Gas station abnormality troubleshooting method based on safety supervision and Internet of Things system and medium

Abstract

The present invention provides a method for troubleshooting abnormalities at a gas filling station based on safety supervision, an Internet of Things system and a medium, the method comprising: generating a gas filling abnormality warning based on gas filling error abnormality information and gas filling vehicle abnormality information, and sending the warning to a control console of a gas filling system of a corresponding gas filling station and a smart gas government safety supervision and management platform; generating an equipment inspection instruction based on the gas filling abnormality warning and equipment characteristics of the gas filling equipment; determining additional monitoring parameters corresponding to the equipment to be checked based on the equipment inspection instruction and uploading the parameters to the equipment object platform; generating a monitoring instruction, and sending the monitoring instruction to a monitoring device associated with the equipment to be checked through the equipment object platform; generating an initial inspection result based on the additional monitoring result and the conventional monitoring result and uploading the result to the smart gas government safety supervision and management platform, and storing the initial inspection result in a database.

Description

Technical Field

[0001] The present specification relates to the field of gas station safety supervision, and in particular to a gas station abnormality troubleshooting method based on safety supervision and an Internet of Things system and medium. Background Art

[0002] Gas stations are usually built in cities to refuel vehicles, or in industrial areas of towns and villages where there are no gas pipelines to supply natural gas. The main gas station can use a compressor to pressurize and store natural gas, and a special transport vehicle will transport the compressed natural gas to the sub-station, which will then refuel cars or other gas-using equipment.

[0003] The gas filling system of a gas filling station includes multiple facilities, such as compressors, air intake buffer tanks, pretreatment systems, cooling systems, lubrication systems, gas storage systems, etc. When a gas filling station encounters metering errors or metering fluctuations, it is often necessary to check the facilities in the gas filling system one by one, which may cause high labor and time costs, and each check of the gas filling system is not always effective, resulting in reduced gas filling efficiency and inconvenience for the operation of the gas filling station.

[0004] Therefore, it is necessary to provide a method that can efficiently and accurately troubleshoot abnormalities in the gas filling system to reduce manpower and time costs and improve the gas filling efficiency of the gas filling station. Summary of the invention

[0005] One or more embodiments of the present specification provide a method for troubleshooting abnormalities at a gas filling station based on safety supervision, which is executed by a gas company management platform in a smart gas filling station Internet of Things system, and the method includes: collecting gas filling information of at least one gas filling vehicle within a predetermined time period through a smart gas equipment object platform; processing the gas filling information to generate gas filling error abnormality information; receiving gas filling vehicle abnormality information uploaded by a gas user object platform through an intelligent control center; generating a gas filling abnormality warning based on the gas filling error abnormality information and the gas filling vehicle abnormality information, and sending it to a control console of the gas filling system of the corresponding gas filling station and a smart gas government safety supervision management platform; the gas filling information collected within the predetermined time period is associated with the abnormal vehicle and / or the corresponding gas filling time corresponding to the gas filling vehicle abnormality information; generating an equipment inspection instruction based on the gas filling abnormality warning and the equipment characteristics of the gas filling equipment obtained from the gas filling system; and controlling the monitoring device to determine based on the equipment inspection instruction The additional monitoring parameters corresponding to the equipment to be checked are uploaded to the smart gas equipment object platform; based on the smart gas equipment object platform, the aggregated additional monitoring parameters are obtained; the additional monitoring parameters include additional monitoring batches and additional enabled monitoring equipment; a first monitoring instruction is generated, and the first monitoring instruction is sent to the first monitoring equipment associated with the equipment to be checked through the smart gas equipment object platform; the first monitoring instruction is used to adjust the existing monitoring parameters of the first monitoring equipment, and the first monitoring equipment is the monitoring equipment that is performing monitoring; a second monitoring instruction is generated, and the second monitoring instruction is sent to the second monitoring equipment associated with the equipment to be checked through the smart gas equipment object platform; the second monitoring instruction is used to start a new monitoring, and the second monitoring equipment refers to the monitoring equipment that has not started monitoring; based on the additional monitoring results and the routine monitoring results, an initial inspection result is generated and uploaded to the smart gas government safety supervision and management platform, and the initial inspection result is stored in the database.

[0006] One of the embodiments of the present specification provides a smart gas filling station Internet of Things system, including: a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, and a smart gas government safety supervision object platform. The smart gas government safety supervision object platform includes a gas company management platform; the smart gas government safety supervision sensor network platform interacts with the smart gas government safety supervision management platform and the gas company management platform respectively; it also includes a gas company sensor network platform, and the gas user object platform and the smart gas equipment object platform that interact with the gas company sensor network platform; wherein the gas company management platform is provided It is located in the intelligent control center of the gas company, and the intelligent control center includes a server, a storage device, and a data transmission component. The gas company management platform is connected to the smart gas equipment object platform of the gas station through a communication network, and the communication network is controlled and operated by the gas company sensor network platform; the gas company sensor network platform includes sensor network sub-platforms corresponding to the gas station, and the sensor network sub-platforms are respectively configured on the communication servers of the gas station; the smart gas equipment object platform is set in the control center of the gas station and is connected to the corresponding gas filling system of the gas station through a communication network, and the gas filling system includes a control console and multiple gas filling equipment.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for troubleshooting abnormalities at a gas station based on safety supervision.

[0008] Beneficial effects: The gas station abnormality troubleshooting method based on safety supervision and the Internet of Things system and medium of the present application can process the abnormal information of gas filling and gas filling vehicles, and generate a gas filling abnormality warning, and selectively troubleshoot the gas filling equipment based on the generated gas filling abnormality warning. For example, strengthen the monitoring of gas filling equipment that may have abnormalities. Based on the monitoring results, determine whether the gas filling equipment has abnormalities. It can narrow the scope of the investigation, which is conducive to improving the efficiency and accuracy of the investigation. Reduce the equipment occupancy caused by troubleshooting normal gas filling equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0010] Figure 1 is an exemplary schematic diagram of a smart gas filling station Internet of Things system according to some embodiments of this specification;

[0011] Figure 2is an exemplary flow chart of a method for troubleshooting abnormalities in a gas station based on safety supervision according to some embodiments of this specification;

[0012] Figure 3 is an exemplary flow chart for generating gas filling error abnormal information according to some embodiments of this specification;

[0013] Figure 4 is an exemplary schematic diagram of generating abnormal information of filling error through a filling model according to some embodiments of this specification;

[0014] Figure 5 It is a schematic diagram of generating device self-test parameters according to some embodiments of this specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0019] The gas filling system of a gas station contains complex facilities. When encountering problems such as metering errors during the gas filling process, it often takes a lot of manpower and time to check the gas filling facilities one by one, and there may be ineffective inspections in the process, which leads to reduced gas filling efficiency of the gas station.

[0020] In view of this, in some embodiments of the present specification, it is desired to provide an improved method for troubleshooting abnormalities at gas stations based on safety supervision.

[0021] Figure 1 It is an exemplary schematic diagram of a smart gas filling station Internet of Things system according to some embodiments of this specification.

[0022] like Figure 1 As shown, the smart gas filling station Internet of Things system 100 may include a smart gas government safety supervision management platform 110, a smart gas government safety supervision sensor network platform 120, a smart gas government safety supervision object platform 130, a gas company sensor network platform 140, and a gas user object platform 151 and a smart gas equipment object platform 152 that interact with the gas company sensor network platform 140.

[0023] The smart gas government safety supervision and management platform 110 may refer to an Internet of Things platform that coordinates and coordinates the connections and collaborations between various functional platforms and provides perception management and control management.

[0024] In some embodiments, the smart gas government safety supervision management platform 110 may receive the calibration inspection results generated by the gas company management platform. In some embodiments, the smart gas government safety supervision management platform 110 may generate a gas filling quality ranking based on the calibration inspection results of multiple gas filling stations corresponding to the same gas source, and send the gas filling quality ranking to the gas company management platform 131.

[0025] The smart gas government safety supervision sensor network platform 120 can be a platform for realizing interactive connection between the smart gas government safety supervision management platform 110 and the smart gas government safety supervision object platform 130 .

[0026] In some embodiments, the smart gas government safety supervision sensor network platform 120 can interact with the smart gas government safety supervision management platform 110.

[0027] The smart gas government safety supervision object platform 130 may be a functional platform for sensing information generation and controlling information final execution. In some embodiments, the smart gas government safety supervision object platform 130 may include a gas company management platform 131 .

[0028] In some embodiments, the smart gas government safety supervision sensor network platform 120 can interact with the smart gas government safety supervision object platform 130.

[0029] In some embodiments, the gas company management platform 131 is set in the intelligent control center of the gas company. The intelligent control center includes a server, a storage device, and a data transmission component. The data transmission component may include devices such as routers, gateways, and switches.

[0030] In some embodiments, the gas company sensor network platform 140 can interact with the smart gas equipment object platform 152. In some embodiments, the gas company management platform 131 can be connected to the smart gas equipment object platforms 152 of multiple gas stations based on a communication network, wherein the communication network is controlled and operated by the gas company sensor network platform 140.

