Intelligent gas cross-regional data interaction method and system

Abstract

The invention discloses an intelligent gas cross-regional data interaction method and system, and relates to the technical field of intelligent gas scheduling transmission. The intelligent gas cross-regional data interaction system comprises a data acquisition module used for acquiring gas time sequence monitoring data of a plurality of regions; the data transmission module is responsible for transmitting data to the receiving module and monitoring the network stability, the data receiving module is used for receiving and verifying the data, and the data interaction module is used for analyzing the verified data and predicting the fuel gas demand index of each area in the next monitoring time period. And the scheduling module is used for carrying out descending order arrangement on the gas demand indexes of the next monitoring time period of each region, generating a gas demand scheduling table and carrying out priority scheduling based on the gas scheduling table, so that dynamic optimal configuration of gas resources among the plurality of regions is realized, waste or shortage of gas supply among the regions is effectively avoided, and the service life of the regions is prolonged. And the integrity and timeliness of the data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent gas scheduling and transmission technology, and specifically to a smart gas cross-regional data interaction method and system. Background Art

[0002] With the acceleration of urbanization, gas, as an important energy resource, has become an indispensable part of modern urban life and industrial production. Especially in the process of gas dispatching between multiple regions or cities, how to ensure the safe, stable and efficient supply of gas has become a difficult problem that needs to be solved urgently. In the face of complex changes in gas demand and supply between multiple regions, traditional technologies often find it difficult to quickly and accurately adjust the allocation of gas resources, resulting in delayed or inaccurate dispatching decisions.

[0003] Prior art, such as the patent application with announcement number: CN113259483B, discloses a cross-regional data interaction method for smart gas, which includes the following steps: establishing a smart gas Internet of Things; the sensor network platform senses the first data; the management platform forms an in-network cloud platform; the out-of-network cloud platform collects the first data when performing out-of-network calculations, and generates a first database; when the user platform initiates a gas meter calibration request, the management platform generates the first calibration data, and sends it to the user platform through the service platform; the user platform calibrates the smart gas meter according to the first calibration data. The present invention discloses a cross-regional data interaction system for smart gas. The cross-regional data interaction method and system for smart gas of the present invention realizes the unmanned and automated calibration of smart gas meters, which saves manpower and material resources for calibrating smart gas meters on the one hand, and forms industry big data on the other hand, providing a reference for the government and relevant resource departments.

[0004] Based on the above scheme, it is found that the limitations of the existing technology include at least the following problems: in the process of gas demand scheduling, the existing technology fails to make full use of real-time gas demand data for prediction and dynamic scheduling, which makes it difficult for gas scheduling to achieve efficient resource allocation when facing complex and changeable demand conditions, which may easily lead to resource waste or insufficient supply in some areas, thereby affecting the overall operating efficiency and reliability. In addition, the existing technology lacks real-time network stability monitoring and dynamic adjustment mechanism, and it is difficult to adjust the data transmission strategy according to the network stability of different areas, which may easily lead to delayed transmission of gas demand data in actual applications, affecting the timeliness of scheduling, and further affecting the supply and scheduling efficiency of gas. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a smart gas cross-regional data interaction method and system, which solves the problems that the existing technology fails to fully utilize real-time gas demand data for prediction and dynamic scheduling during the gas demand scheduling process, and lacks real-time network stability monitoring and dynamic adjustment mechanism.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart gas cross-regional data interaction system, including: a data acquisition module, a data transmission module, a data receiving module, a data interaction module, and a gas scheduling module; the data acquisition module is used to acquire the gas timing monitoring data of several regions; the data transmission module is used to transmit the gas timing monitoring data of each region acquired by the data acquisition module to the data receiving module, and monitor the network stability during the transmission process in real time, and take corresponding transmission measures based on the network stability; the data receiving module is used to receive the gas timing monitoring data of each region transmitted by the data transmission module, and perform verification processing; the data interaction module is used to perform data analysis on the gas timing monitoring data of each region after verification processing, obtain the gas demand index of each monitoring period of each region, and perform predictive analysis to obtain the gas demand index of the next monitoring period of each region; the gas scheduling module is used to arrange the gas demand index of the next monitoring period of each region in descending order, generate a gas demand scheduling table, and perform priority scheduling based on the gas scheduling table.

[0007] Furthermore, the gas time series monitoring data includes the gas flow rate value, gas density value, gas mass flow value, gas calorific value, leakage index, meteorological factor, gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period.

[0008] Furthermore, the specific steps of real-time monitoring of the network stability during the transmission process and taking corresponding transmission measures based on the network stability are as follows: obtaining the network congestion value, signal interference index, spectrum utilization value, and channel capacity value during the transmission process of each area in real time, and performing standardization processing; performing a comprehensive analysis based on the network congestion value, signal interference index, spectrum utilization value, and channel capacity value during the transmission process of each area after the standardization processing to obtain the network stability during the transmission process of each area; comparing and analyzing the network stability during the transmission process of each area with the preset network stability threshold; if the network stability during the transmission process of each area is lower than the preset network stability threshold, taking the first transmission measure; if the network stability during the transmission process of each area is not lower than the preset network stability threshold, taking the second transmission measure.

