A beidou short message bypass monitoring method and device for a field monitoring station

Abstract

The application discloses a Beidou short message bypass monitoring method and device for a field monitoring station, and belongs to the bypass monitoring field. The running state data of all components of the field monitoring station are collected by front-end dual-machine equipment, and the switching of the main equipment and the standby equipment is automatically completed. The front-end dual-machine equipment is designed as a bypass, and does not affect the work of the original system of the field monitoring station. The data can be reported to the back-end analysis platform in real time and reliably in the field such as an uninhabited area through the Beidou satellite link. In addition, the back-end analysis platform can analyze and determine the failure probability of each component of each field monitoring station based on the reported data of each field monitoring station, output a failure high-risk list based on the failure probability, and analyze and determine the personnel maintenance planning result and the route planning result of each maintenance personnel based on the failure probability of each component of each field monitoring station, the failure high-risk list and the location of the failure monitoring station, so that the maintenance and troubleshooting work of each monitoring station site can be facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bypass monitoring, in particular to a Beidou short message bypass monitoring method and device for a field monitoring station. BACKGROUND

[0002] The monitoring station arranged in an uninhabited area, a border, and an open sea needs a running state monitoring device to collect the running conditions of various working components of the monitoring station, and then report to the backend.

[0003] However, the traditional running state monitoring device is usually integrated in the internal system of the field monitoring station, and usually adopts 4G communication, 5G communication, wired network and the like when reporting the state. The integration in the system will affect the working state of the original system to some extent, and when the organization structure of other links in the system changes, the original system needs to be modified adaptively, which greatly increases the influence of the monitoring device on the working components of the monitoring station and reduces the stability of the working system of the monitoring station. At the same time, the traditional reporting link needs the participation of an operator, a base station or a wired network, and cannot complete the state data reporting work in special environments such as an uninhabited area, a border and an open sea.

[0004] In addition, the traditional running state monitoring device usually adopts single machine operation, and when the single machine fails, the state cannot be continuously reported. In some scenes sensitive to real-time and reliability of reported data, the working task cannot be effectively completed.

[0005] Therefore, the traditional running state monitoring device cannot efficiently and stably complete the task in remote areas. When the distance to the city is relatively short and the maintenance cost is low, the problem is not obvious, but as the deployment distance of the monitoring station increases and moves away from the urban area, the traditional running state monitoring device cannot complete the related task.

[0006] Therefore, it is urgent to provide a Beidou short message bypass monitoring method and device for a field monitoring station. SUMMARY

[0007] In order to solve the problem that the traditional running state monitoring device not only affects the working state of the original system, but also cannot realize real-time and reliable reporting of data in the field such as an uninhabited area, the present application provides a Beidou short message bypass monitoring method and device for a field monitoring station.

[0008] On one hand, a Beidou short message bypass monitoring method for a field monitoring station is provided, and a bypass monitoring device includes a front-end double-machine device and a backend analysis platform. The front-end double-machine device includes an A device and a B device, and the two devices are arranged beside the bypass of the field monitoring station. The method comprises the following steps:

[0009] The A device and the B device respectively collect operation state data of each component of the field monitoring station, and send the operation state data collected by themselves and their own working states to each other;

[0010] For the A device and the B device, the consistency of the operation state data collected by themselves and the operation state data collected by the other party is compared, and the reporting device and the reporting data are determined based on the working state of the device and the working state of the other party;

[0011] After the backend analysis platform receives the reporting data of the field monitoring station sent by the reporting device via the Beidou satellite link, the fault probability of each component of each field monitoring station is determined based on the reporting data of each field monitoring station, and a fault high-risk list is output based on the fault probability. Based on the fault probability of each component of each field monitoring station, the fault high-risk list and the location of the fault monitoring station, the personnel maintenance planning result and the route planning result of each maintenance personnel are analyzed and determined. The fault high-risk list includes a monitoring station fault list and a component fault list of each fault monitoring station.

