An intelligent dispatching method and system for gas transportation based on the Internet of Things

Abstract

The present invention relates to the technical field of gas dispatching, and specifically to an intelligent gas transportation dispatching method and system based on the Internet of Things, including: S1, when obtaining gas dispatching, obtaining the user set corresponding to the gas dispatching and determining the gas consumption in the user set; S2, identifying the changes in gas consumption of the user set at different time lines and positions, and drawing a gas consumption curve that changes with time; S3, analyzing the gas consumption curve to determine the main influencing factors of gas consumption; S4, according to the main influencing factors of gas consumption and the gas consumption curve, calculating the distribution of gas consumption at different positions and determining the gas dispatching plan; S5, according to the gas dispatching plan, obtaining the relationship between gas dispatching and positions, identifying the frequently dispatched areas, and adjusting the gas dispatching plan according to the characteristics of the frequently dispatched areas; thus achieving the accuracy and efficiency of gas dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of gas dispatching, and specifically to an intelligent dispatching method and system for gas transportation based on the Internet of Things. Background Art

[0002] Urban gas dispatching is the centralized monitoring and command of the urban gas supply system. In order to achieve the balance between gas supply and demand, ensure safe supply, maintain the best operating conditions and economic operation. The urban gas gate station collects information on the gas source, transmission and distribution, and application of gas, and then transmits it to the gas dispatching center. The gas dispatching center sorts, analyzes, predicts and judges, and issues instructions for production, transmission and distribution, and operation.

[0003] For example, Chinese Patent Publication No. CN111126859A discloses a digital acquisition system and method based on the industrial Internet. The digital acquisition system includes a data measurement module, a data receiving module and a data analysis module. The data measurement module is used to measure gas data on the gas pipeline. The data receiving module includes a gas gate station receiving module and a gas dispatching center receiving module. The gas gate station receiving module is used for the gas gate station to receive the gas data transmitted by the gas pipeline, and the gas dispatching center receiving module is used for the gas dispatching center to receive the gas data transmitted by the gas gate station. The data analysis module is used for the gas dispatching center to schedule and allocate the gas supply volume according to the gas data.

[0004] The prior art sets marks on gas pipelines and gas gate stations, etc., and determines the corresponding gas distribution according to the marks and the measured data. However, when analyzing gas dispatching, it is necessary to determine the relevant trends of users' gas consumption, and identify the relevant parameters during dispatching according to this part of the trend, so as to determine whether the current dispatching plan can be normally executed, so as to achieve the stability and efficiency of gas dispatching supply and transportation. Summary of the Invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: an intelligent dispatching method for gas transportation based on the Internet of Things, including: S1, when obtaining gas dispatching, obtaining the user set corresponding to the gas dispatching, and determining the gas consumption in the user set.

[0006] S2, identifying the changes in gas consumption of the user set at different time lines and positions, and drawing a gas consumption curve that changes with time.

[0007] S3, analyzing the gas consumption curve to determine the main influencing factors of gas consumption.

[0008] S4, according to the main influencing factors of gas consumption and the gas consumption curve, calculating the distribution of gas consumption at different positions, and determining the gas dispatching plan.

[0009] S5. According to the gas scheduling plan, obtain the relationship between gas scheduling and location, identify the frequently scheduled areas, and adjust the gas scheduling plan according to the characteristics of the frequently scheduled areas.

[0010] An intelligent gas transportation scheduling system based on the Internet of Things, comprising: a gas consumption data acquisition module: used to collect the gas consumption data of users in real time through Internet of Things devices, and associate the data with the location information of the users to form a structured data set.

[0011] A gas consumption curve drawing module: used to process the structured data set by using an autoregressive moving average model, output a gas consumption curve, and determine the change trend of the gas consumption curve through the maximum likelihood estimation method.

[0012] A gas consumption analysis module: used to identify the seasonal factors, gas usage habits, and gas pipeline network factors among the main influencing factors of gas consumption by screening the change trend of the gas consumption curve.

[0013] A gas scheduling calculation module: used to calculate the distribution of gas consumption at different locations according to the main influencing factors of gas consumption and the gas consumption curve, and determine the gas scheduling plan.

[0014] A gas scheduling adjustment module: used to obtain the relationship between gas scheduling and location according to the gas scheduling plan, identify the frequently scheduled areas, and adjust the gas scheduling plan according to the characteristics of the frequently scheduled areas.

