PON gateway monitoring method and device based on multi-source data fusion, equipment and medium

Abstract

The invention discloses a PON gateway monitoring method and device based on multi-source data fusion, equipment and a medium. The PON gateway monitoring method comprises the steps of collecting PON gateway parameter data; obtaining a user demand of a target user, constructing a weight matrix based on the user demand, and performing data fusion on the PON gateway parameter data by using the weight matrix and a Kalman filtering algorithm to obtain PON gateway fusion data; and determining an initial threshold value based on the PON gateway fusion data, and monitoring the PON gateway by using the initial threshold value and the PON gateway fusion data. According to the method and the device, the problem that the monitoring index is not matched with the actual experience of the user due to the fact that the fusion result cannot distinguish the demand difference of different users when Kalman filtering is used for data fusion is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a PON gateway monitoring method, device, equipment and medium based on multi-source data fusion. Background Art

[0002] As a key device in the fiber access network, the PON (passive optical network) gateway has a direct impact on the user's network experience due to its operational stability and performance. The bandwidth, latency, packet loss rate and other parameters of the PON gateway are important indicators for measuring network performance. Once an abnormality occurs, it often indicates that the network may have problems such as congestion, equipment failure or configuration errors. If these abnormalities are not identified and handled in a timely manner, it will not only lead to a decline in user network experience, but may also cause large-scale service interruptions and even economic losses.

[0003] In order to ensure the accuracy and pertinence of PON gateway monitoring, it is usually more inclined to adopt a multi-source data fusion method compared to relying on a single parameter for monitoring. The Kalman filter algorithm can be used for multi-source data fusion, but the algorithm has certain limitations: its fusion results cannot distinguish the differences in the needs of different users, resulting in a mismatch between the monitoring indicators and the actual user experience, and cannot provide personalized and accurate monitoring results for different users, limiting its effectiveness in PON gateway monitoring. Summary of the invention

[0004] The main purpose of this application is to provide a PON gateway monitoring method, device, equipment and medium based on multi-source data fusion, aiming to solve the technical problem that when using Kalman filtering for data fusion, the fusion result cannot distinguish the differences in needs of different users, resulting in a mismatch between monitoring indicators and actual user experience.

[0005] To achieve the above-mentioned purpose, the present application provides a PON gateway monitoring method based on multi-source data fusion, including: collecting PON gateway parameter data; obtaining user needs of target users and constructing a weight matrix based on the user needs, using the weight matrix and a Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; determining an initial threshold based on the PON gateway fusion data, and monitoring the PON gateway using the initial threshold and the PON gateway fusion data.

[0006] Optionally, constructing a weight matrix based on the user demand includes: determining the importance of each of the PON gateway parameter data based on the user demand; and constructing a weight matrix based on the importance of each of the PON gateway parameter data.

[0007] Optionally, the using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data includes: multiplying the weight matrix with the observation matrix in the Kalman filter algorithm to obtain a corrected observation matrix; based on the corrected observation matrix and using the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data.

[0008] Optionally, before determining the initial threshold based on the PON gateway fusion data, the method also includes: acquiring PON gateway historical data and determining PON gateway historical fusion data based on the PON gateway historical data; determining the initial threshold based on the PON gateway fusion data includes: determining the initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data.

[0009] Optionally, determining the initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data includes: performing anomaly detection on the PON gateway fusion data and the PON gateway historical fusion data using a preset algorithm; and determining the initial threshold based on the anomaly detection result.

[0010] Optionally, after monitoring the PON gateway using the initial threshold and the PON gateway fusion data, the method further includes: adjusting the initial threshold based on a monitoring result to obtain an adjusted threshold; and continuously monitoring the PON gateway based on the adjusted threshold.

[0011] Optionally, before obtaining user needs of target users and constructing a weight matrix based on the user needs, the method further includes: performing data cleaning on the PON gateway parameter data; wherein the data cleaning at least includes: unifying the dimensions of the PON gateway parameter data.

[0012] In addition, to achieve the above-mentioned purpose, the present application also provides a PON gateway monitoring device based on multi-source data fusion, including: a data acquisition module, used to collect PON gateway parameter data; a data fusion module, used to obtain user needs of target users and construct a weight matrix based on the user needs, and use the weight matrix and Kalman filtering algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; a monitoring module, used to determine an initial threshold based on the PON gateway fusion data, and use the initial threshold and the PON gateway fusion data to monitor the PON gateway.

