Intelligent gas pipe network fault safety processing internet-of-things system and method

Abstract

The invention provides an intelligent gas pipe network fault safety processing Internet of Things system and method, and relates to the field of pipe network fault safety processing. The Internet of Things system comprises a government safety supervision management platform. The method is executed by a government safety supervision and management platform, and comprises the following steps: executing in a first preset period: for platform fault data of which the fault type is unknown in the first preset period, determining a gas adjustment parameter and sending the gas adjustment parameter to a gas regulation and control device in response to a condition that the data volume is greater than a preset volume; in response to the fact that the data volume is smaller than the preset volume, fault processing parameters are determined based on the platform fault data in the first preset period; generating an adjustment instruction based on the fault processing parameter, and sending the adjustment instruction to the supervision device, the target platform and / or the personnel interaction device; and in a second preset period, determining platform fault data based on the monitoring data of the supervision equipment and the communication characteristics of the target platform. According to the invention, platform faults can be automatically monitored and effectively diagnosed and processed.

Description

Technical Field

[0001] This specification relates to the field of pipeline network fault safety processing, and in particular to an Internet of Things system and method for smart gas pipeline network fault safety processing. Background Art

[0002] As smart city construction continues to advance, the stable operation of smart gas systems, a key component of urban infrastructure, is crucial for ensuring the safety and efficiency of urban gas supply. However, in actual operation, smart gas systems may encounter various failures. When a smart gas system fails, troubleshooting and troubleshooting are critical issues that need to be addressed.

[0003] Therefore, it is necessary to provide an IoT system and method for safe handling of smart gas pipeline network faults, which can automatically monitor and effectively diagnose platform faults of the smart gas system, and quickly locate and handle platform faults to ensure the safe operation of the gas pipeline network. Summary of the Invention

[0004] In order to solve the problem of how to automatically monitor, diagnose, locate and handle platform failures of smart gas systems, the present invention provides an Internet of Things system and method for safely handling smart gas pipeline network failures.

[0005] The invention includes an IoT system for handling smart gas pipeline network fault safety. The IoT system includes a government safety supervision and management platform configured to execute a method for handling smart gas pipeline network fault safety. The IoT system also includes a government safety supervision service platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, a smart gas equipment object platform, and a gas maintenance object platform.

[0006] The invention content includes a smart gas pipeline network fault safety processing method, which is executed by a government security supervision and management platform in a smart gas pipeline network fault safety processing Internet of Things system. The method includes: executing within a first preset period: obtaining platform fault data within the first preset period; for the platform fault data of unknown fault type: in response to the data volume being greater than a preset volume, determining gas adjustment parameters and sending them to a gas control device; in response to the data volume being less than the preset volume: determining fault processing parameters based on the platform fault data within the first preset period; generating adjustment instructions based on the fault processing parameters and sending them to a supervision device, a target platform and / or a personnel interaction device to adjust the supervision parameters of the supervision device, the communication parameters of the target platform and / or on-site personnel arrangement; wherein the first preset period includes one or more second preset periods; obtaining the platform fault data within the first preset period includes: periodically obtaining the platform fault data of the one or more second preset periods according to a second preset period; executing within the second preset period: obtaining monitoring data of the supervision device and communication characteristics of the target platform; and determining the platform fault data based on the communication characteristics and the monitoring data.

[0007] The beneficial effects brought about by the above invention include but are not limited to: (1) by obtaining platform fault data within the second preset period, and autonomously determining whether it is necessary to adjust the gas adjustment parameters according to the amount of fault data within the first preset period, the gas control device can be adjusted in time or corresponding fault handling measures can be prepared, which can effectively avoid problems in the subsequent gas pipeline operation, help improve the fault response speed, optimize resource allocation, and enhance the reliability and safety of the entire gas supply network; (2) by comprehensively considering the importance of monitoring data and the data transmission priority, the calculated processing confidence can more accurately reflect the degree of conformity between the actual processing results and the expected processing results; by setting the processing confidence, the effect of the platform processing data can be quantified, which helps to more accurately evaluate the performance of the platform; based on the processing confidence and communication characteristics, the accuracy of the platform fault data can be improved to ensure the accuracy and efficiency of fault handling, reduce misjudgment and duplication of work; (3) the importance of the fault can be accurately assessed based on the platform fault data, and reasonable fault handling parameters can be determined based on the fault importance, which is conducive to the reasonable allocation of resources and time, thereby improving the overall stability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein: Figure 1This is a schematic diagram of the platform structure of a smart gas pipeline network fault safety processing Internet of Things system according to some embodiments of this specification; Figure 2 is an exemplary flow chart of a method for safely handling smart gas network faults according to some embodiments of this specification; Figure 3 is an exemplary schematic diagram of determining platform fault data according to some embodiments of this specification; Figure 4 This is an exemplary schematic diagram of determining fault handling parameters according to some embodiments of this specification. DETAILED DESCRIPTION

[0009] The following is a brief introduction to the drawings used in describing the embodiments. The drawings do not represent all embodiments. When describing the operations performed in steps, unless otherwise specified, the order of the steps is interchangeable, steps may be omitted, and additional steps may be included in the operation process.

[0010] Figure 1 This is a schematic diagram of the platform structure of a smart gas pipeline network fault safety processing Internet of Things system shown in some embodiments of this specification.

[0011] In some embodiments, as Figure 1 As shown, the smart gas pipeline fault safety processing Internet of Things system 100 may include a government safety supervision service platform 110, a government safety supervision management platform 120, a government safety supervision sensor network platform 130, a government safety supervision object platform 140, a gas company management platform 141, key gas-using enterprises 142, a gas company sensor network platform 150, a smart gas equipment object platform 160, and a gas maintenance object platform 170.

[0012] The government security supervision service platform 110 refers to a platform that provides supervision services to the government and can be configured as a server.

[0013] The government safety supervision and management platform 120 is a platform for government safety supervision and management. It can be configured as a processor and / or server and memory. The processor can process data and / or information related to the smart gas pipeline network fault safety management IoT system 100. By way of example only, the processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), etc.

[0014] In some embodiments, the processor can interact with multiple platforms included in the smart gas pipeline fault safety processing Internet of Things system 100, and can also be configured in multiple platforms.

[0015] In some embodiments, the government security supervision management platform 120 can interact with the government security supervision service platform 110 for data.

[0016] In some embodiments, the government safety supervision management platform 120 can be configured to execute a smart gas pipeline network fault safety handling method.

[0017] The government security supervision sensor network platform 130 refers to a platform used by the government to supervise and manage sensor network information, and can be configured as a communication device and a gateway.

[0018] In some embodiments, the government security supervision sensor network platform 130 may be used for communication between the government security supervision management platform 120 and the government security supervision object platform 140 .

[0019] The government security supervision object platform 140 refers to an object platform for generating perception information and executing control information.

[0020] The government safety supervision object platform 140 may include a gas company management platform 141 and key gas-using enterprises 142 .

[0021] The gas company management platform 141 refers to a comprehensive management platform for relevant information of the gas company, and may be configured as a processor and / or server and memory.

[0022] Key gas-consuming enterprises 142 refer to enterprises that require special attention to their gas usage, such as chemical plants that consume large amounts of gas.