[0015] The beneficial effects of the present invention are as follows: First, the present invention realizes the accurate prediction of gas consumption through the Internet of Things technology and the autoregressive moving average model, and improves the accuracy of the scheduling plan.

[0016] Second, the present invention formulates a more scientific and reasonable scheduling plan by comprehensively analyzing the influencing factors of gas consumption, and improves the stability and efficiency of gas supply.

[0017] Third, the present invention discovers and solves the safety problems in the gas transportation process in a timely manner through real-time monitoring and intelligent scheduling, and ensures the safety of gas supply. Description of the Drawings

[0018] The present invention will be further described below in conjunction with the drawings and embodiments.

[0019] Figure 1 It is a schematic flow chart of an intelligent gas transportation scheduling method based on the Internet of Things.

[0020] Figure 2 It is a schematic flow chart of step S2 of an intelligent gas transportation scheduling method based on the Internet of Things.

[0021] Figure 3 It is a schematic flow diagram of step S4 of an intelligent gas transportation scheduling method based on the Internet of Things.

[0022] Figure 4 It is a schematic diagram of a system of an intelligent gas transportation scheduling system based on the Internet of Things. Specific Embodiments

[0023] The embodiments of the present invention will be described in detail below. The embodiments described below are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention. For those not specified in the embodiments, the technologies or conditions described in the literature in the art or according to the product specifications are followed.

[0024] Refer to Figure 1 , an intelligent gas transportation scheduling method based on the Internet of Things, includes: S1, when obtaining gas scheduling, obtaining the user set corresponding to the gas scheduling and determining the gas consumption in the user set.

[0025] S2, identifying the changes in gas consumption of the user set at different time lines and positions, and drawing a gas consumption curve changing with time.

[0026] S3, analyzing the gas consumption curve to determine the main influencing factors of gas consumption.

[0027] S4, according to the main influencing factors of gas consumption and the gas consumption curve, calculating the distribution of gas consumption at different positions and determining the gas scheduling plan.

[0028] S5, according to the gas scheduling plan, obtaining the relationship between gas scheduling and positions, identifying the frequently scheduled areas, and adjusting the gas scheduling plan according to the characteristics of the frequently scheduled areas.

[0029] The user set includes different types of gas consumers such as residential users, commercial users, and industrial users.

[0030] As Figure 2 shown, step S2 includes the following implementation methods: S21, obtaining the gas consumption of the user set at each time point and the geographical location information of the users, sorting the collected data according to the time line and position to obtain a structured data set.

[0031] S22, according to the structured data set, drawing a gas consumption curve according to a preset period and determining the change trend of the gas consumption curve.

[0032] The length of the preset period is four dimensions: 1 day, 1 week, 1 month, and 1 year to quantify the user's demand for gas consumption.

[0033] The gas consumption in step S21 is obtained from the gas consumption of the corresponding user in the user set. The set user set includes, in addition to the corresponding type of gas consumers, the information on the corresponding gas consumption, as well as the user-related location and the time when the gas is consumed.

[0034] The way to draw the gas consumption curve is to input the structured data set into the autoregressive moving average model, and the autoregressive moving average model outputs the gas consumption curve. The gas consumption curve is expressed as follows.

[0035] Y t = c + φ1Y t-1 + φ2Y t-2 +,,, + φ p Y t-p + θ1ε t-1 + θ2ε t-2 +,,, + θ q ε t-q + ε t ; where Y t , Y t-1 ,,, Y t-p represents

[0036] the gas consumption from time point t to time point t-p, c represents the constant term, φ1, φ2,,, φ p represents the autoregressive parameter, p represents the number of autoregressive parameters, θ1, θ2,,, θ q represents the moving average parameter, q represents the number of moving average parameters, ε t , ε t-1 ,,, ε t-q represents the error term from time point t to time point t-q. The values of p and q are both less than the value of the current time point t.

[0037] At this time, the set autoregressive parameters and moving average parameters can identify the currently set gas consumption curve and determine the maximum likelihood value of the current gas consumption curve to determine the change value that will occur in the current gas consumption curve within the expected time.

[0038] The processing method of the autoregressive moving average model also includes obtaining the likelihood function of the gas consumption curve based on the gas consumption curve output by the autoregressive moving average model, determining the maximum likelihood value according to the obtained likelihood function, and determining the change trend of the current gas consumption curve according to the maximum likelihood value.

