Beidou short message bypass monitoring method and device for field monitoring station

Abstract

The invention discloses a Beidou short message bypass monitoring method and device for a field monitoring station, and belongs to the field of bypass monitoring. Front-end double-machine equipment is adopted to collect running state data of all parts of the field monitoring station, and switching between main equipment and standby equipment is automatically completed. The front-end double-computer equipment is in a bypass design, so that the work of the original system of the field monitoring station is not influenced; data are reported to a back-end analysis platform through a Beidou satellite link, and real-time and reliable data reporting can be achieved in the fields such as unmanned areas. Moreover, the back-end analysis platform can analyze and determine the fault probability of each component of each field monitoring station based on the reported data of each field monitoring station, so as to output a fault high-risk list based on the fault probability, and can determine the fault probability of each component of each field monitoring station based on the fault probability of each component of each field monitoring station, the fault high-risk list and the position of the fault monitoring station. And a personnel maintenance planning result and a route planning result of each maintenance personnel are analyzed and determined, so that maintenance and troubleshooting work of each monitoring station site can be conveniently carried out.

Description

Technical Field

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

[0002] Monitoring stations set up in uninhabited areas, borders, and offshore areas require operating status monitoring equipment to collect the operating status of each working component of the monitoring station and then report it to the back-end.

[0003] However, traditional operating status monitoring equipment is typically integrated into the working systems of field monitoring stations, and status reporting typically utilizes 4G communications, 5G communications, or wired networks. Integration within the system can, to a certain extent, impact the existing system's operating status. Furthermore, changes to the organizational structure of other parts of the system require adaptive modifications to the existing system. This significantly increases the impact of monitoring equipment on the working components of the monitoring station and reduces the stability of the monitoring station's operating system. Furthermore, traditional reporting links require the participation of operators, base stations, or wired networks, making it impossible to complete status data reporting in special environments such as uninhabited areas, border crossings, and offshore areas.

[0004] In addition, traditional operation status monitoring equipment usually operates on a single machine. When the single machine fails, it cannot guarantee that the status will continue to be reported. In some scenarios that are sensitive to the real-time and reliability of the reported data, it cannot effectively complete the work tasks.

[0005] As a result, traditional health monitoring equipment is unable to efficiently and reliably perform its tasks in remote areas. This problem was not significant when monitoring stations were deployed closer to cities and maintenance costs were low. However, as the distance between monitoring stations and urban areas increases, traditional health monitoring equipment becomes inadequate.

[0006] Therefore, there is an urgent need to provide a Beidou short message bypass monitoring method and device for field monitoring stations. Summary of the Invention

[0007] In order to solve the problem that traditional operation status monitoring equipment not only affects the operation of the original system but also cannot realize real-time and reliable reporting of data in the wild such as uninhabited areas, the embodiment of the present invention provides a Beidou short message bypass monitoring method and device for field monitoring stations.

[0008] On the one hand, a Beidou short message bypass monitoring method for a field monitoring station is provided. The bypass monitoring device includes a front-end dual-machine device and a back-end analysis platform. The front-end dual-machine device includes device A and device B. The two devices are set at the field monitoring station bypass. The method includes: Device A and Device B respectively collect the operation status data of each component of the field monitoring station, and send the operation status data they collect and their own working status to each other; For both Device A and Device B, the following operations are performed: compare the consistency of the operation status data collected by itself and the operation status data collected by the other party, and determine the reporting device and the reported data based on its own working status and the working status of the other party; After receiving the reported data of the field monitoring station sent by the reporting device via the Beidou satellite link, the back-end analysis platform determines the failure probability of each component of each field monitoring station based on the reported data of each field monitoring station, so as to output a high-risk failure list based on the failure probability, and based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failed monitoring station, analyze and determine the personnel maintenance plan result and the route planning result of each maintenance personnel; the high-risk failure list includes the monitoring station failure list and the component failure list of each failed monitoring station.