[0013] The present application also provides a PON gateway monitoring device based on multi-source data fusion, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable 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 above-mentioned PON gateway monitoring method based on multi-source data fusion.

[0014] The present application also provides a computer-readable storage medium, comprising: a computer program stored therein, wherein when the computer program is executed by a processor, the above-mentioned PON gateway monitoring method based on multi-source data fusion is implemented.

[0015] The PON gateway monitoring method, device, equipment and medium based on multi-source data fusion proposed in this application can dynamically adjust the monitoring strategy according to the needs of different users, thereby providing more accurate monitoring results and avoiding monitoring deviations caused by different user needs, by analyzing the PON gateway parameter data of different users, i.e., various parameters of the PON gateway, constructing a weight matrix and using the weight matrix to correct the Kalman filter algorithm. In addition, the monitoring results after data fusion can better identify potential network congestion, equipment failure and other problems, and take corresponding measures in a timely manner, significantly improving the stability of the network and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a PON gateway monitoring method based on multi-source data fusion according to the first implementation mode of the present application; Figure 2 It is a flow chart of a PON gateway monitoring method based on multi-source data fusion according to a second implementation mode of the present application; Figure 3 It is a flow chart of a PON gateway monitoring method based on multi-source data fusion according to a third implementation mode of the present application; Figure 4 It is a structural block diagram of a PON gateway monitoring device based on multi-source data fusion according to an implementation mode of the present application; Figure 5 The present invention is a schematic diagram of the structure of a PON gateway monitoring device based on multi-source data fusion according to an embodiment of the present application.

[0017] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] In order to ensure the accuracy and pertinence of PON gateway monitoring, it is usually more inclined to adopt a multi-source data fusion method compared to relying on a single parameter for monitoring. The Kalman filter algorithm can be used for multi-source data fusion, but the algorithm has certain limitations: its fusion results cannot distinguish the differences in the needs of different users, resulting in a mismatch between the monitoring indicators and the actual user experience, and cannot provide personalized and accurate monitoring results for different users, limiting its effectiveness in PON gateway monitoring.

[0020] To solve the above problems, the present application provides a PON gateway monitoring method, device, equipment and medium based on multi-source data fusion. The present application scheme is introduced in detail below.

[0021] Figure 1 FIG. 1 is a flowchart of a PON gateway monitoring method based on multi-source data fusion according to a first embodiment of the present application. The PON gateway monitoring method based on multi-source data fusion can be executed by a PON gateway monitoring device with data processing capability. The PON gateway monitoring device can be, for example, a PON gateway monitoring device based on multi-source data fusion. Figure 1 The PON gateway monitoring method based on multi-source data fusion may include the following steps: S11, collecting PON gateway parameter data.

[0022] Among them, the PON gateway parameter data represents various parameters of the PON gateway. For example, the PON gateway parameter data may include memory usage, network traffic, number of processes, bandwidth, network delay, etc. It can be understood that the PON gateway parameter data is multi-source data.

[0023] In the specific implementation process, firstly, the specific parameters of the PON gateway to be monitored, namely the PON gateway parameter data, are obtained. It should be noted that the PON gateway parameter data can be collected by monitoring devices such as PON gateway, OLT device, ONU device, etc. through SNMP protocol or custom interface.

[0024] S12, obtaining user needs of target users and constructing a weight matrix based on the user needs, and using the weight matrix and a Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data.

[0025] The weight matrix can represent user demand, that is, the user's attention to various parameters of the PON gateway.

[0026] It should be noted that the steps of using the traditional Kalman filter algorithm to fuse the PON gateway parameter data include: first, determining the parameters to be fused, i.e., the PON gateway parameter data in this embodiment, and establishing a corresponding state space model; then, initializing the state vector and covariance matrix of the Kalman filter; then, updating the state estimate and covariance matrix through the prediction step, and then correcting them in combination with the actual observation value through the measurement step; finally, outputting the fused result, i.e., a comprehensive indicator, which is used to reflect the overall performance of the network.

[0027] However, the traditional Kalman filter does not take into account the different concerns of different users on network parameters, that is, different users have different monitoring requirements for PON gateways. For example, enterprise users usually have higher bandwidth requirements, while home users are more concerned about network latency. The traditional Kalman filter uses the same weight to fuse all PON gateway parameter data, resulting in the inability to personalize the fusion results. As a result, when the fused data is used for gateway monitoring, there will be a problem that the monitoring indicators do not match the actual user experience. For example, enterprise users may have a poor experience due to insufficient bandwidth, but the fusion results fail to highlight the importance of bandwidth; and home users may feel network lag due to excessive latency, but the fusion results fail to effectively reflect the impact of latency. This problem will reduce the accuracy and practicality of monitoring, and it is impossible to provide targeted network optimization suggestions for different users.