[0023] The gas company sensor network platform 150 refers to a comprehensive management platform for the gas company's sensor information, and can be configured as a communication network and a gateway, etc.

[0024] In some embodiments, the gas company sensor network platform 150 can be used for communication between the gas company management platform 141 and the smart gas equipment object platform 160 and the gas maintenance object platform 170 .

[0025] The smart gas equipment object platform 160 is a functional platform for real-time monitoring and intelligent regulation of the gas pipeline network. In some embodiments, the smart gas equipment object platform 160 includes at least monitoring equipment and gas control devices deployed in the gas pipeline network.

[0026] Supervision equipment refers to related equipment used to monitor and record the operating status of the gas pipeline network.

[0027] In some embodiments, the monitoring device is configured to monitor the gas pipeline network. For example, a pressure sensor is used to monitor the gas pressure in the gas pipeline, a temperature sensor is used to monitor the temperature changes of the gas in the pipeline and the surrounding environment, and a flow sensor is used to monitor the gas flow in the pipeline.

[0028] Gas control devices refer to equipment used to control and regulate gas within pipelines. Examples include valves used to shut off gas pipelines and pressure regulators used to reduce or increase gas pressure within pipelines.

[0029] The gas maintenance object platform 170 is a platform for interacting with gas workers. Gas workers are people who work with the gas pipeline network, such as safety officers, maintenance workers, and storage and transportation workers.

[0030] In some embodiments, the gas maintenance object platform 170 includes at least one human interaction device.

[0031] The human interaction device refers to a device that interacts with gas workers, such as a mobile phone, a computer, etc. In some embodiments, the human interaction device can receive adjustment instructions issued by the government safety supervision management platform 120 to adjust the on-site personnel arrangements.

[0032] For more information about the above platforms, please refer to Figure 2-Figure 4 and related instructions.

[0033] In some embodiments of this specification, based on the smart gas pipeline network fault safety processing Internet of Things system 100, an information operation closed loop can be formed between various functional platforms, and coordinated and regularly operated under the unified management of the government security supervision and management platform, thereby realizing the informatization and intelligence of smart gas pipeline network fault safety processing.

[0034] It should be noted that the above description of the smart gas pipeline network fault safety handling IoT system and its platform is for convenience only and does not limit the present invention to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various platforms or form subsystems that connect to other platforms without departing from these principles.

[0035] Figure 2 This is an exemplary flow chart of a method for handling smart gas pipeline network fault safety according to some embodiments of this specification. In some embodiments, process 200 can be executed by the government safety supervision and management platform 120. For example, it can be executed by a processor in the government safety supervision and management platform 120.

[0036] In some embodiments, as Figure 2As shown, the processor may execute steps S210 to S220 within a first preset period.

[0037] Step S210: Acquire platform fault data within a first preset period.

[0038] The first preset period is a preset period from the start of acquiring platform fault data to determining fault handling parameters. In some embodiments, the first preset period can be set by default by the processor or by a technician based on historical experience. For example, 6 hours before the current time.

[0039] In some embodiments, the processor may process platform fault data within a first preset period in the same batch.

[0040] Platform failure data refers to data related to platform failures in the smart gas network fault safety handling IoT system. In some embodiments, the platform failure data may include the failed platform and / or the type of failure that occurred on the failed platform.

[0041] A faulty platform refers to a platform in the smart gas network fault safety handling IoT system where a fault occurs. In some embodiments, a platform in the system is determined to be a faulty platform if it is unable to properly perform its intended function or service.

[0042] The fault type refers to the type of fault that occurs on the faulty platform. In some embodiments, the fault type may include storage type, forwarding type, computing type, and unknown type.

[0043] Storage failures refer to failures related to data storage, such as the platform being unable to write or read data.

[0044] Forwarding failures refer to failures related to data transmission, such as data loss or transmission delays during platform data transmission.

[0045] Computational failures refer to failures related to data processing or analysis, such as errors in platform calculation results or decreased processing speed.

[0046] Unknown faults are those where the specific cause cannot be determined and require further diagnosis and analysis. For example, a key parameter may have an abnormal reading or stop updating, but initial inspection cannot determine the specific cause.

[0047] In some embodiments, step S210 may further include step S211 , and the processor may execute step S211 to obtain platform fault data within a first preset period.

[0048] Step S211 : periodically acquiring platform fault data of one or more second preset periods according to a second preset period.

[0049] The second preset period is a preset period for obtaining platform fault data. Within one second preset period, the processor may perform one determination of a faulty platform and a fault type.

[0050] In some embodiments, the first preset period may include one or more second preset periods, and the processor may periodically obtain platform fault data according to the second preset period, that is, collect platform fault data once in one second preset period. For platform fault data within one or more second preset periods in a first preset period, the processor may process them together in the same batch to determine fault processing parameters.

[0051] In some embodiments, the second preset period can be set by default by the processor or by a technician based on historical experience, for example, 1 hour.

[0052] Fault handling parameters refer to parameters related to fault handling. In some embodiments, the fault handling parameters may include adjustment parameters of one or more gas control devices.

[0053] In some embodiments, the length of the first preset period may be related to a fault response speed, and the length of the second preset period may be related to the complexity of the gas pipeline network.

[0054] Fault response speed refers to the speed of responding to and handling a fault. In some embodiments, the processor may statistically obtain an average value of multiple fault handling times corresponding to multiple historical fault handling processes and determine it as the fault response speed.

[0055] The faster the fault response speed, the shorter the average time required to resolve the fault. Therefore, the first preset cycle can be appropriately extended to obtain more platform fault data, so that more faults can be handled in the same batch, thereby improving fault handling efficiency.

[0056] In some embodiments, the complexity of the gas pipeline network can be characterized by the number of nodes where the pipelines intersect and the number of devices connected to the pipelines. The greater the number of nodes where the pipelines intersect and the number of devices connected to the pipelines, the more complex the gas pipeline network is.

[0057] In some embodiments, the length of the second preset period can be negatively correlated with the complexity of the gas pipeline network. The more complex the gas pipeline network, the more difficult it is to diagnose and trace the source of the problem. Consequently, the second preset period can be shortened. Collecting platform fault data at shorter intervals facilitates rapid identification of abnormal data and tracing the source of the problem.

[0058] In some embodiments of this specification, faster fault response speeds shorten the average time required to resolve faults. By extending the first preset period, more faults can be processed within the same batch within the same period, thereby improving processing efficiency. The more complex the gas pipeline network, the more difficult it is to trace the source of the fault. By shortening the second preset period, abnormal data can be quickly discovered, improving the system's risk response capabilities.

[0059] In some embodiments, step S211 may further include step S211 - 1 and step S211 - 2 , and the processor may execute step S211 - 1 and step S211 - 2 within a second preset period to obtain platform fault data within the second preset period.

[0060] Step S211 - 1 , obtaining monitoring data of the supervisory device and communication characteristics of the target platform.

[0061] For more information about supervisory devices, see Figure 1 The corresponding description.

[0062] Monitoring data refers to data collected by the monitoring device during the monitoring process. In some embodiments, the monitoring data may include the monitored object and the monitored content of the monitored object.

[0063] The monitoring object refers to the target monitored by the supervisory device, for example, the temperature inside gas pipeline 1.

