Comprehensive evaluation method and system for refined service operation and maintenance model

Abstract

The invention discloses a comprehensive evaluation method and system for a refined service operation and maintenance model, and relates to the technical field of service operation and maintenance evaluation, and the method comprises the steps: 1, obtaining earthquake early warning equipment information, 2, obtaining earthquake early warning network information, and 3, carrying out earthquake early warning evaluation. Through an earthquake early warning platform, equipment data of each prediction of earthquake early warning are obtained, equipment performance indexes of each prediction of the earthquake early warning platform are analyzed, network transmission data of each prediction of earthquake early warning are obtained from the earthquake early warning platform, network quality indexes of each prediction of the earthquake early warning platform are analyzed, and the network quality indexes of each prediction of the earthquake early warning platform are analyzed. The problem that the accurate index of the receiver cannot be predicted is reduced, the situation that the technician determines the system problem is improved, the problem that technical optimization and system upgrading work lacks pertinence is reduced, the level of an earthquake early warning platform is improved, the performance of equipment is improved, data transmission delay is reduced, and the reliability of the system is improved. And an early warning error caused by data loss is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of business operation and maintenance evaluation, and in particular to a refined business operation and maintenance model comprehensive evaluation method and system. Background Art

[0002] With the continuous development of earthquake monitoring technology, a large number of advanced earthquake sensors have been deployed in different geographical areas. These sensors can collect rich earthquake data in real time, including multi-dimensional information such as the amplitude, frequency, and propagation direction of earthquake waves. However, the processing and integration of massive data faces huge challenges. Delays, packet loss, and compatibility issues of different data formats in the data transmission process often occur, which seriously affect the timeliness and accuracy of earthquake information. The comprehensive evaluation method and system of refined business operation and maintenance models need to collect a large amount of data for evaluation, including equipment performance indicators, system operation status, network traffic and other information. Similarly, the earthquake early warning system also needs to collect seismic wave data and equipment operation status data from earthquake monitoring stations. Therefore, it is necessary to conduct a comprehensive evaluation of the refined business operation and maintenance model of the earthquake early warning platform.

[0003] Prior art, such as the invention application patent with announcement number: CN116862417A, discloses a method for identifying seismic performance based on refined assessment of earthquake disaster risk, which relates to the technical field of earthquake disaster data processing. The seismic performance identification method based on refined assessment of earthquake disaster risk includes the following steps: data processing; image mapping; construction of a seismic assessment model; determination of the stress distribution range. The invention collects seismic data in a preset area and processes the data, and uses seismic refraction waves to obtain underground structure information in a preset area in the seismic image waveform and performs image mapping, then combines the propagation characteristics of seismic waves to construct a seismic assessment model and conducts an earthquake simulation assessment, and finally determines the stress distribution range based on the results of the earthquake simulation assessment and formulates corresponding risk prevention and control measures according to the risk control target, thereby achieving the effect of improving the efficiency of earthquake disaster risk assessment and solving the problem of long time consumption of earthquake disaster risk assessment in the prior art.

[0004] Based on the above solutions, it can be found that a refined business operation and maintenance model comprehensive evaluation method and system in the existing technology can meet the basic requirements, but there are also some potential defects and challenges, which are specifically reflected in the following aspects:

[0005] 1. The existing technology has not conducted in-depth research on the equipment performance index of each prediction of the earthquake early warning platform. The equipment performance index of each prediction of the earthquake early warning platform has a crucial impact on earthquake early warning. Due to the inaccurate magnitude error values, time error values ​​and position error values ​​in various regions, it is impossible to predict the accurate index of the receiver, making it difficult for technical personnel to identify the problems of the system, resulting in a lack of pertinence in technical optimization and system upgrades, and an inability to effectively solve key problems, causing the earthquake early warning platform to hover at a low level and difficult to improve its performance.