[0031] In some embodiments, the gas company management platform 131 may execute a gas station abnormality troubleshooting method based on safety supervision.

[0032] For example, the gas company management platform 131 can collect the refueling information of at least one refueling vehicle within a predetermined time period through the smart gas equipment object platform 152. For another example, the gas company management platform 131 can process the refueling information of at least one refueling vehicle to generate refueling error abnormality information. For another example, the gas company management platform 131 can also generate a refueling abnormality warning based on the refueling error abnormality information and the refueling vehicle abnormality information, and send it to the console of the refueling system of the corresponding gas station.

[0033] For more information about the abnormality troubleshooting method of gas stations based on safety supervision, please refer to Figure 2 and related instructions.

[0034] In some embodiments, the gas company management platform 131 can be used to process the refueling information of the refueling vehicle and generate refueling error abnormality information.

[0035] For example, the gas company management platform 131 can perform metering fluctuation analysis at a preset judgment frequency, wherein the metering fluctuation analysis includes the following steps: according to the current moment, determine the current analysis interval; within the current analysis interval, execute the first analysis instruction; determine whether there is target gas filling information during a first preset number of gas filling intensive periods; in response to the existence of target gas filling information, generate a metering fluctuation set based on the target gas filling information; generate gas filling error abnormality information based on the metering fluctuation set, and store it in the memory of the intelligent control center.

[0036] In some embodiments, the first preset number can be determined based on the concentration of different gas-filling intensive periods in the judgment interval. Wherein, no less than a preset proportion of gas-filling information is distributed in the first preset number of gas-filling intensive periods. For more descriptions on determining the first preset number, please refer to the relevant descriptions elsewhere in this specification.

[0037] In response to the absence of target gas filling information, the gas company management platform 131 executes a second analysis and judgment instruction, including: selecting a target interval from the current analysis and judgment interval as the expanded interval to be judged, wherein the target interval is a time interval that is not selected by the first analysis and judgment instruction and the second analysis and judgment instruction; determining the target gas filling information in the expanded interval to be judged; and finally generating gas filling error abnormality information of the target gas filling information in the current analysis and judgment interval; wherein the number of the second analysis and judgment instructions does not exceed a second preset number, and the second preset number can be determined based on time characteristics and a preset judgment frequency.

[0038] For more information about generating gas error exception information, see Figure 3 and related instructions.

[0039] In some embodiments, the gas company management platform 131 can be used to generate a gas filling abnormality warning, and send the gas filling abnormality warning to the console of the gas filling system of the corresponding gas filling station and the gas filling user terminal of the corresponding abnormal vehicle. For example, the gas company management platform 131 can generate gas filling error abnormality information based on the gas filling vehicle abnormal information and the metering fluctuation data corresponding to the gas filling information. For another example, the gas company management platform 131 can also generate a gas filling abnormality warning based on the gas filling error abnormality information and the gas filling vehicle abnormal information, and send it to the console of the gas filling system of the corresponding gas filling station and the gas filling user terminal of the corresponding abnormal vehicle. For more descriptions on generating gas filling abnormality warnings, please refer to the descriptions elsewhere in this specification.

[0040] In some embodiments, the gas company management platform 131 may also generate abnormal gas filling error information through a gas filling model. For example, the gas company management platform 131 may determine abnormal gas filling error information through a gas filling model based on abnormal gas filling vehicle information, a metering fluctuation set composed of metering fluctuation data, and environmental parameters. The abnormal gas filling error information may include at least one of abnormal time and abnormal amount. In some embodiments, the gas filling model may be a machine learning model.

[0041] In some embodiments, the gas filling model can be obtained by training multiple sets of labeled training samples. In some embodiments, the training samples can be determined based on historical monitoring data or generated based on platform simulation. Among them, the training samples generated based on platform simulation can include training samples generated based on simulation of target historical data, and the target historical data can include historical monitoring data with abnormal information of gas filling errors.

[0042] For more information about the gas-filled model, please visit Figure 4 and related instructions.

[0043] In some embodiments, the gas company management platform 131 can also be used to generate equipment self-test parameters corresponding to the equipment gas filling station, and send the equipment self-test parameters to the gas filling station. For example, the gas company management platform 131 can generate correction inspection results based on environmental parameters, additional monitoring results and conventional monitoring results, and send the correction inspection results to the smart gas government safety supervision management platform 110. For another example, the gas company management platform 131 can also obtain the gas filling quality ranking generated by the smart gas government safety supervision management platform 110, and based on the gas filling quality ranking, generate equipment self-test parameters corresponding to the gas filling station and send them to the gas filling station.

[0044] For more information about generating device self-test parameters, see Figure 5 and related instructions.

[0045] The gas company sensor network platform 140 may be a platform for realizing interactive connection between the management platform and the object platform.

[0046] In some embodiments, the gas company sensor network platform 140 can upload the refueling information of the refueling vehicle to the intelligent control center where the gas company management platform 131 is located. In some embodiments, the gas company sensor network platform 140 can include at least one sensor network sub-platform corresponding to the gas station. The sensor network sub-platform is respectively configured on the communication server of at least one gas station.

[0047] The gas user object platform 151 may be a functional platform for sensing information generation and controlling information final execution. In some embodiments, the gas user object platform 151 may interact with the gas company sensor network platform 140 .

[0048] The gas user object platform 151 is connected to the user terminal of the user of the gas filling vehicle. In some embodiments, the gas user object platform 151 can obtain at least one abnormal information of the gas filling vehicle uploaded by the user terminal and upload it to the intelligent control center where the gas company management platform 131 is located.

[0049] The smart gas equipment object platform 152 may be a functional platform for generating perception information and ultimately executing control information.

[0050] In some embodiments, the smart gas equipment object platform 152 can be used to collect the refueling information of at least one refueling vehicle within a predetermined time period. The refueling information may include at least one of the refueling start time and refueling end time, refueling type (e.g., fast charging or slow charging), and refueling volume. In some embodiments, the smart gas equipment object platform 152 can interact with the gas company sensor network platform 140. In some embodiments, the gas company management platform 131 can communicate with the smart gas equipment object platforms 152 of multiple gas stations based on a communication network, wherein the communication network is controlled and operated by the gas company sensor network platform 140.

[0051] In some embodiments, the smart gas equipment object platform 152 is set in the control center of the gas station and is connected to a corresponding gas filling system of each gas station, wherein each gas filling system includes a control console and a plurality of gas filling equipment.

[0052] For those skilled in the art, after understanding the principle of the system, it is possible to apply the smart gas filling station Internet of Things system 100 to any suitable scenario without deviating from this principle.

[0053] It should be noted that the above description of the system and its components is only for convenience of description and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various components, or form a subsystem connected to other components without deviating from this principle. For example, the various components can share a storage device, or each component can have its own storage device. Such variations are all within the scope of protection of this specification.

[0054] Figure 2 This is an exemplary flow chart of a method for troubleshooting abnormalities in a gas station based on safety supervision according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a gas company management platform.

[0055] Step 210: Collect the refueling information of at least one refueling vehicle within a predetermined time period through the smart gas equipment object platform.

[0056] For more information about the smart gas equipment object platform, see Figure 1 Related description.

[0057] The scheduled time period refers to a pre-set time interval.

[0058] In some embodiments, the gas company management platform can obtain the predetermined time period in a variety of ways. For example, the gas company management platform can determine the predetermined time period based on the business hours of the gas station. For another example, the gas company management platform can determine at least one intensive gas filling period based on the time period of concentrated gas filling of gas filling users. The time interval formed by at least one intensive gas filling period, or the time interval between at least one intensive gas filling period and another intensive gas filling period, is determined as the predetermined time period.

[0059] For more details about the intensive gas filling period, please refer to the detailed description of step 320 below.

[0060] Gas-filled vehicles refer to vehicles that are refueled using smart gas filling stations.

[0061] The refueling information refers to information related to the refueling process and / or refueling situation of the refueling vehicle. In some embodiments, the refueling information may include at least one of the refueling time, the refueling type, and the refueling flow rate corresponding to multiple time points.

[0062] The refueling time may include at least one of a refueling start time, a refueling end time, a refueling duration, etc. The refueling type may include at least one of fast charging, slow charging, etc.

[0063] In some embodiments, the smart gas equipment object platform can obtain the refueling information in a variety of ways. For example, by controlling a timer to record the refueling time, etc. By obtaining manual input, the refueling type is determined. By controlling a flow sensor, the refueling flow is obtained. In some embodiments, the smart gas equipment object platform can upload the obtained refueling information to the gas company management platform.

[0064] Step 220, processing the gas filling information to generate gas filling error abnormal information.

[0065] Abnormal filling error information refers to filling information whose difference from the filling standard value exceeds the preset error range.

[0066] In some embodiments, the standard value for filling gas may include at least one of a standard time for filling gas, a standard flow rate for filling gas, and the like.

[0067] In some embodiments, the abnormal gas filling error information may include at least one of the abnormal gas filling error time, the abnormal gas filling error flow rate, etc.

[0068] In some embodiments, the preset error range may include at least one of a time error range, a flow error range, and the like.