[0039] The likelihood function L is expressed as:

[0040] Perform the maximum logarithmization on the obtained likelihood function.

[0041] Among them, L represents the likelihood function, ln(L) represents the maximized logarithmic likelihood function, p represents the number of autoregressive parameters, q represents the number of moving average parameters, T represents the number of time points, σ represents the standard deviation of the error term, π represents pi, e represents the exponential constant, and Y t represents the gas consumption at time point t, represents the expected value of the gas consumption, where t = p + q + 1, p + q + 2,,, T.

[0042] When determining the maximum likelihood value, the autoregressive parameters, error terms, moving average parameters, and constant terms are iterated until the value at the next time point of the current gas consumption curve can be calculated as the most similar value through these calculations, and the autoregressive parameters, error terms, moving average parameters, and constant terms at this time are recorded as the change trend of the currently identified gas consumption curve.

[0043] The likelihood function obtained at this time can output the current change trend of the gas consumption curve, and predict the changes in the corresponding gas consumption curve at different times and schedules based on the obtained trend; the change trend output at this time can extract the influencing factors and related parameters corresponding to the gas consumption curve during the change, so as to be able to perform quantitative analysis on the relevant data based on these parameters separated in the trend.

[0044] The implementation method of analyzing the gas consumption curve in step S3 to determine the main influencing factors of the gas consumption includes: screening out the relevant factors of the current change trend according to the change trend of the gas consumption curve, the time when each change trend appears, and the index value corresponding to the change trend. The relevant factors include seasonal factors, gas usage habits, and gas pipeline network factors; determine the weight of each relevant factor in the current change trend in turn, and take the data with the largest weight among the seasonal factors, gas usage habits, and gas pipeline network factors as the main influencing factor of the gas consumption.

[0045] For the weight of each relevant factor in the current change trend, by extracting the value of the corresponding trend quantity from the currently calculated change trend, and according to the type of the current trend quantity, determine the weight of each relevant factor. The weight set at this time is obtained according to the value corresponding to the type of the current trend quantity set in advance to obtain the weight of each relevant factor.

[0046] As Figure 3 shown, step S4 calculates the distribution of the gas consumption at different positions according to the main influencing factors of the gas consumption and the gas consumption curve. The processing method of determining the gas scheduling plan also includes S41, obtaining each pipeline node at each position, and determining the scheduling time and scheduling parameters during gas scheduling according to the position and main influencing factors of each pipeline node.

[0047] S42. Determine the throughput of each pipeline node during scheduling according to the scheduling time and scheduling parameters; determine the current gas scheduling plan based on the throughput of each pipeline node.

[0048] The scheduling time indicates at what time point the gas is scheduled, and the scheduling parameters include the pressure, flow rate, and flow rate distribution ratio of the pipeline nodes during transmission and distribution to determine the implementation method of the current scheduling.

[0049] For the throughput of each pipeline node, it is necessary to determine the scheduling parameters and the trend shown by the gas consumption curve to select the amount to be scheduled currently. For example, control the flow rate of each pipeline node during scheduling to determine the current scheduling plan.

[0050] Measure each pipeline node using a pressure sensor to obtain the pressure of each pipeline node; the flow rate of each pipeline node can be determined by obtaining the flow between each pipeline node. For example, if there is a section of pipeline flowing from section 1 to section 2, at this time, obtain the pressure, height, cross-sectional area, and density of the gas at section 1 and section 2, and apply Bernoulli's equation to calculate the velocity of the gas flow. The flow rate of the gas flowing in the current pipeline is obtained according to the product of the cross-sectional area and the velocity. The parameters of the leakage alarm are obtained by counting the number and type of alarms in the history of the pipeline to identify whether there is a leakage risk in the current pipeline, so as to verify whether the current pipeline is stable during scheduling.

[0051] For the scheduling time, according to the main influencing factors corresponding to the gas consumption curve, select the time point corresponding to the part with the largest value among the current main influencing factors as the scheduling time. At this time, the processing of the main influencing factors also includes: determining the time when the scheduling time is affected by seasonal factors, gas usage habits, and the main influencing factors corresponding to the gas pipeline network, and determining the proportional value of each main influencing factor in turn. Adjust the scheduling time proportionally according to the calculated proportional value.