[0009] On the other hand, a Beidou short message bypass monitoring device for a field monitoring station based on the steps of any method embodiment of the specification is provided. The device includes: a front-end dual-device and a back-end analysis platform. The front-end dual-device includes Device A and Device B, and the two devices are arranged on the bypass of the field monitoring station; Device A and Device B are respectively used to collect the operation status data of each component of the field monitoring station, and send the operation status data they collect and their own working status to each other; compare the consistency of the operation status data collected by itself and the operation status data collected by the other party, and determine the reporting device and the reported data based on its own working status and the working status of the other party; The back-end analysis platform is used to, after receiving the reported data of the field monitoring station sent by the reporting device via the Beidou satellite link, determine the failure probability of each component of each field monitoring station based on the reported data of each field monitoring station, so as to output a high-risk failure list based on the failure probability, and based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failed monitoring station, analyze and determine the personnel maintenance plan result and the route planning result of each maintenance personnel; the high-risk failure list includes the monitoring station failure list and the component failure list of each failed monitoring station.

[0010] On the other hand, a computer device is provided. The computer device includes 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 implement the steps of the above method.

[0011] On the other hand, a computer-readable storage medium is provided, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0012] On the other hand, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0013] The technical solution provided by the present invention can at least bring the following beneficial effects: The front-end dual-machine device is used to collect the operation status 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 status collection process, the main and standby devices collect data simultaneously, summarize and compare them, and then report them to ensure the accuracy of the reported data. Moreover, the front-end dual-machine device is designed as a bypass, which will not affect the operation of the original system of the field monitoring station; the data is reported to the back-end analysis platform through the Beidou satellite link, and real-time and reliable data reporting can be realized in the wild such as uninhabited areas.

[0014] 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 can analyze and determine the personnel maintenance plan 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 failed monitoring station, which is convenient for carrying out the maintenance and troubleshooting work of each monitoring site. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a Beidou short message bypass monitoring method for a field monitoring station provided by an embodiment of the present invention; Figure 2 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] The following describes the specific implementation of the above concept.

[0019] Please refer to Figure 1 , a Beidou short message bypass monitoring method for a field monitoring station provided by an embodiment of the present invention. The bypass monitoring device includes a front-end dual-device and a back-end analysis platform. The front-end dual-device includes device A and device B. The two devices are arranged on the bypass of the field monitoring station. The method includes: Step 100: Device A and device B respectively collect the operation status data of each component of the field monitoring station, and send the operation status data collected by themselves and their own working status to each other; Step 102: For both device A and device B, perform: compare the consistency of the operation status data collected by themselves and the operation status data collected by the other party, and determine the reporting device and the reporting data based on their own working status and the working status of the other party; Step 104: After the back-end analysis platform receives the reporting data of the field monitoring station sent by the reporting device via the Beidou satellite link, determine the failure probability of each component of each field monitoring station based on the reporting data analysis of each field monitoring station, so as to output a high-risk failure list based on the failure probability, and analyze and determine the personnel maintenance plan 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 failed monitoring station; the high-risk failure list includes the monitoring station failure list and the component failure list of each failed monitoring station.

[0020] In the embodiments of the present invention, a front-end dual-device is used to collect the operation status data of all components of the field monitoring station. When a single device fails, the main device and the standby device are automatically switched; during the status collection process, the main and standby devices collect data simultaneously, summarize and compare them before reporting to ensure the accuracy of the reported data. Moreover, the front-end dual-device is designed as a bypass and will not affect the operation 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 real-time and reliable data reporting can be achieved in the wild such as uninhabited areas.

[0021] In addition, the backend 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, so as to output a high-risk failure list based on the failure probability. Moreover, it can analyze and determine the personnel maintenance plan 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 failed monitoring station, which is convenient for carrying out the maintenance and troubleshooting work of each monitoring site.

[0022] The following describes Figure 1 the execution manner of each step shown.

[0023] For step 100: In the embodiment of the present invention, the data types of the operation status data collected by device A and device B for each component include cooperative data and non-cooperative data; the cooperative data is the status data reported in response to the communication protocol, such as communication protocols like ModBus, SNMP, Onvif, GB / T28181, and custom TCP / UDP communication protocols, and the failure value and working status can be reported in response to the communication protocol.

[0024] The non-cooperative data refers to the situation where the monitored system does not provide the corresponding communication protocol, and the system analyzes the working status through information such as ARP packets, ICMP packets, working power supply voltage and current, and temperature at specific points.

