Passive room subsystem source fault early warning method, system, medium and equipment

Abstract

The application provides a passive room distribution system source fault early warning method, system, medium and equipment, and belongs to the technical field of source fault early warning, which comprises the following steps: periodically scanning the source of the passive room distribution system by using an analog sweep frequency monitoring device, collecting and storing signal parameters, and performing abnormal value detection to identify the number of abnormal signal parameters; performing noise reduction processing on the identified abnormal signal parameter data; based on the identified abnormal signal parameter data, performing fault determination, extracting corresponding signal characteristics, outputting fault information data, generating alarm information, and notifying maintenance personnel to realize fault early warning notification. The application improves the timeliness and accuracy of source fault discovery, reduces the duration of signal attenuation or interruption problems caused by untimely source fault discovery, and significantly improves the communication experience of users. Furthermore, through intelligent monitoring means, the workload of manual inspection is reduced, the work efficiency is improved, and the labor input cost is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of source fault early warning, and particularly relates to a passive room distribution system source fault early warning method, system, medium and equipment. BACKGROUND

[0002] The passive room distribution system plays a very key role in the field of indoor signal coverage. Its performance is like the lifeline of the communication network, and is closely related to the communication experience of users. Whether the intelligibility and stability of voice calls or the speed and accuracy of data transmission are all affected by it. However, in the actual running scene, the source fault is like a time bomb. Once it occurs, it may cause a large signal attenuation, an intermittent call, a slow data loading, or even a direct signal interruption, which may instantly lose the communication ability of the user, seriously damage the overall performance of the system, and greatly reduce the user's satisfaction with the communication service.

[0003] At present, the traditional means for dealing with signal fault detection is manual inspection. This mode has obvious disadvantages. On the one hand, manual inspection consumes a lot of manpower, material resources and time cost. The inspection personnel need to shuttle in the corners of the complex indoor distribution system regularly, and check the equipment and lines one by one, which is extremely low in efficiency. On the other hand, manual inspection cannot realize real-time monitoring. During the interval between two inspections, the fault may occur without being noticed, and the problem cannot be found in time, let alone be solved quickly. It has already been unable to meet the urgent needs of modern communication network for real-time monitoring. SUMMARY

[0004] The present application provides a passive room distribution system source fault early warning method, system, medium and equipment to solve the problems in the prior art.

[0005] In a first aspect, a passive room distribution system source fault early warning method is provided, which comprises,

[0006] Periodically scanning the source of the passive room distribution system using an analog frequency sweep monitoring device, collecting and storing signal parameters;

[0007] Performing outlier detection on the signal parameters to identify signal parameter abnormal data, and performing noise reduction processing on the identified signal parameter abnormal data;

[0008] Based on the identified signal parameter abnormal data, performing fault determination, extracting corresponding signal features, and outputting fault information data;

[0009] Based on the output fault information data, generating an alarm information and notifying the maintenance personnel to realize fault early warning.

[0010] Further, the signal parameters include acquisition time, signal source position, operator, frequency band, received signal strength and reference signal received power.

[0011] Further, the signal parameter is subjected to outlier detection, specifically including that the RSSI parameters and RSRP parameters of the same signal source position, operator and frequency band in the signal parameters are subjected to outlier detection by using a Z-score method.

[0012] Further, the identified signal parameter abnormal data is subjected to noise reduction processing, specifically including that, for the identified signal parameter abnormal data, an interpolation method is used to replace the mean value of adjacent data points of the RSSI parameters or RSRP parameters of the same signal source position, operator and frequency band of the same analog sweep frequency monitoring device, to achieve noise reduction processing of the signal data.

[0013] Further, the signal parameter abnormal data is subjected to noise reduction processing, specifically including that, for the identified signal parameter abnormal data, an interpolation method is used to replace the mean value of adjacent data points of the RSSI parameters or RSRP parameters of the same signal source position, operator and frequency band of the same analog sweep frequency monitoring device, to achieve noise reduction processing of the signal data.

[0014] When the signal parameter abnormal data is RSSI parameter abnormal data, RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive room distribution system at the same time, the same signal source position, the same operator and the same frequency band are extracted and compared with the RSSI parameter abnormal data, and fault judgment is performed according to the comparison result;

[0015] When the signal parameter abnormal data is RSRP parameter abnormal data, the RSRP parameter is compared with the RSSI parameter, and fault judgment is performed according to the comparison result.