[0069] In some embodiments, the gas company management platform can determine the gas filling standard value and the preset error range in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data. For example, the preset error range can be ±1% of the gas filling standard value.

[0070] For more information on determining abnormal gas filling error information, see Figure 3 Related description.

[0071] Step 230: Receive abnormal information of gas filling vehicles uploaded by the gas user object platform through the intelligent control center.

[0072] For more information about the intelligent control center and gas user object platform, please refer to Figure 1 Related description.

[0073] Abnormal information of a gas-filling vehicle refers to information related to abnormal conditions of a gas-filling vehicle. In some embodiments, the abnormal information of a gas-filling vehicle may include at least one of abnormal readings of a gas tank of the gas-filling vehicle, abnormal gas-filling time, and the like.

[0074] In some embodiments, the gas user object platform can obtain abnormal information of gas-filling vehicles in a variety of ways, such as obtaining it from the user end of the gas-filling user, etc. The user end may include at least one of a mobile phone, a computer, etc.

[0075] Step 240, based on the abnormal gas filling error information and the abnormal gas filling vehicle information, a gas filling abnormality warning is generated and sent to the control console of the gas filling system of the corresponding gas filling station and the smart gas government safety supervision and management platform.

[0076] The abnormal gas filling warning refers to a warning notification used to remind a gas filling device in a gas filling station that there is an abnormality during gas filling, and to identify the gas filling device as an abnormal gas filling device.

[0077] Gas filling equipment refers to equipment used to fill gas for gas users' vehicles in a gas filling station. For example, at least one of a compressor, an air intake buffer device, a gas pretreatment device, a cooling system, a gas storage device, and a gas filling pipeline. Gas filling abnormal equipment refers to gas filling equipment that may have a fault and needs to be repaired. In some embodiments, when different gas filling equipment fails, different errors may occur in the gas filling information.

[0078] In some embodiments, the gas company management platform may number the gas filling equipment, and the abnormal gas filling warning may include a reminder that there is an abnormality in the gas filling equipment corresponding to a certain number.

[0079] In some embodiments, the gas company management platform can determine the abnormal gas filling equipment in a variety of ways, and then determine the abnormal gas filling warning. For example, the gas company management platform can establish a first data table based on the historical gas filling error abnormality information, historical gas filling vehicle abnormality information, and historical gas filling abnormal equipment determined by manual inspection / troubleshooting or equipment alarm in the historical data.

[0080] The gas company management platform can query the first data table based on the current gas filling error abnormality information and gas filling vehicle abnormality information, determine the historical gas filling error abnormality information and historical gas filling vehicle abnormality information corresponding to the current gas filling error abnormality information and gas filling vehicle abnormality information, and determine the historical gas filling abnormality equipment corresponding to the historical gas filling error abnormality information and historical gas filling vehicle abnormality information as the current gas filling abnormality equipment.

[0081] In some embodiments, the refueling information collected within a predetermined time period is associated with the abnormal vehicle corresponding to the abnormal information of the refueling vehicle and / or the corresponding refueling time.

[0082] In some embodiments, the vehicle corresponding to the gas filling information collected within the predetermined time period and the abnormal vehicle corresponding to the abnormal information of the gas filling vehicle may be the same vehicle. In some embodiments, the gas filling information collected within the predetermined time period may include multiple gas filling information corresponding to the gas filling time of the abnormal vehicle.

[0083] Step 250, generating an equipment inspection instruction according to the abnormal gas filling warning and the equipment characteristics of the gas filling equipment obtained from the gas filling system.

[0084] Equipment characteristics refer to characteristics related to the gas filling equipment. In some embodiments, the equipment characteristics may include at least one of equipment operating parameters, cumulative operating time, current continuous operating time, maintenance times, etc. corresponding to the gas filling equipment.

[0085] Among them, equipment operating parameters refer to the type and model of the gas filling equipment.

[0086] In some embodiments, the gas filling system can record and store the device characteristics. The gas filling system can upload the device characteristics to the gas company management platform.

[0087] The equipment inspection instruction refers to an instruction to inspect at least one device to be inspected. In some embodiments, the equipment inspection instruction may include equipment operating parameters and / or ranking of the equipment to be inspected. For example, the compressor, the air intake buffer device, and the gas pretreatment device are inspected in sequence.

[0088] The equipment to be inspected refers to the gas filling equipment that needs to be inspected.

[0089] In some embodiments, the gas company management platform can generate equipment inspection instructions in a variety of ways. For example, the gas company management platform can determine the equipment to be inspected based on the abnormal gas filling warning. Determine the inspection score of the equipment to be inspected based on the equipment characteristics of the equipment to be inspected. Sort the equipment to be inspected based on the inspection score. In some embodiments, the sorting may include sorting from large to small based on the inspection score. Prioritize the inspection of the gas filling equipment with the largest inspection score. Generate equipment inspection instructions based on the sorting results.

[0090] The inspection score is the score associated with the inspection order of the equipment to be inspected.

[0091] In some embodiments, the gas company management platform can determine the inspection score in a variety of ways. In some embodiments, the inspection score can be positively correlated to the initial score, the accumulated operating time, the current continuous operating time, and the number of repairs. The initial score is a preset value for determining the inspection score, which can be preset based on experience.

[0092] In some embodiments, the gas company management platform can calculate the inspection score through a first preset algorithm based on the initial score, the accumulated operating time, the current continuous operating time, and the number of maintenance times. In some embodiments, the first preset algorithm can include formula (1):

[0093] (1)

[0094] in, To check the score, is the initial score, is the cumulative running time, is the current running time, is the number of maintenance times. The inspection scores corresponding to different gas filling equipment may be the same or different. In some embodiments, the unit of the accumulated operation time may be years. The unit of the current continuous operation time may be hours.

[0095] Step 260: The control monitoring device determines the additional monitoring parameters corresponding to the equipment to be checked based on the equipment inspection instruction and uploads them to the smart gas equipment object platform.

[0096] The monitoring device refers to a device for monitoring the gas filling equipment. In some embodiments, the monitoring device can monitor at least one of the pressure, temperature, flow rate, etc. of the gas in the corresponding gas filling equipment during the gas filling process. In some embodiments, the monitoring device can monitor the working parameters of the gas filling equipment. For example, at least one of the power and temperature of the gas filling equipment.

[0097] The additional monitoring parameters refer to the relevant parameters for controlling the additional monitoring devices to perform additional monitoring on the equipment to be inspected. In some embodiments, the additional monitoring parameters may include at least one of additional monitoring batches, additional enabled monitoring devices, and the like.

[0098] Additional monitoring batches refer to parameters related to the frequency of additional monitoring of the equipment to be inspected by additional monitoring devices, for example, monitoring every half hour, every hour, etc.

[0099] The additional monitoring device is a monitoring device that is turned on to monitor the gas filling equipment when necessary. The additional monitoring device is different from the conventional monitoring device. The conventional monitoring device is a monitoring device that is always kept in working state to facilitate real-time monitoring of the gas filling equipment.

[0100] In some embodiments, the additional monitoring parameters may include the type and / or number of additional monitoring devices that are enabled. For example, the additional monitoring parameters may include enabling an additional monitoring device that monitors the compressor to monitor the compressor at a frequency of once every half hour.

[0101] In some embodiments, the monitoring device may include multiple sensors, such as at least one of a pressure sensor, a temperature sensor, a flow sensor, and the like.

[0102] In some embodiments, the gas company management platform can determine the additional monitoring parameters in a variety of ways. For example, the gas company management platform can determine the historical equipment inspection instructions and the corresponding historical additional monitoring parameters based on historical data, and establish a second data table based on the historical equipment inspection instructions and the historical additional monitoring parameters. Based on the equipment inspection instructions, query the second data table to determine the historical equipment inspection instructions that are the same or similar to the equipment inspection instructions, and determine the historical additional monitoring parameters corresponding to the historical equipment inspection instructions as the target additional monitoring parameters.

[0103] Step 270, based on the smart gas equipment object platform, obtain the aggregated additional monitoring parameters.

[0104] In some embodiments, the smart gas equipment object platform can aggregate multiple additional monitoring parameters and upload them to the gas company management platform.

[0105] Step 281 , generating a first monitoring instruction, and sending the first monitoring instruction to a first monitoring device associated with the device to be checked through the device object platform.

[0106] The first monitoring instruction refers to a control instruction generated by the gas company management platform and used to control the first monitoring device. In some embodiments, the first monitoring instruction can be used to adjust the existing monitoring parameters of the first monitoring device.

[0107] In some embodiments, the existing monitoring parameters are similar to the additional monitoring parameters. For more information about the existing monitoring parameters, refer to the relevant description of the additional monitoring parameters.

[0108] In some embodiments, the first monitoring instruction may be used to adjust the existing monitoring parameters corresponding to the existing monitoring batch and the additional monitoring batch to remain the same.

[0109] In some embodiments, the gas company management platform may generate the first monitoring instruction in a variety of ways.

[0110] The first monitoring device refers to a conventional monitoring device associated with each device to be checked. For more information about the conventional monitoring device, please refer to the relevant description above.

[0111] Step 282: Generate a second monitoring instruction, and send the second monitoring instruction to a second monitoring device associated with the device to be checked through the device object platform.