[0064] The monitoring content refers to the data actually monitored by the monitoring device for the monitoring object. For example, if the monitoring object is the temperature inside the gas pipeline 1, the monitoring content of the monitoring object can be a sequence of the temperature inside the gas pipeline 1 at multiple time points.

[0065] In some embodiments, monitoring data can be obtained based on the supervision device in the smart gas device object platform.

[0066] The target platform refers to the platform whose communication characteristics need to be acquired. Target platforms include government security supervision service platforms, government security supervision management platforms, government security supervision sensor network platforms, government security supervision object platforms, gas company sensor network platforms, smart gas equipment object platforms, and gas maintenance object platforms.

[0067] Communication characteristics refer to the characteristics of the platform's communication data. Communication data refers to the data transmitted when the platforms communicate with each other.

[0068] In some embodiments, the communication data of the target platform may refer to monitoring data processed by the target platform, that is, the platform fault data is determined by transmitting the monitoring data between the target platforms.

[0069] In some embodiments, the communication characteristics may include the data type, data flow, data sending and receiving time, transmission priority and data source platform of the communication data, that is, the communication characteristics of the target platform may include the data type, data flow, data sending and receiving time, transmission priority and data source platform of the monitoring data processed by the target platform.

[0070] The data type refers to the nature or category of the communication data. In some embodiments, the data type may include storage data, calculation data, and forwarding data.

[0071] Storage data refers to data that will ultimately be stored on the target platform. Computational data refers to data that is processed on the target platform. Forwarding data refers to data that is transmitted only through the target platform and is not stored or computed on the target platform. In some embodiments, the processor can determine the data type of communication data by analyzing its processing method, transmission path, and function in the system.

[0072] Data traffic refers to the total amount of data transmitted per unit time and can be used to measure the data transmission rate. For example, 100 Mbps. In some embodiments, the processor can use a network monitoring tool to capture and analyze the data transmission status of the network interface to obtain data traffic.

[0073] Data transmission and reception time refers to the time difference between the target platform's start of receiving and sending communication data, that is, the time interval between data flowing into and out of the platform. Specifically, if the target platform serves as the destination of data inflow, meaning it only receives data and does not transmit data out, the data transmission and reception time can be represented by the time interval between the first data byte flowing into the platform and the last data byte flowing into the platform. If the target platform serves as the origin of data outflow, meaning it only sends data and does not receive data, the data transmission and reception time can be represented by the time interval between the first data byte flowing out of the platform and the last data byte flowing out of the platform.

[0074] In some embodiments, the processor may determine the data sending and receiving time through a network monitoring tool or a timestamp recorded in a system log.

[0075] Transmission priority refers to the priority level of communication data transmitted by the target platform. Communication data with higher transmission priority will be transmitted before communication data with lower transmission priority.

[0076] In some embodiments, the transmission priority can be represented by an integer value between 1 and 10. The higher the value, the higher the transmission priority. In some embodiments, the transmission priority of data can be set by default by the processor or by a technician based on historical experience.

[0077] The data source platform refers to the platform from which communication data originates. For example, if the communication data received by the government security supervision target platform is transmitted and obtained from the government security supervision management platform, then the government security supervision management platform is the data source platform for that communication data.

[0078] Step S211 - 2 : Determine platform fault data based on the communication characteristics and monitoring data.

[0079] In some embodiments, the processor may determine platform fault data in a variety of ways based on the communication characteristics and monitoring data.

[0080] For example, the processor can determine the expected processing time by querying historical data based on the data type, data flow and transmission priority in the communication characteristics; determine the time deviation based on the expected processing time and the data transmission and reception time; in response to the time deviation being greater than the deviation threshold, determine that the target platform is a faulty platform; and determine the platform fault data based on the data type of the communication data and the data source platform.

[0081] The expected processing time refers to the estimated time it takes for the target platform to process communication data under normal circumstances. The expected processing time can be obtained by the processor based on historical data. For example, for communication data with a storage data type, a data flow of 100Mbps, and a transmission priority of 6, the processor can obtain multiple historical communication data of the same type, calculate the average of the multiple historical actual processing times corresponding to the multiple historical communication data, and determine it as the expected processing time corresponding to the communication data.

[0082] Time deviation refers to the difference between actual processing time and expected processing time, which can be used to measure the efficiency of the system in processing communication data.

[0083] In some embodiments, the processor may determine the difference between the expected processing time and the data transmission and reception time as the time deviation.

[0084] The deviation threshold refers to a preset time deviation threshold. In some embodiments, the deviation threshold may be related to the transmission priority, and the higher the transmission priority, the smaller the deviation threshold.

[0085] In some embodiments, in response to a time deviation corresponding to the target platform being greater than a deviation threshold, the target platform is determined to be a candidate fault platform.

[0086] In some embodiments, for a target platform determined to be a candidate fault platform, if the data type of the communication data of the target platform is storage-type data or forwarding-type data, the processor may determine that the target platform is a faulty platform and determine that the fault type of the target platform corresponds to a storage-type fault or a forwarding-type fault.

[0087] In some embodiments, for a target platform determined to be a candidate fault platform, if the data type of the target platform's communication data is computational data, the processor may obtain the data type of the data processed by the data source platform and compare the data type corresponding to the target platform with the data type corresponding to the data source platform. In response to an inconsistency between the two, the processor may determine that all platforms the communication data flows through during the process of flowing from the data source platform to the target platform are faulty platforms, and the corresponding fault types are all unknown faults; in response to an agreement between the two, the processor may determine that the data source platform and the target platform are faulty platforms, wherein the fault type corresponding to the data source platform is unknown fault, and the fault type corresponding to the target platform is computational fault.

[0088] In some embodiments, the processor may also determine the processing confidence of the target platform based on the communication characteristics and monitoring data; and determine platform fault data based on the processing confidence and communication characteristics of the target platform. For more information on this part, please refer to Figure 3 Related description.

[0089] Step S220: For platform fault data whose fault type is unknown, execute steps S221 and S222.

[0090] In step S221 , in response to the data volume being greater than a preset volume, a gas adjustment parameter is determined and sent to a gas control device.

[0091] Data volume refers to the total amount of data marked and processed when a faulty platform experiences a failure, specifically the total amount of data associated with a specific fault type. Marking refers to categorizing the fault and recording its type. For example, if the platform marked 2GB of data with an unknown fault type during the first preset cycle, the data volume corresponding to the unknown fault type is 2GB.

[0092] The preset volume refers to the maximum amount of fault data that a system or platform can quickly process. In some embodiments, the preset volume can be determined by a processor based on the disaster recovery capabilities of the IoT system for safely handling smart gas pipeline network failures. The greater the disaster recovery capability, the larger the preset volume.

[0093] Among them, disaster recovery capability refers to the ability to quickly restore systems and data when a failure occurs to reduce data loss and business interruption.

[0094] Gas adjustment parameters refer to parameters used to adjust the operation of the gas pipeline network. In some embodiments, the gas adjustment parameters may include operating parameters of one or more gas control devices, such as the valve opening of a gas pressure reducing valve, the flow rate setting of a gas flow meter, and the pressure setting of a gas pressure regulator.