[0016] Further, the RSRP parameter is compared with the RSSI parameter, specifically including that,

[0017] If the RSSI parameter is greater than the RSRP parameter, and the RSSI is marked as abnormal data, then RSSI parameter data collected by other analog sweep frequency monitoring devices in the same room distribution system at the same time, the same signal source position, the same operator and the same frequency band are extracted and compared with the RSSI parameter abnormal data;

[0018] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, then RSRP parameter data collected by other analog sweep frequency monitoring devices in the same room distribution system at the same time, the same signal source position, the same operator and the same frequency band are extracted and compared with the RSRP parameter abnormal data.

[0019] Secondly, a passive room distribution system signal source fault early warning system, the system includes: a signal parameter collection module, a data preprocessing module, a fault identification module and a fault warning module;

[0020] The signal parameter collection module is configured to periodically scan the signal source of the passive room distribution system using an analog sweep monitoring device, collect and store signal parameters;

[0021] The data preprocessing module is configured to perform outlier detection on the signal parameters, identify signal parameter abnormal data, and perform noise reduction processing on the identified signal parameter abnormal data.

[0022] The fault identification module is configured to perform fault determination based on the identified signal parameter abnormal data, extract corresponding signal features, and output fault information data.

[0023] The fault warning module is configured to generate an alarm based on the output fault information data, notify maintenance personnel, and realize fault warning notification.

[0024] Further, the signal parameters include collection time, signal source location, operator, frequency band, received signal strength, and reference signal received power.

[0025] Further, the data preprocessing module is specifically configured to perform outlier detection on the RSSI parameters and RSRP parameters of the same signal source location, operator, and frequency band in the signal parameters using a Z-score method.

[0026] Further, the data preprocessing module is specifically configured to replace the identified signal parameter abnormal data with the mean of adjacent data points of the RSSI parameters or RSRP parameters of the same signal source location, operator, and frequency band using an interpolation method, to achieve noise reduction processing of the signal data.

[0027] Further, the fault identification module is specifically configured to,

[0028] When the signal parameter abnormal data is RSSI parameter abnormal data, the RSSI parameter data collected by other analog sweep monitoring devices in the same passive room distribution system at the same time, same signal source location, same operator, and same frequency band is extracted and compared with the RSSI parameter abnormal data, and fault determination is performed according to the comparison result.

[0029] When the signal parameter abnormal data is RSRP parameter abnormal data, the RSRP parameter is compared with the RSSI parameter, and fault determination is performed according to the comparison result.

[0030] Further, the fault identification module is also configured to,

[0031] If the RSSI parameter is greater than the RSRP parameter, and the RSSI is marked as abnormal data, then the RSSI parameter data collected by other analog sweep frequency monitoring devices of the same room distribution system at the same period, the same source position, the same operator and the same frequency band are extracted and compared with the RSSI parameter abnormal data.

[0032] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, then the RSRP parameter data collected by other analog sweep frequency monitoring devices of the same room distribution system at the same period, the same source position, the same operator and the same frequency band are extracted and compared with the RSRP parameter abnormal data.

[0033] In a third aspect, a computer readable storage medium has stored therein a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned passive room distribution system source fault early warning methods.

[0034] In a fourth aspect, an electronic device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus.

[0035] The memory is used to store a computer program.

[0036] The processor is used to execute the program stored on the memory, and implement the steps of any of the above-mentioned passive room distribution system source fault early warning methods.

[0037] Compared with the prior art, the present application has the following advantages:

[0038] 1. The present application can accurately and timely identify abnormal data in signal parameters by using analog sweep frequency monitoring devices to periodically scan and collect signal parameters of a passive room distribution system, and using the Z-score method to detect outliers in the signal parameters. At the same time, the identified abnormal data is processed by the interpolation method to effectively improve the quality and reliability of the signal data, providing a solid foundation for subsequent fault determination. Based on in-depth analysis and multi-dimensional comparison of abnormal data, the type and location of the fault can be accurately determined, and alarm information can be quickly generated to notify maintenance personnel, realizing rapid early warning and response of the fault.

[0039] 2. Compared with the traditional manual inspection method, the present application greatly improves the timeliness and accuracy of source fault discovery, reduces the duration of signal attenuation or interruption caused by late discovery of source faults, and significantly improves the user's communication experience. Moreover, through intelligent monitoring means, the workload of manual inspection is greatly reduced, the work efficiency is improved, and the labor cost is reduced.

[0040] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the present application will be realized and attained by the structures particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments of the present application, and the ordinary skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0042] Figure 1 A flowchart of a passive room distribution system signal source fault early warning method of the present application is shown.

