A Cross-regional Data Interaction Method and System for Smart Gas

Abstract

The present invention discloses a method and system for cross-regional data interaction of intelligent gas, which relates to the technical field of intelligent gas dispatching and transmission. The intelligent gas cross-regional data interaction system includes: a data acquisition module for acquiring gas time-series monitoring data of several regions; a data transmission module responsible for transmitting the data to a receiving module and monitoring network stability, a data receiving module for receiving and verifying the data, and a data interaction module for analyzing the verified data and predicting the gas demand index of the next monitoring period for each region. Through the gas dispatching module, the present invention arranges the gas demand indexes of the next monitoring period for each region in descending order to generate a gas demand dispatching table, and performs priority dispatching based on the gas demand dispatching table, thereby realizing the dynamic optimal allocation of gas resources among multiple regions, effectively avoiding waste or shortage of gas supply between regions, and ensuring the integrity and timeliness of data.

Description

Technical Field

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

[0002] With the acceleration of the urbanization process, 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 to be solved urgently. And in the face of complex gas demand and supply changes between multiple regions, traditional technologies often have difficulty in quickly and accurately adjusting the allocation of gas resources, resulting in the lag or inaccuracy of dispatching decisions.

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

[0004] Based on the above solutions, it is found that the limitations of the prior art at least include the following problems. In the process of gas demand dispatching, the prior art fails to make full use of real-time gas demand data for prediction and dynamic dispatching. As a result, when facing complex and changeable demand situations, it is difficult for gas dispatching to achieve efficient resource allocation, which is likely to lead to resource waste or insufficient supply in some regions, thus affecting the overall operation efficiency and reliability. Moreover, the prior art lacks real-time network stability monitoring and dynamic adjustment mechanisms, and it is difficult to adjust data transmission strategies according to the network stability of different regions. Therefore, in practical applications, it is easy to cause the delayed transmission of gas demand data, affecting the timeliness of dispatching, and further affecting the supply and dispatching efficiency of gas. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a method and system for cross-regional data interaction of intelligent gas, which solves the problems that in the process of gas demand scheduling in the prior art, real-time gas demand data is not fully utilized for prediction and dynamic scheduling, and there is a lack of real-time network stability monitoring and dynamic adjustment mechanism.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent gas cross-regional data interaction system includes: a data acquisition module, a data transmission module, a data reception 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 reception 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 reception 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 verification processing to 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 sort the gas demand indexes of the next monitoring period of each region in descending order to generate a gas demand scheduling table, and perform priority scheduling based on the gas demand scheduling table.

[0007] Further, the gas time-series monitoring data includes the gas flow velocity 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] Further, 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: Real-time obtain the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission process of each region, and perform standardization processing; Based on the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission process of each region after standardization processing, perform comprehensive analysis to obtain the network stability during the transmission process of each region; Compare and analyze the network stability during the transmission process of each region with the preset network stability threshold respectively; If the network stability during the transmission process of each region is lower than the preset network stability threshold, then take the first transmission measure; If the network stability during the transmission process of each region is not lower than the preset network stability threshold, then take the second transmission measure.

[0009] Further, the specific formula for calculating the network stability of each region is as follows: ; where, For the network stability during the transmission of the th region, , , , successively are the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission of the th region after normalization processing, , , , , successively 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 to obtain the gas demand index for each monitoring period of each region are as follows: comprehensively analyze the gas flow velocity value and gas density value of each node for each monitoring period of each region to obtain the initial gas demand value for each monitoring period of each region; and perform normalization processing on the gas mass flow value, gas calorific value, leakage index, and meteorological factors of each node for each monitoring period of each region; based on the gas mass flow value, gas calorific value, leakage index, and meteorological factors of each node for each monitoring period of each region after normalization processing, comprehensively analyze to obtain the initial gas demand correction factor for each monitoring period of each region; comprehensively analyze the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node for each monitoring period of each region to obtain the gas fluidity index for each monitoring period of each region; and perform normalization processing on the initial gas demand value, initial gas demand correction factor, and gas fluidity index for each monitoring period of each region; and based on the initial gas demand value, initial gas demand correction factor, and gas fluidity index for each monitoring period of each region after normalization processing, comprehensively analyze to obtain the gas demand index for each monitoring period of each region.