[0012] On the other hand, a Beidou short message bypass monitoring device for field monitoring stations based on the steps of any method embodiment of the specification is provided. The device comprises a front-end dual-machine device and a backend analysis platform. The front-end dual-machine device comprises an A device and a B device, which are arranged beside the field monitoring station.

[0013] The A device and the B device are respectively used to collect operation state data of each component of the field monitoring station, and send the operation state data collected by themselves and their own working states to each other; the consistency of the operation state data collected by themselves and the operation state data collected by the other party is compared, and the reporting device and the reporting data are determined based on the working state of the device and the working state of the other party;

[0014] The backend analysis platform is used to receive the reporting data of the field monitoring station sent by the reporting device via the Beidou satellite link, and determine the fault probability of each component of each field monitoring station based on the reporting data of each field monitoring station. Based on the fault probability, a fault high-risk list is output, and based on the fault probability of each component of each field monitoring station, the fault high-risk list and the location of the fault monitoring station, the personnel maintenance planning result and the route planning result of each maintenance personnel are analyzed and determined. The fault high-risk list includes a monitoring station fault list and a component fault list of each fault monitoring station.

[0015] On the other hand, a computer device is provided, which comprises a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to realize the steps of the above-mentioned method.

[0016] In another aspect, a computer readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program, when executed by a processor, implements the steps of the above method.

[0017] In another aspect, a computer program product is provided, comprising a computer program, and the computer program, when executed by a processor, implements the steps of the above method.

[0018] The technical solution provided by the present application can bring at least the following beneficial effects:

[0019] The front-end dual-machine device collects the running state data of all components of the field monitoring station. When a single machine fails, the main device and the standby device are automatically switched. During the state collection process, the main device and the standby device collect and report data after comparison and summarization, ensuring the accuracy of the reported data. The front-end dual-machine device is designed as a bypass, which does not affect the work of the original system of the field monitoring station. The data is reported to the back-end analysis platform through the Beidou satellite link, which can realize real-time and reliable reporting of data in unpopulated areas and other fields.

[0020] In addition, the back-end analysis platform can analyze and determine the failure probability of each component of each field monitoring station based on the reported data of each field monitoring station, output a high-risk failure list based on the failure probability, and analyze and determine the personnel maintenance planning result and the route planning result of each maintenance personnel based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failure monitoring station, so as to facilitate the maintenance and troubleshooting work of each monitoring station. BRIEF DESCRIPTION OF DRAWINGS

[0021] 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 embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is a Beidou short message bypass monitoring method flowchart for a field monitoring station provided by an embodiment of the present application;

[0023] Figure 2 is a hardware architecture diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0025] The specific implementation of the above concept will be described below.

[0026] Please refer to Figure 1 The present application provides a Beidou short message bypass monitoring method for field monitoring stations. The bypass monitoring device includes front-end dual-machine equipment and a back-end analysis platform. The front-end dual-machine equipment includes A equipment and B equipment, and the two devices are arranged beside the field monitoring station bypass. The method includes the following steps.

[0027] Step 100: The A equipment and the B equipment respectively collect the running state data of each component of the field monitoring station, and send the collected running state data and the working state of the equipment to the other equipment.

[0028] Step 102: For the A equipment and the B equipment, the consistency of the collected running state data and the running state data collected by the other equipment is compared, and the reporting equipment and the reporting data are determined based on the working state of the equipment and the working state of the other equipment.

[0029] Step 104: After the back-end analysis platform receives the reporting data of the field monitoring station sent by the reporting equipment via the Beidou satellite link, the fault probability of each component of each field monitoring station is determined based on the reporting data of each field monitoring station, the fault high-risk list is output based on the fault probability, and the personnel maintenance planning result and the route planning result of each maintenance personnel are determined based on the fault probability of each component of each field monitoring station, the fault high-risk list, and the location of the fault monitoring station. The fault high-risk list includes a monitoring station fault list and a component fault list of each fault monitoring station.