[0025] It should be noted that the components of the field monitoring station include intelligent cabinets, power distribution units, visible light cameras, infrared cameras, etc.

[0026] For step 102: In some embodiments, step 102 may include: Both device A and device B compare the consistency of the operation status data they collect with the operation status data collected by the other party; If they are consistent, the data does not need to be merged, and the operation status data collected by itself is the target data; If they are inconsistent, the operation status data of the components with differences is marked as suspected failure data, and the marked position data in the operation status data collected by the other party and itself is merged, and the combination with the mark is used as the target data of this device; Compare the working status of device A and device B; If both device A and device B are normal, determine device A as the reporting device, and the target data stored in device A is used as the reported data; If one device is normal and the other device is abnormal, determine the normal device as the reporting device, and the target data stored in the normal device is used as the reported data; If both devices are abnormal and normal, it is determined that both Device A and Device B are reporting devices, and the unreliable target data identifiers stored in the two devices are sent as reported data.

[0027] In this embodiment, both Device A and Device B collect the operation status data of all components of the field monitoring station. Theoretically, the data is the same. By adopting the two-by-two redundancy technology, dual-machine operation, dual-machine data collection and comparison and merging are carried out, and dual-machine fault determination is performed to automatically complete the switching between the main device and the standby device, making the system have high availability and high reliability.

[0028] After determining the reporting device and the reported data, the reporting device unpacks and sends the reported data. Since the reported data volume of the Beidou short message system is small and the reporting period is long, all data needs to be disassembled.

[0029] The reporting device splits and sends all reported data according to the authorized data length of the Beidou card according to the internal communication protocol structure. The optional split lengths include 30 bytes, 80 bytes, 150 bytes, and 200 bytes long to adapt to different Beidou short message lengths. After unpacking, the reporting device will send the corresponding sequence to the backend analysis platform through the Beidou satellite link to complete the data reporting work.

[0030] Regarding step 104: In some embodiments, based on the reported data analysis of each field monitoring station, the failure probability of each component of each field monitoring station is determined, including steps S1-S8: S1, for each component, execute: read the collaborative data of the current component and determine whether there is a failure value in the collaborative data; S2, when the failure value is 1, directly determine that the failure probability of the current component is 1; S3, when the failure value is 0, read the working power, network traffic, ARP packet response time, and ICMP packet response time of the current component collected from the collaborative data and non-collaborative data of the current component; S4, based on the power threshold range of the current component and the collected working power, calculate the power anomaly probability of the current component.

[0031] In this step, the power anomaly probability of the current component is calculated by the following formula: In the formula, is the power anomaly probability of the current component, is the collected working power, and the power threshold range of the current component is .

[0032] S5. Calculate the network anomaly probability of the current component based on the network traffic threshold range, maximum response time threshold of the current component, and the collected network traffic, ARP packet response time, and ICMP packet response time.

[0033] In this step, the network anomaly 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 response time threshold.

[0034] In this embodiment, for the judgment of network anomalies, traffic monitoring and combined judgment of ARP (Address Resolution Protocol) & ICMP (Internet Control Message Protocol) are usually adopted. Traffic monitoring means that for a specific network device, the theoretical traffic within a certain period of time should satisfy the range . The combined judgment of ARP & ICMP means that the sum of the response times of a specific device to network ARP requests and ICMP requests should theoretically be less than .

[0035] S6. Determine the failure probability of the current component based on the power anomaly probability and network anomaly probability of the current component.

[0036] In this step, the failure probability of the current component is: S7. Obtain the temperature data of the field monitoring station cabinet within the time period, and calculate the temperature anomaly probability based on the temperature fluctuation and average temperature of the cabinet.

[0037] In this step, for the judgment of temperature anomalies, combined judgment of the temperature fluctuation failure factor K1 and the average temperature failure factor K2 is usually adopted.

[0038] The temperature fluctuation failure factor is obtained by collecting the cabinet temperature data within the time period and calculating the temperature standard deviation S K1 , if the temperature fluctuation failure factor coefficient is p, then the temperature fluctuation failure factor K1 = S k1 ×p.