[0095] In some embodiments, in response to the data volume being greater than a preset volume, the processor may determine the gas adjustment parameter in a variety of ways. For example, the processor may determine the gas adjustment parameter according to a preset scheme.

[0096] A preset plan refers to a pre-set plan for reducing system risk. In some embodiments, the preset plan can be determined by technical personnel based on historical experience and reference to historical data. For example, the preset plan may include shutting down some minor gas pipelines when the data volume exceeds a preset volume.

[0097] In some embodiments, the processor may also adaptively adjust gas adjustment parameters based on the difference between the volume of unknown fault data and a preset volume, based on the preset solution. For example, if the preset solution is to reduce the pressure within the gas pipeline, and the volume of unknown fault data is significantly greater than the preset volume, the processor may further reduce the pressure within the gas pipeline based on the preset solution. The corresponding gas adjustment parameters may include a larger valve opening of the gas pipeline's pressure-reducing valve.

[0098] In some embodiments, the government safety supervision management platform can send gas adjustment parameters to the gas control device in the smart gas equipment object platform through the government safety supervision sensor network platform and the gas company sensor network platform to control the gas control device to adjust the operation of the gas pipeline network and reduce overall risk.

[0099] Step S222: In response to the data volume being less than the preset volume, execute steps S222-1 and S222-2.

[0100] Step S222-1: determining fault processing parameters based on platform fault data within a first preset period.

[0101] In some embodiments, the processor may determine fault handling parameters in various ways based on platform fault data within a first preset period. For example, the processor may determine a fault frequency distribution of a faulty platform based on platform fault data within the first preset period, and determine fault handling parameters corresponding to the faulty platform based on a fault probability distribution.

[0102] The failure frequency distribution refers to the frequency distribution of failures of different types occurring on a faulty platform. In some embodiments, the failure frequency distribution can be represented by the frequency of failures of different types occurring on the faulty platform within one or more second preset periods of a first preset period. Each faulty platform corresponds to a single failure frequency distribution. For example, if the first preset period is one day, and the second preset period is each hour within that day, and if faulty platform A experiences two storage-type failures, four computing-type failures, three forwarding-type failures, and one unknown-type failure within the first preset period, then the failure frequency distribution of faulty platform A is (two storage-type failures, four computing-type failures, three forwarding-type failures, and one unknown-type failure).

[0103] In some embodiments, for a faulty platform, the processor may determine the low-probability faults and other probability faults of the faulty platform based on the fault frequency distribution; determine the faults to be processed based on the low-probability faults and other probability faults; and determine the fault processing parameters based on the faults to be processed.

[0104] In some embodiments, the processor may determine the probability of occurrence of various faults on the faulty platform based on the fault frequency distribution; in response to the variance of the various fault occurrence probabilities being less than a variance threshold, determine that the faulty platform does not have a low-probability fault; in response to the variance of the various fault occurrence probabilities being not less than the variance threshold, determine that the faulty platform has a low-probability fault; calculate the probability mean of the various fault occurrence probabilities, determine that faults with a probability less than the probability mean are low-probability faults, and determine that faults that are not low-probability faults are other probability faults. The variance threshold may be set by default by the processor or by a technician based on experience.

[0105] For example, the fault frequency distribution of faulty platform A is (2 storage faults, 4 computing faults, 3 forwarding faults, and 1 unknown fault). The probabilities of storage faults, computing faults, forwarding faults, and unknown faults are 0.2, 0.4, 0.3, and 0.1, respectively, and the variance of the probabilities of these four faults is 0.0125. If the variance threshold is 0.02, there are no low-probability faults, meaning all four faults are other-probability faults. If the variance threshold is 0.01, there are low-probability faults, and the mean probability of the four faults is 0.25. The probabilities of storage faults and unknown faults are both lower than the mean probability. Therefore, storage faults and unknown faults are low-probability faults, while computing faults and forwarding faults are other-probability faults.

[0106] In some embodiments, for a low-probability fault, the processor can determine the last fault time of the low-probability fault, calculate the time difference between the last fault time and the current time, and, if the time difference is greater than the length of a second preset period, identify the low-probability fault as a pending fault. For other probability faults, the processor can directly identify them as pending faults. The last fault time refers to the time when the fault most recently occurred. A pending fault refers to a fault that requires processing. These pending faults include the low-probability faults whose time difference is greater than the length of the second preset period, as well as all other probability faults.

[0107] For example, a storage fault is a low-probability fault. The last fault occurred at 08:00 on October 14th, the current time is 09:30 on October 14th, and the second preset period is 1 hour. The time difference between the last fault and the current time is 1.5 hours, which is greater than the second preset period. Therefore, the storage fault is a pending fault. Computing faults and forwarding faults are other probabilistic faults. Therefore, pending faults include storage faults, computing faults, and forwarding faults.

[0108] In some embodiments, in response to a pending fault being a storage-type fault and / or a forwarding-type fault, the processor may determine a fault handling solution by querying a first preset table, and determine fault handling parameters based on the fault handling solution. The first preset table may include storage-type faults, forwarding-type faults, and corresponding fault handling solutions, and may be constructed by a technician based on historical data and prior experience.

[0109] A fault handling solution refers to a series of predetermined response plans for various types of faults. For example, if a storage-type fault is a fault in the storage device of the faulty platform, the fault handling solution in the first preset table may be a preset fault handling procedure that triggers the storage device. The preset fault handling procedure of the storage device may include connecting to a backup storage medium, enabling a backup storage device, overwriting old data with the latest data, etc. For another example, if a forwarding-type fault is a fault in the communication device of the faulty platform, the fault handling solution in the first preset table may be a preset fault handling procedure that triggers the communication device. The preset fault handling procedure of the communication device may include restoring the default settings of the communication network and restarting the communication device, etc.

[0110] In some embodiments, the processor may factor a preset fault handling procedure into the fault handling parameters.

[0111] In some embodiments, in response to a pending fault being a calculation-type fault and / or an unknown-type fault, the processor may determine a fault handler by querying a personnel scheduling table; and determine fault handling parameters based on the fault handler. The personnel scheduling table may include calculation-type faults, unknown-type faults, and corresponding fault handlers. The personnel scheduling table may be pre-set by relevant departments or units.

[0112] The troubleshooting personnel for computing faults can be the administrator of the faulty platform. The troubleshooting personnel for unknown faults can be the administrator of the faulty platform and / or the administrator of the supervisory equipment.

[0113] In some embodiments, in response to the fault to be processed being a computing fault, the processor may include the management personnel of the fault platform and the platform fault data of the fault platform into the fault processing parameters.

[0114] In some embodiments, in response to the fault to be processed being an unknown fault, if the corresponding unknown fault occurs in the supervisory device, the processor may include the management personnel and default supervisory parameters of the supervisory device in the fault handling parameters; if the corresponding unknown fault occurs in the fault platform, the processor may include the management personnel of the fault platform, the platform fault data and default supervisory parameters of the fault platform in the fault handling parameters.

[0115] Regulatory parameters are the operating parameters of a regulatory device, which determine how the device collects, processes, and responds to data. Examples include the device's operating mode, preset alarm thresholds, and data sampling frequency. Default regulatory parameters are the standard regulatory parameters the system uses when no specific configuration is available or when an unknown fault occurs.