[0009] Furthermore, the specific formula for calculating the network stability of each region is as follows: ; in, For the The network stability during transmission in different regions, , , , After standardization, The network congestion value, signal interference index, spectrum utilization value, and channel capacity value during the transmission process of each area. , , , , They are the congestion coefficient, interference coefficient, spectrum utilization coefficient, channel capacity coefficient, and interaction coefficient stored in the database. 1, 2, 3, ..., , is the number of regions.

[0010] Furthermore, the specific steps for obtaining the gas demand index for each monitoring period in each area are as follows: comprehensively analyze the gas flow rate value and gas density value of each node in each monitoring period in each area to obtain the initial gas demand value for each monitoring period in each area; and normalize the gas mass flow value, gas calorific value, leakage index and meteorological factor of each node in each monitoring period in each area; comprehensively analyze the gas mass flow value, gas calorific value, leakage index and meteorological factor of each node in each monitoring period in each area after normalization to obtain the initial gas demand correction factor for each monitoring period in each area; comprehensively analyze the gas viscosity value, gas compression index, gas oscillation value and friction factor of each node in each monitoring period in each area to obtain the gas fluidity index for each monitoring period in each area; and normalize the initial gas demand value, initial gas demand correction factor and gas fluidity index of each monitoring period in each area; and comprehensively analyze the initial gas demand value, initial gas demand correction factor and gas fluidity index of each monitoring period in each area after normalization to obtain the gas demand index for each monitoring period in each area.

[0011] Furthermore, the specific formula for calculating the initial gas demand correction factor and gas demand index for each monitoring period in each area is as follows: ;in, For the Region The initial gas demand correction factor for each monitoring period, , , , After normalization, Region The first monitoring period Gas mass flow value, gas calorific value, leakage index, meteorological factors of each node, , , , , They are the gas mass flow adjustment coefficient, calorific value adjustment coefficient, leakage adjustment coefficient, meteorological adjustment coefficient, and coupling adjustment coefficient stored in the database. For the Region Gas demand index for each monitoring period, , , After normalization, Region The initial gas demand value, initial gas demand correction factor, and gas liquidity index for each monitoring period. , , , They are the initial adjustment coefficient, correction factor adjustment coefficient, liquidity correction coefficient, and liquidity adjustment coefficient stored in the database. 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

[0012] Furthermore, the specific steps for obtaining the gas fluidity index of each monitoring period in each area are as follows: read the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period in each area, and perform standardization; perform a comprehensive analysis based on the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period in each area after the standardized processing to obtain the gas fluidity index of each monitoring period in each area.

[0013] Furthermore, the specific formula for calculating the gas liquidity index of each monitoring period in each area is as follows: ;in, For the Region Gas liquidity index for each monitoring period, , , , After standardization, Region The first monitoring period The gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node, , , , , They are the viscosity adjustment coefficient, compression adjustment coefficient, oscillation adjustment coefficient, friction adjustment coefficient, and superposition adjustment coefficient stored in the database. 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

[0014] Furthermore, the specific steps for obtaining the gas demand index of each region for the next monitoring period are as follows: perform trend analysis on the gas demand index of each monitoring period in each region to obtain several groups of gas demand index change rates for each region; and perform a comprehensive analysis on the gas demand index of each monitoring period in each region and each group of gas demand index change rates to obtain a gas demand forecast index for each region, which is regarded as the gas demand index of each region for the next monitoring period.

[0015] A smart gas cross-regional data interaction method comprises the following steps: in a data acquisition module, acquiring gas time-series monitoring data of several regions; in a data transmission module, transmitting the gas time-series monitoring data of each region acquired by the data acquisition module to a data receiving module, and monitoring the network stability during the transmission process in real time, and taking corresponding transmission measures based on the network stability; in the data receiving module, receiving the gas time-series monitoring data of each region transmitted by the data transmission module, and performing verification processing; in the data interaction module, performing data analysis on the gas time-series monitoring data of each region after the verification processing, obtaining the gas demand index of each monitoring period of each region, and performing prediction analysis to obtain the gas demand index of the next monitoring period of each region; in the gas scheduling module, arranging the gas demand index of the next monitoring period of each region in descending order, generating a gas demand scheduling table, and performing priority scheduling based on the gas scheduling table.

[0016] The present invention has the following beneficial effects: (1) The smart gas cross-regional data interaction system uses the data interaction module to perform real-time analysis on the gas time series monitoring data of multiple regions, so as to accurately calculate the gas demand index of each region and perform forecast analysis to obtain the gas demand index of the next monitoring period, thereby providing strong decision support for gas scheduling and making priority scheduling. For example, in hot summer weather, the use of air conditioners in some areas surges, and gas demand rises accordingly. Through accurate demand forecasting, the system can dispatch more gas resources to these high-demand areas in advance to avoid gas supply shortages caused by the sharp increase in demand, thereby ensuring the stability of gas supply during peak periods.

[0017] (2) The smart gas cross-regional data interaction system maintains efficient data transmission by real-time monitoring of network stability. If the network is unstable or delayed, the system will automatically take remedial measures, such as adjusting the transmission path or retransmitting data, to ensure the integrity and timeliness of the data. For example, when network problems occur in certain areas, the system can adjust the transmission method in time to ensure uninterrupted data transmission through a more stable network path, thereby preventing data loss or delay caused by network problems, thereby ensuring the real-time and accuracy of gas scheduling decisions.

