Information source fault early warning method, system, medium and equipment for passive indoor distribution system

Abstract

The invention provides an information source fault early warning method, system, medium and equipment for a passive indoor distribution system, and belongs to the technical field of information source fault early warning, and the method comprises the steps: carrying out the regular scanning of an information source of the passive indoor distribution system through employing simulation frequency sweeping monitoring equipment, collecting and storing signal parameters, carrying out the abnormal value detection, and recognizing the abnormal number of the signal parameters; performing noise reduction processing on the identified signal parameter abnormal data; and based on the identified signal parameter abnormal data, carrying out fault determination, extracting corresponding signal features, outputting fault information data, generating alarm information, and notifying maintenance personnel to realize fault early warning notification. According to the invention, the timeliness and accuracy of information source fault discovery are improved, the duration of signal attenuation or interruption caused by untimely information source fault discovery is reduced, and the communication experience of a user is remarkably improved; moreover, through an intelligent monitoring means, the workload of manual inspection is reduced, the working efficiency is improved, and the human input cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of information source fault early warning, and in particular relates to a method, system, medium and equipment for early warning of information source fault in a passive indoor distributed system. Background Art

[0002] Passive indoor distributed systems play a crucial role in indoor signal coverage. Their performance is like the lifeline of the communications network, closely linked to the user's communication experience. Whether it's the clarity and stability of voice calls or the speed and accuracy of data transmission, they all depend on it. However, in actual operational scenarios, signal source failures are like a ticking time bomb. Once they occur, they can cause significant signal attenuation, resulting in intermittent calls and slow data loading. In severe cases, they can even cause outright signal interruption, instantly disrupting users' ability to communicate, severely damaging overall system performance and significantly reducing user satisfaction with communication services.

[0003] Currently, the traditional method for signal fault detection is primarily manual inspection. This model has significant drawbacks. First, manual inspection consumes a significant amount of manpower, material resources, and time. Inspectors must regularly travel through every corner of the complex indoor distribution system, checking equipment and lines one by one, resulting in extremely low efficiency. Second, manual inspections are difficult to achieve real-time monitoring. In the intervals between inspections, faults may quietly occur without anyone noticing, making it impossible to identify problems in a timely manner, let alone respond and resolve them quickly. This model is no longer able to keep up with the urgent need for real-time monitoring in modern communication networks. Summary of the Invention

[0004] The present invention addresses the deficiencies in the prior art and provides a method, system, medium and device for warning of signal source failure in a passive indoor distributed system.

[0005] In a first aspect, a method for early warning of a signal source failure in a passive indoor distributed system is provided, the method comprising: Use analog frequency sweep monitoring equipment to regularly scan the signal source of the passive indoor distributed system, collect and store signal parameters; Performing outlier detection on the signal parameters, identifying abnormal signal parameter data, and performing noise reduction processing on the identified abnormal signal parameter data; Based on the identified signal parameter abnormal data, fault judgment is performed, corresponding signal features are extracted, and fault information data is output; Based on the output fault information data, alarm information is generated and notified to maintenance personnel to realize fault early warning notification.

[0006] Furthermore, the signal parameters include acquisition time, source location, operator, frequency band, received signal strength and reference signal received power.

[0007] Furthermore, the signal parameters are subjected to outlier detection, specifically including performing outlier detection on RSSI parameters and RSRP parameters of the signal parameters with the same source location, operator and frequency band using a Z-score method.

[0008] Furthermore, the noise reduction processing is performed on the identified abnormal signal parameter data, specifically including: for the identified abnormal signal parameter data, an interpolation method is used to replace the RSSI parameters or RSRP parameters of adjacent data points with the same signal source position, operator, and frequency band of the same analog frequency sweep monitoring equipment to achieve noise reduction processing of the signal data.

[0009] Furthermore, the fault determination based on the identified abnormal signal parameter data specifically includes: When the abnormal signal parameter data is RSSI parameter abnormal data, extract RSSI parameter data collected by other analog sweep frequency monitoring equipment in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormal data, and make a fault determination based on the comparison result; When the abnormal signal parameter data is abnormal RSRP parameter data, the RSRP parameter is compared with the RSSI parameter, and a fault determination is performed based on the comparison result.

[0010] Furthermore, the comparing of the RSRP parameter with the RSSI parameter specifically includes: If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; 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 period, same source location, same operator and frequency band collected by other analog frequency sweep monitoring equipment in the same indoor distributed system is extracted and compared with the abnormal RSRP parameter data.