[0112] The second monitoring instruction refers to a control instruction generated by the gas company management platform and used to control the second monitoring device. In some embodiments, the second monitoring instruction can be used to start a new monitoring.

[0113] Starting new monitoring means starting a corresponding number and / or type of monitoring equipment based on additional monitoring instructions, so that the monitoring equipment performs monitoring according to the additional monitoring batches.

[0114] In some embodiments, the gas company management platform may generate the second monitoring instruction in a variety of ways.

[0115] The second monitoring device refers to an additional monitoring device associated with each device to be checked. For more information about the additional monitoring device, please refer to the relevant description above.

[0116] Step 290, based on the additional monitoring results and the regular monitoring results, generate initial inspection results and upload them to the smart gas government safety supervision and management platform, and store the initial inspection results in the database.

[0117] Additional monitoring results refer to additional monitoring data obtained by additionally enabled monitoring devices, such as at least one of additional air pressure data, additional temperature data, additional gas flow data, etc., detected by additionally enabled monitoring devices corresponding to gas filling equipment such as compressors, air intake buffer devices, gas pretreatment devices, cooling systems, gas storage devices, and gas filling pipelines.

[0118] Conventional monitoring results refer to conventional monitoring data obtained by conventional monitoring devices, such as conventional air pressure data, conventional temperature data, conventional gas flow data, etc. detected by conventional monitoring devices corresponding to gas filling equipment such as compressors, air intake buffer devices, gas pretreatment devices, cooling systems, gas storage devices, and gas filling pipelines.

[0119] The initial inspection result refers to the result determined by the gas company management platform as to whether there are any abnormalities in the additional monitoring results and / or routine monitoring results.

[0120] In some embodiments, the gas company management platform can determine the initial inspection result in a variety of ways. For example, the gas company management platform can calculate the difference between the additional monitoring result and / or the conventional monitoring result and a preset normal value, and compare the difference with the monitoring difference threshold.

[0121] If the difference is greater than the monitoring difference threshold, it is determined that the additional monitoring results and / or the conventional monitoring results are abnormal, and the gas filling equipment corresponding to the additional monitoring results and / or the conventional monitoring results is an abnormal gas filling equipment.

[0122] The preset normal value and the monitoring difference threshold are preset values. In some embodiments, the gas company management platform can determine the preset normal value and the monitoring difference threshold in a variety of ways, such as obtaining from historical data, obtaining manual input, etc.

[0123] In some embodiments, the database may be configured in a memory of the intelligent control center.

[0124] For more information about the Smart Control Center, see Figure 1 Related description.

[0125] The gas station abnormality troubleshooting method based on safety supervision provided in some embodiments of this specification can process the abnormal information of gas filling and gas filling vehicles, and generate a gas filling abnormality warning, and selectively troubleshoot the gas filling equipment based on the generated gas filling abnormality warning. For example, strengthen the monitoring of gas filling equipment that may have abnormalities. Based on the monitoring results, determine whether the gas filling equipment has abnormalities. It can narrow the scope of the investigation, which is conducive to improving the efficiency and accuracy of the investigation. Reduce the equipment occupancy caused by troubleshooting normal gas filling equipment.

[0126] Figure 3 is an exemplary flow chart of generating abnormal gas filling error information according to some embodiments of this specification. Figure 3 As shown, the process 300 includes the following steps. In some embodiments, the process 300 can be executed by a gas company management platform.

[0127] In some embodiments, the gas company management platform can perform metering fluctuation analysis at a preset judgment frequency.

[0128] The preset judgment frequency refers to the frequency of judging the measurement fluctuation in a certain time range, which is set in advance. For example, from 7 o'clock to 12 o'clock, judge 3 times. From 12 o'clock to 18 o'clock, judge 5 times, etc.

[0129] Metering fluctuation means that at least one of the refueling information exceeds the preset maximum fluctuation limit, for example, at least one of the refueling start time, refueling end time, and refueling flow rate exceeds the preset maximum fluctuation limit.

[0130] In some embodiments, the gas company management platform can obtain the preset judgment frequency and the maximum fluctuation limit in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data.

[0131] Metering fluctuation analysis refers to the gas company management platform determining whether there is metering fluctuation in gas filling information. In some embodiments, metering fluctuation analysis may include the following steps:

[0132] Step 310, according to the current time, determine the analysis interval of the currently executed measurement fluctuation analysis, which is recorded as the current analysis interval.

[0133] The judgment interval refers to at least one time period obtained by dividing the time range based on the preset judgment frequency. For example, between 6 o'clock and 12 o'clock, the preset judgment frequency is 6 times, and from 6 o'clock to 12 o'clock, each hour is a judgment interval.

[0134] The current judgment interval is the time point of the currently executed measurement fluctuation judgment and the judgment interval it is in. For example, if the time point of the currently executed measurement fluctuation judgment is 8:15, then the current judgment interval is from 8:00 to 9:00.

[0135] In some embodiments, the time length of each judgment interval may be the same or different. In some embodiments, the time interval between two adjacent measurement fluctuation judgments may be the same or different.

[0136] Step 320, executing the first analysis instruction within the current analysis interval.

[0137] The first judgment instruction refers to a control instruction for controlling the smart gas equipment object platform to obtain gas filling information. In some embodiments, the first judgment instruction may include selecting a first preset number of gas filling intensive periods from the current judgment interval and obtaining gas filling information in the gas filling intensive periods.

[0138] The intensive gas filling period refers to a time interval in which the number of gas fillings is greater than or equal to the preset number after a period of time is divided into multiple time intervals based on the second preset rule. For example, the current judgment interval from 8 to 9 o'clock is divided into six time intervals from 8 to 8:10, ... 8:50 to 9:00 according to the second preset rule with an interval of 10 minutes. The number of gas fillings corresponding to the six time intervals is 30, 10, 45, 20, 40, and 15 respectively. If the preset number is 30, the intensive gas filling period is the time intervals from 8 to 8:10, 8:20 to 8:30, and 8:40 to 8:50.

[0139] The preset number of times is a preset value used to screen the gas-intensive filling period.

[0140] In some embodiments, the smart gas equipment object platform can record the number of gas fillings in each time period and upload it to the gas company management platform.

[0141] The first preset number refers to the number of pre-set selected gas filling intensive periods. In some embodiments, the smart gas equipment object platform can randomly select a corresponding number of gas filling intensive periods from a plurality of gas filling intensive periods based on the first preset number.

[0142] In some embodiments, the gas company management platform can obtain the preset number of times, the second preset rule, and the first preset number in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data.

[0143] Step 330 , performing a metering fluctuation analysis on the gas filling information to determine whether there is target gas filling information during the first preset number of gas filling intensive periods.

[0144] Metering fluctuation analysis refers to the gas company management platform conducting metering fluctuation analysis on gas filling information to determine whether there is metering fluctuation in the gas filling information.

[0145] For more information about measurement fluctuations, please refer to the relevant description in the previous article.

[0146] Target gas filling information refers to gas filling information with metering fluctuations.

[0147] Step 341 : In response to the existence of target gas filling information, a metering fluctuation set is generated based on the target gas filling information.

[0148] The metering fluctuation set refers to a data set formed by at least one metering fluctuation data. In some embodiments, the data format of the metering fluctuation set can be the same as the data format of the gas filling information.

[0149] Metering fluctuation data refers to the gas filling information with metering fluctuation.

[0150] In some embodiments, the gas company management platform may generate a metering fluctuation set based on at least one metering fluctuation data.

[0151] For more information about gas filling and metering fluctuations, please refer to the relevant description in the previous article.

[0152] Step 351, generating gas filling error abnormality information based on the metering fluctuation set and storing it in the memory of the intelligent control center.

[0153] For more information about abnormal gas filling errors, see Figure 2 Related description.

[0154] In some embodiments, the gas company management platform can generate a waveform chart based on the metering fluctuation set, wherein the horizontal axis of the waveform chart is the gas filling time, and the vertical axis is the gas filling amount.

[0155] In some embodiments, the gas company management platform may calculate the metered average value of the gas volume added in the time period between each positive fluctuation peak and each negative fluctuation trough in the waveform graph.

[0156] In some embodiments, the gas company management platform may calculate the deviation between the metering mean and the mean of the gas filling amount in the uniform gas filling time period. If the deviation is greater than a preset deviation threshold, at least one gas filling information in the time period is determined as gas filling error abnormal information.

[0157] In some embodiments, the absolute value of at least one of the peak value and the trough value in the waveform diagram is greater than the maximum fluctuation limit. For more description of the maximum fluctuation limit, please refer to the relevant description above.

[0158] In some embodiments, the gas company management platform can obtain the gas filling amount mean value and the preset deviation threshold value in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data.

[0159] In some embodiments, the uniform gas filling time period may include a time period without metering fluctuations. The gas filling amount mean may include a ratio of the total gas filling amount to the total gas filling time during the uniform gas filling time period.

[0160] In some embodiments, the preset deviation threshold may be a first preset amplitude. For more description of the first preset amplitude, see Figure 2 Related description.