[0018] (3) The smart gas cross-regional data interaction system realizes dynamic optimization configuration of gas resources between multiple regions through accurate gas demand forecasting and intelligent scheduling. By generating a gas demand scheduling table and prioritizing scheduling based on the demand index, the system can effectively avoid waste or shortage of gas supply between regions. For example, in certain areas with low demand periods, the system can reduce the gas supply in these areas and dispatch the remaining resources to areas with higher demand, thereby achieving optimal resource allocation, thereby improving the utilization efficiency of gas resources and reducing overall operating costs, thereby reducing unnecessary energy waste, and then helping to improve the overall benefits of gas scheduling, thereby ensuring efficient, safe and fair distribution of energy. (4) The cross-regional data interaction method of smart gas realizes coordinated scheduling among different regions through cross-regional data interaction and intelligent analysis. For example, the peak of gas demand in a certain city may affect the gas supply in neighboring cities. Through the cross-regional coordination mechanism of this method, these regions can be coordinated and scheduled to ensure the balance of gas supply among regions, so as to avoid insufficient or waste of gas in other regions due to excessive demand in one area, thereby improving the utilization efficiency of gas resources and the overall operational benefits, and then ensuring the efficient operation of gas scheduling in complex environments.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a block diagram of a smart gas cross-regional data interaction system of the present invention.

[0021] Figure 2 This is a flow chart of the steps of obtaining the gas demand index of each monitoring period in each region in a smart gas cross-regional data interaction system of the present invention.

[0022] Figure 3 This is a flow chart of a smart gas cross-regional data interaction method of the present invention. DETAILED DESCRIPTION

[0023] See also Figure 1 The embodiment of the present invention provides a technical solution: a smart gas cross-regional data interaction system, comprising: a data acquisition module, a data transmission module, a data receiving module, a data interaction module, and a gas scheduling module; the data acquisition module is used to acquire gas time-series monitoring data of several regions; the data transmission module is used to transmit the gas time-series monitoring data of each region acquired by the data acquisition module to the data receiving module, and monitor the network stability (i.e., transmission stability) during the transmission process in real time, and take corresponding transmission measures based on the network stability; the data receiving module is used to receive the gas time-series monitoring data of each region transmitted by the data transmission module, and perform verification processing; the data interaction module is used to perform data analysis on the gas time-series monitoring data of each region after the verification processing, obtain the gas demand index of each monitoring period of each region, and perform prediction analysis to obtain the gas demand index of the next monitoring period of each region; the gas scheduling module is used to arrange the gas demand index of the next monitoring period of each region in descending order, generate a gas demand scheduling table, and perform priority scheduling based on the gas scheduling table (i.e., schedule the regions corresponding to each sequence of the gas demand scheduling table in turn, such as, prioritize the regions corresponding to the first sequence).

[0024] The specific process of verification processing is as follows: first perform data integrity verification, and after the data integrity verification is correct, perform time synchronization verification, data format verification, and consistency verification simultaneously. If any of the verifications fails, a retransmission mechanism will be triggered.

[0025] Data integrity check: Check whether there is any data loss or damage during the transmission process, that is, check whether the gas timing monitoring data in each area is complete, confirm whether all data packets are successfully received, and ensure that the data has not been tampered with during the transmission process by comparing the checksum or hash value.

[0026] Time synchronization check: Compare the received time series data with the expected time format to ensure that the timestamps are consistent and avoid time zone deviation or time loss.

[0027] Data format validation: Ensure that the received data conforms to the expected format. For example, check the field type (number, string, etc.), whether the number of fields is correct, and whether the value range is reasonable.

[0028] Consistency check: Check whether the monitoring data from different areas are consistent, ensure that the data format and structure of different areas are consistent, and avoid data inconsistency caused by transmission errors.

[0029] The gas time series monitoring data includes the gas flow rate value, gas density value, gas mass flow value, gas calorific value, leakage index, meteorological factor, gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period.

[0030] The gas flow rate value can be obtained through a flow meter.

[0031] The gas density value can be obtained by a gas density meter.

[0032] The gas mass flow rate is the gas mass passing through the node per unit time, which can be obtained by a thermal mass flow meter.

[0033] The calorific value of gas is the heat released when a unit mass of gas is completely burned, which can be obtained through an online calorific value analyzer.

[0034] The leakage index is the risk of gas leakage during the monitoring period. The pressure value (which can be obtained by a pressure sensor) and the methane concentration value (which can be obtained by a methane detector) at each time point in the monitoring period are obtained, and the average pressure value and the average methane concentration value are obtained by averaging. At the same time, the pressure reference value (obtained by obtaining the pressure values ​​at several historical time points and averaging) and the methane concentration reference value (obtained through the safety standards stored in the database) are obtained respectively, and ratio analysis is performed respectively (such as |pressure value-pressure reference value| / pressure reference value). Weighted processing is performed based on the ratio analysis result, and the result obtained is the parameter.

[0035] Meteorological factors are meteorological conditions that affect gas demand, mainly including external temperature, external humidity, external air pressure, and external wind speed. The external temperature, external humidity, external air pressure, and external wind speed within the monitoring period are obtained, standardized, and weighted based on the standardized processing results. The result is the parameter.