[0006] 2. The existing technology does not pay enough attention to judging whether the earthquake early warning platform needs to be adjusted. It cannot match the appropriate prediction methods of the equipment in various regions and the adjustment parameters of the network to judge the earthquake early warning platform. It cannot timely screen out the network transmission and network status with poor performance in abnormal areas, resulting in increased warning time errors and missed precious risk avoidance time. It may cause the system to frequently malfunction when an earthquake occurs and fail to perform the early warning function normally, thereby affecting the long-term stable operation of the system. Summary of the invention

[0007] The purpose of the present invention is to provide a refined business operation and maintenance model comprehensive evaluation method and system to solve the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a refined business operation and maintenance model comprehensive evaluation method and system, including: step one, earthquake early warning equipment information, step two, earthquake early warning network information and step three, earthquake early warning evaluation.

[0009] Step 1. Earthquake early warning equipment information: Through the earthquake early warning platform, obtain the equipment data of each earthquake early warning prediction, and analyze the equipment performance index of each prediction of the earthquake early warning platform.

[0010] Step 2. Earthquake early warning network information: Obtain network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyze the network quality index of each prediction of the earthquake early warning platform.

[0011] Step 3, earthquake early warning evaluation: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, determine whether the earthquake early warning platform needs to be adjusted. If adjustment is required, match the appropriate prediction method of the equipment in each region and the adjustment parameters of the network.

[0012] Furthermore, the specific analysis method of the equipment performance index of each prediction of the earthquake early warning platform is: based on the obtained equipment data of each earthquake early warning prediction, wherein the equipment data includes: magnitude error value, time error value and position error value of each region, and extracting the magnitude reference error, time reference error value and position reference error value of the earthquake early warning platform from the database, and then analyzing the equipment performance index of each prediction of the earthquake early warning platform, and the specific calculation formula is: Among them, a' represents the magnitude reference error of the earthquake early warning platform, a vs It is represented by the magnitude error of the sth region predicted by the earthquake early warning platform in the vth time, d' is represented by the time reference error value of the earthquake early warning platform, d vs It is expressed as the time error of the sth region in the vth prediction of the earthquake early warning platform, f vs It represents the position error of the sth region in the vth prediction of the earthquake early warning platform, and f' represents the position reference error value of the earthquake early warning platform, where s represents the region number, s = 1, 2, ..., g, g represents the number of regions, v represents the prediction number, v = 1, 2, ..., h, and h represents the number of predictions.

[0013] Furthermore, the network quality index of each prediction of the earthquake early warning platform is specifically analyzed by: based on the obtained network transmission data of each earthquake early warning prediction, wherein the network transmission data includes the signal transmission speed, signal coverage area and signal packet loss rate of each region, the network transmission index M of each prediction of the earthquake early warning platform is analyzed. v and state index W v , and then analyze the network quality index of each prediction of the earthquake early warning platform. The specific calculation formula is: v =W v +M v .

[0014] Furthermore, the network status index of each prediction of the earthquake early warning platform is specifically analyzed by: based on the signal packet loss rate of each region predicted by the earthquake early warning platform, and extracting the signal packet loss reference rate of the earthquake early warning platform from the database, analyzing the network status index of each prediction of the earthquake early warning platform, and the specific calculation formula is: k' represents the signal packet loss reference rate of the earthquake early warning platform, k vs It is represented as the signal packet loss rate of the s-th region predicted by the earthquake early warning platform for the v-th time.

[0015] Furthermore, the specific analysis method of the network transmission index of each prediction of the earthquake early warning platform is as follows: based on the signal transmission speed and signal coverage area of ​​each region predicted by the earthquake early warning platform, the signal transmission speed and signal coverage area of ​​each region are ratio-processed to obtain the signal amplitude of each region predicted by the earthquake early warning platform, and the signal reference amplitude of the earthquake early warning platform is extracted from the database, and then the network transmission index of each prediction of the earthquake early warning platform is analyzed. The specific calculation formula is: Where j' represents the signal reference amplitude of the earthquake early warning platform, j vs It is expressed as the signal amplitude of the sth region predicted by the earthquake early warning platform for the vth time.