[0011] In a second aspect, a passive indoor distributed system signal source fault warning system comprises: a signal parameter collection module, a data preprocessing module, a fault identification module and a fault warning module; The signal parameter collection module is used to use an analog frequency sweep monitoring device to periodically scan the signal source of the passive indoor distributed system, collect and store signal parameters; The data preprocessing module is used to perform outlier detection on the signal parameters, identify abnormal signal parameter data, and perform noise reduction on the identified abnormal signal parameter data; The fault identification module is used to perform fault determination based on the identified signal parameter abnormality data, extract the corresponding signal features, and output fault information data; The fault warning module is used to generate alarm information based on the output fault information data and notify maintenance personnel to realize fault warning notification.

[0012] Furthermore, the signal parameters include acquisition time, source location, operator, frequency band, received signal strength and reference signal received power.

[0013] Furthermore, the data preprocessing module is specifically used to perform outlier detection on the RSSI parameters and RSRP parameters of the signal parameters with the same source location, operator and frequency band using a Z-score method.

[0014] Furthermore, the data preprocessing module is specifically used to use the interpolation method to replace the identified abnormal signal parameter data with the mean of the RSSI parameters or RSRP parameters of the same source position, operator, and frequency band of the same analog frequency sweep monitoring equipment to achieve noise reduction processing of the signal data.

[0015] Furthermore, the fault identification module is specifically used to: When the abnormal signal parameter data is RSSI parameter abnormal data, extract RSSI parameter data collected by other analog sweep frequency monitoring equipment in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormal data, and make a fault determination based on the comparison result; When the abnormal signal parameter data is abnormal RSRP parameter data, the RSRP parameter is compared with the RSSI parameter, and a fault determination is performed based on the comparison result.

[0016] Furthermore, the fault identification module is further configured to: If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; 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 period, same source location, same operator and frequency band collected by other analog frequency sweep monitoring equipment in the same indoor distributed system is extracted and compared with the abnormal RSRP parameter data.

[0017] In a third aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned passive indoor distributed system signal source fault early warning methods are implemented.

[0018] 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 communicate with each other via the communication bus; Memory for storing computer programs; The processor is used to implement the steps of any of the above-mentioned passive indoor distributed system signal source fault early warning methods when executing the program stored in the memory.

[0019] Compared with the prior art, the present invention has the following advantages: 1. The present invention uses analog frequency sweep monitoring equipment to regularly scan and collect signal parameters of the passive indoor distributed system, and then uses the Z-score method to detect outliers on the signal parameters, which can accurately and timely identify abnormal data in the signal parameters. At the same time, the interpolation method is used to reduce the noise of the identified abnormal data, which effectively improves the quality and reliability of the signal data and provides a solid foundation for subsequent fault determination. Based on in-depth analysis and multi-dimensional comparison of abnormal data, the fault type and location can be accurately determined, and alarm information can be quickly generated to notify maintenance personnel, realizing rapid warning and response to faults.

[0020] 2. Compared to traditional manual inspection methods, this invention significantly improves the timeliness and accuracy of signal source fault detection, reduces the duration of signal attenuation or interruption caused by untimely fault detection, and significantly improves the user's communication experience. Furthermore, through intelligent monitoring methods, the workload of manual inspections is greatly reduced, work efficiency is improved, and labor costs are reduced.

[0021] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1A schematic flow chart of a method for early warning of a signal source failure in a passive indoor distributed system according to the present invention is shown.

[0024] Figure 2 A schematic diagram of a module of a signal source fault early warning system of a passive indoor distributed system of the present invention is shown. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0026] like Figure 1 As shown, a method for early warning of signal source failure of a passive indoor distributed system comprises the following steps: S1. Use analog frequency sweep monitoring equipment to regularly scan the signal source of the passive indoor distributed system, collect and store signal parameters.

[0027] Optionally, the signal parameters include acquisition time, signal source location, operator, frequency band, received signal strength (RSSI), reference signal received power (RSRP), etc.

[0028] In another embodiment of the present invention, by setting the wake-up time of the analog frequency sweep monitoring device, the analog frequency sweep monitoring device is woken up periodically to complete the scanning and collection of the passive indoor distributed system signal parameters, and after the signal parameter collection is completed, the signal parameter data collected during the scanning process is recorded and stored in real time.