[0052] Therefore, the processing method for the scheduling time is to obtain the seasonal factor influence value, gas usage habit influence value, and gas pipeline network influence value in turn, and obtain the time to be adjusted corresponding to the seasonal factor influence value, gas usage habit influence value, and gas pipeline network influence value to obtain the adjusted time.

[0053] The seasonal factor influence value is expressed as follows: obtain the current temperature and the reference temperature, and determine the temperature change value of the gas caused by the current season. This value is the obtained seasonal factor influence value; the reference temperature set at this time represents the temperature of the gas during normal transmission. Determining the difference between the reference temperature and the current temperature is to verify whether there is a temperature change during the gas transmission. At the same time, the relevant temperature of the gas can be identified by setting temperature sensors on the pipeline nodes.

[0054] Among them, S represents the influence value of seasonal factors, Tem represents the current temperature, Tem0 represents the reference temperature, and β S represents the influence factor of seasonal factors.

[0055] The influence value of gas usage habits is expressed as follows: Obtain the gas consumption in the current time period and the gas consumption in the reference time period, and determine the influence value of gas usage habits; the reference time period is expressed as a preset time period under normal gas usage, and this time period is used to distinguish the gas consumption in the form of time periods, and the gas consumption in the reference time period is set as a verified normal consumption value, so as to be able to identify whether the current gas consumption is excessive or too little.

[0056] Among them, U represents the influence value of gas usage habits, C represents the gas consumption in the current time period, C0 represents the gas consumption in the reference time period, and β U represents the influence factor of gas usage habits.

[0057] The influence value of the gas pipeline network is expressed as follows: Obtain the current pipeline network pressure and the target pipeline network pressure. At this time, the current pipeline network pressure refers to the pressure of the pipeline network set in the corresponding area during gas dispatching, and the target pipeline network is the pipeline network that needs to be passed through during gas dispatching. At this time, determining the pressure of the passed pipeline network can judge the actual flow value of the current gas during dispatching.

[0058] Among them, P represents the influence value of the gas pipeline network, P current represents the current pipeline network pressure, P target represents the target pipeline network pressure, and β P represents the influence factor of the gas pipeline network.

[0059] At this time, the calculated influence values of seasonal factors, gas usage habits, and gas pipeline networks are all values of the same dimension, and the corresponding values are all normalized to determine the proportion value of each influence value in the overall influence value.

[0060] Therefore, the dispatching time is expressed as follows: Obtain the adjusted time corresponding to the influence value of seasonal factors, the influence value of gas usage habits, and the influence value of the gas pipeline network, and the proportion values corresponding to seasonal factors, gas usage habits, and the gas pipeline network, to obtain the dispatching time; for the method of obtaining the adjusted time corresponding to the influence value of seasonal factors, the influence value of gas usage habits, and the influence value of the gas pipeline network, by selecting the time period corresponding to the current value from historical data and verifying the size difference between this time period and the current set time, after adding the average value of these calculated differences to the normal set base value, the corresponding adjusted time can be obtained.

[0061] D = WS ×D S +W U ×D U +W P ×D P ; wherein

[0062] wherein, D represents the scheduling time, and W S represents the proportional value corresponding to the seasonal factor, and W U represents the proportional value corresponding to the gas usage habit, and W P represents the proportional value corresponding to the gas pipeline network, and D S represents the time for adjusting the value affected by the seasonal factor, and D U represents the time for adjusting the value affected by the gas usage habit, and D P represents the time for adjusting the value affected by the gas pipeline network.

[0063] The processing method for the scheduling parameters is as follows: obtain the positions between each pipeline node, calculate the maximum allowable flow rate of each pipeline node, and determine the flow rate distribution ratio of each pipeline node during scheduling according to the maximum allowable flow rate of each pipeline node; according to the obtained flow rate distribution ratio, adjust the opening degree of the current pressure regulating valve, and determine whether the gas flow rate after adjusting the opening degree of the current pressure regulating valve meets the change trend of the gas consumption curve, and use the relevant parameters that meet the change trend of the gas consumption curve as the current scheduling parameters; the relevant parameters described at this time are expressed as the maximum allowable flow rate, the flow rate distribution ratio, and the opening degree of the pressure regulating valve; at the same time, meeting the change trend of the gas consumption curve means that the supply volume of each current pipeline node meets the corresponding user set of each pipeline node, that is, the scenario where the supply volume and the demand volume reach balance, and use the parameters in this scenario as the required scheduling parameters.