[0011] Furthermore, the specific formulas for calculating the initial gas demand correction factor and gas demand index for each monitoring period of each region are as follows: ; where is the initial gas demand correction factor for the th monitoring period of the th region, , , , successively are the th region after normalization processing and the The gas mass flow value, gas calorific value, leakage index, and meteorological factors of the th node of each monitoring period, , , , , are successively the gas mass flow adjustment coefficient, calorific value adjustment coefficient, leakage adjustment coefficient, meteorological adjustment coefficient, and coupling adjustment coefficient stored in the database, is the gas demand index of the th region in the th monitoring period, , , are successively the initial gas demand value, initial gas demand correction factor, and gas fluidity index of the th region in the th monitoring period after normalization processing, , , , are successively the initial adjustment coefficient, correction factor adjustment coefficient, fluidity correction coefficient, and fluidity 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 to obtain the gas fluidity index for each monitoring period in each region are as follows: Read the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period of each region and perform standardization processing; Based on the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period of each region after standardization processing, conduct comprehensive analysis to obtain the gas fluidity index for each monitoring period in each region.

[0013] Furthermore, the specific formula for calculating the gas fluidity index for each monitoring period in each region is as follows: ; where is the gas fluidity index of the th region in the th 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:

[0017] (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.

[0018] (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.

[0019] (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.

[0020] (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.

[0021] 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

[0022] Figure 1 This is a block diagram of a smart gas cross - regional data interaction system according to the present invention.

[0023] Figure 2 This is a flowchart of the steps to obtain the gas demand index for each monitoring period in each region in a smart gas cross - regional data interaction system according to the present invention.

[0024] Figure 3 This is a flowchart of a smart gas cross - regional data interaction method according to the present invention. Detailed implementation manners

[0025] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a smart gas cross - regional data interaction system, including: a data acquisition module, a data transmission module, a data reception 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 reception module, and real - time monitor the network stability (i.e., transmission stability) during the transmission process, and take corresponding transmission measures based on the network stability; the data reception 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 verification processing to obtain the gas demand index for each monitoring period in each region, and perform prediction analysis to obtain the gas demand index for the next monitoring period in each region; the gas scheduling module is used to sort the gas demand indexes for the next monitoring period in each region in descending order to generate a gas demand scheduling table, and perform priority scheduling based on the gas demand scheduling table (i.e., sequentially schedule according to the regions corresponding to each sequence of the gas demand scheduling table, for example, preferentially schedule the region corresponding to the first sequence).

[0026] Among them, the specific process of verification processing is as follows: first, perform data integrity verification. After the data integrity verification is correct, synchronously perform time synchronization verification, data format verification, and consistency verification. If any of the verifications fails, a re - transmission mechanism will be triggered.

[0027] Data integrity verification: Verify whether there is data loss or damage during the transmission process, that is, check whether the gas time - series monitoring data of each region is complete, confirm whether all data packets are successfully received, and ensure that the data has not been tampered with during the transmission process through checksum or hash value comparison.

[0028] Time synchronization verification: 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.

[0029] Data format verification: Ensure that the received data conforms to the expected format. For example, check the field types (numbers, strings, etc.), the correct number of fields, and the reasonable value range.

[0030] Consistency verification: Verify whether the monitoring data from different regions is consistent, ensure that the data formats and structures in different regions are the same, and avoid data inconsistency caused by transmission errors.

[0031] 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 factors, gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period.

[0032] Among them, the gas flow rate value can be obtained through a flow meter.

[0033] The gas density value can be obtained through a gas densitometer.

[0034] The gas mass flow is the mass of gas passing through the node per unit time, which can be obtained through a thermal mass flow meter.

[0035] The gas calorific value is the heat released when unit mass of gas is completely burned, which can be obtained through an on-line calorific value analyzer.