[0029] S2. Perform outlier detection on the collected important signal parameters, identify abnormal signal parameter data, and perform noise reduction on the identified abnormal signal parameter data.

[0030] Optionally, a Z-score (standard score) method is used to detect outliers for RSSI parameters and RSRP parameters of the same source location, operator, and frequency band (Band) among the collected signal parameters.

[0031] In another embodiment of the present invention, the Z-score method is used to calculate the standard deviation distance between each data point of the RSSI parameter and the mean to identify outliers, and the steps include: A. Calculate the mean of RSSI parameters and standard deviation , its formula is expressed as,

[0032]

[0033] in, is the i-th RSSI value; is the mean RSSI; is the standard deviation of RSSI; N is the number of samples; B. Calculate the standard deviation distance between each data point and the mean of the RSSI parameter to identify outliers. The formula is:

[0034] in, is the standard score, which indicates the standard deviation distance between each data point of the RSSI parameter and the mean; C. Set the outlier threshold. When the RSSI exceeds the set threshold, the current RSSI parameter is identified as an abnormal value.

[0035] In another embodiment of the present invention, the Z-score method is used to calculate the standard deviation distance between each data point of the RSRP parameter and the mean to identify outliers, and the steps include: a. Calculate the mean of RSRP parameters and standard deviation , its formula is expressed as,

[0036]

[0037] in, is the i-th RSRP value; is the mean value of RSRP; is the standard deviation of RSRP; N is the number of samples; b. Calculate the standard deviation distance between each data point of RSRP parameter and the mean to identify outliers. The formula is expressed as follows:

[0038] in, is the standard score, which indicates the standard deviation distance between each data point of RSRP parameter and the mean; c. Set the outlier threshold. When the set threshold is exceeded, the current RSRP parameter is identified as an abnormal value.

[0039] In another embodiment of the present invention, for the identified abnormal signal parameter data, the interpolation method is used to replace the RSSI parameters or RSRP parameters of the same analog frequency sweep monitoring equipment with the same signal source location, operator, and frequency band with the average value of the adjacent data points to achieve noise reduction processing of the signal data, effectively eliminate the influence of abnormal values ​​of RSSI parameters and RSRP parameters when the signal source fails, improve the quality of signal parameter data, and support subsequent fault analysis.

[0040] S3. Based on the identified signal parameter abnormal data, perform fault judgment, extract corresponding signal features, and output fault information data.

[0041] Optionally, when the abnormal signal parameter data of the current monitoring device is identified as an RSSI parameter abnormality, it is also necessary to extract RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormality data; If the corresponding frequency bands of other monitoring devices are normal or some devices have the same abnormal situation, the signal level of some frequency bands of the current device is considered to be too low. The fault is determined to be a low signal level alarm of the monitoring device frequency band, which may be due to a hardware failure or antenna problem of the device. Fault information data such as the fault judgment result, acquisition time, device number and signal source location are output; If the RSSI parameters of other monitoring devices with the same signal source location, operator, and frequency band are abnormal, further extract the RSSI signal parameters of the current monitoring device with the same signal source location and other frequency bands and compare them with the RSSI parameter abnormal data; If the RSSI parameters of other frequency bands at the same signal source location as the current monitoring device also have the same abnormal situation, the fault is determined to be too low signal level strength in all frequency bands, and an alarm may be issued for a problem in the indoor distribution link; conversely, if the RSSI parameters of other frequency bands do not have the same abnormal situation, the fault is determined to be a partial signal source failure in the indoor distribution system, and fault information data such as the fault judgment result, acquisition time, device number, and signal source location are output; Optionally, when it is identified that the abnormal signal parameter data of the currently monitored device is an abnormal RSRP parameter, the RSRP parameter is compared with the RSSI parameter; If the RSSI parameter is greater than the RSRP parameter and the RSSI parameter is not marked as abnormal data, extract the RSRP parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare it with the abnormal RSRP parameter data; If other monitoring devices also have the same abnormal situation, the fault is determined to be a large amount of noise or interference in the signal parameters, resulting in a decrease in signal quality. The fault information data such as the fault judgment result, acquisition time, device number, and signal source location are output; If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; If the corresponding frequency bands of other monitoring devices are normal or some devices have the same abnormal situation, the signal level of some frequency bands of the current device is considered to be too low. The fault is determined to be a low signal level alarm of the monitoring device frequency band, which may be due to a hardware failure or antenna problem of the device. Fault information data such as the fault judgment result, acquisition time, device number and signal source location are output; If the RSSI parameters of other monitoring devices with the same signal source location, operator, and frequency band are abnormal, further extract the RSSI signal parameters of the current monitoring device with the same signal source location and other frequency bands and compare them with the RSSI parameter abnormal data; If the RSSI parameters of other frequency bands at the same signal source location of the current monitoring device also have the same abnormal situation, the fault is determined to be that the signal level strength of all frequency bands is too low, and there may be a problem alarm in the indoor distribution link; conversely, if the RSSI parameters of other frequency bands do not have the same abnormal situation, the fault is determined to be a partial signal source failure in the indoor distribution system, and fault information data such as the fault judgment result, collection time, device number and signal source location are output.

