Intelligent fuel gas filling station maintenance method based on safety supervision and Internet of Things system

Abstract

The invention provides an intelligent fuel gas filling station maintenance method based on safety supervision and an Internet of Things system, and relates to the field of fuel gas management. The method comprises the following steps: acquiring historical gas filling data of a gas filling station in a preset time period; determining an estimated use feature based on the historical gas filling data; acquiring historical operation data; determining historical operation characteristics based on the historical operation data; determining operation and maintenance parameters based on the estimated use features and the historical operation features, and generating a maintenance instruction; obtaining the number of reference vehicles; and generating a regulation and control instruction in response to the fact that the number of the reference vehicles is greater than the reference threshold. The Internet of Things system comprises a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform and a gas equipment object platform. According to the invention, the gas filling equipment can be reasonably maintained.

Description

Technical Field

[0001] This specification relates to the field of gas management, and in particular to a smart gas filling station maintenance method and Internet of Things system based on safety supervision. Background Art

[0002] In addition to repairs when a gas filling station fails, the filling equipment also requires daily maintenance to reduce risks. Therefore, it is extremely important to determine the daily maintenance methods for different filling equipment and auxiliary equipment in the gas filling station, while reducing risks and trying not to affect the daily operation of the gas filling station.

[0003] Traditional maintenance often relies on manual experience and fixed maintenance plans, lacks intelligent prediction of the future usage and equipment status of gas filling equipment, and lacks the ability to dynamically adjust maintenance strategies based on real-time data.

[0004] Therefore, it is necessary to provide a smart gas filling station maintenance method and Internet of Things system based on safety supervision. Summary of the invention

[0005] In order to reasonably maintain the gas filling equipment and reduce the impact of maintenance on the daily operation of the gas station while ensuring the stable operation of the gas filling equipment, this manual provides a smart gas filling station maintenance method and Internet of Things system based on safety supervision.

[0006] The invention content includes a smart gas filling station maintenance method based on safety supervision, which is executed by a gas station maintenance Internet of Things system, and the Internet of Things system includes a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and a gas equipment object platform, and the government safety supervision object platform includes a gas company management platform. The method includes: the government safety supervision management platform responds to the current moment being a preset moment for updating maintenance parameters, and the remaining computing resources of the Internet of Things system at the current moment are greater than the computing threshold: through the gas equipment object platform, the historical gas filling data of the gas filling station within a preset time period is obtained; based on the historical gas filling data, the estimated usage characteristics of the gas filling station in the future time period are determined; through the gas equipment object platform, the historical operation data of multiple gas filling equipment of the gas filling station within the preset time period is obtained; the historical operation data of multiple gas filling equipment are collected at a first preset frequency, and the first preset frequency of different gas filling equipment is The frequencies are different; based on the historical operation data of multiple gas filling equipment, the historical operation characteristics of each gas filling equipment are determined; based on the estimated usage characteristics of the gas station and the historical operation characteristics of each gas filling equipment, the operation and maintenance parameters of each gas filling equipment are determined, and a maintenance instruction is generated and sent to the gas company management platform; the operation and maintenance parameters include the operation and maintenance frequency, the operation and maintenance period, and the operation and maintenance items; for each gas filling equipment, the time distance is determined based on the operation and maintenance frequency of the gas filling equipment, and the time distance is the time difference between the current moment and the next maintenance period of the gas filling equipment, and the first preset frequency corresponding to the gas filling equipment is adjusted based on the time distance; through the government safety supervision service platform, the number of reference vehicles is obtained at a second preset frequency, and the reference vehicle is a vehicle that is traveling and has a gas station as its destination; in response to the number of reference vehicles being greater than a reference threshold, a control instruction is generated, and the control instruction is sent to the reference vehicle through the government safety supervision service platform; the control instruction includes determining candidate gas stations, and the candidate gas stations are other gas stations within the preset range of the gas station.

[0007] The invention content includes a smart gas filling station maintenance Internet of Things system based on safety supervision, the Internet of Things system includes a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and a gas equipment object platform, the government safety supervision object platform includes a gas company management platform; the Internet of Things system is configured as follows: the government safety supervision management platform responds to the current moment being a preset moment for updating maintenance parameters, and the remaining computing resources of the Internet of Things system at the current moment are greater than the computing threshold: through the gas equipment object platform, the historical gas filling data of the gas filling station within a preset time period is obtained; based on the historical gas filling data, the estimated usage characteristics of the gas filling station in the future time period are determined; through the gas equipment object platform, the historical operation data of multiple gas filling equipment of the gas filling station within the preset time period is obtained; the historical operation data of multiple gas filling equipment is collected at a first preset frequency, and the first preset frequency of different gas filling equipment is different; based on multiple The historical operation data of each gas filling device are used to determine the historical operation characteristics of each gas filling device; the operation and maintenance parameters of each gas filling device are determined based on the estimated usage characteristics of the gas station and the historical operation characteristics of each gas filling device, and a maintenance instruction is generated and sent to the gas company management platform; the operation and maintenance parameters include operation and maintenance frequency, operation and maintenance period, and operation and maintenance items; for each gas filling device, the time distance is determined based on the operation and maintenance frequency of the gas filling device, and the time distance is the time difference between the current moment and the next maintenance period of the gas filling device, and the first preset frequency corresponding to the gas filling device is adjusted based on the time distance; through the government safety supervision management platform, the number of reference vehicles is obtained at a second preset frequency, and the reference vehicle is a vehicle that is traveling and has a gas filling station as its destination; in response to the number of reference vehicles being greater than a reference threshold, a control instruction is generated, and the control instruction is sent to the reference vehicle through the government safety supervision sensor network platform; the control instruction includes determining candidate gas filling stations, and the candidate gas filling stations are other gas filling stations within the preset range of the gas filling station.