[0161] In some embodiments, process 300 further includes the following steps:

[0162] Step 342: In response to the absence of target gas filling information, execute a second analysis instruction.

[0163] The second judgment instruction refers to a control instruction for controlling the smart gas equipment object platform to obtain the expansion interval to be judged. In some embodiments, the second judgment instruction may include selecting a target interval from the current judgment interval as the expansion interval to be judged.

[0164] The target interval refers to a time interval among multiple time intervals divided by the current judgment interval, which is not selected by the first judgment instruction and is not selected by the executed second judgment instruction.

[0165] For more information about time intervals, please refer to the previous description.

[0166] The expanded interval to be assessed refers to the target interval selected from the target interval and to be assessed in the future.

[0167] In some embodiments, the smart gas equipment object platform can select the expansion interval to be determined in a variety of ways, such as random selection.

[0168] Step 352, performing metering fluctuation analysis on the gas filling information in the expansion interval to be determined, and determining the target gas filling information in the expansion interval to be determined.

[0169] For more information about metering fluctuations and target gas filling information, please refer to Figure 3 , and related description of step 330.

[0170] Step 362, based on the target gas filling information obtained by the first judgment instruction and / or the second judgment instruction, and the metering fluctuation set corresponding to the target gas filling information, generate gas filling error abnormality information of the target gas filling information in the current judgment interval.

[0171] In some embodiments, the number of the second analysis instructions does not exceed a second preset number.

[0172] The second preset number refers to the preset number of selected expansion intervals to be determined. In some embodiments, the smart gas equipment object platform can randomly select a corresponding number of expansion intervals to be determined from multiple target intervals based on the second preset number.

[0173] In some embodiments, the gas company management platform can obtain the second preset number in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data.

[0174] In some embodiments, the second preset number can be determined based on the time characteristic and the preset judgment frequency.

[0175] The time feature refers to a feature related to the number of gas-intensive periods. In some embodiments, the time feature may be the number of gas-intensive periods. In some embodiments, the second preset number may be positively correlated with the time feature and negatively correlated with the preset judgment frequency.

[0176] In some embodiments, the gas company management platform may determine the second preset number by a second preset algorithm. The second preset algorithm may include formula (2):

[0177] (2)

[0178] in, is the second preset quantity, is the time feature, To preset the judgment frequency, is the adjustment factor.

[0179] In some embodiments, the gas company management platform may determine the adjustment coefficient in a variety of ways, such as obtaining at least one of manual input and obtaining from historical data.

[0180] In some embodiments, the gas company management platform can determine the adjustment coefficient by consulting a third data table based on the gas source type and the frequency range of abnormal gas filling equipment.

[0181] The gas source type refers to the type of gas added to the vehicle by the gas station, for example, at least one of liquefied natural gas (LNG), compressed natural gas (CNG), etc.

[0182] In some embodiments, the third data table may include the gas source type, the frequency range of abnormal gas filling equipment, and the corresponding adjustment coefficient. In some embodiments, the gas company management platform may obtain the historical gas source type, the historical frequency range, and the corresponding historical adjustment coefficient from historical data, and construct the third data table based on the historical gas source type, the historical frequency range, and the corresponding historical adjustment coefficient.

[0183] In some embodiments, the historical data may include historical data of the same period (eg, between 6 o'clock and 12 o'clock) corresponding to different gas source types.

[0184] In some embodiments, the gas company management platform can sort the scheduled time periods or gas-filling intensive periods corresponding to the same period in chronological order, detect the gas-filling equipment from the first scheduled time period or gas-filling intensive period, determine the ranking of the scheduled time period or gas-filling intensive period where the abnormal gas-filling equipment first appears, and use the ranking as the frequency range.

[0185] In some embodiments, the adjustment coefficient may be equal to the frequency range. For example, after the gas company management platform sorts the multiple scheduled time periods, an abnormal gas filling device is detected in the fifth scheduled time period, and the adjustment coefficient is determined to be 5.

[0186] For more information about scheduled time periods and gas-intensive periods, please refer to the relevant descriptions in the previous article.

[0187] In some embodiments, after all target intervals within the current determination interval are selected as the expansion intervals to be determined and the second determination instruction is executed, the second determination instruction is no longer executed.

[0188] For more information about preset judgment frequency, generation of metering fluctuation set, and generation of abnormal gas filling error information, please refer to the relevant description in the previous article.

[0189] When there is no target gas filling information during the intensive gas filling period, the expansion interval to be judged can be judged to determine whether there is target gas filling information in the expansion interval to be judged, which is conducive to improving the accuracy of determining the target gas filling information, thereby improving the accuracy of subsequent determination of gas filling error abnormal information.

[0190] In some embodiments, the first preset number can be determined based on the concentration of different gas filling intensive periods in the judgment interval.

[0191] The degree of concentration refers to data related to the amount of gas filling information distributed in the gas filling intensive period. The smaller the number of gas filling intensive periods containing the same amount of gas filling information, the higher the degree of concentration.

[0192] In some embodiments, the gas company management platform can determine the number of gas-filling intensive periods in which a preset proportion of gas-filling information is distributed, and use this number as the first preset number. For example, in the current judgment interval from 8 to 10 o'clock, there are 6 gas-filling intensive periods, and the current judgment interval includes 100 gas-filling information, and the preset ratio is 90%. If 90 gas-filling information are distributed in 5 gas-filling intensive periods, the first preset number is 5. If 90 gas-filling information are distributed in 6 gas-filling intensive periods, the first preset number is 6. If 90 gas-filling information are distributed in 1 gas-filling intensive period, the first preset number is 1.

[0193] In some embodiments, the gas company management platform can determine the preset ratio in a variety of ways, such as obtaining it from historical data, obtaining manual input, etc.

[0194] A gas filling intensive period is selected from the current judgment interval, and the target gas filling information is determined based on the gas filling information in the gas filling intensive period, and the gas filling error abnormal information is further generated. The predetermined time period can be divided, and the gas filling information in different time intervals can be selectively obtained (for example, the gas filling information corresponding to the gas filling intensive period is preferentially selected), which can reduce the data processing volume of the gas company management platform, improve data processing efficiency, and can focus on the key time period (for example, the gas filling intensive period) for targeted analysis to accurately determine the gas filling abnormal information.

[0195] By making the first preset number related to the concentration degree, when selecting the first preset number of gas-filling intensive periods, priority can be given to selecting the gas-filling intensive periods with more concentrated gas-filling information, so that gas-filling error abnormality information can be generated based on more data in the subsequent period, which is conducive to further improving the accuracy of the gas-filling error abnormality information.

[0196] In some embodiments, the gas company management platform may generate abnormal gas filling error information based on abnormal gas filling vehicle information and metering fluctuation data corresponding to the gas filling information.

[0197] For more information about abnormal information and metering fluctuation data of gas-filling vehicles, please refer to the relevant description in the previous article.

[0198] In some embodiments, when there is abnormal information about a refueling vehicle, the gas company management platform may determine the metering fluctuation data corresponding to the abnormal information about the refueling vehicle as abnormal information about the refueling error. That is, the metering fluctuation data of the refueling equipment corresponding to the refueling vehicle during the refueling time is determined as abnormal information about the refueling error.

[0199] For more information about abnormal gas filling errors, see Figure 2 Related description.

[0200] In some embodiments, the maximum fluctuation limit associated with the metering fluctuation may be set by the gas station. The preset error range associated with the abnormal gas filling error information may be set by the gas company. In some embodiments, the maximum fluctuation limit may be less than or equal to the maximum boundary value of the preset error range.

[0201] For more information about the maximum fluctuation limit and the preset error range, please refer to the relevant description of step 220 above.

[0202] In some embodiments, the gas company management platform can determine the abnormal gas filling equipment based on the abnormal gas filling error information. For example, the gas company management platform can obtain the equipment operating parameters and conventional monitoring data in the time period corresponding to the abnormal gas filling error information, calculate the difference between the equipment operating parameters and the conventional monitoring data and the preset normal values, and compare the difference with the monitoring difference threshold. If the difference is greater than the monitoring difference threshold, it is determined that the additional monitoring results and / or the conventional monitoring results are abnormal.

[0203] For more information about routine monitoring data, preset normal values, and monitoring difference thresholds, please refer to Figure 2 Related description.

[0204] In some embodiments, the gas company management platform may send a gas filling abnormality warning to the gas filling user terminal corresponding to the abnormal vehicle.

[0205] The gas filling user terminal refers to a terminal device used by the gas filling user to communicate with the gas filling station, for example, at least one of a mobile phone and a computer.

[0206] When generating abnormal information of filling error, by considering abnormal information of filling vehicle, the accuracy of generating abnormal information of filling error can be improved, and unnecessary redundant troubleshooting work caused by misjudgment due to abnormality of filling vehicle can be avoided.

[0207] Figure 4 It is an exemplary schematic diagram of generating aeration error abnormality information through aeration model according to some embodiments of this specification.

[0208] like Figure 4 As shown, the gas company management platform 131 can determine the refueling error abnormal information 430 through the refueling model 420 based on the refueling vehicle abnormal information 410-1, the metering fluctuation set 410-2 composed of metering fluctuation data, and the environmental parameter 410-3.