[0030] In the embodiments of the present application, the front-end dual-machine equipment is used to collect the running state data of all components of the field monitoring station. When a single machine fails, the main equipment and the standby equipment are automatically switched. During the state collection process, the main equipment and the standby equipment are used to collect, aggregate, compare, and report. The accuracy of the reported data is ensured. The front-end dual-machine equipment is designed as a bypass, which does not affect the work of the original system of the field monitoring station. The data is reported to the back-end analysis platform via the Beidou satellite link, and the real-time and reliable reporting of the data can be realized in the field such as unpopulated areas.

[0031] In addition, the back-end analysis platform can determine the failure probability of each component of each field monitoring station based on the reported data of each field monitoring station, output a high-risk failure list based on the failure probability, and analyze and determine personnel maintenance planning results and route planning results of each maintenance personnel based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failure monitoring station, so as to facilitate the maintenance and troubleshooting work of each monitoring station.

[0032] The execution mode of each step is described below. Figure 1 The execution mode of each step is described below.

[0033] For step 100:

[0034] In the embodiment of the application, the data types of the running state data collected by the A device and the B device for each component include cooperative data and non-cooperative data. The cooperative data is state data reported in response to a communication protocol, such as ModBus, SNMP, Onvif, GB / T28181, and a self-defined TCP / UDP communication protocol. The failure value and the working state can be reported in response to the communication protocol.

[0035] The non-cooperative data refers to a monitored system that does not provide a corresponding communication protocol. The system analyzes the working state through ARP packets, ICMP packets, working power voltage and current, and temperature information of specific points.

[0036] It should be noted that the components of the field monitoring station include an intelligent cabinet, a power distribution unit, a visible light camera, an infrared camera, and the like.

[0037] For step 102:

[0038] In some embodiments, step 102 can include:

[0039] The A device and the B device compare the consistency of the running state data collected by themselves and the running state data collected by the other party.

[0040] If they are consistent, the running state data collected by the device itself is the target data and there is no need to merge the data.

[0041] If they are inconsistent, the component running state data with differences is marked as suspected failure data, and the marked position data in the running state data collected by the other party and the running state data collected by the device itself is merged, and the combination is used as the target data of the device.

[0042] The working states of the A device and the B device are compared.

[0043] If the A device and the B device are both normal, the A device is determined as the reporting device, and the target data stored in the A device is used as the reported data.

[0044] If one device is normal and the other is abnormal, the normal device is determined as the reporting device, and the target data stored in the normal device is determined as the reporting data;

[0045] If both devices are normal, both A device and B device are determined as the reporting device, and the target data stored in both devices is determined as the reporting data and sent.

[0046] In the embodiment, A device and B device collect the running state data of all components of the field monitoring station, and the data is theoretically the same. By using the two-by-two voting technology, double machines are operated, double machines collect data for comparison and merging, and double machine fault judgment is performed, so that the automatic switching of the main device and the standby device is completed, and the system has high availability and high reliability.

[0047] After determining the reporting device and the reporting data, the reporting device sends the reporting data. Since the Beidou short message system has small reporting data volume and long reporting period, all data need to be disassembled.

[0048] The reporting device disassembles and sends all reporting data according to the Beidou card authorized data length according to the internal communication protocol structure. The optional disassembly length includes 30 bytes, 80 bytes, 150 bytes and 200 bytes long to adapt to different Beidou short message lengths. After disassembly, the reporting device sends the corresponding sequence to the back-end analysis platform through the Beidou satellite link to complete the data reporting work.