[0028] Based on this, this embodiment adds a weight matrix on the basis of the traditional Kalman filter algorithm, and dynamically adjusts the weight matrix for different users, so that the fusion result can more accurately reflect the actual needs of users. Therefore, when using the fusion result for gateway monitoring, it can provide performance indicators that are more in line with user needs, thereby providing a more targeted and practical reference for network optimization and troubleshooting.

[0029] It is understandable that different users have different monitoring requirements for PON gateways. For example, enterprise users usually have higher bandwidth requirements, while home users are more concerned about network latency. Furthermore, in order to better monitor the PON gateway, it is necessary to increase the weight matrix according to the specific needs of the user, so that the obtained PON gateway fusion data can better reflect the specific needs of different users.

[0030] In the specific implementation process, the collected PON gateway parameter data is first cleaned to obtain the user needs of the target users, wherein the data cleaning at least includes: unifying the dimensions of the PON gateway parameter data so as to subsequently perform data fusion on the PON gateway parameter data.

[0031] In one embodiment, in step S12, constructing a weight matrix based on the user demand may specifically include: S121. Determine the importance of each of the PON gateway parameter data based on the user demand; S122: construct a weight matrix based on the importance of each of the PON gateway parameter data.

[0032] In a specific implementation process, the importance of multiple different types of users to each PON gateway parameter data is obtained, and then the importance of the target user to each PON gateway parameter data is determined according to the similarity between the user needs of the target user and the user needs of each type of user.

[0033] Further, the PON gateway parameter data constitutes the observation vector in the Kalman filter algorithm, and a weight matrix is ​​constructed according to the position of each PON gateway parameter data in the observation vector, wherein the diagonal elements in the weight matrix are the importance of each PON gateway parameter data, and the elements in other positions in the weight matrix are all 0. It can be understood that the weight matrix is ​​a diagonal matrix.

[0034] Specifically, take the PON gateway parameter data as bandwidth and network delay as an example. If the observation vector is [bandwidth, network delay] T , then the weight matrix is:

[0035] And if the observation vector is [network delay, bandwidth] T , then the weight matrix is:

[0036] Therefore, a weight matrix is ​​constructed based on user needs, and the weight matrix can be used to correct the original Kalman filter algorithm, and then the corrected Kalman filter algorithm is used to perform data fusion on the PON gateway parameter data.

[0037] In one embodiment, in step S12, using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain the PON gateway fusion data may specifically include: S123, multiplying the weight matrix by the observation matrix in the Kalman filter algorithm to obtain a modified observation matrix; S124. Based on the modified observation matrix and using a Kalman filter algorithm, data fusion is performed on the PON gateway parameter data to obtain PON gateway fusion data.

[0038] Among them, the observation matrix in the Kalman filter algorithm is usually used to map the actual observation value to the state estimation space of the system. Since the present embodiment performs data fusion on the PON gateway parameter data, the observation matrix in this example is an N*1 column vector, where N represents the number of PON gateway parameter data. Since the PON gateway parameter data in this embodiment, i.e., the relevant parameters of the PON gateway, are obtained by direct observation, the vector values ​​in the observation matrix are all 1.

[0039] In the specific implementation process, the observation matrix is ​​first determined based on the number of PON gateway parameter data, and then the weight matrix is ​​multiplied by the observation matrix to obtain a modified observation matrix. It should be noted that since the weight matrix is ​​an N*N matrix and the observation matrix is ​​N*1, the multiplication of the weight matrix and the observation matrix satisfies the matrix multiplication condition.

[0040] Furthermore, the observation matrix in the Kalman filter algorithm is replaced with a modified observation matrix, and the Kalman filter algorithm is used to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data.

[0041] It is understandable that this embodiment replaces the observation matrix in the Kalman filter algorithm with a modified observation matrix. The modified observation matrix can dynamically adjust the importance of different parameters, that is, different PON gateway parameter data in the fusion process by introducing a weight matrix that characterizes user needs, so as to better reflect the personalized needs of users. For example, when enterprise users encounter insufficient bandwidth problems, the corrected fusion results can more sensitively reflect bandwidth anomalies, thereby helping operation and maintenance personnel to quickly locate problems; and for home users, the modified observation matrix can more accurately capture delay fluctuations and improve user experience. Therefore, the introduction of the modified observation matrix not only enhances the accuracy of PON gateway monitoring, but also improves its practicality and user satisfaction in actual applications.