[0064] For the maximum allowable flow rate, calculate the capacity of the current pipeline by multiplying the cross-sectional area and the actual volume inside the current pipeline node, set a flow meter inside the current pipeline node, count the actual gas volume passing through the current pipeline node at different times, and take the maximum value of the actual gas volume passing through the current pipeline node at different times as the maximum allowable flow rate of the current pipeline node; if the current pipeline is a newly established pipeline, then use the average value of the adjacent pipeline nodes connected to the current pipeline node as the maximum allowable flow rate of the current pipeline node.

[0065] The flow distribution ratio is determined by obtaining the ratio of the current pipeline node in the historical flow distribution. The flow distribution ratio is obtained by comparing the flow of the current pipeline node with the flow of the overall pipeline. At this time, the change value of the current pipeline node in the historical flow distribution is compared. If the change value of the flow distribution ratio exceeds the preset change value within the current preset period, the average value of the flow distribution ratio in the previous preset period is used as the value of the current flow distribution ratio; if the change value of the flow distribution ratio is less than the preset change value within the current preset period, the median value of the current flow distribution ratio is used as the value of the current flow distribution ratio.

[0066] The opening of the pressure regulating valve is expressed as the product of the difference between the current pipe network pressure and the target pipe network pressure and the pressure coefficient, that is, the pressure value most relevant to the current gas flow during gas scheduling is determined, and the opening of the pressure regulating valve is calculated; at this time, the processing method for the opening of the pressure regulating valve is to determine the pressure deviation between the current pipe network pressure and the target pipe network pressure, determine the pressure deviation curve of the pressure deviation under the corresponding gas scheduling. If there are extreme points in the pressure deviation curve, the moving average value of the extreme points of the pressure deviation curve is used as the current pressure deviation, and the current pipe network pressure is adjusted according to the current pressure deviation; if there are no extreme points in the pressure deviation curve, the average value of the pressure deviation curve is used as the current pressure deviation, and the current pipe network pressure is adjusted according to the current pressure deviation.

[0067] At this time, the adjusted and selected maximum allowable flow rate, flow distribution ratio, and opening of the pressure regulating valve, when meeting the gas supply and demand balance, the corresponding maximum allowable flow rate, flow distribution ratio, and opening of the pressure regulating valve are used as the output adjustment parameters.

[0068] At this time, the method for determining the throughput of each pipeline node during scheduling according to the scheduling time and scheduling parameters is to calculate the total required scheduling volume by multiplying the maximum allowable flow rate of the current pipeline node by the scheduling time, and obtain the throughput of each pipeline node according to the flow distribution ratio of each pipeline.

[0069] The method for determining the current gas scheduling plan according to the throughput of each pipeline node is to compare the scheduling time and scheduling parameters of each pipeline node with the scheduling plan, and use the scheduling plan with the highest similarity to the current scheduling time and scheduling parameters as the current gas scheduling plan.

[0070] The processing method for the frequently scheduled area in step S5 includes using the clustering method to cluster all the scheduling locations during gas scheduling to determine the frequently scheduled area. The clustering method used at this time is the K-means algorithm to determine the relevant clusters of all scheduling locations in terms of location, and the several clusters with the largest number are used as the currently identified frequently scheduled areas.

[0071] Adjusting the gas scheduling plan according to the characteristics of the frequently scheduled area means obtaining the layout relationship and layout method of each pipeline node. At this time, the layout relationship and layout method of each pipeline node represent identifying the topological structure of the current pipeline node. For example, if the current pipeline node is in the form of a branched, looped, or grid-like shape, then the corresponding layout relationship and layout method represent how the pipelines are combined in terms of shape.

[0072] According to the layout relationship and layout method of each pipeline node, determine the pressure stability analysis and capacity evaluation analysis of each pipeline node. Based on the pressure stability analysis and capacity evaluation analysis, obtain the comprehensive analysis result. Based on the obtained comprehensive analysis result, adjust the gas scheduling plan.

[0073] The pressure stability analysis means obtaining the length, diameter, friction coefficient, and gas flow velocity of the pipeline node to obtain the pressure loss coefficient.

[0074] Among them, ΔP loss represents the pressure loss coefficient, f represents the friction coefficient, L represents the pipeline length, PD represents the pipeline diameter, v represents the gas flow velocity, and ρ represents the gas density.