[0042] S4. Based on the output fault information data, generate alarm information and notify maintenance personnel to realize fault early warning notification. The alarm information is notified to the maintenance personnel of the passive indoor distributed system in the form of a work order or email to realize fault early warning notification.

[0043] like Figure 2 As shown, a passive indoor distributed system signal source fault warning system includes: a signal parameter collection module, a data preprocessing module, a fault identification module and a fault warning module.

[0044] 1. The signal parameter collection module is used to periodically scan the signal source of the passive indoor distributed system using an analog frequency sweep device to collect and store signal parameters.

[0045] Optionally, the signal parameters include acquisition time, signal source location, operator received signal strength (RSSI), reference signal received power (RSRP), etc.

[0046] In another embodiment of the present invention, the signal parameter collection module realizes periodic wake-up of the analog frequency sweeping device by setting the wake-up time of the analog frequency sweeping device, completes the scanning and collection of the signal parameters of the passive indoor distributed system, and after the signal parameter collection is completed, records and stores the signal parameter data collected during the scanning process in real time.

[0047] 2. The data preprocessing module is used to detect outliers on the collected signal parameters, identify abnormal signal parameter data, and perform noise reduction on the identified abnormal signal parameter data.

[0048] Optionally, for the collected signal parameters, the data preprocessing module uses a Z-score (standard score) method to detect outliers on the RSSI parameters and RSRP parameters of the same signal source location, operator, and frequency band.

[0049] In another embodiment of the present invention, the data preprocessing module calculates the standard deviation distance between each data point of the RSSI parameter and the mean by the Z-score method to identify outliers, and the steps include: A. Calculate the mean of RSSI parameters and standard deviation , its formula is expressed as,

[0050]

[0051] in, is the i-th RSSI value; is the mean RSSI; is the standard deviation of RSSI; N is the number of samples; B. Calculate the standard deviation distance between each data point and the mean of the RSSI parameter to identify outliers. The formula is:

[0052] in, is the standard score, which indicates the standard deviation distance between each data point of the RSSI parameter and the mean; C. Set the outlier threshold. When the RSSI exceeds the set threshold, the current RSSI parameter is identified as an abnormal value.

[0053] In another embodiment of the present invention, the data preprocessing module further calculates the standard deviation distance between each data point of the RSRP parameter and the mean by a Z-score method to identify outliers, the steps of which include: a. Calculate the mean of RSRP parameters and standard deviation , which is expressed as

[0054]

[0055] in, is the i-th RSRP value; is the mean value of RSRP; is the standard deviation of RSRP; N is the number of samples; b. Calculate the standard deviation distance between each data point of RSRP parameter and the mean to identify outliers. The formula is expressed as follows:

[0056] in, is the standard score, which indicates the standard deviation distance between each data point of RSRP parameter and the mean; c. Set the outlier threshold. When the set threshold is exceeded, the current RSRP parameter is identified as an abnormal value.

[0057] In another embodiment of the present invention, for the identified abnormal signal parameter data, an interpolation method is used to replace the RSSI parameters or RSRP parameters of adjacent data points with the same signal source location, operator, and frequency band of the same analog frequency sweep monitoring device to achieve noise reduction processing of the signal data, effectively eliminate the influence of abnormal values ​​of RSSI parameters and RSRP parameters when the signal source fails, improve the quality of signal parameter data, and support subsequent fault analysis.

[0058] 3. The fault identification module is used to make fault judgments based on the identified signal parameter abnormal data, extract corresponding signal features, and output fault information data.