[0042] S13, determining an initial threshold based on the PON gateway fusion data, and monitoring the PON gateway using the initial threshold and the PON gateway fusion data.

[0043] The initial threshold represents a threshold for alarming the PON gateway. In this embodiment, the initial threshold can be pre-defined by an operator according to the PON gateway fusion data.

[0044] In the specific implementation process, the initial threshold is first determined based on the PON gateway fusion data. Then, when the PON gateway fusion data at the next moment reaches the preset warning standard, i.e., the initial threshold, the PON gateway monitoring device automatically triggers an alarm and notifies the operator. At the same time, an alarm report is generated to facilitate subsequent troubleshooting and optimization.

[0045] The PON gateway monitoring method based on multi-source data fusion proposed in the embodiment of the present application analyzes the PON gateway parameter data of different users, i.e., various parameters of the PON gateway, constructs a weight matrix and uses the weight matrix to correct the Kalman filter algorithm. It can dynamically adjust the monitoring strategy according to the differences in needs of different users, thereby providing more accurate monitoring results and avoiding monitoring deviations caused by different user needs. In addition, the monitoring results after data fusion can better identify potential network congestion, equipment failures and other problems, and take corresponding measures in a timely manner, significantly improving the stability of the network and user experience.

[0046] Based on the above embodiments, Figure 2 is a flow chart of a PON gateway monitoring method based on multi-source data fusion according to a second embodiment of the present application, Figure 2 Based on Figure 1 The corresponding preferred embodiment of the PON gateway monitoring method based on multi-source data fusion, the PON gateway monitoring method based on multi-source data fusion can be executed by a PON gateway monitoring device with data processing capability, the PON gateway monitoring device can be, for example, a PON gateway monitoring device based on multi-source data fusion, refer to Figure 2 The PON gateway monitoring method based on multi-source data fusion may include the following steps: S21, collecting PON gateway parameter data; S22, obtaining user needs of target users and constructing a weight matrix based on the user needs, and using the weight matrix and Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; S23, obtaining PON gateway historical data and determining PON gateway historical fusion data based on the PON gateway historical data; S24: Determine an initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data, and monitor the PON gateway using the initial threshold and the PON gateway fusion data.

[0047] In a specific implementation process, in step S23, the PON gateway monitoring device may first obtain PON gateway historical data, where the PON gateway historical data includes historical data of various parameters of the PON gateway. It can be understood that each PON gateway historical data corresponds to historical data of PON gateway parameter data.

[0048] Furthermore, the PON gateway historical data is processed using a Kalman filter algorithm that replaces the observation matrix with a modified observation matrix to obtain multiple PON gateway historical fusion data.

[0049] In one embodiment, in step S24, determining the initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data may specifically include: S241, using a preset algorithm to perform anomaly detection on the PON gateway fusion data and the PON gateway historical fusion data; S242: Determine an initial threshold based on the abnormality detection result.

[0050] Among them, the preset algorithm is an anomaly detection algorithm. This embodiment takes the LOF anomaly detection algorithm as an example for explanation. In other embodiments, other anomaly detection algorithms can also be used as preset algorithms. This embodiment does not specifically limit the preset algorithm.

[0051] In the specific implementation process, the LOF anomaly detection algorithm is used to perform anomaly detection on multiple PON gateway historical fusion data and PON gateway fusion data to obtain a number of anomaly values, and then the several anomaly values ​​are analyzed to determine the initial threshold.

[0052] It should be noted that, in this embodiment, in step S24, the initial threshold value may also be preset by the operator according to the PON gateway fusion data and the PON gateway historical fusion data.

[0053] In this exemplary embodiment, the PON gateway historical data is used to determine the initial threshold on the basis of the above embodiment. Since the historical data can reflect the behavior pattern of the PON gateway in different time periods, the threshold can be set more accurately by analyzing the historical data, thereby improving the accuracy of PON gateway monitoring.