[0043] Figure 2 A module diagram of a passive room distribution system signal source fault early warning system of the present application is shown. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the ordinary skilled in the art without any creative effort are within the protection scope of the present application.

[0045] As shown in Figure 1 A passive room distribution system signal source fault early warning method, the steps of which include:

[0046] S1, using an analog frequency sweep monitoring device to periodically scan the signal source of the passive room distribution system, collecting and storing signal parameters.

[0047] Optionally, the signal parameters include acquisition time, signal source position, operator, frequency band, received signal strength (RSSI) and reference signal received power (RSRP), etc.

[0048] In another embodiment of the present application, by setting the wake-up time of the analog frequency sweep monitoring device, the analog frequency sweep monitoring device is periodically woken up to complete the collection of signal parameters of the passive room distribution system, and after the collection of signal parameters is completed, the signal parameter data collected in the scanning process is recorded and stored in real time.

[0049] S2, performing outlier detection on the collected important signal parameters, identifying signal parameter abnormal data, and performing noise reduction processing on the identified signal parameter abnormal data.

[0050] Optionally, for the collected signal parameters of the same signal source position, operator and frequency band (Band), the Z-score method is used for outlier detection.

[0051] In another embodiment of the present application, the Z-score method is used to calculate the standard deviation distance of each data point of the RSSI parameter from the mean value to identify outliers, and the steps include:

[0052] A. Calculate the mean value of the RSSI parameter and the standard deviation , the formula is

[0053]

[0054]

[0055] wherein, is the ith RSSI value; is the mean value of RSSI; is the standard deviation of RSSI; N is the sample size;

[0056] B. Calculate the standard deviation distance of each data point of the RSSI parameter from the mean value to identify outliers, the formula is

[0057]

[0058] wherein, is the standard score, indicating the standard deviation distance of each data point of the RSSI parameter from the mean value;

[0059] C. Set the outlier threshold, when exceeds the set threshold, the current RSSI parameter is identified as an outlier.

[0060] In another embodiment of the present application, the Z-score method is also used to calculate the standard deviation distance of each data point of the RSRP parameter from the mean value to identify outliers, and the steps include:

[0061] a. Calculate the mean value of the RSRP parameter and the standard deviation , the formula is

[0062]

[0063]

[0064] wherein, is the ith RSRP value; is the mean of RSRP; is the standard deviation of RSRP; N is the sample number;

[0065] b. Calculate the standard deviation distance of each data point of the RSRP parameter from the mean to identify outliers, which is expressed as,

[0066]

[0067] wherein, is the standard score, indicating the standard deviation distance of each data point of the RSRP parameter from the mean;

[0068] c. Set an outlier threshold, when when the threshold is exceeded, the current RSRP parameter is identified as an outlier.

[0069] In another embodiment of the application, for the identified signal parameter abnormal data, an interpolation method is used to replace the mean of adjacent data points of the RSSI parameter or RSRP parameter of the same source position, operator, and frequency band of the same analog sweep frequency monitoring device, to realize noise reduction processing of signal data, effectively eliminate the influence of RSSI parameter and RSRP parameter outliers when the source fails, improve the quality of signal parameter data, and support subsequent fault analysis.

[0070] S3, based on the identified signal parameter abnormal data, fault determination is performed, and corresponding signal features are extracted, and fault information data is output.

[0071] Optionally, when it is identified that the signal parameter abnormal data of the current monitoring device is RSSI parameter abnormality, RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive room subsystem at the same time, the same source position, the same operator, and the same frequency band are also needed to be extracted and compared with the RSSI parameter abnormal data;

[0072] If the corresponding frequency band of other monitoring devices is normal or some devices have the same abnormal situation, it is considered that the signal level of part of the frequency band of the current device is too low, and the fault is determined to be a monitoring device frequency band signal level too low alarm, which may exist hardware failure or antenna problem of the device, and the fault determination result, collection time, device number, and source position are output as fault information data;

[0073] If the RSSI parameters of other monitoring devices at the same source position, operator, and frequency band are all abnormal, the RSSI signal parameters of the current monitoring device at the same source position and other frequency bands are further extracted and compared with the RSSI parameter abnormal data;

[0074] If the RSSI parameter of the current monitoring device is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0075] Optionally, when it is identified that the signal parameter abnormal data of the current monitoring device is RSRP parameter abnormality, the RSRP parameter is compared with the RSSI parameter;

[0076] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0077] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0078] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0079] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0080] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0081] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data of the same time period, the same source position, the same operator and the same frequency band collected by other analog frequency sweep monitoring devices of the same room distribution system is extracted and compared with the RSRP parameter abnormal data;

[0082] S4, based on the output fault information data, generating an alarm information and notifying a maintenance personnel, realizing fault early warning notification, wherein the alarm information is notified to the passive room distribution system maintenance personnel in the form of a work order or an email, realizing fault early warning notification.