[0116] In some embodiments, for a fault platform, the processor may combine multiple fault processing parameters corresponding to all pending faults of the fault platform to determine the fault processing parameters of the fault platform.

[0117] In some embodiments, the processor may determine the fault severity of the fault based on the platform fault data within the first preset period; and determine the fault handling parameters based on the fault severity. For more information about this part, please refer to Figure 4 Related description.

[0118] Step S222-2: Generate an adjustment instruction based on the fault handling parameters and send it to the supervisory device, target platform and / or human interaction device to adjust the supervisory parameters of the supervisory device, the communication parameters of the target platform and / or arrange personnel on site.

[0119] In some embodiments, the adjustment instructions can be used to guide the supervisory device to adjust supervisory parameters, guide the target platform to adjust communication parameters, guide the personnel interaction device to adjust on-site personnel arrangements, guide other devices to adjust working parameters, etc.

[0120] In some embodiments, the adjustment instruction can be determined based on a fault handling parameter. For example, for a storage-type fault, the fault handling parameter is a preset fault handling procedure for the storage device. If the preset fault handling procedure is to enable a backup storage device, the corresponding adjustment instruction can be to enable the backup storage device. The government security supervision and management platform sends this adjustment instruction to the backup storage device to instruct it to be enabled.

[0121] For another example, for a forwarding fault, if the fault handling parameter is optimizing the transmission path, the corresponding adjustment instruction could be adjusting network routing settings or encrypted transmission protocols. The government security supervision and management platform would send this adjustment instruction to the relevant communication equipment to optimize the data transmission process. For another example, for a computing fault, if the fault handling parameters are the administrator of the faulty platform and the platform fault data of the faulty platform, the corresponding adjustment instruction could be assigning the administrator of the faulty platform. The government security supervision and management platform would send this adjustment instruction to the personnel interaction device to adjust the on-site staffing arrangements.

[0122] For example, for unknown faults, the fault handling parameters can be the administrator of the fault platform, the platform fault data of the fault platform, and the default supervision parameters. The corresponding adjustment instructions can be to assign the administrator of the fault platform and enable the default supervision parameters. The government security supervision management platform sends these instructions to the supervision device or personnel interaction device to adjust the on-site personnel arrangement and restore the default supervision parameters.

[0123] Communication parameters refer to the operating parameters of a communication device, which determine how it sends and receives data on the network. Examples include packet size, transmission rate, and network protocol.

[0124] On-site personnel are personnel responsible for operating and maintaining the gas network on-site. In some embodiments, the personnel interaction device can adjust the on-site personnel according to the adjustment instruction.

[0125] In some embodiments of the present specification, the processor obtains platform fault data within a second preset period, and autonomously determines whether it is necessary to adjust the gas adjustment parameters based on the amount of fault data within a first preset period, thereby timely adjusting the gas control device or preparing corresponding fault handling measures, which can effectively avoid subsequent problems in the operation of the gas pipeline network, help improve fault response speed, optimize resource allocation, and enhance the reliability and safety of the entire gas supply network.

[0126] It should be noted that the above description of process 200 is for illustration and purpose only and does not limit the scope of application of the present invention. Those skilled in the art may make various modifications and variations to process 200 under the guidance of the present invention. However, such modifications and variations are still within the scope of the present invention.

[0127] Figure 3 This is an exemplary schematic diagram of determining platform fault data according to some embodiments of this specification.

[0128] In some embodiments, the monitoring data may also include data importance. Figure 3 As shown, the processor may determine the processing confidence 330 of the target platform based on the communication characteristics 310 and the monitoring data 320 ; and determine the platform fault data 350 based on the processing confidence 330 and the communication characteristics 310 of the target platform.

[0129] Data importance refers to the importance of the monitoring object included in the monitoring data. In some embodiments, the monitoring data may include the monitoring object, the monitoring content of the monitoring object, and the data importance.

[0130] In some embodiments, data importance may be represented by a numerical value between 0 and 1. The higher the numerical value, the higher the data importance.

[0131] In some embodiments, the processor may determine the data importance corresponding to the monitoring data by querying a second preset table. The second preset table may include the pipeline area of the monitoring object and the data importance of the monitoring data. The division of the pipeline area may be pre-set by a technician based on experience.

[0132] For example, monitoring data The monitoring object included is the temperature inside the gas pipeline 1, and the pipeline area of the gas pipeline 1 is the area , the area can be determined by querying the second preset table The corresponding data importance is 0.6, then the monitoring data The data importance is 0.6.

[0133] For more information about monitoring objects and monitoring contents, please refer to Figure 2 Related description.

[0134] Processing confidence refers to the degree of conformity between the platform's actual processing results and the expected processing results. In some embodiments, processing confidence can refer to the degree of conformity between the platform's actual processing results and the expected processing results in the time dimension, that is, the degree of conformity between the data transmission and reception time and the expected processing time.

[0135] For details on data sending and receiving times and expected processing times, see Figure 2 and related descriptions.

[0136] In some embodiments, the processing confidence can be represented by a numerical value of 0-1. The larger the numerical value, the higher the processing confidence, and the higher the degree of conformity between the actual processing result of the platform and the expected processing result.

[0137] In some embodiments, the processor can determine the processing confidence of the target platform in a variety of ways based on the communication characteristics and monitoring data. For example, for a piece of monitoring data processed by a target platform, the processor can determine the deviation threshold of the monitoring data based on the data importance of the monitoring data and the transmission priority in the communication characteristics; determine the processing confidence corresponding to the target platform processing the monitoring data based on the deviation threshold and the time deviation; and determine the statistical value of multiple processing confidences corresponding to the target platform processing multiple monitoring data as the processing confidence of the target platform. The statistical value can be a mean, a mode, etc.

[0138] For example, for the target platform Processing a monitoring data , its deviation threshold can be determined by the following formula (1): (1) in, and is a constant of magnitude, For monitoring data The deviation threshold, is the basic deviation threshold, For monitoring data The importance of data, For monitoring data The order of magnitude constant and the basic deviation threshold can be set by default by the processor or by a technician based on experience.

[0139] Exemplary target platform Processing monitoring data The corresponding processing confidence can be determined by the following formula (2): (2) in, For monitoring data The processing confidence, For monitoring data The deviation threshold, is the time deviation.

[0140] Correspondingly, if the target platform The monitoring data processed include monitoring data , monitoring data and monitoring data , and the corresponding processing confidences are 、 、 , then the target platform The processing confidence can be .

[0141] For more information about communication data, transmission priority, deviation threshold, and time deviation, see Figure 2 and its related descriptions.

[0142] In some embodiments, the processor may further obtain processing flow data of the monitoring data; and determine the processing confidence level of the monitoring data on the target platform based on the monitoring data, the processing flow data, and the communication characteristics.

[0143] For details about monitoring data, communication characteristics, and processing confidence, please refer to the previous description.

[0144] Processing process data refers to relevant data involved in the data processing process. In some embodiments, the processing process data may include data change information of one or more processing links and the target platform for processing.

[0145] The processing link refers to each link in which the monitoring data is processed, such as the forwarding link, the calculation link, the storage link, etc. In some embodiments, the processing link can be represented by the target platform where the monitoring data is processed, that is, one processing link corresponds to one target platform.