[0049] For step 104:

[0050] In some embodiments, the failure probability of each component of each field monitoring station is determined based on the reporting data of each field monitoring station, including steps S1-S8:

[0051] S1, for each component, read the cooperative data of the current component, and determine whether there is a fault value in the cooperative data;

[0052] S2, when the fault value is 1, the failure probability of the current component is directly determined as 1;

[0053] S3, when the fault value is 0, the working power, network traffic, ARP packet response time and ICMP packet response time of the current component are read from the cooperative data and non-cooperative data of the current component;

[0054] S4, based on the power threshold range of the current component and the collected working power, the power abnormal probability of the current component is calculated.

[0055] In this step, the power abnormal probability of the current component is calculated by the following formula:

[0056]

[0057] wherein, is the power abnormality probability of the current component, is the collected working power, the power threshold range of the current component is .

[0058] S5, based on the network traffic threshold range of the current component, the maximum threshold of response time and the collected network traffic, ARP packet response time, ICMP packet response time, the network abnormality probability of the current component is calculated.

[0059] In this step, the network abnormality probability of the current component is calculated by the following formula:

[0060]

[0061]

[0062]

[0063] wherein, is the network abnormality probability of the current component, is the traffic abnormality probability, is the response abnormality probability, is the collected network traffic, is the network traffic threshold range of the current component, is the sum of ARP packet response time and ICMP packet response time, is the maximum threshold of response time.

[0064] In this embodiment, the judgment of network abnormality usually adopts traffic monitoring and ARP (Address Resolution Protocol) & ICMP (Internet Control Message Protocol) joint judgment. Traffic monitoring refers to that the theoretical traffic of a specific network device within a certain time should meet the range . ARP & ICMP joint judgment refers to that the sum of the response time of a specific device to network ARP request and ICMP request should be less than .

[0065] S6, based on the power abnormality probability and the network abnormality probability of the current component, the failure probability of the current component is determined.

[0066] In this step, the failure probability of the current component is:

[0067]

[0068] S7, the temperature data of the cabinet of the field monitoring station within a time period is obtained, and based on the temperature fluctuation and average temperature of the cabinet, the temperature abnormality probability is calculated.

[0069] In this step, for the temperature anomaly judgment, usually adopt temperature fluctuation fault factor K1 and average temperature fault factor K2 joint judgment.

[0070] Temperature fluctuation fault factor through the collection of time period of cabinet temperature data, and calculate the temperature standard deviation S K1 , temperature fluctuation fault factor coefficient for p, then the temperature fluctuation fault factor K1=S k1 ×p.

[0071] Average temperature fault factor through the collection of time period of cabinet temperature data, calculate the temperature average value k avg . According to the deployment location and the equipment requirements, set up the rated temperature k 额定 , average temperature fault factor coefficient for q, then the average temperature fault factor K2=q×(k avg -k 额定 ) / k avg .

[0072] Then the temperature anomaly probability is the sum of K1 and K2, that is, P 温度 =K1+K2.

[0073] S8, based on the temperature anomaly probability and the failure probability of all components in the field monitoring station, determine the overall failure probability of the field monitoring station:

[0074]

[0075] In the formula, n is the number of components in the field monitoring station, and i is the component sequence number.

[0076] Therefore, according to the front-end data received from the Beidou satellite link, the back-end platform can freely build related equipment state monitoring applications, and specify maintenance strategies according to the field situation, and complete related business work.

[0077] The back-end maintenance platform has the functions of centralized receiving of front-end information, intelligent analysis of fault points and dynamic adjustment of maintenance forces. The front-end dual machine equipment sends the running state to the back-end analysis platform through the wireless communication link. The communication link can be selected from Beidou 2nd generation, Beidou 3rd generation short message, 4G, 5G and other technical schemes. After the back-end analysis platform receives the data completely, it carries out summary analysis. According to the list of components of the field monitoring station and the overall failure probability and single component failure probability of the monitoring station obtained by intelligent analysis, the high-risk failure list is output. It can be understood that the high-risk failure list includes the monitoring station failure list and the component failure list of each fault monitoring station. The platform analyzes the fault points comprehensively, and outputs the personnel maintenance planning results and the route planning results of each maintenance personnel according to the fault type and fault point distance and other factors. In order to facilitate the subsequent maintenance personnel to carry out point maintenance and troubleshooting work according to the route planning suggestion.