[0209] The gas filling model may be a model for determining gas filling error abnormal information. In some embodiments, the gas filling model may be a machine learning model, such as a deep neural network model (DNN), a recurrent neural network (RNN) model, etc.

[0210] The input of the refueling model 420 includes refueling vehicle abnormal information 410-1, metering fluctuation set 410-2, and environmental parameters 410-3 corresponding to each refueling information in at least one refueling information, and the output may include refueling error abnormal information 430 corresponding to each refueling information in at least one refueling information.

[0211] Gas vehicle abnormal information refers to abnormal conditions related to gas vehicles obtained from the user end. For example, the gas tank reading of the gas vehicle is unreasonable, the gas filling time is abnormal, etc. Gas vehicle abnormal information can be uploaded by the gas user object platform. For further explanation of gas vehicle abnormal information, see Figure 2 The corresponding content.

[0212] In some embodiments, the metering fluctuation set may include a plurality of metering fluctuation data corresponding to the refueling information. The metering fluctuation data refers to the refueling information of the refueling equipment corresponding to the refueling vehicle that fluctuates during the refueling time.

[0213] In some embodiments, the system platform (such as a gas company management platform) can determine the refueling information corresponding to the refueling vehicle abnormal information based on the refueling vehicle abnormal information 410-1, and determine the refueling information of several other vehicles with the same or similar time period as the refueling time of the refueling vehicle, and then determine several metering fluctuation data to obtain a metering fluctuation set 410-2 composed of the metering fluctuation data.

[0214] Environmental parameters refer to parameters related to the environment of the gas filling equipment monitored by the environmental monitoring device. For example, the ambient temperature of the environment where the gas filling equipment is located. Environmental parameters can be obtained through environmental monitoring devices that communicate with the smart gas equipment object platform. For more information about environmental parameters, see Figure 5 and related instructions.

[0215] The abnormal gas filling error information refers to abnormal gas filling error information. The gas filling error can be determined based on the ratio of the difference between the actual gas filling flow rate and the preset flow rate within a predetermined time period to the preset flow rate.

[0216] In some embodiments, the abnormal gas filling error information includes at least one of the abnormal gas filling error time and the abnormal gas filling error amount. The abnormal gas filling error time may be the gas filling start time, gas filling end time and gas filling duration time with errors.

[0217] In some embodiments, the gas filling model can be obtained by multiple sets of labeled training samples. In some embodiments, the training samples can be determined based on historical monitoring data or generated based on platform simulation. In some embodiments, the training samples generated based on platform simulation include: generating training samples through platform simulation based on target historical data.

[0218] The target historical data refers to the historical monitoring data used to simulate and generate training samples. In some embodiments, the target historical data may include historical monitoring data containing abnormal information of gas filling errors.

[0219] In some embodiments, the gas filling model can be obtained by training multiple groups of labeled training samples. For example, multiple groups of labeled training samples can be input into the initial gas filling model, and a loss function can be constructed by using the labels and the results of the initial gas filling model, and the parameters of the initial gas filling model can be iteratively updated based on the loss function. In some embodiments, the iterative update of the parameters of the initial gas filling model can be performed using a variety of methods, for example, it can be updated based on the gradient descent method. When the preset conditions are met, the model training is completed and a trained gas filling model is obtained. Among them, the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0220] In some embodiments, the training samples may include sample gas filling vehicle abnormal information, sample metering fluctuation set, and sample environmental parameters. The label may be gas filling error abnormal information.

[0221] In some embodiments, training samples can be determined based on historical monitoring data and / or generated based on platform simulation. For example, the gas filling error in the historical monitoring data is used as a label, and based on the historical monitoring data, training samples containing abnormal gas filling error information and training samples not containing abnormal gas filling error information are generated through system platform simulation, for example, through simulation generation by a gas company management platform. Among them, the gas filling error in the historical monitoring data can be a real gas filling error determined by actual monitoring. The label (i.e., model output) corresponding to the training sample that does not contain abnormal gas filling error information can be no abnormal gas filling error information or the abnormal amount is 0.

[0222] In some embodiments, the training samples that do not contain abnormal information on filling errors can be generated through simulation by the system platform based on the filling time of the filling vehicle and the maximum fluctuation limit of the metering fluctuation data. In some embodiments, the algorithm for simulating and generating training samples by the system platform may include but is not limited to a pseudo-random number generator, an inverse transformation method, etc. For more information about the maximum fluctuation limit of the filling time and metering fluctuation data of the filling vehicle, please refer to the relevant description above.

[0223] Through the gas filling model described in some embodiments of this specification, the abnormal gas filling error information can be determined relatively quickly and accurately, providing data support for the subsequent determination of abnormal gas filling equipment; by simulating and generating training samples for the gas filling model through the system platform, the model training effect and model recognition accuracy can be improved; in addition, determining the abnormal gas filling error information based on the abnormal information of the gas filling vehicle can improve accuracy and avoid unnecessary redundant troubleshooting work caused by misjudgment.

[0224] Figure 5 It is a schematic diagram of generating device self-test parameters according to some embodiments of this specification.

[0225] In some embodiments, the gas company management platform can obtain environmental parameters 511 through an environmental monitoring device that is communicatively connected to the smart gas equipment object platform.

[0226] The environment monitoring device refers to a device for monitoring the environment parameter 511. In some embodiments, the environment monitoring device may include at least one of a temperature sensor, a humidity sensor, and the like.

[0227] Environmental parameters 511 refer to parameters monitored by the environmental monitoring device and related to the environment in which the gas filling device is located. In some embodiments, environmental parameters 511 may include at least one of the ambient temperature and ambient humidity of the environment in which the gas filling device is located.

[0228] In some embodiments, the environmental monitoring device can obtain environmental parameters 511 and upload them to the smart gas equipment object platform 152 .

[0229] In some embodiments, the gas company management platform can generate correction check results 520 based on environmental parameters 511, additional monitoring results 512 and regular monitoring results 513, and send the correction check results 520 to the smart gas government safety supervision management platform.

[0230] For more information about additional monitoring results 512 and regular monitoring results 513, see Figure 2 Related description.

[0231] Corrected inspection result 520 refers to an inspection result obtained by correcting the initial inspection result by taking into account the influence of environmental factors.

[0232] In some embodiments, the initial inspection result determined by the gas company management platform is that the additional monitoring result 512 and / or the conventional monitoring result 513 are abnormal, and the gas filling equipment corresponding to the additional monitoring result 512 and / or the conventional monitoring result 513 is an abnormal gas filling equipment. The gas company management platform can determine the impact of the environmental parameter 511 on the gas filling equipment based on the environmental parameter 511, and adjust the monitoring difference threshold based on the impact. The gas company management platform can re-compare the difference with the adjusted monitoring difference threshold to determine whether there is an abnormality in the additional monitoring result 512 and / or the conventional monitoring result 513. Among them, the difference is the difference between the additional monitoring result 512 and / or the conventional monitoring result 513 and the preset normal value.

[0233] The impact range refers to the degree of impact of the environmental parameter 511 on the gas filling equipment. For example, when the ambient temperature is 35°C, the working efficiency of the compressor will be affected.

[0234] In some embodiments, the gas company management platform can determine the impact range in a variety of ways. For example, the gas company management platform can construct a fourth data table based on the historical environmental parameters 511 and the historical impact range in the historical data. The fourth data table includes the historical impact range of the historical environmental parameters 511 and the corresponding gas filling equipment. The gas company management platform can query the fourth data table based on the environmental parameters 511 and the abnormal gas filling equipment. Determine the historical environmental parameters 511 corresponding to the environmental parameters 511, and use the historical impact range of the historical environmental parameters 511 on the gas filling equipment corresponding to the abnormal gas filling equipment as the impact range of the environmental parameters 511 on the abnormal gas filling equipment.

[0235] In some embodiments, the gas company management platform can determine the historical impact range based on multiple historical monitoring data in the historical data. In some embodiments, the gas company management platform can determine the number of inspections of the equipment to be inspected in the historical time period, and the number of non-abnormal times determined after the inspection, where the monitoring results are abnormal due to environmental parameters 511 but the equipment to be inspected itself is not abnormal. The gas company management platform can calculate the ratio of the non-abnormal number to the number of inspections, and use the ratio as the impact range.

[0236] For example, in the historical monitoring data, there are 10 inspection times for the equipment to be inspected (for example, a compressor, etc.), 9 of which were due to the excessively high ambient temperature in the environmental parameter 511, resulting in abnormalities in the additional monitoring results 512 and / or the conventional monitoring results 513 corresponding to the compressor, but the actual compressor itself has no problem, and the number of non-abnormal times is 9, then the impact amplitude can be 0.9.

[0237] In some embodiments, the gas company management platform can adjust the monitoring difference threshold in a variety of ways. In some embodiments, the adjusted monitoring difference threshold can be positively correlated to the impact amplitude. In some embodiments, the gas company management platform can calculate the adjusted monitoring difference threshold based on a third preset algorithm. The third preset algorithm can include formula (3):

[0238] (3)

[0239] in, Adjusted monitoring difference threshold, To monitor the difference threshold, The impact range.