[0008] The beneficial effects brought about by the above invention content include but are not limited to: (1) The various functional platforms of the Internet of Things system coordinate and operate regularly to form an information operation closed loop, thereby realizing the informationization and intelligence of real-time detection and daily maintenance of gas filling equipment in gas stations; (2) By deploying reference vehicles to other gas stations, it is helpful to alleviate traffic congestion, save the time of gas filling users, and improve the user experience; by determining the operation and maintenance parameters of different gas filling equipment in gas stations during daily maintenance, the efficiency of equipment operation and maintenance is improved and the risk of failure is reduced; (3) The estimated usage characteristics are determined by the prediction model, which comprehensively considers the weather, the characteristics of each gas station itself, and the connection between gas stations, thereby improving the accuracy of determining the estimated usage characteristics and helping to determine operation and maintenance parameters that are more in line with the actual situation. 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 It is a structural diagram of a smart gas filling station maintenance Internet of Things system based on safety supervision according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flow chart of a smart gas filling station maintenance method based on safety supervision according to some embodiments of this specification;

[0012] Figure 3 is an exemplary model diagram of a prediction model according to some embodiments of this specification;

[0013] Figure 4 is an exemplary model diagram of a fault model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0014] 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.

[0015] 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 "include" and "comprise" 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.

[0016] 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.

[0017] Figure 1 It is a structural diagram of a smart gas filling station maintenance Internet of Things system based on safety supervision as shown in some embodiments of this specification.

[0018] In some embodiments, Figure 1As shown, the smart gas filling station maintenance Internet of Things system 100 based on safety supervision may include a government safety supervision service platform 110, a government safety supervision management platform 120, a government safety supervision sensor network platform 130, a government safety supervision object platform 140, a gas company sensor network platform 150 and a gas equipment object platform 160, and the government safety supervision object platform 140 includes a gas company management platform 141.

[0019] The government safety supervision service platform 110 refers to a platform that provides gas supervision services to users.

[0020] The government security supervision management platform 120 refers to a comprehensive management platform for government management information.

[0021] In some embodiments, the government security supervision management platform 120 may interact with the government security supervision service platform 110 and the government security supervision sensor network platform 130 .

[0022] The government security supervision sensor network platform 130 refers to a platform for comprehensive management of government sensor information, such as communication base stations, routers, wireless WiFi devices, etc. In some embodiments, the government security supervision sensor network platform 130 can interact with the government security supervision management platform 120 and the government security supervision object platform 140.

[0023] The government safety supervision object platform 140 refers to a platform for generating government supervision information and controlling information execution. In some embodiments, the government safety supervision object platform 140 includes a gas company management platform 141, which refers to a platform for information management of gas companies.

[0024] In some embodiments, the government safety regulation object platform 140 may interact with the government safety regulation sensor network platform 130 and the gas company sensor network platform 150 .

[0025] The gas company sensor network platform 150 refers to a platform for comprehensive management of sensor information of the gas company. In some embodiments, the gas company sensor network platform 150 includes communication base stations, routers, wireless WiFi and other equipment. In some embodiments, the gas company sensor network platform 150 can interact with the government safety supervision object platform 140 and the gas equipment object platform 160.

[0026] In some embodiments, the gas equipment object platform 160 can be configured as a monitoring device and / or a pipe network device to obtain the operation data of multiple gas filling devices of the gas filling station. For example, the gas equipment object platform 160 may include a camera device, a positioning device, a gas compressor, a gas pipeline, a flow meter, a pressure gauge, etc.

[0027] In some embodiments, the gas equipment object platform 160 may interact with the government safety supervision object platform 140 through the gas company sensor network platform 150 .

[0028] For more information about the functions of the smart gas filling station maintenance IoT system 100 based on safety supervision, please refer to Figure 2-Figure 4 and its related instructions.

[0029] In some embodiments of the present specification, based on the gas station maintenance Internet of Things system, the various functional platforms coordinate and operate regularly to form an information operation closed loop, thereby realizing the real-time detection and daily maintenance of gas station gas filling equipment in an information-based and intelligent manner.

[0030] Figure 2 It is an exemplary flow chart of a smart gas filling station maintenance method based on safety supervision as shown in some embodiments of this specification.

[0031] In some embodiments, in response to the current moment being the preset moment for updating the maintenance parameters and the remaining computing resources of the IoT system at the current moment being greater than the computing threshold, the government security supervision management platform 120 may execute the following Figure 2 Flow 200 is shown.

[0032] In some embodiments, the government security supervision management platform 120 can determine multiple preset times by presetting the update frequency. The preset update frequency can be preset based on experience. For example, if the preset update frequency is 4 times / day, the preset time can be set to 0:00, 6:00, 12:00, and 18:00 every day.

[0033] In some embodiments, when the computing resources of the system are sufficient, that is, if the remaining computing resources of the system at the current moment are greater than the computing threshold, the government safety supervision management platform 120 can execute the gas station maintenance method shown in process 200 to reduce the failure risk of the gas station without affecting the daily operation of the gas station. The computing threshold can be preset based on experience.

[0034] like Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by the smart government security supervision management platform 120.

[0035] Step 210, obtaining historical gas filling data of the gas filling station within a preset period of time through the gas equipment object platform, wherein the preset period of time may be a period of time within the historical period of time.

[0036] Gas filling data refers to data related to gas filling. For example, the gas filling data includes the target vehicle, the gas filling time of the target vehicle, and the gas filling amount. The target vehicle is a vehicle that has been filled with gas at a gas filling station. Historical gas filling data refers to gas filling data within a preset time period. In some embodiments, the historical gas filling data can be obtained from the gas equipment object platform 160.

[0037] Step 220, based on the historical gas filling data, determine the estimated usage characteristics of the gas filling station in the future period.

[0038] The estimated usage feature refers to the degree to which the gas station is used in the future period. In some embodiments, the estimated usage feature may reflect the degree to which the gas station provides gas in the future period. In some embodiments, the estimated usage feature may reflect the gas supply pressure of the gas station in the future. In some embodiments, the estimated usage feature may include an estimated gas supply feature, which refers to the total amount of gas provided by the gas station in the future period.

[0039] In some embodiments, the government safety supervision management platform 120 can intercept a historical period equal to the length of the future period, obtain the total amount of gas provided by the gas station in the historical gas filling data of the historical period, determine it as the estimated gas supply feature, and use the estimated gas supply feature as the estimated usage feature. For more information on determining the estimated usage feature, please refer to Figure 3 and its related description.

[0040] Step 230, obtaining historical operation data of multiple gas filling equipment of the gas filling station within a preset time period through the gas equipment object platform.

[0041] Gas filling equipment refers to related equipment used for gas filling work, such as gas storage equipment, gas filling machine, submersible liquefied natural gas pump, gas filling column, gas release column, vent pipe, etc.