[0078] In some embodiments, the personnel maintenance planning result is determined by the following steps B1-B6:

[0079] B1, obtaining a set of locations of the fault monitoring stations and a fault level of each fault monitoring station in the fault list of the monitoring stations.

[0080] In this step, the set of locations of the fault monitoring stations is L={l1, li,..., ln},

[0081] The fault level of each fault monitoring station is E={e1, ei,..., en}, where i is the sequential number of the fault monitoring station.

[0082] B2, obtaining a skill vector and a current location of each maintenance personnel.

[0083] B3, calculating a maintenance time threshold of each fault monitoring station based on the fault level of each fault monitoring station.

[0084] The maintenance time threshold of each fault monitoring station is calculated by the following formula:

[0085]

[0086] In the formula, is the maintenance time threshold of the i-th fault monitoring station, is the fault level of the i-th fault monitoring station.

[0087] It can be understood that the fault level of the fault monitoring station can be set according to the emergency degree, which is divided into 1-5 levels according to the emergency degree, The larger the value is, the more urgent it is, and the smaller the maintenance time threshold is.

[0088] B4, calculating the adaptation degree of each maintenance personnel to each fault monitoring station based on the required skill vector of each fault monitoring station and the skill vector of each maintenance personnel.

[0089] The adaptation degree of each maintenance personnel to each fault monitoring station is calculated by the following formula:

[0090]

[0091] In the formula, is the adaptation degree of the j-th maintenance personnel to the i-th fault monitoring station, is the skill vector of the j-th maintenance personnel, is the required skill vector of the i-th fault monitoring station.

[0092] B5, based on the fitness, the location set of the fault monitoring station and the current location of each maintenance personnel, calculate the work cost of each maintenance personnel to maintain each fault monitoring station.

[0093] The work cost of each maintenance personnel to maintain each fault monitoring station is calculated by the following formula:

[0094]

[0095] In the formula, is the work cost of the jth maintenance personnel to maintain the ith fault monitoring station, is the Euclidean distance, is the current location of the jth maintenance personnel, is the location of the ith fault monitoring station, is the fitness of the jth maintenance personnel to the ith fault monitoring station.

[0096] B6, based on the work cost of each maintenance personnel to maintain each fault monitoring station and the maintenance time threshold of each fault monitoring station, determine the planning target to find the personnel maintenance planning result with optimal work cost and maintenance time.

[0097] The planning target is:

[0098]

[0099] In the formula, a and b are weight coefficients, n is the number of fault monitoring stations, is the maintenance time threshold of the ith fault monitoring station, and m is the number of maintenance personnel, is the work cost of the jth maintenance personnel to maintain the ith fault monitoring station.

[0100] It can be seen that, according to the fault type (i.e. the required skill vector of the fault monitoring station), the urgency, and the personnel skill matching degree, the maintenance tasks are dynamically allocated, and the utilization rate of human resources is optimized.

[0101] In some embodiments, the route planning result of each maintenance personnel is determined by analyzing the following steps H1-H4:

[0102] H1, for the route planning of each maintenance personnel, execute: based on the personnel maintenance planning result, determine the fault monitoring stations required to be maintained by the current maintenance personnel, to determine all paths of the current maintenance personnel.

[0103] For example, the maintenance personnel is assigned three fault monitoring station maintenance tasks, and there are 6 paths, i.e. 6 point maintenance sequences.

[0104] H2, determine the pheromone update rule, and calculate the pheromone of each path by using the ant colony algorithm.

[0105] In this step, the pheromone update rule is:

[0106]

[0107]

[0108] wherein, is the pheromone, and p is the evaporation coefficient, is the pheromone update parameter, and k is the path number, is the path time, is the waiting time, is the pheromone increment coefficient.