[0016] Furthermore, the determination of whether the earthquake early warning platform needs to be adjusted is specifically analyzed as follows: based on the obtained equipment performance index of each prediction of the earthquake early warning platform, the equipment performance index of each prediction of the earthquake early warning platform is compared with the equipment performance index safety range of the earthquake early warning platform stored in the database; if the equipment performance index of a certain prediction of the earthquake early warning platform is not within the equipment performance index safety range of the earthquake early warning platform, the predicted equipment performance index is recorded as an abnormal equipment state, indicating that the earthquake early warning platform needs to be adjusted, thereby matching the appropriate prediction method for the equipment in each region.

[0017] Based on the obtained network quality index of each prediction of the earthquake early warning platform, the network quality index of each prediction of the earthquake early warning platform is compared with the network quality index safety range of the earthquake early warning platform stored in the database. If the network quality index of a certain prediction of the earthquake early warning platform is not within the network quality index safety range of the earthquake early warning platform, the predicted network quality index is recorded as a network abnormal state, indicating that the earthquake early warning platform needs to be adjusted, and then the adjustment parameters of the network in each region are matched.

[0018] Furthermore, the specific analysis method of the suitable prediction method for matching equipment in various regions is as follows: based on the obtained abnormal status of the equipment, the regions corresponding to the abnormal status of each device are screened, and the seismic activity background of each region is obtained from the earthquake early warning platform, and the suitable prediction methods are matched with each earthquake-prone region and each region with less earthquake activity, and equipment is maintained and upgraded for each earthquake-prone region, and a regular equipment inspection system is established. For each region with less earthquake activity, equipment calibration is performed regularly to ensure the normal operation of the equipment.

[0019] Furthermore, the specific analysis method for matching the adjustment parameters of the network in each region is as follows: based on the obtained network abnormal state, screening each region corresponding to each network abnormal state, and obtaining each geographical environment from the earthquake early warning platform, matching the network adjustment parameters of each plain area, each basin area, each plateau area and each mountain area, increasing the earthquake monitoring stations added according to the network transmission demand in each plain area, improving the monitoring density, using shielded cables and installing shielded cabinets in each basin area to reduce the impact of seismic waves on network signals, taking cold-proof and warm-keeping measures for existing network equipment in each plateau area, and adopting a distributed network architecture in each mountain area to disperse the network equipment in different peaks and valleys, connected by wireless links or optical fibers to form a network covering the entire mountain area, reducing the impact of a single device failure on the entire network, and improving the reliability and stability of the network.

[0020] The system of a refined business operation and maintenance model comprehensive evaluation method described in the second aspect of the present invention is characterized in that it includes: an earthquake early warning equipment information module: through the earthquake early warning platform, obtaining the equipment data of each earthquake early warning prediction, and analyzing the equipment performance index of each prediction of the earthquake early warning platform.

[0021] Earthquake early warning network information module: obtains network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyzes the network quality index of each prediction of the earthquake early warning platform.

[0022] Earthquake early warning evaluation module: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, it is determined whether the earthquake early warning platform needs to be adjusted. If adjustment is required, the appropriate prediction method for equipment in each region and the adjustment parameters of the network are matched.

[0023] The beneficial effects of the present invention are: 1. Through the earthquake early warning platform, the equipment data of each earthquake early warning prediction is obtained, the equipment performance index of each prediction of the earthquake early warning platform is analyzed, and the network transmission data of each earthquake early warning prediction is obtained from the earthquake early warning platform, and the network quality index of each prediction of the earthquake early warning platform is analyzed, which reduces the occurrence of the problem of being unable to predict the accurate index of the receiver, improves the situation where technical personnel can clearly identify the problems of the system, reduces the problem of lack of pertinence in technical optimization and system upgrade work, improves the level of the earthquake early warning platform, enhances the performance of the equipment, reduces data transmission delays, and avoids early warning errors caused by data loss.