[0039] The average temperature failure factor calculates the average temperature k by collecting the cabinet temperature data within the acquisition time period. avg According to the deployment location and the requirements of the equipment inside the cabinet, the rated temperature k is set. 额定 If the average temperature failure factor coefficient is q, then the average temperature failure factor K2 = q×(k avg - k 额定 ) / k avg .

[0040] Then the temperature anomaly probability is the sum of K1 and K2, that is, P 温度 = K1 + K2.

[0041] S8. Based on the temperature anomaly probability and the failure probabilities of all components in the field monitoring station, determine the overall failure probability of the field monitoring station: In the formula, n is the number of components inside the field monitoring station, and i is the component sequence number.

[0042] Therefore, based on the front-end data received from the Beidou satellite link, the back-end platform can freely construct relevant equipment status monitoring applications, specify maintenance strategies according to the on-site situation, and complete relevant business work.

[0043] The back-end maintenance platform has functions such as centralized reception of front-end information, intelligent analysis of fault points, and dynamic adjustment of maintenance forces. The front-end dual-machine equipment sends the operating status to the back-end analysis platform through a wireless communication link. The communication link can optionally adopt technical solutions such as Beidou Generation 2, Beidou Generation 3 short messages, 4G, 5G, etc. After the back-end analysis platform completely receives the data, it conducts summary analysis. According to the component list of the field monitoring station stored in the back-end and the overall failure probability and single-component failure probability of the monitoring station obtained through intelligent analysis, it outputs a high-risk failure list. It can be understood that the high-risk failure list includes the monitoring station failure list and the component failure list of each faulty monitoring station. The platform comprehensively analyzes the fault points, and conducts comprehensive analysis according to factors such as fault type and distance of the fault points, and outputs the personnel maintenance planning results and the route planning results of each maintenance personnel. So that subsequent maintenance personnel can carry out maintenance and troubleshooting work at each point according to the route planning suggestions.

[0044] In some embodiments, the personnel maintenance planning results are analyzed and determined in the following manner shown in steps B1 - B6: B1. Obtain the location set of the faulty monitoring stations in the monitoring station failure list and the failure levels of each faulty monitoring station.

[0045] In this step, the location set of the faulty monitoring stations is L = {l1, li,..., ln}, The fault levels of each fault monitoring station are E = {e1, ei,..., en}, where i is the sequential number of the fault monitoring station.

[0046] B2. Obtain the skill vectors and current positions of each maintenance personnel.

[0047] B3. Based on the fault levels of each fault monitoring station, calculate the maintenance time thresholds of each fault monitoring station.

[0048] Calculate the maintenance time thresholds of each fault monitoring station through the following formula: 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.

[0049] It can be understood that the fault levels of the fault monitoring stations can be set according to the urgency level, which is divided into levels 1 - 5 according to the urgency level. The larger the value, the more urgent it is, and the smaller the maintenance time threshold will be.

[0050] B4. Based on the required skill vectors of each fault monitoring station and the skill vectors of each maintenance personnel, calculate the fitness of each maintenance personnel for each fault monitoring station.

[0051] Calculate the fitness of each maintenance personnel for each fault monitoring station through the following formula: In the formula, is the fitness of the j-th maintenance personnel for 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.

[0052] B5. Based on the fitness, the position set of the fault monitoring stations and the current positions of each maintenance personnel, calculate the working costs of each maintenance personnel for repairing each fault monitoring station.

[0053] Calculate the working costs of each maintenance personnel for repairing each fault monitoring station through the following formula: In the formula, is the working cost of the j-th maintenance personnel for repairing the i-th fault monitoring station, is the Euclidean distance, is the current position of the j-th maintenance personnel, is the position of the i-th fault monitoring station, is the fitness of the j-th maintenance personnel for the i-th fault monitoring station.

[0054] B6. Determine the planning objective based on the working costs of each maintenance personnel for repairing each fault monitoring station and the maintenance time threshold of each fault monitoring station, so as to find the personnel maintenance planning result with the optimal working cost and maintenance time.

[0055] The planning objective is: In the formula, a and b are weight coefficients, n is the number of fault monitoring stations, is the maintenance time threshold of the i-th fault monitoring station, m is the number of maintenance personnel, is the working cost of the j-th maintenance personnel for repairing the i-th fault monitoring station.