[0054] Based on the above embodiments, Figure 3 is a flow chart of a PON gateway monitoring method based on multi-source data fusion according to a third embodiment of the present application, Figure 3 Based on Figure 1 The corresponding preferred embodiment of the PON gateway monitoring method based on multi-source data fusion is shown in FIG. Figure 3 The PON gateway monitoring method based on multi-source data fusion may include the following steps: S31, collecting PON gateway parameter data; S32, obtaining user needs of target users and constructing a weight matrix based on the user needs, and using the weight matrix and Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; S33, obtaining PON gateway historical data and determining PON gateway historical fusion data based on the PON gateway historical data; S34, determining an initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data, and monitoring the PON gateway using the initial threshold and the PON gateway fusion data; S35, adjusting the initial threshold based on the monitoring result to obtain an adjusted threshold; S36: Continuously monitor the PON gateway based on the adjustment threshold.

[0055] It should be noted that if frequent false alarms occur when using the initial threshold for continuous monitoring, it means that the threshold is set too low and the threshold needs to be increased; if no alarms occur continuously, it may be that the threshold is set too high, resulting in missed alarms, and the threshold needs to be lowered.

[0056] In a specific implementation process, after step S34, the monitoring result is first obtained, and the initial threshold is adjusted based on the monitoring result.

[0057] Specifically, when the PON gateway is continuously monitored, the ratio of the number of data exceeding the initial threshold to the total number of data before the time point corresponding to the fusion data of each PON gateway is added to the inverse of the ratio as the necessity of adjusting the fusion data of each PON gateway.

[0058] Furthermore, the initial threshold is adjusted based on the necessity of adjustment of the fused data of each PON gateway.

[0059] It is understandable that the greater the necessity for adjustment, the more cases of under-reporting and false positives the initial threshold has, and the more adjustment is needed.

[0060] In this exemplary embodiment, the thresholds in the monitoring process are dynamically adjusted on the basis of the above-mentioned embodiments, which can improve the flexibility and accuracy of monitoring; in addition, by dynamically adjusting the thresholds through real-time data, it is possible to accurately monitor abnormal conditions in the PON gateway and reduce missed faults or false alarms due to improper threshold settings.

[0061] Based on the above embodiments, Figure 4 FIG. 1 is a structural block diagram of a PON gateway monitoring device based on multi-source data fusion according to an embodiment of the present application, such as Figure 4 As shown, the PON gateway monitoring device 400 based on multi-source data fusion may include: a data acquisition module 410, a data fusion module 420, and a monitoring module 430, wherein: The data collection module 410 is used to collect PON gateway parameter data; The data fusion module 420 is used to obtain user needs of target users and construct a weight matrix based on the user needs, and use the weight matrix and Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; The monitoring module 430 is used to determine an initial threshold based on the PON gateway fusion data, and monitor the PON gateway using the initial threshold and the PON gateway fusion data.

[0062] In an exemplary embodiment, the data fusion module 420 may also be used to determine the importance of each of the PON gateway parameter data based on the user demand; and construct a weight matrix based on the importance of each of the PON gateway parameter data.

[0063] In an exemplary embodiment, the data fusion module 420 can also be used to multiply the weight matrix with the observation matrix in the Kalman filter algorithm to obtain a modified observation matrix; based on the modified observation matrix and using the Kalman filter algorithm, the PON gateway parameter data is data-fused to obtain PON gateway fusion data.

[0064] In an exemplary embodiment, the monitoring module 430 can also be used to obtain PON gateway historical data and determine PON gateway historical fusion data based on the PON gateway historical data; determining the initial threshold based on the PON gateway fusion data includes: determining the initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data.

[0065] In an exemplary embodiment, the monitoring module 430 may also be used to perform anomaly detection on the PON gateway fusion data and the PON gateway historical fusion data using a preset algorithm; and determine an initial threshold based on the anomaly detection result.

[0066] In an exemplary embodiment, the monitoring module 430 may also be used to adjust the initial threshold based on the monitoring result to obtain an adjusted threshold; and continuously monitor the PON gateway based on the adjusted threshold.

[0067] In an exemplary embodiment, the data fusion module 420 may also be used to perform data cleaning on the PON gateway parameter data; wherein the data cleaning at least includes: unifying the dimensions of the PON gateway parameter data.

[0068] Those skilled in the art should understand that the division of the various modules in the embodiment is only a division of logical functions, and can be fully or partially integrated into one or more actual carriers in actual application, and these modules can be all implemented in the form of software called by the processing unit, or all implemented in the form of hardware, or implemented in the form of a combination of software and hardware. It should be noted that each module in a PON gateway monitoring device based on multi-source data fusion in this embodiment corresponds one by one to each step in a PON gateway monitoring method based on multi-source data fusion in the aforementioned embodiment. Therefore, the specific implementation method of this embodiment can refer to the implementation method of the aforementioned PON gateway monitoring method based on multi-source data fusion, which will not be repeated here.