[0024] 2. Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, determine whether the earthquake early warning platform needs to be adjusted. If adjustment is required, match the appropriate prediction method of the equipment in each region and the adjustment parameters of the network, thereby reducing the time error of the early warning, avoiding the problem of missing precious risk avoidance time, reducing the problem of frequent system failures when an earthquake occurs, improving the early warning function, and promoting the long-term stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0027] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 creative work are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the present invention provides a refined business operation and maintenance model comprehensive evaluation method and system, including: step one, earthquake early warning equipment information, step two, earthquake early warning network information and step three, earthquake early warning evaluation.

[0030] Step 1. Earthquake early warning equipment information: Through the earthquake early warning platform, obtain the equipment data of each earthquake early warning prediction, and analyze the equipment performance index of each prediction of the earthquake early warning platform.

[0031] In the above embodiment, the specific analysis method of the equipment performance index of each prediction of the earthquake early warning platform is: based on the equipment data of each prediction of the earthquake early warning obtained, wherein the equipment data includes: the magnitude error value, time error value and position error value of each region, and extracting the magnitude reference error, time reference error value and position reference error value of the earthquake early warning platform from the database, and then analyzing the equipment performance index of each prediction of the earthquake early warning platform, the specific calculation formula is: Among them, a' represents the magnitude reference error of the earthquake early warning platform, a vs It is represented by the magnitude error of the sth region predicted by the earthquake early warning platform in the vth time, d' is represented by the time reference error value of the earthquake early warning platform, d vs It is expressed as the time error of the sth region in the vth prediction of the earthquake early warning platform, f vs It represents the position error of the sth region in the vth prediction of the earthquake early warning platform, and f' represents the position reference error value of the earthquake early warning platform, where s represents the region number, s = 1, 2, ..., g, g represents the number of regions, v represents the prediction number, v = 1, 2, ..., h, and h represents the number of predictions.

[0032] Through the earthquake early warning platform, the equipment data of each earthquake early warning prediction is obtained, the equipment performance index of each prediction of the earthquake early warning platform is analyzed, and the network transmission data of each earthquake early warning prediction is obtained from the earthquake early warning platform, and the network quality index of each prediction of the earthquake early warning platform is analyzed. This reduces the occurrence of problems such as inability to predict the accurate index of the receiver, improves the ability of technical personnel to clearly identify system problems, reduces the lack of targeted technical optimization and system upgrades, improves the level of the earthquake early warning platform, enhances the performance of the equipment, reduces data transmission delays, and avoids warning errors caused by data loss.

[0033] Step 2. Earthquake early warning network information: Obtain network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyze the network quality index of each prediction of the earthquake early warning platform.

[0034] In the above embodiment, the network quality index of each prediction of the earthquake early warning platform is specifically analyzed by: based on the obtained network transmission data of each prediction of the earthquake early warning, wherein the network transmission data includes the signal transmission speed, signal coverage area and signal packet loss rate of each region, the network transmission index M of each prediction of the earthquake early warning platform is analyzed. v and state index W v , and then analyze the network quality index of each prediction of the earthquake early warning platform. The specific calculation formula is: v =W v +M v .

[0035] In the above embodiment, the network status index of each prediction of the earthquake early warning platform is specifically analyzed by: based on the signal packet loss rate of each region predicted by the earthquake early warning platform, and extracting the signal packet loss reference rate of the earthquake early warning platform from the database, analyzing the network status index of each prediction of the earthquake early warning platform, and the specific calculation formula is: k' represents the signal packet loss reference rate of the earthquake early warning platform, k vs It is represented as the signal packet loss rate of the s-th region predicted by the earthquake early warning platform for the v-th time.