[0036] The gas viscosity value can be obtained by using a viscometer to obtain the viscosity value at each time point during the monitoring period, and then averaged, and the result is the parameter.

[0037] The gas compression index is the degree of volume change of gas under different pressure and temperature conditions. The parameter is obtained by obtaining temperature and pressure values, performing standardization, and performing weighted processing based on the processing results.

[0038] The gas oscillation value is a measure of the volatility caused by pressure changes when the gas flows in the pipeline. The pressure value at each time point in the monitoring period is obtained and statistically analyzed to obtain the maximum pressure and minimum pressure. The pressure value at each time point is averaged to obtain the pressure mean, and the maximum pressure, minimum pressure and pressure mean are ratioed, that is, (maximum pressure - minimum pressure) / pressure mean.

[0039] The friction factor is the resistance generated by the interaction between the pipe surface and the gas when the gas flows through the pipe. The parameter is obtained by obtaining the surface roughness of the pipe (obtained from the standardized roughness value table stored in the database) and the diameter (obtained from the pipe design data stored in the database) and performing calculations based on the Haaland formula.

[0040] Specifically, the specific steps of real-time monitoring of network stability during transmission and taking corresponding transmission measures based on network stability are as follows: real-time acquisition of network congestion value, signal interference index, spectrum utilization value, and channel capacity value during transmission in each area, and standardization (i.e., de-unitization); comprehensive analysis of the network congestion value, signal interference index, spectrum utilization value, and channel capacity value during transmission in each area after standardization to obtain the network stability during transmission in each area; comparison and analysis of the network stability during transmission in each area with a preset network stability threshold; if the network stability during transmission in each area is lower than the preset network stability threshold, measures are taken. Take the first transmission measure (i.e., adjust the transmission path or protocol, for example, by adopting a more stable protocol or selecting an alternative path for data transmission; increase redundant transmission, increase the data transmission path, ensure data integrity, perform signal interference control, and reduce signal interference by adjusting the transmission frequency or using anti-interference technology); if the network stability during the transmission process in each area is not lower than the preset network stability threshold, take the second transmission measure (i.e., continue normal data transmission, continue data transmission according to the current transmission strategy, ensure that the gas timing monitoring data can be smoothly transmitted to the data receiving module, and enhance data transmission efficiency, for example, select a more efficient data compression method, or speed up the data transmission process by increasing the bandwidth of data transmission).

[0041] Among them, the network congestion value measures the degree to which the network is congested during transmission, the actual bandwidth used (obtained through bandwidth monitoring tools, such as PRTG, SolarWinds and other network monitoring tools) / maximum bandwidth (obtained through the network configuration file stored in the database).

[0042] The signal interference index is the degree of influence of external interference on signal quality. It is obtained by obtaining the signal-to-noise ratio and the total interference value (that is, obtaining the strength of each interference source through a spectrum analyzer to obtain the total interference level), and then performing normalization processing. Based on the normalization result, weighted processing is performed, and the result obtained is the parameter.

[0043] The spectrum utilization value is the actual spectrum ratio used, which can be obtained through a spectrum analyzer.

[0044] The channel capacity value is the average of the maximum amount of information that each channel can transmit under ideal conditions. The bandwidth, signal strength, and noise strength are obtained and analyzed based on Shannon's theorem. The result is the parameter, and the bandwidth, signal strength, and noise strength can all be obtained through a spectrum analyzer.

[0045] The specific formula for calculating the network stability of each region is as follows: ;in, For the The network stability during transmission in different regions, For the The network congestion value of the transmission process in each area, is the congestion coefficient stored in the database, For the The signal interference index during transmission in each area, is the interference coefficient stored in the database, For the The spectrum utilization value of the transmission process in each area, is the spectrum utilization coefficient stored in the database, For the The channel capacity value during the transmission process of each area, is the channel capacity coefficient stored in the database, is the interaction coefficient stored in the database, 1, 2, 3, ..., , is the number of regions.

[0046] It should be explained that the formula This item is used to adjust the superposition effect of network congestion value, signal interference index, spectrum utilization value, and channel capacity value to avoid network stability being too high or too low.

[0047] , , , , It can be obtained through the following steps: using historical data, combined with network congestion value, signal interference index, spectrum utilization value, channel capacity value and other indicators, to conduct statistical regression analysis, quantify the specific impact of each factor on network stability, and thus fit the initial weight value; secondly, using the sensitivity analysis method, adjust the value range of each coefficient, observe its impact on the network stability evaluation results, and ensure the stability and rationality of the model.

[0048] The specific implementation example of calculating the network stability of each area is as follows. The following data are available: network congestion value, signal interference index, spectrum utilization value, and channel capacity value during transmission in five areas. The specific data is shown in Table 1: Table 1 Data example of regional network performance The data example of regional network performance in Table 1 is normalized to obtain Table 2: Table 2. Example of data on regional network performance after normalization Congestion coefficient stored in the database Approximately: 0.48.

[0049] Interference coefficients stored in the database Approximately: 0.37.

[0050] Spectrum utilization coefficients stored in the database Approximately: 0.42.

[0051] Channel capacity coefficients stored in the database Approximately: 0.51.

[0052] Interaction coefficients stored in the database Approximately: 0.78.