[0036] The leakage index is the risk of gas leakage during the monitoring period. By obtaining the pressure value (which can be obtained through a pressure sensor) and methane concentration value (which can be obtained through a methane detector) at each time point during the monitoring period, and performing mean processing to obtain the mean pressure and mean methane concentration. At the same time, obtain the pressure reference value (by obtaining the pressure values at several historical time points and performing mean processing) and methane concentration reference value (obtained from the safety standards stored in the database) respectively, and perform ratio analysis (such as, |pressure value - pressure reference value| / pressure reference value) respectively. Based on the ratio analysis results, perform weighted processing, and the obtained result is this parameter.

[0037] The meteorological factors are the meteorological conditions that affect gas demand, mainly including external temperature, external humidity, external air pressure, and external wind speed. By obtaining the external temperature, external humidity, external air pressure, and external wind speed during the monitoring period, performing standardization processing, and performing weighted processing based on the standardization processing results, the obtained result is this parameter.

[0038] The gas viscosity value can be obtained by obtaining the viscosity value at each time point during the monitoring period through a viscometer and performing mean processing, and the obtained result is this parameter.

[0039] The gas compression index represents the degree of volume change of gas under different pressure and temperature conditions. By obtaining the temperature value, pressure value, and performing normalization processing, and then conducting weighted processing based on the processing results, the obtained result is this parameter.

[0040] The gas oscillation value measures the volatility caused by pressure changes when gas flows in a pipeline. By obtaining the pressure value at each time point during the monitoring period and conducting statistical analysis, the maximum pressure value, minimum pressure value are obtained, and the average value of the pressure value at each time point is calculated to obtain the average pressure value. Then, the ratio of the maximum pressure value, minimum pressure value, and average pressure value is processed, that is, (maximum pressure value - minimum pressure value) / average pressure value.

[0041] The friction factor is the resistance generated by the interaction between the pipeline surface and the gas when the gas flows through the pipeline. By obtaining the surface roughness of the pipeline (obtained from the standardized roughness value table stored in the database) and the diameter (obtained from the pipeline design data stored in the database), and performing calculation processing based on the Haaland formula, the obtained result is this parameter.

[0042] Specifically, the specific steps for real-time monitoring of network stability during the transmission process and taking corresponding transmission measures based on network stability are as follows: Real-time obtain the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission process in each area, and perform normalization processing (i.e., unit removal processing); Based on the normalized network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission process in each area, conduct comprehensive analysis to obtain the network stability during the transmission process in each area; Compare and analyze the network stability during the transmission process in each area with the preset network stability threshold respectively; If the network stability during the transmission process in each area is lower than the preset network stability threshold, then take the first transmission measure (i.e., adjust the transmission path or protocol, such as by using a more stable protocol or selecting an alternative path for data transmission; increase redundant transmission, add data transmission paths to ensure data integrity, and conduct signal interference control, such as reducing 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, then 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 successfully transmitted to the data receiving module, and enhance the data transmission efficiency. For example, select a more efficient data compression method, or accelerate the data transmission process by increasing the data transmission bandwidth).

[0043] Among them, the network congestion degree value measures the degree of congestion in the network during the transmission process, which is the actual used bandwidth (obtained through bandwidth monitoring tools such as PRTG, SolarWinds and other network monitoring tools) / the maximum bandwidth (obtained through the network configuration file stored in the database).

[0044] The signal interference index is the degree of influence of external interference on the signal quality. By obtaining the signal-to-noise ratio and the total interference value (that is, obtaining the intensity of each interference source through a spectrum analyzer to obtain the total interference level), and then performing standardization processing, and performing weighted processing based on the results of the standardization processing, the obtained result is this parameter.

[0045] The spectrum utilization rate value is the ratio of the actually used spectrum, which can be obtained through a spectrum analyzer.