[0036] In the above embodiment, the network transmission index predicted by the earthquake early warning platform is specifically analyzed by the following method: based on the signal transmission speed and signal coverage area of ​​each region predicted by the earthquake early warning platform, the signal transmission speed and signal coverage area of ​​each region are ratio-processed to obtain the signal amplitude of each region predicted by the earthquake early warning platform, and the signal reference amplitude of the earthquake early warning platform is extracted from the database, and then the network transmission index predicted by the earthquake early warning platform is analyzed. The specific calculation formula is: Where j' represents the signal reference amplitude of the earthquake early warning platform, j vs It is expressed as the signal amplitude of the sth region predicted by the earthquake early warning platform for the vth time.

[0037] Step 3, earthquake early warning evaluation: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, determine whether the earthquake early warning platform needs to be adjusted. If adjustment is required, match the appropriate prediction method of the equipment in each region and the adjustment parameters of the network.

[0038] In the above embodiment, the specific analysis method for judging whether the earthquake early warning platform needs to be adjusted is as follows: based on the obtained equipment performance index of each prediction of the earthquake early warning platform, the equipment performance index of each prediction of the earthquake early warning platform is compared with the equipment performance index safety range of the earthquake early warning platform stored in the database. If the equipment performance index of a certain prediction of the earthquake early warning platform is not within the equipment performance index safety range of the earthquake early warning platform, the predicted equipment performance index is recorded as an abnormal equipment state, indicating that the earthquake early warning platform needs to be adjusted, so as to match the appropriate prediction method for the equipment in each region.

[0039] Based on the obtained network quality index of each prediction of the earthquake early warning platform, the network quality index of each prediction of the earthquake early warning platform is compared with the network quality index safety range of the earthquake early warning platform stored in the database. If the network quality index of a certain prediction of the earthquake early warning platform is not within the network quality index safety range of the earthquake early warning platform, the predicted network quality index is recorded as a network abnormal state, indicating that the earthquake early warning platform needs to be adjusted, and then the adjustment parameters of the network in each region are matched.

[0040] In the above embodiment, the specific analysis method of the suitable prediction method for matching the equipment in each region is as follows: based on the obtained abnormal status of the equipment, the regions corresponding to the abnormal status of each device are screened, and the earthquake activity background of each region is obtained from the earthquake early warning platform, and the suitable prediction method is matched with each earthquake-prone region and each region with less earthquake activity, and the equipment is maintained and upgraded for each earthquake-prone region, and a regular equipment inspection system is established. For each region with less earthquake activity, the equipment is calibrated regularly to ensure the normal operation of the equipment.

[0041] In the above embodiment, the adjustment parameters of the network matching each region are specifically analyzed as follows: based on the obtained network abnormal state, the regions corresponding to the network abnormal state are screened, and the geographical environment is obtained from the earthquake early warning platform, and the network adjustment parameters of each plain area, each basin area, each plateau area and each mountain area are matched; in each plain area, additional earthquake monitoring stations are added according to the network transmission demand to improve the monitoring density; in each basin area, shielded cables are used and shielded cabinets are installed to reduce the impact of seismic waves on network signals; in each plateau area, cold-proof and warm-keeping measures are taken for existing network equipment; in each mountain area, a distributed network architecture is adopted to disperse the network equipment in different peaks and valleys, and connected through wireless links or optical fibers to form a network covering the entire mountain area, thereby reducing the impact of a single device failure on the entire network and improving the reliability and stability of the network.

[0042] Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, it is judged whether the earthquake early warning platform needs to be adjusted. If adjustment is required, the appropriate prediction method of the equipment in each region and the adjustment parameters of the network are matched, which reduces the time error of the warning, avoids the problem of missing precious risk avoidance time, reduces the problem of frequent system failures when earthquakes occur, improves the warning function, and promotes the long-term stable operation of the system.