[0059] Optionally, when the abnormal signal parameter data of the current monitoring device is identified as an RSSI parameter abnormality, it is also necessary to extract RSSI parameter data collected by other analog sweep frequency monitoring devices in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormality data; If the corresponding frequency bands of other monitoring devices are normal or some devices have the same abnormal situation, the signal level of some frequency bands of the current device is considered to be too low. The fault is determined to be a low signal level alarm of the monitoring device frequency band, which may be due to a hardware failure or antenna problem of the device. Fault information data such as the fault judgment result, acquisition time, device number and signal source location are output; If the RSSI parameters of other monitoring devices with the same signal source location, operator, and frequency band are abnormal, further extract the RSSI signal parameters of the current monitoring device with the same signal source location and other frequency bands and compare them with the RSSI parameter abnormal data; If the RSSI parameters of other frequency bands at the same signal source location as the current monitoring device also have the same abnormal situation, the fault is determined to be too low signal level strength in all frequency bands, and an alarm may be issued for a problem in the indoor distribution link; conversely, if the RSSI parameters of other frequency bands do not have the same abnormal situation, the fault is determined to be a partial signal source failure in the indoor distribution system, and fault information data such as the fault judgment result, acquisition time, device number, and signal source location are output; Optionally, when it is identified that the abnormal signal parameter data of the currently monitored device is an abnormal RSRP parameter, the RSRP parameter is compared with the RSSI parameter; If the RSSI parameter is greater than the RSRP parameter and the RSSI parameter is not marked as abnormal data, extract the RSRP parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare it with the abnormal RSRP parameter data; If other monitoring devices also have the same abnormal situation, the fault is determined to be a large amount of noise or interference in the signal parameters, resulting in a decrease in signal quality. The fault information data such as the fault judgment result, acquisition time, device number, and signal source location are output; If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; If the corresponding frequency bands of other monitoring devices are normal or some devices have the same abnormal situation, the signal level of some frequency bands of the current device is considered to be too low. The fault is determined to be a low signal level alarm of the monitoring device frequency band, which may be due to a hardware failure or antenna problem of the device. Fault information data such as the fault judgment result, acquisition time, device number and signal source location are output; If the RSSI parameters of other monitoring devices with the same signal source location, operator, and frequency band are abnormal, further extract the RSSI signal parameters of the current monitoring device with the same signal source location and other frequency bands and compare them with the RSSI parameter abnormal data; If the RSSI parameters of other frequency bands at the same signal source location of the current monitoring device also have the same abnormal situation, the fault is determined to be that the signal level strength of all frequency bands is too low, and there may be a problem alarm in the indoor distribution link; conversely, if the RSSI parameters of other frequency bands do not have the same abnormal situation, the fault is determined to be a partial signal source failure in the indoor distribution system, and fault information data such as the fault judgment result, collection time, device number and signal source location are output.

[0060] 4. The fault warning module is used to generate alarm information based on the output fault information data and notify maintenance personnel to realize fault warning notification; among them, the alarm information is notified to the passive indoor distributed system maintenance personnel in the form of a work order or email to realize fault warning notification.

[0061] Based on the above disclosure, the present invention also provides an electronic device. The electronic device according to an embodiment of the present invention includes at least one processor and at least one storage medium electrically connected to each other, wherein the storage medium is electrically connected to 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 perform the method described above.

[0062] Based on the same inventive concept, the present invention further provides a storage medium, which stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor to enable the at least one processor to execute the above method.

[0063] The above description and accompanying drawings sufficiently illustrate the embodiments of the present invention to enable those skilled in the art to practice them. Other embodiments may include structural and other changes. The embodiments represent only possible variations. Unless expressly required, individual components and functions are optional, and the order of operations may vary. Portions and features of some embodiments may be included in or replace portions and features of other embodiments. The embodiments of the present invention are not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for warning of signal source failure in a passive indoor distributed system, characterized in that: The method comprises, Use analog frequency sweep monitoring equipment to regularly scan the signal source of the passive indoor distributed system, collect and store signal parameters; Performing outlier detection on the signal parameters, identifying abnormal signal parameter data, and performing noise reduction processing on the identified abnormal signal parameter data; Based on the identified signal parameter abnormal data, fault judgment is performed, corresponding signal features are extracted, and fault information data is output; Based on the output fault information data, alarm information is generated and notified to maintenance personnel to realize fault early warning notification.

2. The passive indoor distributed system signal source fault early warning method according to claim 1, characterized in that: The signal parameters include acquisition time, source location, operator, frequency band, received signal strength and reference signal received power.