[0069] Based on the above embodiments, Figure 5 FIG. 1 is a schematic diagram of a PON gateway monitoring device based on multi-source data fusion according to an embodiment of the present application. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a PON gateway monitoring method based on multi-source data fusion, the method comprising: collecting PON gateway parameter data; obtaining user requirements of target users and constructing a weight matrix based on the user requirements, using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; determining an initial threshold based on the PON gateway fusion data, and monitoring the PON gateway using the initial threshold and the PON gateway fusion data.

[0070] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0071] On the basis of the above embodiments, on the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the PON gateway monitoring method based on multi-source data fusion provided by the above methods, the method including: collecting PON gateway parameter data; obtaining user needs of target users and constructing a weight matrix based on the user needs, using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; determining an initial threshold based on the PON gateway fusion data, and monitoring the PON gateway using the initial threshold and the PON gateway fusion data.

[0072] On the basis of the above embodiments, on another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the PON gateway monitoring method based on multi-source data fusion provided by the above methods, the method comprising: collecting PON gateway parameter data; obtaining user requirements of target users and constructing a weight matrix based on the user requirements, using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; determining an initial threshold based on the PON gateway fusion data, and using the initial threshold and the PON gateway fusion data to monitor the PON gateway.

[0073] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A PON gateway monitoring method based on multi-source data fusion, characterized in that: include: Collect PON gateway parameter data; Obtaining user needs of target users and constructing a weight matrix based on the user needs, and using the weight matrix and a Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; An initial threshold is determined based on the PON gateway fusion data, and the PON gateway is monitored using the initial threshold and the PON gateway fusion data.

2. The PON gateway monitoring method based on multi-source data fusion according to claim 1, characterized in that: The constructing a weight matrix based on the user demand includes: Determining the importance of each of the PON gateway parameter data based on the user needs; A weight matrix is ​​constructed based on the importance of each of the PON gateway parameter data.

3. The PON gateway monitoring method based on multi-source data fusion according to claim 1, characterized in that: The method of using the weight matrix and the Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data includes: Multiplying the weight matrix by the observation matrix in the Kalman filter algorithm to obtain a modified observation matrix; Based on the modified observation matrix and using a Kalman filter algorithm, data fusion is performed on the PON gateway parameter data to obtain PON gateway fusion data.

4. The PON gateway monitoring method based on multi-source data fusion according to claim 1, characterized in that: Before determining the initial threshold based on the PON gateway fusion data, the method further includes: Acquire PON gateway historical data and determine PON gateway historical fusion data based on the PON gateway historical data; The determining an initial threshold based on the PON gateway fusion data comprises: An initial threshold is determined based on the PON gateway fusion data and the PON gateway historical fusion data.

5. The PON gateway monitoring method based on multi-source data fusion according to claim 4, characterized in that: The determining the initial threshold based on the PON gateway fusion data and the PON gateway historical fusion data comprises: Performing anomaly detection on the PON gateway fusion data and the PON gateway historical fusion data using a preset algorithm; An initial threshold is determined based on the anomaly detection results.

6. The PON gateway monitoring method based on multi-source data fusion according to claim 1, characterized in that: After the PON gateway is monitored by using the initial threshold and the PON gateway fusion data, the method further includes: Adjusting the initial threshold based on the monitoring result to obtain an adjusted threshold; The PON gateway is continuously monitored based on the adjustment threshold.

7. The PON gateway monitoring method based on multi-source data fusion according to claim 1, characterized in that: Before acquiring the user needs of the target users and constructing the weight matrix based on the user needs, the method further includes: Performing data cleaning on the PON gateway parameter data; The data cleaning at least includes: unifying the dimensions of the PON gateway parameter data.

8. A PON gateway monitoring device based on multi-source data fusion, characterized in that: include: Data acquisition module, used to collect PON gateway parameter data; A data fusion module, used to obtain user needs of target users and construct a weight matrix based on the user needs, and use the weight matrix and Kalman filter algorithm to perform data fusion on the PON gateway parameter data to obtain PON gateway fusion data; The monitoring module is used to determine an initial threshold based on the PON gateway fusion data, and monitor the PON gateway using the initial threshold and the PON gateway fusion data.

9. PON gateway monitoring equipment based on multi-source data fusion, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable 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 PON gateway monitoring method based on multi-source data fusion as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the PON gateway monitoring method based on multi-source data fusion as claimed in any one of claims 1 to 7 is implemented.

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