[0053] Substituting the above coefficients and the data in Table 2 into the specific formula for calculating the network stability of each region, we obtain: Network stability in the first region = ln(1+(1 / (1+0.86)) 0.48 *(1 / (1+0.81)) 0.37 *0.89 0.42 *0.78 0.51 ) / (exp(-0.78*0.86*0.81*0.89*0.78))))≈0.54.

[0054] The network stability of the second region == ln(1+(((1 / (1+0.69)) 0.48 *(1 / (1+0.71)) 0.37 *0.78 0.42 *0.65 0.51 ) / (exp(-0.78*0.69*0.71*0.78*0.65))))≈0.45.

[0055] The network stability of the third region == ln(1+(((1 / (1+0.39)) 0.48 *(1 / (1+0.53)) 0.37 *0.59 0.42 *0.73 0.51 ) / (exp(-0.78*0.39*0.53*0.59*0.73))))≈0.43.

[0056] Network stability in the fourth region == ln(1+(((1 / (1+0.92)) 0.48 *(1 / (1+0.89)) 0.37 *0.46 0.42 *0.54 0.51 ) / (exp(-0.78*0.92*0.89*0.46*0.54))))≈0.30.

[0057] The network stability of the fifth region == ln(1+(((1 / (1+0.36)) 0.48 *(1 / (1+0.42)) 0.37 *0.34 0.42 *0.59 0.51 ) / (exp(-0.78*0.36*0.42*0.34*0.59))))≈0.32.

[0058] In this implementation scheme, by real-time monitoring and analysis of key performance indicators of the network (such as network congestion, signal interference index, spectrum utilization and channel capacity), the stability of the network during data transmission can be comprehensively evaluated. Through standardization, these data can eliminate unit differences and ensure that data from different regions can be compared and analyzed under the same standard, so as to more accurately determine whether there are unstable factors in the network. When network instability is detected, corresponding transmission measures are automatically taken, such as adjusting the transmission path, adding redundant transmission or anti-interference technology, etc., to ensure the stability and reliability of data transmission. Secondly, by monitoring various performance indicators of the network, especially network congestion and channel capacity, the network bottleneck area can be identified in advance. When network congestion is found, the transmission path or protocol is adjusted to avoid these bottleneck areas and optimize the data transmission path, so as to reduce the waste of network resources, thereby improving the utilization efficiency of network resources and ensuring the stable operation of the system in a complex network environment. Finally, by introducing a variety of transmission measures (such as selecting a more stable protocol, adding redundant transmission paths, signal interference control, etc.), it is ensured that the system can still maintain normal operation when the network is unstable. Even in the event of sudden network problems or interference, automatic adjustment and optimization measures can be used to avoid data transmission interruptions or large-scale delays, thereby ensuring the timely update and accuracy of gas monitoring data, thereby adapting to the dynamic and complex network environment, and helping to improve the real-time nature of gas scheduling and the ability to respond to emergencies.

[0059] Specifically, Figure 2 As shown, the specific steps for obtaining the gas demand index of each monitoring period in each area are as follows: comprehensively analyze the gas flow rate value and gas density value of each node in each monitoring period in each area (i.e., perform product processing and sum processing) to obtain the initial gas demand value of each monitoring period in each area; and normalize the gas mass flow value, gas calorific value, leakage index, and meteorological factor of each node in each monitoring period in each area (i.e., remove the unit); comprehensively analyze the gas mass flow value, gas calorific value, leakage index, and meteorological factor of each node in each monitoring period in each area after normalization. The initial gas demand correction factor of each monitoring period in each area is obtained; the gas viscosity value, gas compression index, gas oscillation value and friction factor of each node in each monitoring period in each area are comprehensively analyzed to obtain the gas fluidity index of each monitoring period in each area; the initial gas demand value, initial gas demand correction factor and gas fluidity index of each monitoring period in each area are normalized; and the initial gas demand value, initial gas demand correction factor and gas fluidity index of each monitoring period in each area after normalization are comprehensively analyzed to obtain the gas demand index of each monitoring period in each area.

[0060] The specific formula for calculating the initial gas demand correction factor and gas demand index for each monitoring period in each area is as follows: ; in, For the Region The initial gas demand correction factor for each monitoring period, After normalization Region The first monitoring period The gas mass flow value of each node, is the gas mass flow adjustment coefficient stored in the database, After normalization Region The first monitoring period The calorific value of gas at each node, is the calorific value adjustment coefficient stored in the database, After normalization Region The first monitoring period The leakage index of each node, is the leakage adjustment coefficient stored in the database, After normalization Region The first monitoring period The meteorological factors of each node, is the meteorological adjustment coefficient stored in the database, is the coupling adjustment coefficient stored in the database, For the Region Gas demand index for each monitoring period, After normalization Region The initial gas demand value for each monitoring period, is the initial adjustment coefficient stored in the database, After normalization Region The initial gas demand correction factor for each monitoring period, is the correction factor adjustment coefficient stored in the database, After normalization Region Gas liquidity index for each monitoring period, is the liquidity correction factor stored in the database, is the liquidity adjustment coefficient stored in the database, 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

[0061] It should be explained that the formula This term is used to adjust for the cumulative risk of leakage under high load conditions.