[0042] Historical operation data refers to data related to the historical operation of the gas filling equipment, such as the frequency of use of the gas filling equipment, the sequence of operating parameters, etc. In some embodiments, the historical operation data can reflect the loss of the gas filling equipment caused by multiple historical uses, and can also reflect the probability of future failure of the gas filling equipment.

[0043] An operating parameter sequence refers to a sequence consisting of data of operating parameters of a gas filling device. In some embodiments, the types of operating parameters that need to be supervised and collected by different gas filling devices are different, and the types of operating parameters that need to be supervised and collected can be preset. At a certain moment, data corresponding to a set of preset types of operating parameters of a certain gas filling device can be collected, and multiple sets of data of a certain gas filling device can be collected at multiple moments, which constitutes an operating parameter sequence.

[0044] The following is an explanation of the types of operating parameters of different refueling equipment by way of examples.

[0045] For example, for gas storage equipment, the types of operating parameters may include temperature, pressure, audio data, opening status, current time, etc. For another example, for a gas dispenser, the types of operating parameters may include temperature, pressure, audio data, opening status, current time, gas filling speed, etc. For another example, for a submersible pump, the types of operating parameters may include temperature, pressure, audio data, opening status, current time, vibration data, etc. For another example, for a gas filling column, a gas release column, and a vent pipe, the types of operating parameters may include temperature, audio data, opening status, current time, etc.

[0046] Among them, temperature, pressure, filling speed, and vibration data can be obtained through temperature sensors, pressure sensors, speed sensors, and vibration sensors installed on the corresponding filling equipment, and audio data can be obtained through audio equipment installed on the corresponding filling equipment.

[0047] In some embodiments, historical operation data of multiple gas filling devices may be collected at a first preset frequency, and the first preset frequencies of different gas filling devices may be different. The first preset frequency may be determined according to a preset, for example, the initial first preset frequency may be set to t0.

[0048] In some embodiments, the first preset frequency can be adjusted according to the historical maintenance times of the gas filling equipment. For example, the more the historical maintenance times of the gas filling equipment are, the first preset frequency is adjusted upward on the basis of the initial first preset frequency. In some embodiments, the first preset frequency can be adjusted according to the time distance. For example, the shorter the time distance is, the first preset frequency is adjusted upward on the basis of the initial first preset frequency. For more information about time distance, please see below.

[0049] Step 240 , based on the historical operation data of the plurality of gas filling devices, determine the historical operation characteristics of each gas filling device.

[0050] The historical operation characteristics reflect the historical usage of the gas filling equipment. In some embodiments, different gas filling equipment has different historical operation characteristics. For example, for a gas storage device, its historical operation characteristics may include a temperature change sequence, a pressure change sequence, an abnormal degree of audio data, etc.

[0051] In some embodiments, the government safety supervision management platform 120 can determine the historical operation characteristics of each gas filling device based on the historical operation data of multiple gas filling devices through statistical analysis and other methods. For example, the government safety supervision management platform 120 can obtain the temperature collected at multiple moments in the historical operation data, calculate the temperature change rate of each two adjacent moments, and form a temperature change sequence with multiple temperature change rates; the determination method of the pressure change sequence is similar to that of the temperature change sequence.

[0052] For another example, the government safety supervision management platform 120 can calculate the similarity of the audio data in the historical operation data collected during a preset time period with the standard audio data in the audio database one by one, and regard the audio data with a similarity lower than a similarity threshold as suspicious audio data, and then calculate the ratio of the suspicious audio data to the total amount of all audio data obtained during the preset time period, and use the ratio as the degree of abnormality of the audio data, wherein the audio database is a database that is preset to store a large amount of standard audio data, and the similarity threshold can be preset and determined based on experience.

[0053] Step 250, based on the estimated usage characteristics of the gas station and the historical operation characteristics of each gas filling equipment, determine the operation and maintenance parameters of each gas filling equipment, and generate maintenance instructions to send to the gas company management platform.

[0054] Operation and maintenance parameters refer to data related to the operation and maintenance of the gas filling equipment. In some embodiments, the operation and maintenance parameters may include operation and maintenance frequency, operation and maintenance period, and operation and maintenance items. The operation and maintenance frequency refers to the frequency of performing operation and maintenance work, the operation and maintenance period refers to the time period for performing operation and maintenance work, and the operation and maintenance items may include the work items required for maintenance.

[0055] In some embodiments, the government safety supervision management platform 120 can determine the operation and maintenance parameters based on vector matching. The government safety supervision management platform 120 can construct a first vector database, which includes multiple first reference vectors and their corresponding reference operation and maintenance parameters. Each first reference vector can be constructed based on the historical estimated usage characteristics of the gas station and the historical operation characteristics of each gas filling device; the reference operation and maintenance parameters can be constructed based on the historical operation and maintenance parameters corresponding to the first reference vector, and the historical operation and maintenance parameters are the operation and maintenance parameters when the gas station is operating normally.

[0056] The government safety supervision management platform 120 can construct a target feature vector based on the estimated usage characteristics of the current gas station and the historical operation characteristics of each gas filling equipment; based on the target feature vector, it can be matched in the first vector database to obtain the first reference vector with the smallest vector distance, and the historical operation and maintenance parameters corresponding to the first reference vector can be determined as the operation and maintenance parameters.

[0057] In some embodiments, for each gas filling equipment, the government safety supervision management platform 120 can determine the time distance based on the operation and maintenance frequency of the gas filling equipment, and the time distance is the time difference between the current moment and the next maintenance period of the gas filling equipment; the government safety supervision management platform 120 can adjust the first preset frequency corresponding to the gas filling equipment based on the time distance. For example, the smaller the time distance, the higher the first preset frequency. That is, the closer the current moment is to the maintenance period, the higher the first preset frequency, which is conducive to the maintenance personnel to grasp the gas filling equipment data at the most recent moment and facilitate the maintenance work.

[0058] A maintenance order refers to an order to mobilize maintenance personnel to go to a gas station for maintenance. For example, a maintenance order may include a maintenance period, maintenance items, etc. In some embodiments, the government safety supervision management platform 120 may generate a maintenance order based on operation and maintenance parameters. For example, the government safety supervision management platform 120 may determine the maintenance period in the maintenance order based on the operation and maintenance frequency and the operation and maintenance period, and use the operation and maintenance items as maintenance items in the maintenance order.

[0059] For more information on determining operational parameters, see Figure 3 and its description.