[0075] The capacity evaluation analysis means obtaining the maximum allowable flow rate and the theoretical maximum flow rate of the current pipeline node to determine the capacity factor. The capacity factor is expressed as the ratio of the maximum allowable flow rate and the theoretical maximum flow rate of the current pipeline node.

[0076] The calculation method for the comprehensive analysis result is to normalize the pressure loss coefficient and the capacity factor and then calculate the weighted average to obtain the comprehensive analysis result.

[0077] Based on the obtained comprehensive analysis result, the method for adjusting the gas scheduling plan is as follows: Based on the obtained comprehensive evaluation result, determine the operating state of the current pipeline node. At this time, the operating state of the pipeline node is represented by the relevant pressure loss and capacity evaluation to determine whether the pipeline node can operate stably. If the operating state of the current pipeline node is abnormal, obtain the scheduling plan with the largest correlation coefficient value for the abnormal operating state of the current pipeline node as the adjusted gas scheduling plan. If the operating state of the current pipeline node is normal, use the scheduling plan with the highest similarity to the index value of the comprehensive analysis result as the adjusted gas scheduling plan.

[0078] When the recognized operating status is normal at this time, select the pressure loss coefficient and capacity factor in the current comprehensive analysis result, and select the plan with the highest similarity to the pressure loss coefficient and capacity factor of the current pipeline node from the set scheduling plans to adjust the current scheduling plan. At the same time, the similarity calculated at this time is to compare the data corresponding to the pressure loss coefficient and capacity factor with the corresponding data in the set scheduling plan to determine whether the data corresponding to the pressure loss coefficient and capacity factor is completely included in the set scheduling plan, so as to make further adjustments to the current gas scheduling, enabling dynamic adjustment as a whole and improving the applicability of gas scheduling.

[0079] The abnormal operating status recognized at this time includes flow abnormality and pressure abnormality; the flow abnormality means that there is a sudden increase or decrease in the flow rate of a pipeline node, resulting in the flow rate of the pipeline node exceeding the normal range; the pressure abnormality means that there is a sudden increase or decrease in the pressure of some nodes in the pipeline node, resulting in the pressure of the corresponding pipeline node exceeding the normal range; the processing method for the abnormal operating status is to determine the distance distribution of the abnormal points where abnormalities occur in the current pipeline node and the number of occurrences of the abnormal points, search for at least one emergency treatment point corresponding to the current abnormal point from the set scheduling plan, determine the location, scheduling time and scheduling parameters of the emergency treatment point, and judge the change in the gas volume received by the corresponding user set after using the emergency treatment point. If, after using the emergency treatment point, the correlation coefficient between the adjusted scheduling time and scheduling parameters and the original scheduling plan is greater than the preset correlation coefficient value, the scheduling plan corresponding to the existence of the emergency treatment point is used as the scheduling plan for adjusting the gas; at this time, the correlation coefficient is calculated by calculating the Pearson correlation coefficient between the adjusted scheduling time and scheduling parameters and the parameters in the original scheduling plan to determine whether there is a linear correlation between the current parameters and the adjusted parameters. The value of the correlation coefficient at this time is from 0 to 1, and the preset correlation coefficient value is set to 0.8, so as to select the most suitable scheduling plan for the current plan. If there are multiple scheduling plans with a correlation coefficient greater than the preset correlation coefficient value, select the scheduling plan with the largest correlation coefficient value for adjustment. If there is no scheduling plan with a correlation coefficient greater than the preset correlation coefficient value, select to close the relevant pipeline node and upload the corresponding situation to the peripheral terminal.

[0080] As Figure 4 shown, the present invention also provides an intelligent gas transmission scheduling system based on the Internet of Things, including: a gas consumption data acquisition module, a gas consumption curve drawing module, a gas consumption analysis module, a gas scheduling calculation module, and a gas scheduling adjustment module.

[0081] The gas consumption data acquisition module: is used to collect the gas consumption data of users in real time through Internet of Things devices, and associate the data with the location information of the users to form a structured data set.

[0082] Gas consumption curve plotting module: It is used to process the structured data set by using the autoregressive moving average model, output the gas consumption curve, and determine the change trend of the gas consumption curve by the maximum likelihood estimation method.

[0083] Gas consumption analysis module: It is used to identify the seasonal factors, gas usage habits and gas pipeline network factors among the main influencing factors of gas consumption by screening the change trend of the gas consumption curve.