[0240] For more information about additional monitoring results 512, routine monitoring results 513, preset normal values, initial examination results, and monitoring difference thresholds, please refer to Figure 2 Related description.

[0241] In some embodiments, the gas company management platform may determine the allowable value of the operating parameter of the equipment to be inspected based on the environmental parameter 511 and the inspection score corresponding to the equipment inspection instruction.

[0242] For more information about equipment inspection instructions and inspection scores, see Figure 2 Related description.

[0243] The operating parameters refer to the relevant parameters of the device to be checked when it is working, for example, the operating power of the device to be checked.

[0244] In some embodiments, the gas company management platform may obtain the operating parameters in a variety of ways, such as obtaining at least one of manual input, obtaining from historical data, and obtaining from manufacturers.

[0245] The allowable value refers to a parameter related to the tolerance of the working intensity of a device to be checked under different environmental parameters 511. The lower the allowable value, the wider or greater the tolerance. In some embodiments, the allowable values ​​corresponding to different devices to be checked may be the same or different.

[0246] The tolerance level refers to the degree to which the load of the gas filling equipment can be reduced when the gas filling equipment is working in an abnormal environment (for example, a high temperature environment with a temperature exceeding 35°C). By reducing the load of the gas filling equipment, failure of the gas filling equipment when working in an abnormal environment can be avoided.

[0247] In some embodiments, the gas company management platform can obtain the allowable value in a variety of ways. In some embodiments, the allowable value can be positively correlated with the inspection score and negatively correlated with the impact amplitude. In some embodiments, the gas company management platform can calculate the allowable value by a fourth preset algorithm. The fourth preset algorithm can include formula (4):

[0248] (4)

[0249] in, is the allowable value, To check the score, For more information about the inspection score and impact range, please refer to the previous description.

[0250] In some embodiments, the gas company management platform may revise the initial inspection result based on the allowable value in combination with the additional monitoring result 512 and the regular monitoring result 513 to generate a revised inspection result 520 .

[0251] In some embodiments, the gas company management platform may compare the allowable value with the allowable threshold value. When the allowable value is less than the allowable threshold value, the gas filling device corresponding to the allowable value is confirmed as a normal device.

[0252] The allowable threshold refers to a preset value for comparison with the allowable value. In some embodiments, the gas company management platform can obtain the allowable threshold in a variety of ways, such as obtaining from historical data, obtaining manual input, etc.

[0253] In some embodiments, the gas company management platform sorts the devices to be inspected according to the inspection scores, and the gas company management platform can use the lowest inspection score corresponding to the gas filling devices that are not gas filling abnormal devices in the initial inspection results as the allowable threshold. Among them, the lowest inspection score corresponding to the gas filling devices that are not gas filling abnormal devices can be a statistical result. For example, if the gas filling devices corresponding to the lowest inspection score are not gas filling abnormal devices for a preset number of times (for example, 3 times, 5 times, etc.), then the lowest inspection score can be used as the allowable threshold.

[0254] For more information about the additional monitoring results 512 , the routine monitoring results 513 , the initial inspection results, the correction inspection results 520 , and the inspection scores, please refer to the relevant description above.

[0255] When generating the correction inspection result 520 , the influence of the abnormal environment on the gas filling equipment is taken into account by considering the allowable value, which is helpful to eliminate the interference data generated by the abnormal environment and improve the accuracy of the determined correction inspection result 520 .

[0256] In some embodiments, the gas company management platform can obtain the gas filling quality ranking 530 generated by the smart gas government safety supervision management platform, and the gas filling quality ranking 530 is generated based on the calibration inspection results 520 of the gas stations corresponding to the same gas source.

[0257] For more information about the Smart Gas Government Safety Supervision and Management Platform, please refer to Figure 1 Related description.

[0258] The gas filling quality ranking 530 refers to the ranking order of the gas filling quality of the gas filling stations. In some embodiments, the gas filling quality ranking 530 may be negatively correlated to the number of abnormalities of the gas filling equipment.

[0259] In some embodiments, the smart gas government safety supervision and management platform can determine the gas filling quality ranking 530 in a variety of ways. For example, the smart gas government safety supervision and management platform can count the number of abnormalities of gas filling equipment of multiple gas filling stations corresponding to the same gas source (e.g., CNG, LNG, etc.) based on the calibration inspection results 520 in the statistical historical time period. The gas filling stations are ranked based on the number of abnormalities of the gas filling equipment.

[0260] In some embodiments, the more times the gas filling equipment has abnormalities, the lower the ranking of the gas filling station. In some embodiments, if the number of abnormalities of the gas filling equipment is the same, the farther the time of the most recent abnormality of the gas filling equipment is from the current time, the lower the ranking of the gas filling station.

[0261] In some embodiments, the gas company management platform can generate equipment self-test parameters 540 corresponding to the gas filling station based on the gas filling quality ranking 530 and send them to the gas filling station.

[0262] The equipment self-check parameter 540 refers to a control instruction sent to a gas filling station to control the gas filling station to check the corresponding gas filling equipment. In some embodiments, the equipment self-check parameter 540 includes a self-check cycle 541 and a self-check equipment.

[0263] In some embodiments, the gas company management platform can determine the equipment self-test parameter 540 in a variety of ways, such as based on at least one of experience, obtaining manual input, and obtaining from historical data.

[0264] The self-check cycle 541 refers to the cycle at which the gas station checks the corresponding gas filling equipment, for example, once a day, once every two days, etc.

[0265] In some embodiments, the length of the self-check period 541 may be related to the ranking of the gas filling quality ranking 530 of the gas filling station. For example, the lower the ranking of the gas filling quality ranking 530, the shorter the self-check period 541.

[0266] The self-checking equipment 542 refers to the gas filling equipment that needs to be checked by the gas filling station, for example, at least one of a compressor, an air intake buffer device, a gas pre-treatment device, a cooling system, a gas storage device, and a gas filling pipeline.

[0267] In some embodiments, the self-test device 542 may include a gas filling anomaly device.

[0268] For more information about abnormal gas filling equipment, please refer to Figure 2 Related description.

[0269] In some embodiments, the gas filling quality ranking 530 may be generated based on the calibration inspection results 520 of the gas filling stations corresponding to the same and different gas sources.

[0270] In some embodiments, the gas company management platform can classify and count the calibration inspection results 520 of different gas filling stations by gas source type, and determine the gas filling quality rankings 530 corresponding to different gas source types, so that the gas filling stations in the same gas filling quality ranking 530 have the same gas source type.

[0271] In some embodiments, the equipment self-test parameter 540 may correspond to gas filling stations corresponding to different gas sources.

[0272] In some embodiments, in response to at least one gas station among multiple gas source types, the sum of the number of abnormal gas filling equipments corresponding to the gas station is the highest or the ranking exceeds the ranking threshold, or exceeds the mean of the historical average number of abnormal gas filling equipments. The gas company management platform can calculate the ratio of the sum of the number of abnormal gas filling equipments to the mean of the historical average number of abnormal gas filling equipments, and adjust the self-test period 541 in the self-test parameters corresponding to the gas station.

[0273] The ranking threshold is a preset value, for example, the third place, the fourth place, etc. The gas company management platform can determine the ranking threshold in a variety of ways, for example, obtaining at least one of manual input, obtaining from historical data, etc.

[0274] The mean of the historical average number of abnormal gas filling equipment times refers to the average number of abnormal gas filling equipment times that occurred at multiple gas filling stations of one or more gas source types in a historical time period. In some embodiments, the gas company management platform can calculate the ratio of the sum of the number of abnormal gas filling equipment times that occurred at multiple gas filling stations of one or more gas source types in a historical time period to the total number of gas filling stations, and use the ratio as the mean of the historical average number of abnormal gas filling equipment times.

[0275] In some embodiments, the gas company management platform can determine the adjusted self-checking period 541 in a variety of ways. In some embodiments, the adjusted self-checking period 541 can be negatively correlated to the ratio of the sum of the number of abnormal gas filling equipment times to the average of the historical average number of abnormal gas filling equipment times.

[0276] In some embodiments, the gas company management platform may calculate the adjusted self-check period 541 by a fifth preset algorithm. The fifth preset algorithm may include formula (5):

[0277] (5)

[0278] in, The adjusted self-test cycle is 541. For the self-test cycle 541, is the total number of abnormalities of the gas filling equipment, It is the average of the historical average number of abnormalities of gas filling equipment.

[0279] Different gas stations and different periods may use different gas source types, which may affect the monitoring data of the gas filling equipment. By classifying the gas filling stations according to the gas source type and ranking the gas filling quality 530 of the gas filling stations of the same category, the statistical deviation caused by the different gas source types can be eliminated, and the equipment self-test parameters 540 of the gas equipment can be determined in a targeted manner according to the gas source type, thereby improving the accuracy of the determined equipment self-test parameters 540. By adjusting the self-test cycle 541, the self-test cycle 541 of the gas filling station with a large number of gas filling equipment abnormalities can be adjusted in a targeted manner, which is conducive to reducing the number of gas filling equipment abnormalities at the gas filling station.