[0060] Step 260, obtaining the number of reference vehicles at a second preset frequency through the government safety supervision service platform.

[0061] The reference vehicle refers to a vehicle that is traveling and whose destination is a gas station. In some embodiments, the government safety supervision service platform 110 can obtain the number of reference vehicles at a second preset frequency. The second preset frequency can be determined manually or based on experience.

[0062] Step 270 , in response to the number of reference vehicles being greater than a reference threshold, generating a control instruction, and sending the control instruction to the reference vehicles through the government safety supervision service platform.

[0063] In some embodiments, the reference threshold value may be preset. In some embodiments, the reference threshold value may be determined based on the actual vehicle flow within a preset range of the gas station. For example, the relationship between the actual vehicle flow and the reference threshold value may be negatively correlated. The preset range refers to a spatial range centered on the gas station, and the specific size may be determined manually or by preset.

[0064] The control instruction is an instruction for controlling the reference vehicle to other gas stations. In some embodiments, the control instruction may include determining candidate gas stations, where the candidate gas stations are other gas stations within a preset range of the gas stations.

[0065] Since the greater the actual traffic volume, the more likely it is to cause traffic jams, by setting the reference threshold to a smaller value, more reference vehicles can be deployed to other gas stations, which can help alleviate traffic jams, save time for gas users, and improve user experience.

[0066] In some embodiments, the government safety supervision management platform 120 can predict the probability sequence and time sequence based on the preset range of the gas station, the location of other gas stations, the current location of the reference vehicle, the remaining gas amount of the reference vehicle, and the type of the reference vehicle, and select at least one of the other gas stations that meets the preset conditions as a candidate gas filling station.

[0067] The locations of other gas stations refer to the spatial locations of other gas stations. The current location of the reference vehicle refers to the spatial location of the reference vehicle. The remaining gas amount of the reference vehicle refers to the amount of gas currently remaining in the reference vehicle. The type of the reference vehicle refers to the vehicle type of the reference vehicle (e.g., car, truck, etc.).

[0068] The probability sequence is composed of multiple success probabilities. The success probability is the probability that the reference vehicle successfully drives to a certain other gas station. Each of the reference vehicles successfully driving to other gas stations corresponds to a success probability, and multiple success probabilities constitute a probability sequence.

[0069] For example, the government safety supervision management platform 120 can obtain the estimated distance and estimated travel time of the reference vehicle from the current location of the reference vehicle to the location of a certain gas station (hereinafter referred to as an optional gas station) among other gas stations at the current moment. In some embodiments, the government safety supervision management platform 120 can communicate with the electronic map software to obtain the estimated distance and estimated travel time from the electronic map software.

[0070] The government safety supervision management platform 120 further determines the gas consumption per unit distance based on the type of the reference vehicle through the first preset correspondence, and determines the reference gas consumption to reach the optional gas station based on the estimated distance and the gas consumption per unit distance, for example, reference gas consumption = estimated distance × gas consumption per unit distance. The government safety supervision management platform 120 determines the success probability of the optional gas station based on the remaining gas volume of the reference vehicle and the second preset correspondence between the reference gas consumption and the success probability. Based on the success probability of each of the other gas stations, a probability sequence is obtained.

[0071] The time sequence is composed of multiple time sums. Each time sum is the sum of the time taken for the reference vehicle to travel to an optional gas station and complete gas refueling at the optional gas station. Each of the other gas stations to which the reference vehicle travels corresponds to a time sum, and multiple time sums constitute a time sequence.

[0072] In some embodiments, the government safety supervision management platform 120 can determine the amount of gas to be added based on the full amount of gas corresponding to the type of reference vehicle (the amount of gas stored when fully filled, which can be obtained through the vehicle's factory data), for example, the amount of gas to be added = the full amount of gas - the remaining gas of the reference vehicle). The refueling time is related to the amount of gas to be added and the preset reference refueling speed, for example, the refueling time = the amount of gas to be added ÷ the preset reference refueling speed. The sum of the estimated driving time of the reference vehicle to the optional gas station and the refueling time is the total time consumption, and the sum of the time consumption of the reference vehicle to all optional gas stations constitutes a time consumption sequence.

[0073] In some embodiments, the preset conditions may include that the success probability is higher than the success threshold, and the refueling time is lower than the time threshold. That is, the government safety supervision management platform 120 selects at least one of the other gas stations with a success probability higher than the success threshold and a refueling time lower than the time threshold as a candidate gas refueling station. The success threshold and time threshold can be determined based on experience.

[0074] In some embodiments of the present specification, by deploying reference vehicles to other gas stations, it is helpful to alleviate traffic congestion, save time for gas users, and improve user experience; by determining the operation and maintenance parameters of different gas filling equipment at the gas station during daily maintenance, the efficiency of equipment operation and maintenance can be improved and the risk of failure can be reduced.

[0075] In some embodiments, the government safety supervision management platform 120 may also divide the future period into multiple future sub-periods, and estimate the estimated usage characteristics of the gas station in each future sub-period through a prediction model.

[0076] The future sub-periods may be divided manually or by other methods.

[0077] Figure 3 It is an exemplary model diagram of the prediction model shown in some embodiments of this specification.

[0078] A prediction model refers to a model used to determine the estimated usage characteristics. The prediction model can be a machine learning model, for example, a Graph Neural Networks (GNN) model.

[0079] In some embodiments, Figure 3 As shown, the input of the prediction model 330 may include the gas filling map 310 and the future sub-period 320, and the output may include the estimated usage feature 340. Each estimated usage feature output by the prediction model 330 corresponds to each future sub-period.

[0080] The gas filling graph 310 refers to a graph that can represent the relationship between gas filling stations. The gas filling graph may include nodes and edges. A node represents a gas filling station, and an edge is used to connect two gas filling stations that can be connected. For example, if a vehicle can pass between the two gas filling stations, the two gas filling stations are considered to be connected. For more information about the estimated usage characteristics, see Figure 2 Description.

[0081] In some embodiments, the nodes of the gas filling map 310 may correspond to various gas filling stations. One node corresponds to one gas filling station. Figure 3As shown in the figure, gas station A, gas station A1, gas station A2, gas station A3, and gas station A4 are all nodes. The node features of the gas filling map can include the historical filling data of the gas station, the location of the gas station, the gas composition provided by the gas station, the scale of the gas station, and the weather data for the future period. For an explanation of the historical filling data and the location of the gas station, please refer to Figure 2 Related description.