[0084] Gas scheduling calculation module: It is used to calculate the distribution of gas consumption at different locations according to the main influencing factors of gas consumption and the gas consumption curve, and determine the gas scheduling plan.

[0085] Gas scheduling adjustment module: It is used to obtain the relationship between gas scheduling and location according to the gas scheduling plan, identify the frequently scheduled areas, and adjust the gas scheduling plan according to the characteristics of the frequently scheduled areas.

[0086] In summary, the system realizes the intelligent scheduling of gas transportation by integrating multiple functional modules.

[0087] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, and still be covered by the protection scope of the present invention.

Claims

1. A gas transmission intelligent scheduling method based on the Internet of Things, characterized in that: include: S1, when obtaining gas scheduling, the user set corresponding to the gas scheduling is determined, and the gas consumption in the user set is determined; S2, identify the changes in gas consumption of user sets at different timelines and locations, and draw a gas consumption curve that changes over time; The method of drawing the gas consumption curve is to input the structured data set into the autoregressive moving average model, and the autoregressive moving average model outputs the gas consumption curve. The gas consumption curve is expressed as: ； in, represents the gas consumption from time point t to time point tp, represents a constant term, represents the autoregressive parameter, represents the number of autoregressive parameters, represents the moving average parameter, represents the number of moving average parameters, represents the error term from time point t to time point tq; The processing method of the autoregressive moving average model also includes obtaining a likelihood function of the gas consumption curve based on the gas consumption curve output by the autoregressive moving average model, determining a maximum likelihood value of the output according to the obtained likelihood function, and determining a change trend of the current gas consumption curve according to the maximum likelihood value; The likelihood function L is expressed as: ; ； The obtained likelihood function is logarithmized; ； in, represents the likelihood function, represents the function that maximizes the logarithmic likelihood, represents the number of autoregressive parameters, represents the number of moving average parameters, represents the number of time points, represents the standard deviation of the error term, represents pi, represents the exponential constant, represents the gas consumption at time point t, represents the expected value of gas consumption, ; S3, analyzing the gas consumption curve and determining the main factors affecting the gas consumption; S4, according to the main influencing factors of gas consumption and the gas consumption curve, calculate the distribution of gas consumption at different locations and determine the gas scheduling plan; S5, according to the gas scheduling plan, obtain the relationship between gas scheduling and location, identify the frequent scheduling area, and adjust the gas scheduling plan according to the characteristics of the frequent scheduling area.

2. According to the method of intelligent scheduling of gas transportation based on the Internet of Things in claim 1, it is characterized in that: Step S2 includes the following implementations: S21, obtaining the gas consumption of the user set at each time point and the user's geographical location information, and arranging the collected data according to the timeline and location to obtain a structured data set; S22, drawing a gas consumption curve according to a preset period based on the structured data set, and determining a change trend of the gas consumption curve.

3. According to the method of intelligent scheduling of gas transportation based on the Internet of Things in claim 1, it is characterized in that: In step S3, the gas consumption curve is analyzed to determine the main factors affecting the gas consumption, including: According to the changing trend of the gas consumption curve and the time of occurrence of each changing trend and the indicator value corresponding to the changing trend, the relevant factors of the current changing trend are screened out, and the relevant factors include seasonal factors, gas usage habits and gas pipeline network factors; the weight of each relevant factor in the current changing trend is determined in turn, and the data with the largest weight among seasonal factors, gas usage habits and gas pipeline network factors is taken as the main influencing factor of gas consumption.

4. According to claim 3, a method for intelligent dispatching of gas transportation based on the Internet of Things is characterized in that: The processing method of step S4 also includes: S41, obtaining each pipeline node at each position, and determining the scheduling time and scheduling parameters for gas scheduling according to the position of each pipeline node and the main influencing factors; S42, determining the throughput of each pipeline node during scheduling according to the scheduling time and scheduling parameters; and determining the current gas scheduling plan according to the throughput of each pipeline node.

5. The method for intelligent dispatching of gas transportation based on the Internet of Things according to claim 4 is characterized in that: The method for processing the scheduling time is to obtain the impact value of seasonal factors, the impact value of gas usage habits and the impact value of the gas pipeline network in turn, and obtain the time that needs to be adjusted corresponding to the impact value of seasonal factors, the impact value of gas usage habits and the impact value of the gas pipeline network to obtain the adjustment time.