[0056] It can be seen that in this embodiment, the maintenance tasks are dynamically allocated according to the fault type (i.e., the required skill vector of the fault monitoring station), the urgency, and the personnel skill matching degree, so as to optimize the utilization rate of human resources.

[0057] In some implementation manners, the route planning results of each maintenance personnel are analyzed and determined by the following steps H1 - H4: H1. For the route planning of each maintenance personnel, execute: Based on the personnel maintenance planning result, determine the fault monitoring stations that need to be repaired by the current maintenance personnel, so as to determine all the paths of the current maintenance personnel.

[0058] For example, if a maintenance personnel is assigned the maintenance tasks of three fault monitoring stations, then there are 6 paths, that is, 6 kinds of point repair sequences.

[0059] H2. Determine the pheromone update rule, and use the ant colony algorithm to calculate the pheromone of each path.

[0060] In this step, the pheromone update rule method is: In the formula, is the pheromone, ρ is the evaporation coefficient, is the pheromone update parameter, k is the path number, is the path time, is the waiting time, is the pheromone increment coefficient.

[0061] H3. Based on the static distance and dynamic traffic delay of each path, calculate the traffic delay coefficient of each path.

[0062] In this step, the calculation method of the traffic delay coefficient of each path is: In the formula, is the traffic delay coefficient for each path, is the static distance from monitoring station i to monitoring station j, such as geographical distance or fixed path length. The shorter the distance, the larger the value, and the stronger the path attractiveness. represents the dynamic traffic delay from monitoring station i to monitoring station j, such as real-time factors like congestion and road conditions. The higher the dynamic traffic delay, the smaller the value, and the weaker the path attractiveness. is the weight coefficient, used to balance the influence of static distance and dynamic traffic delay. If = 0, then the dynamic traffic delay is ignored and only the static distance is relied on; if > 0, then the influence of dynamic traffic delay increases with the increase of .

[0063] 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.

[0064] In this step, the selection probability of each path is: In the formula, is the selection probability of each path, is the pheromone, is the traffic delay coefficient of each path, α is the pheromone heuristic factor, indicating the influence degree of pheromone concentration on path selection. The larger the value of α, the more ants tend to choose the path with high pheromone concentration, that is, the path that has been frequently selected in historical experience; if α = 0, the algorithm only relies on heuristic information, such as distance, and ignores pheromone accumulation. β is the heuristic information factor, indicating the influence degree of heuristic information on path selection. The larger the value of β, the more ants tend to choose the path with short distance or smooth traffic; if β = 0, the algorithm only relies on pheromone concentration and may fall into local optimum. By adjusting the ratio of α and B, the emphasis of the algorithm on "historical experience" and "real-time road conditions" can be balanced. For example: α > β, more trust in pheromone accumulation, suitable for static environment. β > α, more attention to real-time traffic data, suitable for dynamic environment. Usually set α ∈ [1, 2], β ∈ [2, 5], and specific values need to be optimized through experiments.

[0065] In some embodiments, the backend analysis platform adopts LSTM-based fault prediction to manage the spare parts inventory to remind the maintenance personnel to purchase spare parts in time for replenishment.

[0066] In this embodiment, an LSTM fault prediction model is used to predict and output the fault probabilities at several future moments based on the historical fault probabilities of each component respectively. When the average fault probability of the component within the average set time is greater than the set value and the inventory of the component is less than the set threshold, a spare part replenishment purchase notice is triggered.

[0067] An embodiment of the present invention provides a Beidou short message bypass monitoring device for a field monitoring station. The device includes: a front-end dual-device and a back-end analysis platform. The front-end dual-device includes device A and device B, and the two devices are arranged on the bypass of the field monitoring station. Device A and device B are respectively used to collect the operation status data of each component of the field monitoring station, and send the operation status data collected by themselves and their own working status to each other; compare the consistency of the operation status data collected by themselves and the operation status data collected by the other party, and determine the reporting device and the reporting data based on their own working status and the working status of the other party. The back-end analysis platform is used to determine the fault probabilities of each component of each field monitoring station based on the reporting data of each field monitoring station received via the Beidou satellite link, so as to output a high-risk fault list based on the fault probabilities, and analyze and determine the personnel maintenance plan result and the route planning result of each maintenance personnel based on the fault probabilities, the high-risk fault list and the location of the fault monitoring station of each field monitoring station; the high-risk fault list includes the monitoring station fault list and the component fault list of each fault monitoring station.