[0062] , , , , It can be obtained through the following steps: Based on historical monitoring data, the initial impact weights of each variable (gas mass flow value, gas calorific value, leakage index, meteorological factors) on the initial gas demand correction factor are determined through statistical regression analysis. Then, the value range of the coefficient is adjusted using the sensitivity analysis method to evaluate the stability and applicability of these parameters to the formula output. Next, the weights are further fitted through model optimization (such as machine learning algorithm or multi-objective optimization) to ensure that the formula can accurately reflect the specific correction status of the actual initial gas demand correction factor, and the coefficient is fine-tuned based on the characteristics of different regions to ensure that it is suitable for specific initial gas demand correction needs.

[0063] In the formula This item is used to regulate the positive effect of liquidity on gas demand, making the demand index more flexible and realistic.

[0064] , , , It can be obtained through the following steps: using historical monitoring data, combined with the initial gas demand value, the initial gas demand correction factor, and the gas liquidity index, statistical regression analysis is performed to quantify the specific impact of each factor on the gas demand index, thereby fitting the initial weight value; secondly, using the sensitivity analysis method, adjust the value range of each coefficient, observe its impact on the gas demand index evaluation results, ensure the stability and rationality of the model, and based on regional characteristics and actual conditions, correct and optimize the preliminary fitting coefficients, and finally determine the coefficient value applicable to the specific area.

[0065] In this implementation plan, by comprehensively analyzing and normalizing multiple indicators such as gas flow rate, gas density, gas mass flow, gas calorific value, leakage index and meteorological factors in each area, the gas demand index of each area in different monitoring periods can be accurately calculated, especially taking into account the differences between different nodes (such as meteorological factors and leakage index), so as to more carefully reflect the actual gas demand situation, and by reasonably adjusting these factors, a more accurate quantitative prediction of gas demand can be made according to regional characteristics, so as to avoid over-scheduling or waste of resources, and improve the efficiency and response speed of scheduling. Secondly, by adjusting the weights of factors such as gas mass flow, calorific value, leakage index, meteorological factors, etc. , making the initial gas demand correction factor more in line with the actual demand changes, and using sensitivity analysis methods and model optimization techniques, it can be accurately adjusted according to the characteristics of different regions, thereby ensuring that the demand correction factor of each region is highly consistent with the actual situation, thereby reducing unnecessary scheduling and oversupply caused by high risks, and thus ensuring the safety and economy of gas supply. Finally, through comprehensive analysis and regulation of the gas liquidity index, and flexible adjustment of the gas demand index according to factors such as climate conditions and infrastructure in different regions, the system has strong adaptability, thereby ensuring the rationality and accuracy of scheduling decisions, and avoiding waste of resources or insufficient supply due to insufficient liquidity or unreasonable scheduling.

[0066] Specifically, the specific steps for obtaining the gas fluidity index of each monitoring period in each area are as follows: read the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period in each area, and perform standardization (i.e., remove the unit); perform a comprehensive analysis based on the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period in each area after the standardization to obtain the gas fluidity index of each monitoring period in each area.

[0067] The specific formula for calculating the gas liquidity index for each monitoring period in each area is as follows: ;in, For the Region Gas liquidity index for each monitoring period, After standardization Region The first monitoring period The gas viscosity value of each node, is the viscosity adjustment factor stored in the database, After standardization Region The first monitoring period The gas compression index of each node, is the compression adjustment factor stored in the database, After standardization Region The first monitoring period Gas oscillation value of each node, is the oscillation adjustment coefficient stored in the database, After standardization Region The first monitoring period The friction factor of each node, is the friction adjustment coefficient stored in the database, is the overlay adjustment coefficient stored in the database, 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

[0068] It needs to be explained that , , , It can be obtained through the following steps: Based on historical monitoring data, the initial influence weights of various variables (gas viscosity value, gas compression index, gas oscillation value, friction factor) on the gas fluidity index are determined through statistical regression analysis. Then, the range of coefficients is adjusted using the sensitivity analysis method to evaluate the stability and applicability of these parameters to the formula output. Next, the weights are further fitted through model optimization (such as machine learning algorithms) to ensure that the formula can accurately reflect the actual gas flow state. The coefficients are fine-tuned based on the characteristics of different regions to ensure that they are suitable for specific gas flow assessment needs.

[0069] In this implementation scheme, by reading the gas viscosity value, compression index, oscillation value and friction factor and standardizing these parameters, the influence of different units can be eliminated, thereby ensuring that the data of each region and monitoring period are compared under the same standard, and then the gas fluidity index of each region can be accurately calculated, which helps to identify the flow state of gas in the pipeline network, such as whether there is a risk of poor flow or blockage, thereby providing effective data support for gas scheduling and ensuring the smooth operation of the pipeline network. Secondly, based on historical monitoring data and statistical regression analysis, the contribution of various influencing factors (such as gas viscosity, compression index, oscillation value, etc.) to the gas fluidity index can be quantified, and fine-tuned according to the characteristics of different regions to ensure that the fluidity assessment results are more in line with the actual situation, and the weights of each parameter are adjusted through sensitivity analysis and model optimization (such as machine learning algorithm) to ensure the stability and applicability of the formula output. In addition, sensitivity analysis can evaluate the impact of changes in various variables on the gas fluidity index, thereby optimizing the value range of the coefficient and ensuring the accuracy of the model under different conditions, thereby avoiding scheduling errors caused by inappropriate parameter adjustments, and then improving reliability.