[0043] Reference Figure 2As shown, the present invention provides a system for a refined business operation and maintenance model comprehensive evaluation method, characterized in that it includes: an earthquake early warning equipment information module: through an earthquake early warning platform, obtaining equipment data for each earthquake early warning prediction, and analyzing the equipment performance index of each prediction of the earthquake early warning platform.

[0044] Earthquake early warning network information module: obtains network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyzes the network quality index of each prediction of the earthquake early warning platform.

[0045] Earthquake early warning evaluation module: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, it is determined whether the earthquake early warning platform needs to be adjusted. If adjustment is required, the appropriate prediction method for equipment in each region and the adjustment parameters of the network are matched.

[0046] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for a refined business operation and maintenance model, characterized in that: include: Step 1: Earthquake early warning equipment information: Obtain equipment data for each earthquake early warning prediction through the earthquake early warning platform, and analyze the equipment performance index for each earthquake early warning platform prediction; Step 2: Earthquake early warning network information: Obtain network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyze the network quality index of each earthquake early warning platform prediction; Step 3, earthquake early warning evaluation: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, determine whether the earthquake early warning platform needs to be adjusted. If adjustment is required, match the appropriate prediction method of the equipment in each region and the adjustment parameters of the network.

2. According to claim 1, a refined business operation and maintenance model comprehensive evaluation method is characterized in that: The specific analysis method of the equipment performance index of each prediction of the earthquake early warning platform is as follows: Based on the equipment data of each earthquake early warning prediction, the equipment data includes: magnitude error value, time error value and position error value of each region, and the magnitude reference error, time reference error value and position reference error value of the earthquake early warning platform are extracted from the database, and then the equipment performance index of each prediction of the earthquake early warning platform is analyzed. The specific calculation formula is: Among them, a' represents the magnitude reference error of the earthquake early warning platform, a vs It is represented by the magnitude error of the sth region predicted by the earthquake early warning platform in the vth time, d' is represented by the time reference error value of the earthquake early warning platform, d vs It is expressed as the time error of the sth region in the vth prediction of the earthquake early warning platform, f vs It represents the position error of the sth region in the vth prediction of the earthquake early warning platform, and f' represents the position reference error value of the earthquake early warning platform, where s represents the region number, s = 1, 2, ..., g, g represents the number of regions, v represents the prediction number, v = 1, 2, ..., h, and h represents the number of predictions.

3. According to claim 1, a refined business operation and maintenance model comprehensive evaluation method is characterized in that: The specific analysis method of the network quality index of each prediction of the earthquake early warning platform is as follows: Based on the network transmission data of each earthquake early warning prediction, including the signal transmission speed, signal coverage area and signal packet loss rate of each region, the network transmission index M of each earthquake early warning platform prediction is analyzed. v and state index W v , and then analyze the network quality index of each prediction of the earthquake early warning platform. The specific calculation formula is: v =W v +M v .

4. A refined business operation and maintenance model comprehensive evaluation method according to claim 3, characterized in that: The specific analysis method of the network status index of each prediction of the earthquake early warning platform is as follows: Based on the obtained signal packet loss rate of each region predicted by the earthquake early warning platform, the signal packet loss reference rate of the earthquake early warning platform is extracted from the database, and the network status index of each prediction of the earthquake early warning platform is analyzed. The specific calculation formula is: k' represents the signal packet loss reference rate of the earthquake early warning platform, k vs It is represented as the signal packet loss rate of the s-th region predicted by the earthquake early warning platform for the v-th time.

5. According to claim 3, a refined business operation and maintenance model comprehensive evaluation method is characterized in that: The specific analysis method of the network transmission index of each prediction of the earthquake early warning platform is as follows: Based on the signal transmission speed and signal coverage area of ​​each region predicted by the earthquake early warning platform, the signal transmission speed and signal coverage area of ​​each region are ratio processed to obtain the signal amplitude of each region predicted by the earthquake early warning platform, and the signal reference amplitude of the earthquake early warning platform is extracted from the database, and then the network transmission index of each prediction of the earthquake early warning platform is analyzed. The specific calculation formula is: Where j' represents the signal reference amplitude of the earthquake early warning platform, j vs It is expressed as the signal amplitude of the sth region predicted by the earthquake early warning platform for the vth time.