[0083] As shown in the figure, a passive room distribution system signal source fault early warning system comprises a signal parameter collection module, a data preprocessing module, a fault identification module and a fault early warning module. Figure 2

[0084] 1. The signal parameter collection module is used for periodically scanning the signal source of the passive room distribution system using an analog frequency sweeping device, collecting and storing signal parameters.

[0085] Optionally, the signal parameters include acquisition time, signal source position, operator received signal strength (RSSI) and reference signal received power (RSRP) and the like.

[0086] In another embodiment of the present application, the signal parameter collection module realizes periodic awakening of the analog frequency sweeping device by setting the awakening time of the analog frequency sweeping device, completes the signal parameter scanning collection of the passive room distribution system, and records and stores the signal parameter data collected in the scanning process in real time after the signal parameter collection is completed.

[0087] 2. The data preprocessing module is used for detecting abnormal values of the collected signal parameters, identifying signal parameter abnormal data, and performing noise reduction processing on the identified signal parameter abnormal data.

[0088] Optionally, for the collected signal parameters, the data preprocessing module uses the Z-score (standard score) method to detect abnormal values of the RSSI parameters and the RSRP parameters of the same signal source position, operator and frequency band (Band).

[0089] In another embodiment of the present application, the data preprocessing module identifies abnormal values by calculating the standard deviation distance of each data point of the RSSI parameter from the mean value by the Z-score method, and the steps include:

[0090] A. Calculate the mean value of the RSSI parameter and the standard deviation , which is expressed by the formula,

[0091]

[0092]

[0093] wherein, is the i-th RSSI value; is the mean value of the RSSI; ​is the standard deviation of RSSI; N is the number of samples;

[0094] B. Calculate the standard deviation distance of each data point of RSSI parameter from the mean to identify outliers, which is expressed as,

[0095]

[0096] wherein, is the standard score, which represents the standard deviation distance of each data point of RSSI parameter from the mean;

[0097] C. Set the threshold of outliers, when when the set threshold is exceeded, the current RSSI parameter is identified as an outlier.

[0098] In another embodiment of the present application, the data preprocessing module further calculates the standard deviation distance of each data point of RSRP parameter from the mean by Z-score method to identify outliers, the steps of which include:

[0099] a. Calculate the mean of RSRP parameter and the standard deviation , which is expressed as

[0100]

[0101]

[0102] wherein, is the ith RSRP value; is the mean of RSRP; is the standard deviation of RSRP; N is the number of samples;

[0103] b. Calculate the standard deviation distance of each data point of RSRP parameter from the mean to identify outliers, which is expressed as,

[0104]

[0105] wherein, is the standard score, which represents the standard deviation distance of each data point of RSRP parameter from the mean;

[0106] c. Set the threshold of outliers, when when the set threshold is exceeded, the current RSRP parameter is identified as an outlier.

[0107] In another embodiment of the present application, for the identified signal parameter abnormal data, an interpolation method is adopted to replace the mean value of adjacent data points of the RSSI parameter or RSRP parameter of the same source position, operator and frequency band of the same analog sweep frequency monitoring device to realize signal data noise reduction processing, effectively eliminate the influence of RSSI parameter and RSRP parameter abnormal values when the source fails, improve the signal parameter data quality, and support subsequent fault analysis.

[0108] 3. The fault identification module is used for fault determination based on the identified signal parameter abnormal data, and extracts corresponding signal characteristics to output fault information data.