[0109] H3, based on the static distance and dynamic traffic delay of each path, calculate the traffic delay coefficient of each path.

[0110] In this step, the traffic delay coefficient of each path is calculated as:

[0111]

[0112] wherein, is the traffic delay coefficient of each path, is the static distance from monitoring station i to monitoring station j, such as geographic distance or fixed path length. The shorter the distance, the greater the value, the stronger the path attraction. represents the dynamic traffic delay from monitoring station i to monitoring station j, such as congestion, road conditions, and other real-time factors. The higher the dynamic traffic delay, the smaller the value, the weaker the path attraction. is the weight coefficient, used to balance the influence of static distance and dynamic traffic delay. If = 0, the dynamic traffic delay is ignored and only the static distance is relied on; if > 0, the influence of dynamic traffic delay increases with .

[0113] H4, based on the pheromone of each path and the traffic delay coefficient of each path, determine the selection probability of each path to determine the route planning result of the current maintenance personnel.

[0114] In this step, the selection probability of each path is:

[0115]

[0116] wherein, is the selection probability of each path, is the pheromone, Traffic delay coefficient of each path, a is pheromone heuristic factor, indicating pheromone concentration The degree of influence on path selection. The greater the value of a, the more the ant tends to select the path with high pheromone concentration, i.e. the path frequently selected in historical experience; if a = 0, the algorithm only relies on heuristic information such as distance, ignoring pheromone accumulation. Beta is the heuristic information factor, indicating the degree of influence of heuristic information on path selection. The greater the value of beta, the more the ant tends to select the path with short distance or smooth traffic flow; if beta = 0, the algorithm only relies on pheromone concentration, which may fall into local optimum. By adjusting the ratio of a and beta, the emphasis of the algorithm on 'historical experience' and'real-time traffic' can be balanced. For example: a > beta, more trust in pheromone accumulation, suitable for static environment. Beta > a, more attention to real-time traffic data, suitable for dynamic environment. Usually a [1, 2], beta [2, 5], and specific adjustment needs to be optimized through experiments.

[0117] In some embodiments, the back-end analysis platform adopts LSTM-based fault prediction to manage spare parts inventory to remind maintenance personnel to timely replenish spare parts.

[0118] In this embodiment, the LSTM fault prediction model is used to predict the future fault probability at a plurality of time points based on the historical fault probability of each component respectively; when the average fault probability of the component within the average setting time is greater than the set value, and the inventory of the component is less than the set threshold, a spare part replenishment notification is triggered.

[0119] The embodiment of the application provides a Beidou short message bypass monitoring device for a field monitoring station, which comprises: a front-end dual-machine device and a back-end analysis platform, the front-end dual-machine device comprises an A device and a B device, and the two devices are arranged beside the field monitoring station;

[0120] The A device and the B device are respectively used for collecting the running state data of each component of the field monitoring station, and sending the collected running state data and the working state of the device to the other device; the consistency of the collected running state data and the running state data collected by the other device is compared, and the reporting device and the reporting data are determined based on the working state of the device and the working state of the other device;

[0121] The back-end analysis platform is used for receiving the reporting data of the field monitoring station sent by the reporting device via the Beidou satellite link, analyzing and determining the fault probability of each component of each field monitoring station based on the reporting data of each field monitoring station, outputting a fault high-risk list based on the fault probability, and analyzing and determining a personnel maintenance planning result and a route planning result of each maintenance personnel based on the fault probability of each component of each field monitoring station, the fault high-risk list and the location of the fault monitoring station. The fault high-risk list comprises a monitoring station fault list and a component fault list of each fault monitoring station.

[0122] It should be noted that the device embodiments and the method embodiments described above belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0123] Embodiments of the present application also provide a computer device, comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the Beidou short message bypass monitoring method for the field monitoring station provided by the above method embodiments.