[0146] Data change information refers to data that can represent changes in monitoring data. In some embodiments, data change information can include input data and output data of the monitoring data on the target platform. Where the monitoring data is stored on the target platform, i.e., the data change information corresponding to the storage link of the monitoring data may only include the input data of the monitoring data on the target platform.

[0147] For example, for a monitoring data , processing link 1 is the forwarding link, and the corresponding target platform is , monitoring data through the forwarding link No changes have occurred. The data change information is ( , ); Processing link 2 is the calculation link, and the corresponding target platform is , after the calculation phase monitoring data Transformed into , the data change information is ( , ); Processing link 3 is the storage link, and the corresponding target platform is , monitoring data through the storage stage No changes have occurred and no output is made. The data change information is ( ), in summary, monitoring data The processing flow data is .

[0148] In some embodiments, the processor may read data change information of each processing link obtained from a target platform corresponding to each processing link of the monitoring data, thereby obtaining processing flow data of the monitoring data.

[0149] In some embodiments, the processing confidence level of the monitoring data on the target platform may refer to the degree of conformity between the data sending and receiving time and the expected processing time of the monitoring data on the target platform.

[0150] In some embodiments, the processor may determine the processing confidence level of the monitoring data on the target platform based on the monitoring data, the processing flow data of the monitoring data, and the communication characteristics of the target platform using various methods. For example, the processor may determine the processing confidence level by querying a first vector database.

[0151] Wherein, the first vector database may include multiple first standard vectors and corresponding first vector labels. In some embodiments, the first vector database may be constructed by a processor or a technician based on historical data. Wherein, the processor may construct multiple first candidate vectors based on a large amount of historical data, and a first candidate vector is composed of a historical monitoring data, a historical processing flow data of the historical monitoring data, a historical communication feature of a historical target platform, and the actual processing confidence corresponding to the historical monitoring data on the historical target platform; cluster the multiple first candidate vectors to form multiple first cluster centers; construct a historical monitoring data corresponding to a first cluster center, a historical processing flow data of the historical monitoring data, and a historical communication feature of a historical target platform into a first standard vector, and determine the actual processing confidence corresponding to the first cluster center as the first vector label of the first standard vector. Wherein, clustering methods may include but are not limited to K-means clustering, mean shift clustering, etc.

[0152] In some embodiments, the actual processing confidence level can be determined based on the platform failure frequency and data failure frequency. The platform failure frequency refers to the number of times the same failure type occurs on the same platform within a preset time period. The data failure frequency refers to the ratio of the number of target platforms where the monitoring data failure occurred to the total number of platforms that the monitoring data passed within a preset time period. The preset time period can be set by default by the processor or pre-set by a technician.

[0153] Exemplary, historical monitoring data On historical target platforms The corresponding actual processing confidence It can be determined by the following formula (3): (3) in, and is a constant of magnitude, and is a decimal close to 0, used to prevent the denominator from being 0. Indicates the frequency of similar failures on the platform, Indicates the frequency of data failures. 、 、 and All are set by default by the processor or pre-set by a technician.

[0154] In some embodiments, the processor may construct a first vector to be matched based on current monitoring data, processing flow data of the monitoring data, and communication data characteristics of a target platform, determine multiple first similarities between the first vector to be matched and multiple first standard vectors in a first vector database, and determine the first vector label corresponding to the first standard vector with the highest first similarity as the processing confidence level of the monitoring data on the target platform. The first similarity may be Euclidean distance, cosine similarity, Jaccard similarity, or the like.

[0155] In some embodiments, the processing confidence of the monitoring data corresponding to the target platform is also related to the processing flexibility of the supervision object.

[0156] Processing flexibility refers to the degree of flexibility in processing a supervision object. In some embodiments, processing flexibility can be represented by a numerical value of 1-10, where a larger numerical value indicates a higher processing flexibility.

[0157] The regulatory plan refers to a plan for regulating the gas pipeline network. In some embodiments, the regulatory plan may include the intensity of regulation of the regulated object.

[0158] Supervision intensity refers to the intensity of supervision applied to a regulated object. In some embodiments, the supervision intensity of a regulated object can be represented by the monitoring frequency of the regulated object. The higher the monitoring frequency, the greater the supervision intensity.

[0159] Monitoring frequency refers to the number of times the regulated object is monitored within a unit of time.

[0160] In some embodiments, the processor may obtain the gas network supervision plan in various ways, such as directly retrieving a supervision plan that has been manually uploaded in advance.

[0161] In some embodiments, the processor may determine the handling flexibility of the regulated object based on the gas network's regulatory plan in various ways. For example, the handling flexibility of the regulated object may be negatively correlated to the regulatory intensity of the regulated object.

[0162] In some embodiments, the processing confidence of the monitoring data corresponding to the target platform may be positively correlated with the processing flexibility of the supervision object.

[0163] In some embodiments of this specification, based on the regulatory planning of the gas pipeline network, the processing flexibility of the regulatory object can be accurately determined. Correlating the processing confidence with the processing flexibility can reduce misjudgments or missed judgments caused by overly strict confidence, thereby improving overall processing efficiency and accuracy.

[0164] In some embodiments of the present specification, by recording and analyzing the processing flow data, each link from data collection to the final processing result can be clearly understood, thereby improving the transparency of the entire process and enhancing the traceability of the system; the processing confidence obtained by comprehensively considering the processing flow data and communication characteristics provides more accurate data support, and at the same time, considering the possible differences in different processing links and the changes that may occur during the communication process, the reliability of the processing confidence is increased.

[0165] In some embodiments, the processor may determine the platform fault data based on the processing confidence and communication characteristics of the target platform using a variety of methods. For example, the processor may determine the platform fault data by querying a second vector database based on the processing confidence and communication characteristics.

[0166] The second vector database may include multiple second standard vectors and corresponding second vector labels. In some embodiments, the second vector database may be constructed by a processor or technician based on historical data. The processor may construct multiple second candidate vectors based on a large amount of historical data, where each second candidate vector is composed of historical communication characteristics, historical processing confidence, and historical actual platform failure data corresponding to a historical target platform.

[0167] The processor can cluster multiple second candidate vectors to form multiple second cluster centers; construct the historical communication characteristics and historical processing confidence of a historical target platform corresponding to a second cluster center into a second standard vector, and determine the historical actual platform fault data corresponding to the second cluster center as the second vector label of the second standard vector.

[0168] The processor may directly determine historical actual platform fault data based on historical data. Clustering methods may include but are not limited to K-means clustering, mean shift clustering, and the like.

[0169] In some embodiments, the processor may construct a second vector to be matched based on the current communication characteristics and processing confidence, determine multiple second similarities between the second vector to be matched and multiple second standard vectors in the second vector database, and determine the second vector label corresponding to the second standard vector with the highest second similarity as the current platform fault data. The second similarity may be Euclidean distance, cosine similarity, or the like.

[0170] In some embodiments, as Figure 3 As shown, the processor may determine platform fault data 350 using a fault prediction model 340 based on the processing confidence 330 and the communication characteristics 310 of the target platform.