[0082] The gas composition provided by the gas station refers to the components of the gas, which can be obtained through the gas equipment object platform 160. Different gas components may affect the use effect of the gas, and thus affect the speed of gas use. The scale of the gas station refers to the size of the gas station, which can be determined based on its floor area and the number of staff. The larger the floor area and the more staff, the larger the scale of the gas station. The location of the gas station and the scale of the gas station will affect the passenger flow, and thus affect the number of vehicles in the future period. The weather data for the future period refers to the future weather conditions, which can be determined based on weather forecasts and other methods and input into the government safety supervision management platform 120. The weather data will affect travel in the future period, and thus affect the gas supply pressure of the gas station in the future period.

[0083] In some embodiments, the edges of the gas filling graph may correspond to the connectivity relationship between the gas filling stations. For example, if there is a connection relationship between two gas filling stations, the two gas filling stations can be connected; if there is no connection relationship, it means that the two gas filling stations cannot be connected (for example, the path between the two gas filling stations is blocked). For the description of connectivity, please refer to the above. The characteristics of the edge may include the straight-line distance and the driving path between the two gas filling stations connected by the edge.

[0084] In some embodiments, the prediction model may be trained in the following manner.

[0085] First, a first training data set is obtained, wherein the first training data set includes a plurality of first training samples and a first label corresponding to each first training sample; then, multiple rounds of iterations are performed, and when an iteration end condition is met, the iteration is ended to obtain a trained prediction model; wherein at least one round of iterations includes:

[0086] 1) selecting one or more first training samples from the first training data set, inputting the one or more first training samples into the prediction model, and obtaining the prediction model output corresponding to the one or more first training samples;

[0087] 2) Substituting the prediction model output corresponding to the one or more first training samples and the labels of the one or more first training samples into a predefined loss function formula to calculate the value of the loss function;

[0088] 3) According to the value of the loss function, the model parameters in the prediction model are reversely updated by various feasible methods, for example, based on the gradient descent method.

[0089] In some embodiments, the first training sample can be obtained based on historical data. The first label of the first training sample can be obtained by manual annotation. In some embodiments, the first training sample can be a historical gas filling map constructed based on historical data of the first period, and the first label can be an actual usage feature corresponding to the first training sample in the historical data of the second period. For example, the first label is [A1, A2, ..., Am, A1, A2, ..., Am], which respectively corresponds to the actual usage features of m future sub-periods in the first training sample. The first period is earlier than the second period.

[0090] In some embodiments of the present specification, estimated usage characteristics are determined through a prediction model, which comprehensively considers the weather, the characteristics of each gas station itself, and the connections between gas stations, thereby improving the accuracy of determining the estimated usage characteristics and helping to determine operation and maintenance parameters that are more in line with actual conditions.

[0091] In some embodiments, the estimated usage characteristics also include estimated traffic characteristics.

[0092] The estimated traffic flow characteristics refer to the estimated traffic flow conditions in the future time period. In some embodiments, the estimated traffic flow characteristics may include the number of traffic flows of different vehicle types in the gas station in each future sub-time period. Different types of vehicles have different gas storage capacities, and the corresponding number of traffic flows will affect the gas supply pressure of the gas station.

[0093] In some embodiments of the present specification, the accuracy of determining the estimated usage characteristics can be improved by considering the impact of the number of different types of vehicles on the gas supply pressure of a gas station.

[0094] In some embodiments, for each gas filling equipment, the government safety supervision management platform 120 can also determine the failure probability of the gas filling equipment in a future period based on historical operation characteristics, and determine the operation and maintenance parameters based on the failure probability and estimated usage characteristics.

[0095] Failure probability refers to the probability of failure of the gas filling equipment. In some embodiments, the government safety supervision management platform 120 can determine the failure probability in a variety of ways based on historical operating characteristics. For example, the government safety supervision management platform 120 can determine the failure probability based on a first preset table. The first preset table is a table used to characterize the correspondence between historical operating characteristics and failure probability, and can be determined based on experience. For more information on determining failure probability, see Figure 4 and its description.

[0096] In some embodiments, the government security supervision management platform 120 can determine the operation and maintenance parameters in a variety of ways based on the failure probability and the estimated usage characteristics. For example, the government security supervision management platform 120 can determine the operation and maintenance parameters based on the second preset table. The second preset table is a table used to characterize the correspondence between the failure probability and the estimated usage characteristics and the operation and maintenance parameters, which can be preset and determined based on experience. For more information about the operation and maintenance parameters, please see below.

[0097] Figure 4 is an exemplary model diagram of a fault model according to some embodiments of the present specification.

[0098] In some embodiments, the probability of failure may be determined by a failure model.

[0099] A fault model refers to a model used to determine the probability of a fault. The fault model may be a machine learning model, for example, any one or a combination of deep neural networks (DNN) and support vector machines (SVM).

[0100] In some embodiments, Figure 4 As shown, the input of the fault model 470 may include historical operation characteristics 410 and gas composition 420, and the output may include a fault probability 480. The historical operation characteristics 410 refer to the historical operation characteristics of each gas filling device. For more information about the historical operation characteristics, see Figure 2 Gas composition 420 refers to the gas composition provided by the smart gas station, which can be found in Figure 2 The failure probability 480 refers to the failure probability of each gas filling device in the future period. For details about the failure probability, please refer to the previous description. The failure probability of each gas filling device output by the fault model corresponds to the historical operation characteristics of each gas filling device input by the fault model.

[0101] In some embodiments, the fault model can be obtained by joint training with the prediction model.

[0102] The government safety supervision management platform 120 can obtain a second training data set, which includes a number of first training samples and a first label corresponding to each first training sample, and a number of second training samples and a second label corresponding to each second training sample, and perform multiple rounds of iterations. When the iteration end condition is met, the iteration ends to obtain a fault model that has been trained. Among them, at least one round of iteration includes: selecting one or more first training samples from the second training data set, inputting one or more first training samples into the prediction model, and obtaining the prediction model output corresponding to one or more first training samples; inputting the prediction model output corresponding to one or more first training samples and one or more second training samples into the fault model to obtain the corresponding fault model output; according to the prediction model output, the first label corresponding to one or more first training samples, the fault model output, and the second label corresponding to one or more second training samples, substitute into the predefined joint training loss function, and calculate the value of the loss function; according to the value of the loss function, reversely update the model parameters in the prediction model and the fault model at the same time through various feasible methods. For example, update based on the gradient descent method.