6. A refined business operation and maintenance model comprehensive evaluation method according to claim 1, characterized in that: The specific analysis method for determining whether the earthquake early warning platform needs to be adjusted is as follows: Based on the obtained equipment performance index of each prediction of the earthquake early warning platform, the equipment performance index of each prediction of the earthquake early warning platform is compared with the equipment performance index safety interval of the earthquake early warning platform stored in the database. If the equipment performance index of a certain prediction of the earthquake early warning platform is not within the equipment performance index safety interval of the earthquake early warning platform, the predicted equipment performance index is recorded as an abnormal equipment state, indicating that the earthquake early warning platform needs to be adjusted, so as to match the appropriate prediction method of the equipment in each region; Based on the obtained network quality index of each prediction of the earthquake early warning platform, the network quality index of each prediction of the earthquake early warning platform is compared with the network quality index safety range of the earthquake early warning platform stored in the database. If the network quality index of a certain prediction of the earthquake early warning platform is not within the network quality index safety range of the earthquake early warning platform, the predicted network quality index is recorded as a network abnormal state, indicating that the earthquake early warning platform needs to be adjusted, and then the adjustment parameters of the network in each region are matched.

7. A refined business operation and maintenance model comprehensive evaluation method according to claim 6, characterized in that: The specific analysis method of the suitable prediction method for matching equipment in various regions is as follows: Based on the abnormal status of the equipment obtained, the regions corresponding to the abnormal status of each device are screened, and the seismic activity background of each region is obtained from the earthquake early warning platform. The appropriate prediction methods are matched with the earthquake-prone regions and the regions with less earthquake activity. The equipment is maintained and upgraded in the earthquake-prone regions, and a regular equipment inspection system is established. Equipment is calibrated regularly in the regions with less earthquake activity to ensure the normal operation of the equipment.

8. A refined business operation and maintenance model comprehensive evaluation method according to claim 6, characterized in that: The specific analysis method of the adjustment parameters of the networks matching each region is as follows: Based on the obtained network abnormal status, the regions corresponding to each network abnormal status are screened, and the geographical environment is obtained from the earthquake early warning platform to match the network adjustment parameters of each plain area, basin area, plateau area and mountain area. In each plain area, additional earthquake monitoring stations are added to increase the network transmission demand and improve the monitoring density. In each basin area, shielded cables are used and shielded cabinets are installed to reduce the impact of seismic waves on network signals. In each plateau area, cold-proof and warm-keeping measures are taken for existing network equipment. In each mountain area, a distributed network architecture is adopted to disperse network equipment in different peaks and valleys, and connected through wireless links or optical fibers to form a network covering the entire mountain area, reducing the impact of a single device failure on the entire network and improving the reliability and stability of the network.

9. A system for executing a refined business operation and maintenance model comprehensive evaluation method as described in any one of claims 1 to 8, characterized in that: include: Earthquake early warning equipment information module: obtain the equipment data of each earthquake early warning prediction through the earthquake early warning platform, and analyze the equipment performance index of each earthquake early warning platform prediction; Earthquake early warning network information module: obtains network transmission data of each earthquake early warning prediction from the earthquake early warning platform, and analyzes the network quality index of each earthquake early warning platform prediction; Earthquake early warning evaluation module: Based on the equipment performance index of each prediction of the earthquake early warning platform and the network quality index of each prediction of the earthquake early warning platform, it is determined whether the earthquake early warning platform needs to be adjusted. If adjustment is required, the appropriate prediction method for equipment in each region and the adjustment parameters of the network are matched.

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