[0109] Optionally, when it is identified that the signal parameter abnormal data of the current monitoring device is RSSI parameter abnormality, RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive room distribution system at the same period, the same source position, the same operator and the same frequency band are also required to be extracted and compared with the RSSI parameter abnormal data;

[0110] If the corresponding frequency band of the other monitoring devices is normal or some devices have the same abnormality, it is considered that the signal level of the current device in some frequency bands is too low, and the fault is determined to be a monitoring device frequency band signal level too low alarm, which may exist device hardware failure or antenna problem, and the fault determination result, collection time, device number and source position and other fault information data are output;

[0111] If the RSSI parameters of the other monitoring devices at the same source position, operator and frequency band are all abnormal, the RSSI signal parameters of the current monitoring device at the same source position and other frequency bands are further extracted and compared with the RSSI parameter abnormal data;

[0112] If the RSSI parameters of the current monitoring device at the same source position and other frequency bands also have the same abnormality, it is determined that the fault is that the signal level of all frequency bands is too low, and the room distribution link may have a problem alarm; otherwise, if the RSSI parameters of other frequency bands do not have the same abnormality, it is determined that the fault is a part of the source fault of the room distribution system, and the fault determination result, collection time, device number and source position and other fault information data are output;

[0113] Optionally, when it is identified that the signal parameter abnormal data of the current monitoring device is RSRP parameter abnormality, the RSRP parameter is compared with the RSSI parameter;

[0114] If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, the RSRP parameter data collected by other analog sweep frequency monitoring devices in the same room distribution system at the same period, the same source position, the same operator and the same frequency band are extracted and compared with the RSRP parameter abnormal data;

[0115] If other monitoring devices also have the same abnormal situation, it is determined that the fault is that there is a lot of noise or interference in the signal parameters, resulting in a decrease in signal quality, and the fault determination result, collection time, device number, and source location are output as fault information data.

[0116] If the RSSI parameter is greater than the RSRP parameter, and the RSSI is marked as abnormal data, then the RSSI parameter data collected by other analog sweep frequency monitoring devices of the same room distribution system at the same period, the same source location, the same operator and frequency band are extracted and compared with the RSSI parameter abnormal data.

[0117] If other monitoring devices have normal corresponding frequency bands or some devices have the same abnormal situation, it is considered that the signal level of the current device is too low in some frequency bands, and the fault is determined to be a monitoring device frequency band signal level alarm, which may be a hardware failure or antenna problem of the device, and the fault determination result, collection time, device number and source location are output as fault information data.

[0118] If the RSSI parameters of other monitoring devices at the same source location, operator and frequency band are all abnormal, the RSSI signal parameters of the current monitoring device at the same source location and other frequency bands are further extracted and compared with the RSSI parameter abnormal data.

[0119] If the RSSI parameters of the current monitoring device at the same source location and other frequency bands also have the same abnormal situation, it is determined that the fault is that the signal level of all frequency bands is too low, and the room distribution link may have a problem alarm. Otherwise, if the RSSI parameters of other frequency bands do not have the same abnormal situation, it is determined that the fault is a part of the source fault of the room distribution system, and the fault determination result, collection time, device number and source location are output as fault information data.

[0120] 4. The fault warning module is used to generate alarm information based on the output fault information data, and notify the maintenance personnel to realize fault warning notification; wherein the alarm information is notified to the passive room distribution system maintenance personnel in the form of a work order or an email to realize fault warning notification.

[0121] Based on the above disclosure, the present application also provides an electronic device. The electronic device of the embodiment of the present application comprises at least one processor and at least one storage medium electrically connected, the storage medium is electrically connected with the processor, wherein the storage medium stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method as described above.

[0122] Based on the same inventive concept, the application further provides a storage medium storing instructions executable by at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as above.

[0123] The above description and drawings suffice to fully illustrate the embodiments of the application to enable a person skilled in the art to practice them. Other embodiments can include structural and other changes. The embodiments represent only a few of the possible variations. Individual components and functions are optional and the order of operations can vary, unless explicitly required. Parts and features of some embodiments can be included in or replace parts and features of other embodiments. The embodiments of the application are not limited to the structures already described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A passive room subsystem source failure early warning method, characterized in that, The method comprises, periodically scanning the signal source of the passive room distribution system using the analog sweep frequency monitoring device, collecting and storing signal parameters; performing outlier detection on the signal parameters, identifying signal parameter abnormal data, and performing noise reduction processing on the identified signal parameter abnormal data; based on the identified signal parameter abnormal data, performing fault judgment, extracting corresponding signal characteristics, and outputting fault information data; based on the output fault information data, generating alarm information and notifying maintenance personnel to realize fault early warning; The fault judgment based on the identified signal parameter abnormal data specifically includes, when the signal parameter abnormal data is RSSI parameter abnormal data, extract RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive room distribution system at the same time, the same signal source position, the same operator and the same frequency band, and compare it with the RSSI parameter abnormal data, and perform fault judgment according to the comparison result; when the signal parameter abnormal data is RSRP parameter abnormal data, compare the RSRP parameter with the RSSI parameter, and perform fault judgment according to the comparison result; The comparison of the RSRP parameter and the RSSI parameter specifically includes, if the RSSI parameter is greater than the RSRP parameter, and the RSSI is marked as abnormal data, then extract the RSSI parameter data collected by other analog sweep frequency monitoring devices in the same room distribution system at the same time, the same signal source position, the same operator and the same frequency band, and compare it with the RSSI parameter abnormal data; if the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, then extract the RSRP parameter data collected by other analog sweep frequency monitoring devices in the same room distribution system at the same time, the same signal source position, the same operator and the same frequency band, and compare it with the RSRP parameter abnormal data.