[0103] Among them, the loss function of the joint training may include: weighted summing up multiple first sub-loss items based on a first weight set to obtain a first loss item; weighted summing up multiple second sub-loss items based on a second weight set to obtain a second loss item; multiplying or adding the first loss item and the second loss item as the loss function value of the joint training.

[0104] For example, the loss function of joint training = (a1×first sub-loss term 1+...+am×first sub-loss term m)×(b1×second sub-loss term 1+...+bn×second sub-loss term n), where m represents the number of future sub-periods corresponding to the prediction model; n represents the number of gas-filling equipment. For example, the loss function of joint training = (a1×first sub-loss term 1+...+am×first sub-loss term m)+(b1×second sub-loss term 1+...+bn×second sub-loss term n), where m represents the number of future sub-periods corresponding to the prediction model; n represents the number of gas-filling equipment.

[0105] If the estimated usage features of the gas filling station in each future sub-period output by the prediction model are [A10, A20, ..., Am0]; the corresponding first label is [A1, A2, ..., Am], then each element in [(A1-A10), (A2-A20), ..., (Am-Am0)] is the first sub-loss item corresponding to a future sub-period.

[0106] If the failure probability of each gas filling device output by the fault model is [B10, B20, ..., Bn0]; the corresponding second label is [B1, B2, ..., Bn], then each element in [(B1-B10), (B2-B20), ..., (Bn-Bn0)] is the second sub-loss item corresponding to a gas filling device.

[0107] The weight corresponding to the first sub-loss item is determined based on the time distance between the corresponding future sub-period and the current moment, for example, the weight corresponding to the first sub-loss item is negatively correlated with the time distance; the weight corresponding to the second sub-loss item is determined based on the importance of the corresponding gas filling equipment, for example, the weight corresponding to the second sub-loss item is positively correlated with the importance. The importance of the gas filling equipment refers to the frequency of use of the gas filling equipment within a preset period of time.

[0108] When the future sub-period is T1-T2 and the current time is T0, the time distance between the future sub-period and the current time may be T1-T0 or T2-T0.

[0109] In some embodiments, the second training sample can be obtained based on historical data. The second label of the second training sample can be obtained by manual annotation. In some embodiments, the second training sample can be the historical operation characteristics and historical gas composition of the first period, and the second label can be the actual failure probability of each gas filling device corresponding to the second training sample in the historical data of the second period. For example, the second label is [B1, B2, ..., Bn], where B1, B2, ..., Bn are the actual failure conditions of n future sub-periods corresponding to the second training sample (if the gas filling device fails, it is recorded as 1, otherwise it is 0). Among them, the first period is earlier than the second period.

[0110] In some embodiments of the present specification, the fault probability is determined by a fault model, which can improve the reliability and accuracy of determining the fault probability based on massive data.

[0111] In some embodiments, Figure 4 As shown, the input of the fault model also includes at least one of the estimated gas supply feature 430 and the estimated traffic flow feature 440. The estimated gas supply feature 430 refers to the estimated gas supply feature of the gas filling station in the future period. For more information about the estimated gas supply feature, please refer to Figure 2 The estimated traffic flow characteristics 440 refer to the estimated traffic flow characteristics 440 of the future period. For more information about the estimated traffic flow characteristics, see Figure 2 Related instructions.

[0112] The estimated gas supply characteristics can reflect the traffic conditions of different vehicle types in each future sub-period. For example, for buses, the amount of gas they fill is often greater than that of ordinary cars, which will cause the gas filling equipment to continue to operate longer, thereby increasing the probability of failure. For another example, when the type of vehicles being filled with gas is constantly changing, the operating time of the gas filling equipment will also change continuously, thereby increasing the probability of failure.

[0113] In some embodiments, the second training sample may further include at least one of actual air supply characteristics of the second period and actual traffic characteristics of the second period.

[0114] In some embodiments, Figure 4 As shown, the input of the fault model also includes the spatial layout 450 of the gas filling station and the location layout 460 of the gas filling equipment. The spatial layout of the gas filling station is the spatial structure of the gas filling station, which can be determined by the design drawings of the gas filling station.

[0115] The location layout of the gas filling equipment refers to the location of each gas filling equipment in the gas filling station. Taking any point in the gas filling station as the origin (for example, the entrance of the gas filling station), a spatial rectangular coordinate system is established, and the coordinates are used to represent the location of the gas filling equipment, thereby obtaining the location layout of the gas filling equipment. If the spatial layout of the gas filling station is too compact, or the location layout of the gas filling equipment has an unreasonable design, it may affect the efficiency of vehicle entry and exit and gas supply, and thus affect the probability of failure.

[0116] In some embodiments, the second training sample may further include the spatial layout of historical gas filling stations and the location layout of historical gas filling equipment.

[0117] In some embodiments of this specification, by taking the estimated usage characteristics, estimated traffic flow characteristics, the spatial layout of the gas station and the location layout of the gas filling equipment as the input of the fault model, the usage of the gas filling equipment is fully considered, so that the output of the fault model is more in line with the actual situation, and the rationality and accuracy of determining the fault probability are improved. By jointly training the prediction model and the fault model, the accuracy of the model training is improved, and the accuracy of the obtained model is improved.

[0118] In some embodiments, the government safety supervision management platform 120 may also determine operation and maintenance parameters based on the failure probability of each gas filling device in a future period and estimated usage characteristics in response to the failure probability of the gas filling device not exceeding the maintenance threshold.

[0119] In some embodiments, the government safety supervision management platform 120 may generate an emergency instruction in response to the failure probability of the gas filling equipment exceeding a maintenance threshold, and the emergency instruction is used to remind maintenance personnel to immediately repair the gas filling equipment.

[0120] The maintenance threshold refers to a value used to determine whether the gas filling equipment needs maintenance. The maintenance threshold can be preset based on manual experience. In some embodiments, the government safety supervision management platform 120 can also dynamically determine the maintenance threshold. For example, during peak hours or in severe weather conditions, the government safety supervision management platform 120 can automatically lower the maintenance threshold to improve the reliability of the gas filling equipment. For more information about the maintenance threshold, please refer to the relevant description below.