[0280] Generating the correction inspection result 520 based on the environmental parameter 511 takes into account the influence of the environmental parameter 511 on the operation state of the gas filling equipment, which is conducive to improving the accuracy of generating the correction inspection result 520. Generating the equipment self-test parameter 540 based on the gas filling quality ranking 530 can generate different equipment self-test parameters 540 according to the gas filling quality ranking 530, and control different gas filling stations to perform inspections based on the corresponding equipment self-test parameters 540, which can reduce the occurrence of misjudgments. For the gas filling stations with high rankings, frequent manual inspections can be avoided to affect the operation efficiency of the gas filling stations.

[0281] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0282] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0283] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0284] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0285] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0286] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0287] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for troubleshooting abnormalities at a gas station based on safety supervision, characterized in that: It is executed by the gas company management platform in the smart gas filling station IoT system, including: Collecting the refueling information of at least one refueling vehicle within a predetermined time period through the smart gas equipment object platform; Processing the gas filling information to generate gas filling error abnormal information; Receive abnormal information about gas-filling vehicles uploaded by the gas user object platform through the intelligent control center; Based on the abnormal gas filling error information and the abnormal gas filling vehicle information, a gas filling abnormality warning is generated and sent to the console of the gas filling system of the corresponding gas filling station and the smart gas government safety supervision and management platform; the gas filling information collected within the predetermined time period is associated with the abnormal vehicle corresponding to the abnormal gas filling vehicle information and / or the corresponding gas filling time; Generate an equipment inspection instruction according to the abnormal gas filling warning and the equipment characteristics of the gas filling equipment obtained from the gas filling system; The control monitoring device determines the additional monitoring parameters corresponding to the equipment to be checked based on the equipment inspection instruction and uploads them to the smart gas equipment object platform; Based on the smart gas equipment object platform, the aggregated additional monitoring parameters are obtained; the additional monitoring parameters include additional monitoring batches and additionally enabled monitoring devices; Generate a first monitoring instruction, and send the first monitoring instruction to a first monitoring device associated with the device to be checked through the smart gas device object platform; the first monitoring instruction is used to adjust the existing monitoring parameters of the first monitoring device, and the first monitoring device is a monitoring device that is performing monitoring; Generate a second monitoring instruction, and send the second monitoring instruction to a second monitoring device associated with the device to be checked through the smart gas device object platform; the second monitoring instruction is used to start a new monitoring, and the second monitoring device refers to the monitoring device that has not started monitoring; Based on the additional monitoring results and routine monitoring results, initial inspection results are generated and uploaded to the smart gas government safety supervision and management platform, and the initial inspection results are stored in the database.

2. The method according to claim 1, characterized in that The processing of the gas filling information to generate gas filling error abnormal information further includes: The measurement fluctuation analysis is performed at a preset judgment frequency, and the measurement fluctuation analysis includes: According to the current moment, determine the judgment interval of the measurement fluctuation judgment currently being executed, and record it as the current judgment interval; In the current judgment interval, a first judgment instruction is executed; the first judgment instruction includes: selecting a first preset number of gas-filling intensive periods from the current judgment interval, and obtaining the gas-filling information in the gas-filling intensive periods; Performing a metering fluctuation analysis on the gas filling information to determine whether there is target gas filling information during the gas filling intensive period of the first preset number; In response to the existence of the target gas filling information, generating a metering fluctuation set based on the target gas filling information; Gas filling error abnormality information is generated based on the metering fluctuation set and stored in a memory of the intelligent control center.

3. The method according to claim 2, characterized in that The processing of the gas filling information to generate gas filling error abnormal information includes: Generate the gas filling error abnormal information based on the gas filling vehicle abnormal information and the metering fluctuation data corresponding to the gas filling information; Based on the abnormal gas filling error information and the abnormal gas filling vehicle information, a gas filling abnormality warning is generated and sent to the console of the gas filling system of the corresponding gas filling station and the smart gas government safety supervision and management platform, which also includes: The abnormal gas filling warning is sent to the gas filling user terminal corresponding to the abnormal vehicle.

4. The method according to claim 1, characterized in that: The method further comprises: Acquiring environmental parameters through an environmental monitoring device that is in communication with the smart gas equipment object platform; Generate a correction check result according to the environmental parameters, the additional monitoring results and the conventional monitoring results, and send the correction check result to the smart gas government safety supervision and management platform; Obtaining a gas filling quality ranking generated by the smart gas government safety supervision and management platform, wherein the gas filling quality ranking is generated based on the calibration inspection results of the gas filling stations corresponding to the same gas source; Based on the gas filling quality ranking, the equipment self-test parameters corresponding to the gas filling station are generated and sent to the gas filling station, and the equipment self-test parameters include a self-test cycle and a self-test device.

5. The method according to claim 4, characterized in that The gas filling quality ranking is generated based on the calibration inspection results of the gas filling stations corresponding to the same and different gas sources; the equipment self-test parameters correspond to the gas filling stations corresponding to different gas sources.

6. A gas station abnormality troubleshooting Internet of Things system based on safety supervision, characterized in that: include: Smart gas government safety supervision and management platform; Smart gas government safety supervision sensor network platform; Smart gas government safety supervision object platform; A gas company sensor network platform, and a gas user object platform and a smart gas equipment object platform that interact with the gas company sensor network platform; The smart gas government safety supervision object platform includes a gas company management platform; the smart gas government safety supervision sensor network platform interacts with the smart gas government safety supervision management platform and the gas company management platform respectively; The gas company management platform is set in the gas company's intelligent control center, which includes a server, a storage device, and a data transmission component. The gas company management platform is connected to the smart gas equipment object platform of the gas station based on a communication network, and the communication network is controlled and operated by the gas company's sensor network platform. The gas company management platform is configured as follows: Collecting gas filling information of at least one gas filling vehicle within a predetermined time period through the smart gas equipment object platform; Processing the gas filling information to generate gas filling error abnormal information; Receiving abnormal information of gas-filling vehicles uploaded by the gas user object platform through the intelligent control center; Based on the abnormal gas filling error information and the abnormal gas filling vehicle information, a gas filling abnormality warning is generated and sent to the console of the gas filling system of the corresponding gas filling station and the smart gas government safety supervision and management platform; the gas filling information collected within the predetermined time period is associated with the abnormal vehicle corresponding to the abnormal gas filling vehicle information and / or the corresponding gas filling time; Generate an equipment inspection instruction according to the abnormal gas filling warning and the equipment characteristics of the gas filling equipment obtained from the gas filling system; The control monitoring device determines the additional monitoring parameters corresponding to the equipment to be checked based on the equipment inspection instruction and uploads them to the smart gas equipment object platform; Based on the smart gas equipment object platform, the aggregated additional monitoring parameters are obtained; the additional monitoring parameters include additional monitoring batches and additionally enabled monitoring devices; Generate a first monitoring instruction, and send the first monitoring instruction to a first monitoring device associated with the device to be checked through the smart gas device object platform; the first monitoring instruction is used to adjust the existing monitoring parameters of the first monitoring device, and the first monitoring device is a monitoring device that is performing monitoring; Generate a second monitoring instruction, and send the second monitoring instruction to a second monitoring device associated with the device to be checked through the smart gas device object platform; the second monitoring instruction is used to start a new monitoring, and the second monitoring device refers to the monitoring device that has not started monitoring; Based on the additional monitoring results and the regular monitoring results, generate initial inspection results and upload them to the smart gas government safety supervision and management platform, and store the initial inspection results in the database; The gas company's sensor network platform includes a sensor network sub-platform corresponding to the gas station, and the sensor network sub-platform is respectively configured on the communication server of the gas station; the smart gas equipment object platform is set in the control center of the gas station and is communicatively connected with the corresponding gas filling system of the gas station, and the gas filling system includes the console and multiple gas filling equipment.

7. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: The measurement fluctuation analysis is performed at a preset judgment frequency, and the measurement fluctuation analysis includes: According to the current moment, determine the judgment interval of the measurement fluctuation judgment currently being executed, and record it as the current judgment interval; In the current judgment interval, a first judgment instruction is executed; the first judgment instruction includes: selecting a first preset number of gas-filling intensive periods from the current judgment interval, and obtaining the gas-filling information in the gas-filling intensive periods; Performing a metering fluctuation analysis on the gas filling information to determine whether there is target gas filling information during the gas filling intensive period of the first preset number; In response to the existence of the target gas filling information, generating a metering fluctuation set based on the target gas filling information; Gas filling error abnormality information is generated based on the metering fluctuation set and stored in a memory of the intelligent control center.

8. The system according to claim 7, characterized in that The gas company management platform is further configured as follows: Generate the gas filling error abnormal information based on the gas filling vehicle abnormal information and the metering fluctuation data corresponding to the gas filling information; Based on the abnormal gas filling error information and the abnormal gas filling vehicle information, a gas filling abnormality warning is generated and sent to the console of the gas filling system of the corresponding gas filling station and the smart gas government safety supervision and management platform, which also includes: The abnormal gas filling warning is sent to the gas filling user terminal corresponding to the abnormal vehicle.

9. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the gas station abnormality troubleshooting method based on safety supervision as described in any one of claims 1 to 5.

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