[0068] It should be noted that: the above device embodiment and the method embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0069] An embodiment of the present application also provides a computer device. The computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the Beidou short message bypass monitoring method for a field monitoring station provided by the above method embodiments.

[0070] An embodiment of the present application also provides a computer-readable storage medium. At least one instruction, at least one program, a code set or an instruction set is stored on the computer-readable storage medium, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the Beidou short message bypass monitoring method for a field monitoring station provided by the above method embodiments.

[0071] An embodiment of the present application further provides a computer program product, which includes a computer program. The processor of the computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the Beidou short message bypass monitoring method for the field monitoring station in any one of the above embodiments.

[0072] For the convenience of description, when describing the above device or apparatus, various modules or units are described separately according to functions. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0073] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods in various embodiments or some parts of the embodiments of the present application.

[0074] Finally, it should also be noted that in this article, relational terms such as first, second, third, and fourth 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 these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0075] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A Beidou short message bypass monitoring method for field monitoring stations, characterized in that The bypass monitoring device includes a front-end dual-device and a back-end analysis platform. The front-end dual-device includes Device A and Device B, and the two devices are arranged in parallel to the field monitoring station. The method includes: Device A and Device B respectively collect the operation status data of each component of the field monitoring station, and send the operation status data collected by themselves and their own working status to each other; For both Device A and Device B, execute: compare the consistency of the operation status data collected by themselves with the operation status data collected by the other party, and determine the reporting device and the reporting data based on their own working status and the working status of the other party; After the back-end analysis platform receives the reporting data of the field monitoring station sent by the reporting device via the Beidou satellite link, it analyzes and determines the failure probability of each component of each field monitoring station based on the reporting data of each field monitoring station, so as to output a high-risk failure list based on the failure probability, and based on the failure probability of each component of each field monitoring station, the high-risk failure list and the location of the failed monitoring station, analyze and determine the personnel maintenance plan result and the route planning result of each maintenance personnel; the high-risk failure list includes the monitoring station failure list and the component failure list of each failed monitoring station.

2. The method according to claim 1, wherein For both Device A and Device B, execute: compare the consistency of the operation status data collected by themselves with the operation status data collected by the other party, and determine the reporting device and the reporting data based on their own working status and the working status of the other party, including: Both Device A and Device B compare the consistency of the operation status data collected by themselves with the operation status data collected by the other party; If they are consistent, the data does not need to be merged, and the operation status data collected by themselves is the target data; If they are inconsistent, mark the operation status data of the components with differences as suspected failure data, and merge the marked position data in the operation status data collected by the other party and themselves, and combine the marks as the target data of the device; Compare the working status of Device A and Device B; If both Device A and Device B are normal, determine Device A as the reporting device, and the target data stored in Device A is used as the reporting data; If any one device is normal and the other device is abnormal, determine the normal device as the reporting device, and the target data stored in the normal device is used as the reporting data; If both devices are abnormally normal, determine that both Device A and Device B are reporting devices, and send the target data stored in both devices marked as unreliable as the reporting data.

3. The method according to claim 1, wherein The data types of the operation status data of each component collected by Device A and Device B include cooperative data and non-cooperative data; the cooperative data is the status data reported by using the communication protocol; The analysis and determination of the failure probability of each component of each field monitoring station based on the reporting data of each field monitoring station includes: For each component, execute: Read the cooperative data of the current component, and determine whether there is a failure value in the cooperative data; When the failure value is 1, directly determine that the failure probability of the current component is 1; When the fault value is 0, read the working power, network traffic, ARP packet response time, and ICMP packet response time of the current component collected from the cooperative data and non - cooperative data of the current component; Calculate the power anomaly probability of the current component based on the power threshold range of the current component and the collected working power; Calculate the network anomaly probability of the current component based on the network traffic threshold range of the current component, the maximum response time threshold, and the collected network traffic, ARP packet response time, and ICMP packet response time; Determine the fault probability of the current component based on the power anomaly probability and network anomaly probability of the current component; Obtain the temperature data of the field monitoring station cabinet within a time period, and calculate the temperature anomaly probability based on the temperature fluctuation and average temperature of the cabinet; Determine the overall fault probability of the field monitoring station based on the temperature anomaly probability and the fault probabilities of all components in the field monitoring station.