[0124] Embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the Beidou short message bypass monitoring method for the field monitoring station provided by the above method embodiments.

[0125] Embodiments of the present application also provide a computer program product, the computer program product comprising a computer program, the processor of the computer device reading the computer program from the computer readable storage medium, and the processor executing the computer program to enable the computer device to execute the Beidou short message bypass monitoring method for the field monitoring station provided by any of the above embodiments.

[0126] For the convenience of description, the above device or device is described as various modules or units in function respectively. Of course, in the implementation of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0127] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware platforms. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.

[0128] Finally, it needs to be pointed out that, in this article, the relationship terms such as first, second, third and fourth, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0129] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A Beidou short message bypass monitoring method for a field monitoring station, characterized in that, The bypass monitoring device comprises a front-end dual-machine device and a back-end analysis platform, the front-end dual-machine device comprises an A device and a B device, and the two devices are arranged at a bypass of a field monitoring station, and the method comprises the following steps: The A device and the B device respectively collect running state data of each component of the field monitoring station, and send the collected running state data and working state to the other device; For the A device and the B device, the consistency of the running state data collected by the two devices is compared, and the reporting device and reporting data are determined based on the working state of the two devices; After receiving the reporting data of the field monitoring station sent by the reporting device via a Beidou satellite link, the back-end analysis platform analyzes and determines the fault probability of each component of each field monitoring station based on the reporting data, outputs a fault high-risk list based on the fault probability, and analyzes and determines a personnel maintenance planning result and a route planning result of each maintenance personnel based on the fault probability of each component of each field monitoring station, the fault high-risk list and the location of the fault monitoring station; the fault high-risk list comprises a monitoring station fault list and a component fault list of each fault monitoring station; The data types of the running state data collected by the A device and the B device for each component include cooperative data and non-cooperative data; the cooperative data is state data reported by responding to a communication protocol; The fault probability of each component of each field monitoring station is determined based on the reporting data, comprising the following steps: For each component, the following steps are performed: Read the cooperative data of the current component, and determine whether there is a fault value in the cooperative data; When the fault value is 1, the fault probability of the current component is directly determined as 1; When the fault value is 0, the working power, network traffic, ARP packet response time and ICMP packet response time of the current component are read from the cooperative data and non-cooperative data of the current component; Based on the power threshold range and the collected working power of the current component, the power abnormal probability of the current component is calculated; Based on the network traffic threshold range, response time maximum threshold and collected network traffic, ARP packet response time and ICMP packet response time of the current component, the network abnormal probability of the current component is calculated; Based on the power abnormal probability and the network abnormal probability of the current component, the fault probability of the current component is determined; The temperature data of the cabinet of the field monitoring station in a time period is obtained, and the temperature abnormal probability is calculated based on the temperature fluctuation and average temperature of the cabinet; Based on the temperature abnormal probability and the fault probability of all components in the field monitoring station, the overall fault probability of the field monitoring station is determined; The network abnormal probability of the current component is calculated by the following formula: In the formula, is the network anomaly probability of the current component, is the traffic anomaly probability, is the response anomaly probability, is the collected network traffic, is the network traffic threshold range of the current component, is the sum of the ARP packet response time and the ICMP packet response time, is the maximum threshold of the response time; The personnel maintenance planning result is analyzed and determined by the following method: The position set of the fault monitoring station and the fault level of each fault monitoring station in the monitoring station fault list are obtained; The skill vector and current position of each maintenance personnel are obtained; Based on the fault level of each fault monitoring station, the maintenance time threshold of each fault monitoring station is calculated; calculating an adaptation degree of each maintenance personnel to each fault monitoring station based on a required skill vector of each fault monitoring station and a skill vector of each maintenance personnel; calculating a work cost of each maintenance personnel to repair each fault monitoring station based on the adaptation degree, a location set of the fault monitoring station and a current location of each maintenance personnel; determining a planning target to find a personnel maintenance planning result with optimal work cost and repair time based on the work cost of each maintenance personnel to repair each fault monitoring station and a repair time threshold of each fault monitoring station; calculating the repair time threshold of each fault monitoring station by the following formula: wherein is a maintenance time threshold value for the i-th fault monitoring station, is a fault level for the i-th fault monitoring station; calculating the adaptation degree of each maintenance personnel to each fault monitoring station by the following formula: wherein is the fitness of the jth maintenance person to the ith fault monitoring station, is the skill vector of the jth maintenance person, is the required skill vector of the ith fault monitoring station; calculating the work cost of each maintenance personnel to repair each fault monitoring station by the following formula: wherein, is the work cost of the jth maintenance worker to repair the ith fault monitoring station, is the Euclidean distance, is the current position of the jth maintenance worker, is the position of the ith fault monitoring station, is the fitness of the jth maintenance worker to the ith fault monitoring station.