3. The passive indoor distributed system signal source fault early warning method according to claim 1, characterized in that: The performing outlier detection on the signal parameters specifically includes performing outlier detection on the RSSI parameters and RSRP parameters of the signal parameters with the same source location, operator and frequency band using the Z-score method.

4. The passive indoor distributed system signal source fault early warning method according to claim 1, characterized in that: The noise reduction processing of the identified abnormal signal parameter data specifically includes using an interpolation method to replace the identified abnormal signal parameter data with the average value of the RSSI parameters or RSRP parameters of the same analog sweep frequency monitoring equipment, the same source location, operator, and frequency band to achieve noise reduction processing of the signal data.

5. The passive indoor distributed system signal source fault early warning method according to claim 1, characterized in that: The fault determination based on the identified abnormal signal parameter data specifically includes: When the abnormal signal parameter data is RSSI parameter abnormal data, extract RSSI parameter data collected by other analog sweep frequency monitoring equipment in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormal data, and make a fault determination based on the comparison result; When the abnormal signal parameter data is abnormal RSRP parameter data, the RSRP parameter is compared with the RSSI parameter, and a fault determination is performed based on the comparison result.

6. The passive indoor distributed system signal source fault early warning method according to claim 5, characterized in that: The comparing of the RSRP parameter with the RSSI parameter specifically includes: If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; 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 period, same source location, same operator and frequency band collected by other analog frequency sweep monitoring equipment in the same indoor distributed system is extracted and compared with the abnormal RSRP parameter data.

7. A passive indoor distributed system signal source fault warning system, characterized in that: The system includes: a signal parameter collection module, a data preprocessing module, a fault identification module and a fault warning module; The signal parameter collection module is used to use an analog frequency sweep monitoring device to periodically scan the signal source of the passive indoor distributed system, collect and store signal parameters; The data preprocessing module is used to perform outlier detection on the signal parameters, identify abnormal signal parameter data, and perform noise reduction on the identified abnormal signal parameter data; The fault identification module is used to perform fault determination based on the identified signal parameter abnormality data, extract the corresponding signal features, and output fault information data; The fault warning module is used to generate alarm information based on the output fault information data and notify maintenance personnel to realize fault warning notification.

8. The passive indoor distributed system signal source fault early warning system according to claim 7, characterized in that: The signal parameters include acquisition time, source location, operator, frequency band, received signal strength and reference signal received power.

9. The passive indoor distributed system signal source fault early warning system according to claim 7, characterized in that: The data preprocessing module is specifically used to detect outliers using the Z-score method on the RSSI parameters and RSRP parameters of the signal parameters with the same source location, operator and frequency band.

10. The passive indoor distributed system signal source fault early warning system according to claim 7, characterized in that: The data preprocessing module is specifically used to use the interpolation method to replace the identified abnormal signal parameter data with the mean of the RSSI parameters or RSRP parameters of the same analog sweep frequency monitoring equipment, the same source location, operator, and frequency band, to achieve noise reduction processing of the signal data.

11. The passive indoor distributed system signal source fault early warning system according to claim 7, characterized in that: The fault identification module is specifically used to: When the abnormal signal parameter data is RSSI parameter abnormal data, extract RSSI parameter data collected by other analog sweep frequency monitoring equipment in the same passive indoor distributed system at the same time period, same signal source location, same operator and frequency band, and compare them with the RSSI parameter abnormal data, and make a fault determination based on the comparison result; When the abnormal signal parameter data is abnormal RSRP parameter data, the RSRP parameter is compared with the RSSI parameter, and a fault determination is performed based on the comparison result.

12. The passive indoor distributed system signal source fault early warning system according to claim 11, characterized in that: The fault identification module is specifically used to: If the RSSI parameter is greater than the RSRP parameter and the RSSI is marked as abnormal data, the RSSI parameter data collected by other analog frequency sweep monitoring equipment in the same indoor distributed system at the same time period, same signal source location, same operator and frequency band are extracted and compared with the abnormal RSSI parameter data; 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 period, same source location, same operator and frequency band collected by other analog frequency sweep monitoring equipment in the same indoor distributed system is extracted and compared with the abnormal RSRP parameter data.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the passive indoor distributed system signal source fault early warning method according to any one of claims 1 to 6 are implemented.

14. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is used to implement the steps of the passive indoor distributed system signal source fault early warning method described in any one of claims 1-6 when executing the program stored in the memory.

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