[0121] In some embodiments, the maintenance threshold may be determined based on at least one of the estimated air supply characteristics and the estimated traffic flow characteristics. For the description of the estimated air supply characteristics and the estimated traffic flow characteristics, please refer to the above.

[0122] In some embodiments, the maintenance threshold may be negatively correlated with the estimated gas supply characteristics. For example, when the estimated usage characteristics are large, indicating that the gas supply volume in the future period is large, the government safety supervision management platform 120 may lower the maintenance threshold to reduce the operating risk of the gas filling equipment and avoid service interruption.

[0123] In some embodiments, the maintenance threshold is negatively correlated with the estimated traffic flow characteristics. For example, when the estimated traffic flow characteristics are large, indicating that the traffic volume in the future period is large, the government safety supervision management platform 120 can lower the maintenance threshold to reduce the operation risk of the gas filling equipment and avoid service interruption.

[0124] By dynamically adjusting maintenance thresholds based on estimated usage characteristics and estimated traffic flow characteristics, maintenance strategies can be flexibly adjusted according to the real-time status of the gas filling station to avoid service interruptions due to gas filling equipment failures, thereby improving the experience of gas users.

[0125] In some embodiments, the government safety supervision management platform 120 can use the failure probability in the future time period as the clustering indicator to cluster the vectors to be clustered in the clustering database to obtain multiple cluster families, and the cluster family where the failure probability of the gas filling equipment in the future time period is located is the target cluster family; for each gas filling equipment, the operation and maintenance parameters of the gas filling equipment are determined based on the historical estimated usage characteristics and historical operation and maintenance parameters corresponding to multiple first-class clustering vectors in the target cluster family where the gas filling equipment is located.

[0126] A clustering database refers to a database for storing historical data. In some embodiments, the clustering database may store historical maintenance data, such as historical maintenance frequency, historical maintenance period, and historical maintenance items. In some embodiments, the historical data stored in the clustering database may be used for clustering analysis. In some embodiments, the clustering database may also store relevant data involved in the clustering process, such as multiple vectors to be clustered, multiple cluster groups, and target cluster groups.

[0127] In some embodiments, the government safety supervision management platform 120 can construct a cluster database. For example, the government safety supervision management platform 120 can store historical gas filling equipment types, historical probability of failure of the gas filling equipment in the future period, historical estimated usage characteristics, and historical operation and maintenance parameters in the cluster database.

[0128] In some embodiments, the clustering database includes vectors to be clustered.

[0129] The vector to be clustered refers to a vector used by the clustering database for clustering analysis. In some embodiments, the vector to be clustered may include a first type of clustering vector and a second type of clustering vector.

[0130] In some embodiments, the government safety supervision management platform 120 can construct a first type of clustering vector based on the historical gas filling equipment type, the actual failure probability of the gas filling equipment in the future period, the historical estimated usage characteristics, and the historical operation and maintenance parameters. For the description of the gas filling equipment type, failure probability, estimated usage characteristics, and operation and maintenance parameters, please refer to the above.

[0131] In some embodiments, the government safety supervision management platform 120 can construct a second type of clustering vector based on the type of gas filling equipment, the probability of failure of the gas filling equipment in the future period, and the estimated usage characteristics. For the description of the type of gas filling equipment, the probability of failure, and the estimated usage characteristics, please refer to the above.

[0132] The clustering family is the result obtained by grouping the vectors to be clustered by the clustering algorithm, and each clustering family contains a group of similar vectors to be clustered (for example, the first type of clustering vectors and the second type of clustering vectors, etc.). The target clustering family refers to the clustering family containing the second type of clustering vectors. In some embodiments, the clustering family where the failure probability of the gas filling equipment in the future period belongs is the target clustering family.

[0133] In some embodiments, the government safety supervision management platform 120 may perform cluster analysis on the first type of cluster vectors and the second type of cluster vectors by using a clustering algorithm (e.g., a K-means clustering algorithm, etc.). For example, the government safety supervision management platform may perform cluster analysis on the first type of cluster vectors and the second type of cluster vectors by using a K-means clustering algorithm, taking the failure probability of the gas filling equipment in the current future period as a constraint, and obtain multiple target cluster families.

[0134] In some embodiments, the operation and maintenance parameters include operation and maintenance periods, operation and maintenance projects, and operation and maintenance frequencies. In some embodiments, the government security supervision and management platform 120 may use the historical operation and maintenance period with the largest number of occurrences in the historical operation and maintenance parameters of multiple first-category clustering vectors in the target cluster family as the current operation and maintenance period. In some embodiments, the government security supervision and management platform 120 may use the union of the historical operation and maintenance projects in the historical operation and maintenance parameters as the current operation and maintenance project. In some embodiments, the government security supervision and management platform 120 may weighted sum the historical operation and maintenance frequencies in the historical operation and maintenance parameters of the first-category clustering vectors, and use the weighted sum result as the current operation and maintenance frequency.

[0135] In some embodiments, the weight of the weighted summation can be determined based on the historical estimated usage characteristics corresponding to the first type of clustering vectors. For example, the weight can be negatively correlated with the historical estimated usage characteristics corresponding to the first type of clustering vectors. When the historical estimated usage characteristics are larger, that is, the estimated gas supply characteristics and the estimated traffic characteristics are larger, it indicates that the gas supply in the future period will be larger, the traffic volume will be more, and the use of the gas filling equipment will be more. At this time, more maintenance should be carried out, that is, the operation and maintenance frequency should be reduced.

[0136] Based on historical data, by taking the failure probability in the future period as the clustering indicator, the vectors to be clustered in the clustering database are clustered, which can improve the accuracy and effectiveness of determining the operation and maintenance parameters.

[0137] One or more embodiments of the present specification also provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any method of the above embodiments.

[0138] 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.

[0139] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their 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.