4. The method according to claim 1, wherein The personnel maintenance plan result is determined through the following analysis: Obtain the location set of the faulty monitoring stations in the monitoring station fault list and the fault levels of each faulty monitoring station; Obtain the skill vectors and current locations of each maintenance personnel; Calculate the maintenance time threshold of each faulty monitoring station based on the fault level of each faulty monitoring station; Calculate the fitness of each maintenance personnel for each faulty monitoring station based on the required skill vector of each faulty monitoring station and the skill vectors of each maintenance personnel; Calculate the working cost of each maintenance personnel for repairing each faulty monitoring station based on the fitness, the location set of the faulty monitoring stations, and the current locations of each maintenance personnel; Determine the planning objective based on the working cost of each maintenance personnel for repairing each faulty monitoring station and the maintenance time threshold of each faulty monitoring station, so as to find the personnel maintenance plan result with the optimal working cost and maintenance time.

5. The method according to claim 4, wherein The personnel maintenance plan result is determined through the following method: Calculate the maintenance time threshold of each faulty monitoring station through the following formula: Wherein, is the maintenance time threshold of the i-th fault monitoring station, is the fault level of the i-th fault monitoring station; Calculate the fitness of each maintenance personnel for each faulty monitoring station through the following formula: Wherein, is the adaptability 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; Calculate the working cost of each maintenance personnel for repairing each faulty monitoring station through the following formula: In the formula, is the working cost of the j-th maintenance personnel for repairing the i-th fault monitoring station, is the Euclidean distance, is the current position of the j-th maintenance personnel, is the position of the i-th fault monitoring station, is the fitness of the j-th maintenance personnel for the i-th fault monitoring station; The planning objective is: Wherein, a and b are weight coefficients, n is the number of fault monitoring stations, is the maintenance time threshold of the i-th fault monitoring station, m is the number of maintenance personnel, is the working cost of the j-th maintenance personnel for maintaining the i-th fault monitoring station.

6. The method according to claim 1, characterized in that, The route planning results of each maintenance personnel are determined through the following analysis: For the route planning of each maintenance personnel, the following operations are performed: Based on the personnel maintenance plan result, determine each faulty monitoring station that the current maintenance personnel needs to repair, so as to determine all paths of the current maintenance personnel; Determine the pheromone update rule, and use the ant colony algorithm to calculate the pheromone of each path; Calculate the traffic delay coefficient of each path based on the static distance and dynamic traffic delay of each path; Determine the selection probability of each path based on the pheromone of each path and the traffic delay coefficient of each path, so as to determine the route planning result of the current maintenance personnel.

7. A Beidou short message bypass monitoring device for a field monitoring station, which is used to implement the steps of the method according to any one of claims 1-6 above, and is characterized in that, Include: The front - end dual - machine device and the back - end analysis platform. The front - end dual - machine device includes device A and device B, and the two devices are set on the bypass of the field monitoring station; Device A and Device B are respectively used to collect the operation status data of each component of the field monitoring station, and send the collected operation status data and their own working status to each other; compare the consistency of the operation status data collected by themselves and the operation status data collected by the other party, and determine the reporting device and the reporting data based on their own working status and the working status of the other party; 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 then determine the failure probability of each component of each field monitoring station based on the reporting data of each field monitoring station, so as to output a high-risk failure list based on the failure probability, and analyze and determine the personnel maintenance plan 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 failed monitoring station; The high-risk failure list includes a monitoring station failure list and a component failure list of each failed monitoring station.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored on the memory to implement the steps of the method according to any one of claims 1-6 above.

9. A computer-readable storage medium, characterized in that, The storage medium stores computer programs, and when the computer programs are executed by a processor, the steps of the method according to any one of claims 1-6 are implemented.

10. A computer program product, characterized in that, It includes computer programs, and when the computer programs are executed by a processor, the steps of the method according to any one of claims 1-6 are implemented.

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