[0070] Specifically, the specific steps for obtaining the gas demand index for the next monitoring period in each region are as follows: perform a trend analysis on the gas demand index for each monitoring period in each region (i.e., the difference between the gas demand index for the previous period and the next period, divided by the period time) to obtain several groups of gas demand index change rates for each region; and perform a comprehensive analysis on the gas demand index for each monitoring period in each region and the change rate of each group of gas demand indexes to obtain a predicted gas demand index for each region, which is regarded as the gas demand index for the next monitoring period in each region.

[0071] The specific formula for calculating the gas demand forecast index for each region is as follows: ;in, For the Gas demand forecast index for each region, For the Region Gas demand index for each monitoring period, For the Region Group gas demand index change rate, For the Region The weighted coefficient of the change rate of the group gas demand index, is the first change adjustment coefficient stored in the database, For the Region Group gas demand index change rate, For the Region The weighted coefficient of the change rate of the group gas demand index, is the second change adjustment coefficient stored in the database, is the change interaction adjustment coefficient stored in the database, and , 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of gas demand index change rate groups, and = -1.

[0072] It needs to be explained that , It can be obtained by the following steps: , The gas demand index change rate of each region is summed up and analyzed. The gas demand index changes and values ​​are then , The change rate of the gas demand index of each group is respectively related to the The change and value of the group gas demand index are analyzed by proportion, and the result of the proportion analysis is the corresponding weighting coefficient.

[0073] , , It can be obtained through the following steps: obtaining historical data, analyzing the dynamic changes of the change rates of two adjacent groups of gas demand indices, using statistical regression methods to quantify the initial impact of the change rates of two adjacent groups of gas demand indices on the gas demand forecast index, thereby obtaining the initial coefficient value, and then, based on the sensitivity analysis technology, adjusting the value ranges of these coefficients under different scenarios to ensure the applicability of the formula to the gas demand forecast index.

[0074] In this implementation scheme, through the comprehensive analysis of trend analysis and gas demand index change rate, it is possible to dynamically predict the gas demand of each region in the next period, and then make scheduling decisions in advance, and optimize gas scheduling and supply. Secondly, by dynamically adjusting the weighted coefficients of different change rates, the model can adapt to different demand fluctuations according to regional characteristics and time characteristics to avoid gas supply shortages or surpluses. At the same time, by adjusting the coefficients using the sensitivity analysis method, the prediction model can have strong adaptability and flexibility, ensuring that stable demand forecasts can be provided in a variety of scenarios, thereby reducing the system's response time and scheduling errors. By refining the calculation steps of the gas demand prediction index, combining the change rate and various adjustment coefficients, it is helpful to generate more accurate demand forecast values, thereby effectively preventing excessive load or gas waste. Finally, through the comprehensive analysis of multiple gas demand change rate groups, the optimal allocation of resources can be achieved, thereby ensuring that the gas demand in different periods and different regions can be fully met, thereby improving the operational efficiency of scheduling.

[0075] See also Figure 3 The embodiment of the present invention provides a technical solution: a smart gas cross-regional data interaction method, comprising the following steps: in a data acquisition module, acquiring gas time-series monitoring data of several regions; in a data transmission module, transmitting the gas time-series monitoring data of each region acquired by the data acquisition module to a data receiving module, and monitoring the network stability during the transmission process in real time, and taking corresponding transmission measures based on the network stability; in the data receiving module, receiving the gas time-series monitoring data of each region transmitted by the data transmission module, and performing verification processing; in the data interaction module, performing data analysis on the gas time-series monitoring data of each region after the verification processing, obtaining the gas demand index of each monitoring period of each region, and performing prediction analysis to obtain the gas demand index of the next monitoring period of each region; in the gas scheduling module, arranging the gas demand index of the next monitoring period of each region in descending order, generating a gas demand scheduling table, and performing priority scheduling based on the gas scheduling table.

[0076] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A smart gas cross-regional data interaction system, characterized in that: include: Data acquisition module, data transmission module, data receiving module, data interaction module, gas scheduling module; The data acquisition module is used to acquire gas time-series monitoring data of several areas; The data transmission module is used to transmit the gas timing monitoring data of each area acquired by the data acquisition module to the data receiving module, and monitor the network stability during the transmission process in real time, and take corresponding transmission measures based on the network stability; The data receiving module is used to receive the gas time-series monitoring data of each area transmitted by the data transmission module and perform verification processing; The data interaction module is used to perform data analysis on the gas time series monitoring data of each area after verification processing to obtain the gas demand index of each area in each monitoring period, and perform forecast analysis to obtain the gas demand index of the next monitoring period of each area; The gas scheduling module is used to arrange the gas demand index of each area in the next monitoring period in descending order, generate a gas demand scheduling table, and perform priority scheduling based on the gas scheduling table; The gas time series monitoring data includes the gas flow rate value, gas density value, gas mass flow value, gas calorific value, leakage index, meteorological factor, gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period; The specific steps to obtain the gas demand index for each monitoring period in each area are as follows: Comprehensively analyze the gas flow rate value and gas density value of each node in each monitoring period of each area to obtain the initial gas demand value of each monitoring period of each area; The gas mass flow value, gas calorific value, leakage index and meteorological factors of each node in each monitoring period of each area are normalized; Based on the normalized gas mass flow value, gas calorific value, leakage index and meteorological factors of each node in each monitoring period of each area, the initial gas demand correction factor of each monitoring period of each area is obtained; Comprehensively analyze the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period of each area to obtain the gas fluidity index of each monitoring period of each area; The initial gas demand value, initial gas demand correction factor, and gas liquidity index of each monitoring period in each area are normalized; A comprehensive analysis is performed based on the normalized initial gas demand value, initial gas demand correction factor, and gas liquidity index of each monitoring period in each region to obtain the gas demand index of each monitoring period in each region; The specific steps to obtain the gas demand index for the next monitoring period in each area are as follows: Perform trend analysis on the gas demand index of each monitoring period in each area to obtain several groups of gas demand index change rates in each area; A comprehensive analysis is performed on the gas demand index of each monitoring period in each region and the change rate of each group of gas demand indices to obtain the gas demand forecast index of each region, which is regarded as the gas demand index of the next monitoring period in each region.