[0140] 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 smart gas filling station maintenance method based on safety supervision, characterized in that: The method is executed by a smart gas filling station maintenance Internet of Things system, which includes a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and a gas equipment object platform, and the government safety supervision object platform includes a gas company management platform; the method includes: The government security supervision management platform responds that the current moment is a preset moment for updating maintenance parameters, and the remaining computing resources of the Internet of Things system at the current moment are greater than the computing threshold: Obtain historical gas filling data of the gas filling station within a preset period of time through the gas equipment object platform; Determining estimated usage characteristics of the gas station in a future period based on the historical gas filling data; Obtain historical operation data of multiple gas filling equipment of the gas filling station within the preset period through the gas equipment object platform; the historical operation data of multiple gas filling equipment are collected at a first preset frequency, and the first preset frequency of different gas filling equipment is different; Based on the historical operation data of the plurality of gas filling devices, determining the historical operation characteristics of each of the gas filling devices; Based on the estimated usage characteristics of the gas filling station and the historical operation characteristics of each of the gas filling equipment, the operation and maintenance parameters of each of the gas filling equipment are determined, and a maintenance instruction is generated and sent to the gas company management platform; the operation and maintenance parameters include the operation and maintenance frequency, the operation and maintenance period, and the operation and maintenance items; for each of the gas filling equipment, the time distance is determined based on the operation and maintenance frequency of the gas filling equipment, and the time distance is the time difference between the current moment and the next maintenance period of the gas filling equipment, and the first preset frequency corresponding to the gas filling equipment is adjusted based on the time distance; The number of reference vehicles is obtained at a second preset frequency through the government safety supervision service platform; the reference vehicles are vehicles that are traveling and whose destination is the gas station; In response to the number of the reference vehicles being greater than a reference threshold, a control instruction is generated, and the control instruction is sent to the reference vehicles through the government safety supervision service platform; the control instruction includes determining candidate gas stations, and the candidate gas stations are other gas stations within a preset range of the gas stations.

2. The method according to claim 1, characterized in that The step of determining the estimated usage characteristics of the gas filling station in a future period based on the historical gas filling data includes: The future time period is divided into a plurality of future sub-periods, and the estimated usage characteristics of the gas station in each of the future sub-periods are estimated through a prediction model; the prediction model is a machine learning model.

3. The method according to claim 1, characterized in that The determining of the operation and maintenance parameters of each of the gas filling equipment based on the estimated usage characteristics of the gas filling station and the historical operation characteristics of each of the gas filling equipment includes: For each of the gas filling devices, determining a failure probability of the gas filling device in the future period based on the historical operation characteristics; The operation and maintenance parameters are determined based on the failure probability and the estimated usage characteristics.

4. The method according to claim 3, characterized in that The method further includes: in response to the failure probability of the gas filling device not exceeding a maintenance threshold, determining the operation and maintenance parameter based on the failure probability of each of the gas filling devices in the future time period and the estimated usage characteristics.

5. The method according to claim 4, characterized in that The method further comprises: Taking the failure probability in the future time period as a clustering index, clustering the vectors to be clustered in the clustering database to obtain a plurality of cluster groups, wherein the cluster group in which the failure probability of the gas filling equipment in the future time period belongs is a target cluster group; For each of the gas filling devices, the operation and maintenance parameters of the gas filling device are determined based on the historical estimated usage characteristics and historical operation and maintenance parameters corresponding to a plurality of first-category cluster vectors in the target cluster group where the gas filling device is located.

6. A smart gas filling station maintenance Internet of Things system based on safety supervision, characterized in that: The Internet of Things system includes a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and a gas equipment object platform, and the government safety supervision object platform includes a gas company management platform; the Internet of Things system is configured as follows: The government security supervision management platform responds that the current moment is a preset moment for updating maintenance parameters, and the remaining computing resources of the Internet of Things system at the current moment are greater than the computing threshold: Obtaining historical gas filling data of the gas filling station within a preset period of time through the gas equipment object platform; Determining estimated usage characteristics of the gas station in a future period based on the historical gas filling data; Obtain historical operation data of multiple gas filling equipment of the gas filling station within the preset period through the gas equipment object platform; the historical operation data of multiple gas filling equipment are collected at a first preset frequency, and the first preset frequency of different gas filling equipment is different; Based on the historical operation data of the plurality of gas filling devices, determining the historical operation characteristics of each of the gas filling devices; Based on the estimated usage characteristics of the gas filling station and the historical operation characteristics of each of the gas filling equipment, determine the operation and maintenance parameters of each of the gas filling equipment, and generate a maintenance instruction to send to the gas company management platform; The operation and maintenance parameters include operation and maintenance frequency, operation and maintenance period, and operation and maintenance items; for each of the gas filling equipment, a time distance is determined based on the operation and maintenance frequency of the gas filling equipment, the time distance being the time difference between the current moment and the next maintenance period of the gas filling equipment, and the first preset frequency corresponding to the gas filling equipment is adjusted based on the time distance; Obtaining the number of reference vehicles at a second preset frequency through the government safety supervision service platform, wherein the reference vehicles are vehicles that are traveling and whose destination is the gas station; In response to the number of the reference vehicles being greater than a reference threshold, generating a control instruction, and sending the control instruction to the reference vehicles through the government safety supervision service platform; The control instruction includes determining candidate gas filling stations, and the candidate gas filling stations are other gas filling stations within a preset range of the gas filling stations.

7. The Internet of Things system according to claim 6, characterized in that: The step of determining the estimated usage characteristics of the gas filling station in a future period based on the historical gas filling data includes: The future time period is divided into a plurality of future sub-periods, and the estimated usage characteristics of the gas station in each of the future sub-periods are estimated through a prediction model; the prediction model is a machine learning model.

8. The Internet of Things system according to claim 6, characterized in that: The determining of the operation and maintenance parameters of each of the gas filling equipment based on the estimated usage characteristics of the gas filling station and the historical operation characteristics of each of the gas filling equipment includes: For each of the gas filling devices, determining a failure probability of the gas filling device in the future period based on the historical operation characteristics; The operation and maintenance parameters are determined based on the failure probability and the estimated usage characteristics.

9. The Internet of Things system according to claim 8, characterized in that: The Internet of Things system is further configured to: in response to the failure probability of the gas filling equipment not exceeding a maintenance threshold, determine the operation and maintenance parameters based on the failure probability of each of the gas filling equipment in the future time period and the estimated usage characteristics.

10. The Internet of Things system according to claim 9, characterized in that: The Internet of Things system is further configured as follows: Taking the failure probability in the future time period as a clustering index, clustering the vectors to be clustered in the clustering database to obtain a plurality of cluster groups, wherein the cluster group in which the failure probability of the gas filling equipment in the current future time period belongs is the target cluster group; For each of the gas filling devices, the operation and maintenance parameters of the gas filling device are determined based on the historical estimated usage characteristics and historical operation and maintenance parameters corresponding to a plurality of first-category cluster vectors in the target cluster group where the gas filling device is located.

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