[0171] For information on processing confidence, communication characteristics, and platform failure data, see Figure 2 and related descriptions.

[0172] A fault prediction model refers to a model used to determine platform fault data. In some embodiments, the fault prediction model can be a machine learning model, such as a neural network (NN) model or any combination thereof, or other custom model structures.

[0173] In some embodiments, the inputs of the failure prediction model 340 may include the process confidence 330 and the communication characteristics 310 , and the outputs may include the platform failure data 350 .

[0174] In some embodiments, a fault prediction model can be trained and acquired through various methods. For example, the fault prediction model can be trained and acquired using multiple first training samples with first training labels. A set of first training samples can include sample processing confidence and sample communication characteristics of one or more sample target platforms. The first training labels corresponding to the first training samples can be actual platform fault data. The first training samples and first training labels can be acquired based on historical data.

[0175] In some embodiments, the processor may train a fault prediction model based on first training samples and first training labels. Training methods may include, but are not limited to, gradient descent. As an example, the processor may input multiple first training samples with first training labels into an initial fault prediction model, construct a first loss function using the first training labels and the output of the initial fault prediction model, and iteratively update the parameters of the initial fault prediction model based on the first loss function. Model training is completed when a first preset condition is met, resulting in a trained fault prediction model. The first preset condition may include convergence of the first loss function, or the number of iterations reaching a threshold.

[0176] In some embodiments of this specification, by using a fault prediction model to determine platform fault data, the data processing and data analysis capabilities of the model can be fully utilized to obtain accurate and reliable platform fault data in a short time, thereby improving efficiency and accuracy.

[0177] In some embodiments of the present specification, by comprehensively considering the importance of monitoring data and the data transmission priority, the calculated processing confidence can more accurately reflect the degree of conformity between the actual processing results and the expected processing results; by setting the processing confidence, the effect of the platform processing data can be quantified, which helps to more accurately evaluate the performance of the platform; based on the processing confidence and communication characteristics, the accuracy of the platform fault data can be improved to ensure the accuracy and efficiency of fault handling and reduce misjudgments and duplication of work.

[0178] Figure 4 This is an exemplary schematic diagram of determining fault handling parameters according to some embodiments of this specification.

[0179] In some embodiments, as Figure 4 As shown, the processor may determine the fault severity 430 of the fault based on the platform fault data 350 within the current first preset period; and determine the fault handling parameter 460 based on the fault severity 430 .

[0180] For more information about the first preset cycle, fault handling parameters, and platform fault data, see Figure 2 Related description.

[0181] The current first preset period refers to the first preset period at the current moment.

[0182] The fault severity can be used to measure the extent of the loss caused by the fault. In some embodiments, the fault severity can be represented by a value from 1 to 10. The higher the value, the higher the fault severity and the greater the loss caused by the fault.

[0183] In some embodiments, the processor may determine the fault severity based on the platform fault data within the current first preset period using a variety of methods. For example, the processor may determine the fault severity based on the platform fault data within the current first preset period by querying a third preset table. The third preset table includes a plurality of sample platform fault data and corresponding sample fault severity levels.

[0184] In some embodiments, the third preset table can be constructed based on historical data. For example, the processor can use historical platform failure data of historical failures that occurred but were not promptly resolved as sample platform failure data, and use the historical failure importance corresponding to each sample platform failure data as the sample corresponding label. The processor and / or technicians can assess and determine the historical failure importance based on the historical actual losses corresponding to the sample platform failure data, and label the sample corresponding label. The greater the historical actual losses, the higher the historical failure importance.

[0185] In some embodiments, the processor may determine the fault importance based on the platform fault data within the current first preset period by querying a third preset table. For example, a third similarity may be calculated between the platform fault data within the current first preset period and a plurality of sample platform fault data in the third preset table, and the sample corresponding label corresponding to the sample platform fault data with the highest third similarity may be used as the fault importance of the platform fault data within the current first preset period. The third similarity may be one or any combination of Euclidean distance, cosine similarity, and the like.

[0186] In some embodiments, as Figure 4 As shown, the processor can also determine the fault importance 430 based on the platform fault data 350 of one or more second preset periods within the first preset period through the importance prediction model 420. For more information about the second preset period, please refer to Figure 2 and related descriptions.

[0187] The importance prediction model refers to a model used to determine the importance of a fault. In some embodiments, the importance prediction model can be a machine learning model, such as a neural network.

[0188] In some embodiments, the input of the importance prediction model 420 includes platform fault data 350 of one or more second preset periods within the first preset period, and the output includes fault importance 430 .

[0189] In some embodiments, the importance prediction model can be trained and acquired in various ways. For example, the importance prediction model can be trained and acquired using multiple second training samples with second training labels. The second training samples can include platform fault data from one or more second preset periods within the first preset period.

[0190] In some embodiments, the second training samples and second training labels can be obtained based on historical data. A second training sample is historical platform failure data from one or more second preset periods within a first preset period. The second training label corresponding to the second training sample is the historical actual failure importance. The acquisition and labeling of the second training label is similar to the labeling of samples in the third preset table and is not further described here.

[0191] In some embodiments, the processor can train and obtain the importance prediction model based on the second training sample and the second label. For the training process of the importance prediction model, please refer to Figure 3 The relevant instructions for the training process of the fault prediction model in

[15] are similar and will not be repeated here.

[0192] In some embodiments, as Figure 4 As shown, the input of the importance prediction model 420 may also include the regulatory plan 410 of the gas pipeline network.

[0193] More information on regulatory planning can be found in Figure 2 The corresponding description.

[0194] In some embodiments, the second training sample of the importance prediction model may further include a sample supervision plan of a sample gas pipeline network, and the sample supervision plan may be obtained based on historical data.

[0195] In some embodiments, the processor may train an acquisition importance prediction model based on a second training sample including sample platform failure data and a sample supervision plan and corresponding second training labels.

[0196] For the training method of the importance prediction model, please refer to the relevant description above.

[0197] In some embodiments of this specification, using the supervision plan as an input to the importance prediction model can effectively consider the impact of the supervision plan on the fault importance, making the prediction of the fault importance more accurate.

[0198] In some embodiments of this specification, by using an importance prediction model to determine the importance of platform faults, the data processing and data analysis capabilities of the model can be fully utilized to obtain accurate and reliable fault importance in a short time, thereby improving efficiency and accuracy.

[0199] In some embodiments, the processor can determine fault handling parameters based on the fault severity using various methods. For example, the processor can determine the fault handling parameters corresponding to the fault severity by querying a fourth preset table. The fourth preset table can be constructed based on historical data. The fourth preset table can include multiple historical fault severity levels and corresponding historical fault handling parameters.

[0200] In some embodiments, as Figure 4 As shown, the processor may determine that the fault is a current batch fault 450 based on the fault importance 430 and the preset threshold 440 ; and determine a fault processing parameter 460 based on the platform fault data corresponding to the current batch fault 450 .

[0201] For more information about fault severity, platform fault data, and fault handling parameters, see the corresponding descriptions above.

[0202] The preset threshold refers to the preset minimum value of the fault importance of the current batch of faults, for example, 0.6.

[0203] Current batch faults refer to the faults that need to be handled in the current batch.

