable, and to establish a communication state data list of the power communication optical cable, and to establish a communication state evaluation model based on the communication state data list of the power communication optical cable and a machine learning algorithm; An evaluation module is electrically connected to the data processing module and the model building module, and is used to predict the communication status of the power communication optical cable based on the monitoring data of the power communication optical cable in each installation area in combination with the communication status evaluation model, and collect the real-time communication status of the power communication optical cable, and calculate the deviation value from the predicted communication status of the power communication optical cable. If the deviation value exceeds the trigger threshold, a power communication optical cable fault signal is output, and the power communication optical cable operation and maintenance is performed. Otherwise, it is determined that the power communication optical cable is in normal status.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the power communication optical cable operating status monitoring and operation and maintenance method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer-readable program stored thereon, characterized in that: When the computer-readable program is executed by a processor, the method for monitoring and operating the operating status of a power communication optical cable according to any one of claims 1 to 6 is implemented.

Images