2. The method of claim 1, wherein, The A device and the B device are compared with each other in the consistency of the running state data collected by themselves and the running state data collected by the other party, and the reporting device and the reporting data are determined based on the working state of the device itself and the working state of the other party, including: The A device and the B device are compared with each other in the consistency of the running state data collected by themselves and the running state data collected by the other party; If consistent, the running state data collected by the device itself is the target data and no merging is needed; If inconsistent, the component running state data with differences is marked as suspected fault data, and the marked position data in the running state data collected by the other party and the device itself is merged to be combined as the target data of the device; The working states of the A device and the B device are compared with each other; If both the A device and the B device are normal, the A device is determined as the reporting device, and the target data stored in the A device is determined as the reporting data; If either device is normal and the other device is abnormal, the normal device is determined as the reporting device, and the target data stored in the normal device is determined as the reporting data; If both devices are abnormal, the A device and the B device are determined as the reporting devices, and the target data stored in both devices is marked as unreliable and sent as the reporting data.

3. The method of claim 1, wherein, The route planning result of each maintenance personnel is determined by analyzing the following: For the route planning of each maintenance personnel, the following is performed: Based on the personnel maintenance planning result, each fault monitoring station to be repaired by the current maintenance personnel is determined to determine all paths of the current maintenance personnel; An information pheromone updating rule is determined, and an ant colony algorithm is used to calculate the information pheromone of each path; Based on the static distance and dynamic traffic delay of each path, a traffic delay coefficient of each path is calculated; Based on the information pheromone of each path and the traffic delay coefficient of each path, a selection probability of each path is determined to determine the route planning result of the current maintenance personnel.

4. A Beidou short message bypass monitoring device for a field monitoring station, used to implement the steps of the method of any one of claims 1-3, characterized in that, including: The front-end dual-machine device and the back-end analysis platform, the front-end dual-machine device includes an A device and a B device, and the two devices are arranged beside the field monitoring station; A device and B device are respectively used for collecting operation state data of each component of a field monitoring station, and sending the operation state data collected by itself and its working state to the other party; comparing the consistency of the operation state data collected by itself and the operation state data collected by the other party, and determining a reporting device and reporting data based on its working state and the working state of the other party; The back-end analysis platform is used for receiving the reporting data of the field monitoring station sent by the reporting device via a Beidou satellite link, analyzing and determining the failure probability of each component of each field monitoring station based on the reporting data of each field monitoring station, outputting a high-risk failure list based on the failure probability, and analyzing and determining a personnel maintenance planning result and a route planning result of each maintenance personnel based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failure monitoring station. The high-risk failure list includes a monitoring station failure list and a component failure list of each failure monitoring station.

5. A computer device, comprising: The computer device includes a memory and a processor, the memory is used for storing a computer program, and the processor is used for executing the computer program stored on the memory to realize the steps of the method of any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the method of any one of claims 1-3.

7. A computer program product, characterised in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1-3.

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