[0204] In some embodiments, the processor may determine faults whose fault importance is not less than a preset threshold as current batch faults.

[0205] In some embodiments, the preset threshold may be positively correlated with the fault handling cost. The higher the fault handling cost, the greater the loss caused by the fault, the higher the cost-benefit ratio of fault handling, and the larger the preset threshold.

[0206] The fault handling cost refers to the cost required to repair the fault. In some embodiments, the fault handling cost can be represented by the time spent on fault handling, the number of personnel involved, and the amount of equipment involved. The longer the fault handling time, the more personnel involved, and the more equipment involved, the higher the fault handling cost. In some embodiments, the processor can statistically calculate the average of multiple historical fault handling costs corresponding to multiple historical faults and determine this average as the fault handling cost.

[0207] In some embodiments, the preset threshold may also be related to the degree of dispersion of the processing confidence of the monitoring data. The preset threshold may be negatively correlated with the degree of dispersion of the processing confidence. The greater the degree of dispersion, the greater the fluctuation in the processing quality of the monitoring data, the greater the potential risk in the gas pipeline network, and the need to concentrate more faults in the current batch for joint processing, thus requiring a smaller preset threshold.

[0208] The degree of dispersion of the processing confidence of the monitoring data can be represented by the variance of the processing confidence corresponding to the multiple monitoring data. The larger the variance, the greater the degree of dispersion.

[0209] In some embodiments, the processor can determine fault handling parameters in various ways based on the platform fault data corresponding to the current batch of faults. For example, the processor can determine the fault frequency distribution based on the platform fault data corresponding to the current batch of faults; and determine the fault handling parameters based on the fault frequency distribution. For more information about this part, please refer to Figure 2 The process is similar to the description of step S222-1 in , and will not be repeated here.

[0210] In some embodiments of this specification, the fault importance is compared with a preset threshold to quickly distinguish whether the fault is a current batch fault. High-importance faults can be screened out and handled in a timely manner, effectively improving the efficiency of handling important faults and the rationality of fault management, and reducing system losses caused by untimely handling of important faults.

[0211] In some embodiments of this specification, the importance of faults is accurately assessed based on platform fault data, and reasonable fault handling parameters can be determined based on the fault importance, which is conducive to achieving reasonable allocation of resources and time, thereby improving the overall stability and operational efficiency of the system.

[0212] The embodiments of the present invention are only for illustration and description, and do not limit the scope of application of the present invention. For those skilled in the art, various modifications and changes that can be made under the guidance of the present invention are still within the scope of the present invention.

[0213] Furthermore, certain features, structures, or characteristics of one or more embodiments of the present invention may be appropriately combined.

[0214] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the accompanying materials of this invention and the content of this invention, the descriptions, definitions, and / or usage of terms in this invention shall prevail.

Claims

1. A smart gas pipeline network fault safety processing Internet of Things system, characterized by: The Internet of Things system includes a government security supervision and management platform; The government security supervision and management platform is configured to: Execute in the first preset cycle: Acquire platform fault data within the first preset period; for the platform fault data of unknown fault type: In response to the data volume being greater than a preset volume, determining a gas adjustment parameter and sending it to the gas control device; In response to the data volume being less than the preset volume: determining a fault processing parameter based on the platform fault data within the first preset period; Based on the fault handling parameters, an adjustment instruction is generated and sent to the supervisory device, the target platform and / or the human interaction device to adjust the supervisory parameters of the supervisory device, the communication parameters of the target platform and / or arrange personnel on site; The first preset period includes one or more second preset periods; and the government security supervision and management platform is further configured to: periodically acquiring the platform fault data of the one or more second preset periods according to a second preset period; During the second preset period, the following is executed: Acquiring monitoring data of the supervisory device and communication characteristics of the target platform; The platform fault data is determined based on the communication characteristics and the monitoring data.

2. The Internet of Things system according to claim 1, characterized in that The monitoring data also includes data importance, and the government security supervision management platform is further configured to: determining a processing confidence level of the target platform based on the communication characteristics and the monitoring data; The platform fault data is determined based on the processing confidence and the communication characteristics of the target platform.

3. The Internet of Things system according to claim 2, characterized in that: The government security supervision and management platform is further configured to: Acquire processing flow data of the monitoring data; the processing flow data includes data change information of one or more processing links and the target platform for processing; The processing confidence corresponding to the monitoring data on the target platform is determined based on the monitoring data, the processing flow data, and the communication characteristics.

4. The Internet of Things system according to claim 1, characterized in that The government security supervision and management platform is further configured to: Determining the fault importance of the fault based on the platform fault data within the current first preset period; The fault handling parameters are determined based on the fault importance.

5. The Internet of Things system according to claim 4, characterized in that: The government security supervision and management platform is further configured to: Based on the platform fault data of one or more second preset periods within the current first preset period, the fault importance is determined by an importance prediction model; the importance prediction model is a machine learning model.

6. The Internet of Things system according to claim 1, characterized in that: The Internet of Things system also includes a government safety supervision service platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, a smart gas equipment object platform, and a gas maintenance object platform; The government safety supervision object platform includes the gas company management platform and key gas-using enterprises; the smart gas equipment object platform includes the supervision equipment and the gas control device deployed in the gas pipeline network; the supervision equipment is configured to monitor the gas pipeline network; The gas maintenance object platform includes the personnel interaction device.

7. A smart gas network fault safety handling method, characterized in that: The method is executed by a government security supervision and management platform in a smart gas pipeline network fault safety processing Internet of Things system, and the method includes: Execute in the first preset cycle: Acquire platform fault data within the first preset period; for the platform fault data of unknown fault type: In response to the data volume being greater than a preset volume, determining a gas adjustment parameter and sending it to the gas control device; In response to the data volume being less than the preset volume: determining a fault processing parameter based on the platform fault data within the first preset period; Based on the fault handling parameters, an adjustment instruction is generated and sent to the supervisory device, the target platform and / or the human interaction device to adjust the supervisory parameters of the supervisory device, the communication parameters of the target platform and / or arrange personnel on site; The first preset period includes one or more second preset periods; and obtaining platform fault data within the first preset period includes: periodically acquiring the platform fault data of the one or more second preset periods according to a second preset period; During the second preset period, the following is executed: Acquiring monitoring data of the supervisory device and communication characteristics of the target platform; The platform fault data is determined based on the communication characteristics and the monitoring data.

8. The method according to claim 7, characterized in that The monitoring data further includes data importance, and determining the platform fault data based on the communication characteristics and the monitoring data includes: determining a processing confidence level of the target platform based on the communication characteristics and the monitoring data; The platform fault data is determined based on the processing confidence and the communication characteristics of the target platform.

9. The method according to claim 8, characterized in that Determining the processing confidence of the target platform based on the communication characteristics and the monitoring data includes: Acquire processing flow data of the monitoring data; the processing flow data includes data change information of one or more processing links and the target platform for processing; The processing confidence corresponding to the monitoring data on the target platform is determined based on the monitoring data, the processing flow data, and the communication characteristics.

10. The method according to claim 7, characterized in that Determining the fault processing parameter based on the platform fault data within the first preset period includes: Determining the fault importance of the fault based on the platform fault data within the current first preset period; The fault handling parameters are determined based on the fault importance.

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