6. The method for intelligent dispatching of gas transportation based on the Internet of Things according to claim 4 is characterized in that: The processing method for scheduling parameters is to obtain the position between each pipeline node, calculate the maximum allowable flow of each pipeline node, and determine the flow distribution ratio of each pipeline node during scheduling according to the maximum allowable flow of each pipeline node; According to the obtained flow distribution ratio, the current pressure regulating valve opening is adjusted, and it is determined whether the gas flow after adjusting the current pressure regulating valve opening meets the changing trend of the gas consumption curve, and the relevant parameters that meet the changing trend of the gas consumption curve are used as the current scheduling parameters; According to the scheduling time and scheduling parameters, the method of determining the throughput of each pipeline node during scheduling is to obtain the overall required scheduling volume by calculating the product of the maximum allowable flow of the current pipeline node and the scheduling time, and to obtain the throughput of each pipeline node according to the flow distribution ratio of each pipeline; According to the throughput of each pipeline node, the current gas scheduling plan is determined by comparing the scheduling time and scheduling parameters of each pipeline node with the scheduling plan, and taking the scheduling plan with the greatest similarity to the current scheduling time and scheduling parameters as the current gas scheduling plan.

7. The method for intelligent dispatching of gas transportation based on the Internet of Things according to claim 1 is characterized in that: Adjusting the gas dispatching scheme according to the characteristics of the frequently dispatched area in step S5 further includes: obtaining the layout relationship and layout mode of each pipeline node, determining the pressure stability analysis and capacity evaluation analysis of each pipeline node according to the layout relationship and layout mode of each pipeline node, obtaining a comprehensive analysis result according to the pressure stability analysis and capacity evaluation analysis, and adjusting the gas dispatching scheme based on the obtained comprehensive analysis result; Based on the comprehensive analysis results obtained, the processing method for adjusting the gas scheduling plan is to determine the operating status of the current pipeline node based on the comprehensive evaluation results obtained. If the operating status of the current pipeline node is abnormal, the scheduling plan with the largest correlation coefficient with the abnormal operating status of the current pipeline node is obtained as the scheduling plan for adjusting the gas. If the operating status of the current pipeline node is normal, the scheduling plan with the greatest similarity to the indicator value of the comprehensive analysis result is used as the scheduling plan for adjusting the gas.

8. A gas transmission intelligent dispatching system based on the Internet of Things, using the gas transmission intelligent dispatching method based on the Internet of Things as claimed in claim 1, characterized in that: include: Gas consumption data collection module: used to collect users' gas consumption data in real time through IoT devices, and associate the data with users' location information to form a structured data set; Gas consumption curve drawing module: used to process the structured data set using the autoregressive moving average model, output the gas consumption curve, and determine the change trend of the gas consumption curve through the maximum likelihood estimation method; The method of drawing the gas consumption curve is to input the structured data set into the autoregressive moving average model, and the autoregressive moving average model outputs the gas consumption curve. The gas consumption curve is expressed as: ； in, represents the gas consumption from time point t to time point tp, represents a constant term, represents the autoregressive parameter, represents the number of autoregressive parameters, represents the moving average parameter, represents the number of moving average parameters, represents the error term from time point t to time point tq; The processing method of the autoregressive moving average model also includes obtaining a likelihood function of the gas consumption curve based on the gas consumption curve output by the autoregressive moving average model, determining a maximum likelihood value of the output according to the obtained likelihood function, and determining a change trend of the current gas consumption curve according to the maximum likelihood value; The likelihood function L is expressed as: ; ； The obtained likelihood function is logarithmized; ； in, represents the likelihood function, represents the function that maximizes the logarithmic likelihood, represents the number of autoregressive parameters, represents the number of moving average parameters, represents the number of time points, represents the standard deviation of the error term, represents pi, represents the exponential constant, represents the gas consumption at time point t, represents the expected value of gas consumption, ; Gas consumption analysis module: used to identify seasonal factors, gas usage habits and gas pipeline network factors among the main factors affecting gas consumption by screening the changing trend of the gas consumption curve; Gas scheduling calculation module: used to calculate the distribution of gas consumption at different locations and determine the gas scheduling plan based on the main influencing factors of gas consumption and the gas consumption curve; Gas scheduling adjustment module: used to obtain the relationship between gas scheduling and location according to the gas scheduling plan, identify the frequent scheduling areas, and adjust the gas scheduling plan according to the characteristics of the frequent scheduling areas.

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