2. The smart gas cross-regional data interaction system according to claim 1 is characterized in that: The specific steps for real-time monitoring of network stability during transmission and taking corresponding transmission measures based on network stability are as follows: Obtain the network congestion value, signal interference index, spectrum utilization value, and channel capacity value during the transmission process of each area in real time and perform standardized processing; Based on the standardized network congestion value, signal interference index, spectrum utilization value, and channel capacity value in each area during transmission, the network stability in each area during transmission is obtained; Compare and analyze the network stability during the transmission process in each area with the preset network stability threshold; If the network stability during the transmission of each area is lower than a preset network stability threshold, a first transmission measure is taken; If the network stability during the transmission of each area is not lower than a preset network stability threshold, the second transmission measure is taken.

3. The smart gas cross-regional data interaction system according to claim 2 is characterized in that: The specific formula for calculating the network stability of each region is as follows: ； in, For the The network stability during transmission in different regions, , , , After standardization, The network congestion value, signal interference index, spectrum utilization value, and channel capacity value during the transmission process of each area. , , , , They are the congestion coefficient, interference coefficient, spectrum utilization coefficient, channel capacity coefficient, and interaction coefficient stored in the database. 1, 2, 3, ..., , is the number of regions.

4. The smart gas cross-regional data interaction system according to claim 1 is characterized in that: The specific formula for calculating the initial gas demand correction factor and gas demand index for each monitoring period in each area is as follows: ； in, For the Region The initial gas demand correction factor for each monitoring period, , , , After normalization, Region The first monitoring period Gas mass flow value, gas calorific value, leakage index, meteorological factors of each node, , , , , They are the gas mass flow adjustment coefficient, calorific value adjustment coefficient, leakage adjustment coefficient, meteorological adjustment coefficient, and coupling adjustment coefficient stored in the database. For the Region Gas demand index for each monitoring period, , , After normalization, Region The initial gas demand value, initial gas demand correction factor, and gas liquidity index for each monitoring period. , , , They are the initial adjustment coefficient, correction factor adjustment coefficient, liquidity correction coefficient, and liquidity adjustment coefficient stored in the database. 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

5. The smart gas cross-regional data interaction system according to claim 1 is characterized in that: The specific steps to obtain the gas liquidity index for each monitoring period in each area are as follows: Read the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period in each area, and perform standardization processing; Based on the standardized gas viscosity value, gas compression index, gas oscillation value and friction factor of each node in each monitoring period of each area, a comprehensive analysis is performed to obtain the gas fluidity index of each monitoring period of each area.

6. The smart gas cross-regional data interaction system according to claim 5 is characterized in that ,The specific formula for calculating the gas liquidity index of each ,monitoring period in each area is as follows: ； in, For the Region Gas liquidity index for each monitoring period, , , , After standardization, Region The first monitoring period The gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node, , , , , They are the viscosity adjustment coefficient, compression adjustment coefficient, oscillation adjustment coefficient, friction adjustment coefficient, and superposition adjustment coefficient stored in the database. 1, 2, 3, ..., , is the number of regions, 1, 2, 3, ..., , is the number of monitoring periods, 1, 2, 3, ..., , is the number of nodes.

7. A smart gas cross-regional data interaction method, using the smart gas cross-regional data interaction system according to any one of claims 1 to 6, characterized in that: The following steps are involved: In the data acquisition module, the gas time series monitoring data of several areas are obtained; In the data transmission module, the gas timing monitoring data of each area acquired by the data acquisition module is transmitted to the data receiving module, and the network stability during the transmission process is monitored in real time, and corresponding transmission measures are taken based on the network stability; In the data receiving module, the gas timing monitoring data of each area transmitted by the data transmission module is received and verified; In the data interaction module, data analysis is performed on the gas time series monitoring data of each area after verification processing to obtain the gas demand index of each area in each monitoring period, and forecast analysis is performed to obtain the gas demand index of the next monitoring period of each area; In the gas scheduling module, the gas demand index of each area in the next monitoring period is arranged in descending order, a gas demand scheduling table is generated, and priority scheduling is performed based on the gas scheduling table.

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