2. The method of claim 1, wherein the step of determining the failure of the source comprises the steps of: determining whether the source is in a standby mode; and determining whether the source is in a failure mode. The signal parameters include collection time, signal source position, operator, frequency band, received signal strength and reference signal received power.

3. The method of claim 1, wherein the step of determining the failure of the source comprises the steps of: determining whether the source is in a standby mode; and determining whether the source is in a sleep mode. The outlier detection on the signal parameters specifically includes using the Z-score method to perform outlier detection on the RSSI parameters and RSRP parameters of the same signal source position, operator and frequency band in the signal parameters.

4. The method for early warning of signal source failure in a passive indoor distribution system according to claim 1, characterized in that, The noise reduction processing on the identified signal parameter abnormal data specifically includes using the interpolation method to replace the mean value of adjacent data points of the RSSI parameters or RSRP parameters of the same signal source position, operator and frequency band of the same analog sweep frequency monitoring device to realize noise reduction processing of the signal data.

5. A passive room subsystem source failure warning system, characterized by The system comprises a signal parameter collection module, a data preprocessing module, a fault identification module and a fault early warning module; The signal parameter collection module is used to periodically scan the signal source of the passive room distribution system using the analog sweep frequency monitoring device, collect and store signal parameters; The data preprocessing module is used to perform outlier detection on the signal parameters, identify signal parameter abnormal data, and perform noise reduction processing on the identified signal parameter abnormal data; The fault identification module is configured to determine a fault based on the identified signal parameter abnormal data, extract corresponding signal features, and output fault information data. The fault warning module is configured to generate an alarm based on the output fault information data, and notify a maintenance personnel to achieve fault warning notification. The fault identification module is configured to, When the signal parameter abnormal data is RSSI parameter abnormal data, RSSI parameter data of the same time period, same signal source position, same operator, and same frequency band collected by other analog sweep frequency monitoring devices in the same passive room division system is extracted and compared with the RSSI parameter abnormal data, and a fault is determined based on the comparison result. When the signal parameter abnormal data is RSRP parameter abnormal data, the RSRP parameter is compared with the RSSI parameter, and a fault is determined based on the comparison result. The fault identification module is configured to, If the RSSI parameter is greater than the RSRP parameter, and the RSSI is marked as abnormal data, RSSI parameter data of the same time period, same signal source position, same operator, and same frequency band collected by other analog sweep frequency monitoring devices in the same room division system is extracted and compared with the RSSI parameter abnormal data. If the RSSI parameter is greater than the RSRP parameter, and the RSSI parameter is not marked as abnormal data, RSRP parameter data of the same time period, same signal source position, same operator, and same frequency band collected by other analog sweep frequency monitoring devices in the same room division system is extracted and compared with the RSRP parameter abnormal data.

6. The system of claim 5, wherein the system further comprises a fault detection system. The signal parameters include collection time, signal source position, operator, frequency band, received signal strength, and reference signal received power.

7. The system of claim 5, wherein the system further comprises a fault detection system. The data preprocessing module is configured to perform abnormal value detection on the RSSI parameter and the RSRP parameter of the same signal source position, operator, and frequency band in the signal parameters by using a Z-score method.

8. The system of claim 5, wherein the system further comprises a fault detection system. The data preprocessing module is configured to replace the identified signal parameter abnormal data with a mean value of adjacent data points of the RSSI parameter or the RSRP parameter of the same signal source position, operator, and frequency band of the same analog sweep frequency monitoring device by using an interpolation method to achieve signal data noise reduction processing.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the passive room division system signal source fault warning method of any one of claims 1-4.

10. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the passive room division system signal source fault warning method of any one of claims 1-4. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the passive room division system signal source fault warning method of any one of claims 1-4. ​

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