[0046] The channel capacity value is the average value of the maximum amount of information that each channel can transmit under ideal conditions. Obtain the bandwidth, signal strength, and noise strength, and analyze and process them based on Shannon's theorem. The obtained result is the parameter, and the bandwidth, signal strength, and noise strength can all be obtained through a spectrum analyzer.

[0047] The specific formula for calculating the network stability of each area is as follows: ; among them, is the network stability during the transmission of the th area, is the network congestion degree value during the transmission of the th area, is the congestion coefficient stored in the database, is the signal interference index during the transmission of the th area, is the interference coefficient stored in the database, is the spectrum utilization rate value during the transmission of the th area, is the spectrum utilization coefficient stored in the database, is the channel capacity value during the transmission of the th 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 areas.

[0048] It should be explained that the term in the formula is used to adjust the superposition effect of the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value, to avoid the network stability being too high or too low.

[0049] , , , , can be obtained through the following steps: Using historical data, combined with indicators such as network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value, conduct statistical regression analysis to quantify the specific impact of each factor on network stability, so as to fit the initial weight value. Secondly, adopt the sensitivity analysis method to adjust the value range of each coefficient and observe its impact on the network stability evaluation result to ensure the stability and rationality of the model.

[0050] A specific implementation example of calculating the network stability of each region is as follows. The existing data includes the network congestion degree value, signal interference index, spectrum utilization rate value, and channel capacity value during the transmission process of five regions. The specific data is shown in Table 1:

[0051] Table 1 Data example of regional network performance

[0052]

[0053] Perform standardization processing on the data example of regional network performance in Table 1 to obtain Table 2:

[0054] Table 2 Data example of regional network performance after standardization processing

[0055]

[0056] The congestion coefficient stored in the database is approximately: 0.48.

[0057] The interference coefficient stored in the database is approximately: 0.37.

[0058] The spectrum utilization coefficient stored in the database is approximately: 0.42.

[0059] The channel capacity coefficient stored in the database is approximately: 0.51.

[0060] The interaction coefficient stored in the database is approximately: 0.78.

[0061] Substitute the above coefficients and the data in Table 2 into the specific formula for calculating the network stability of each region respectively, and obtain:

[0062] The network stability of 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。

[0063] The network stability of the second area == 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。

[0064] The network stability of the third area == 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。

[0065] The network stability of the fourth area == 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。

[0066] The network stability of the fifth area == 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。

[0067] In this implementation scheme, by monitoring and analyzing the key performance indicators of the network in real time (such as network congestion degree, signal interference index, spectrum utilization rate, and channel capacity), it is possible to comprehensively evaluate the stability of the network during data transmission. Through standardization processing, these data can eliminate unit differences and ensure that data in different regions can be compared and analyzed under the same standard, so as to more accurately judge whether there are unstable factors in the network. When the network is detected to be unstable, corresponding transmission measures are automatically taken, such as adjusting the transmission path, increasing 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 degree and channel capacity, network bottleneck areas can be identified in advance. When network congestion is detected, these bottleneck areas can be avoided by adjusting the transmission path or protocol, optimizing the data transmission path, thereby reducing the waste of network resources, improving the utilization efficiency of network resources, and ensuring the stable operation of the system in a complex network environment. Finally, by introducing various transmission measures (such as selecting a more stable protocol, increasing redundant transmission paths, signal interference control, etc.), it is ensured that the system can still operate normally when the network is unstable. Even in the case of sudden network problems or interference, data transmission interruptions or large-scale delays can be avoided through automatic adjustment and optimization measures, ensuring the timely update and accuracy of gas monitoring data, adapting to a dynamic and complex network environment, and contributing to improving the real-time performance of gas dispatching and the ability to respond to emergencies.

[0068] Specifically, as Figure 2 shown, the specific steps to obtain the gas demand index for each monitoring period in each region are as follows: comprehensively analyze the gas flow rate value and gas density value of each node in each monitoring period of each region (i.e., perform multiplication processing and summation processing) to obtain the initial gas demand value for each monitoring period in each region; and perform normalization processing (i.e., unit removal processing) on the gas mass flow value, gas calorific value, leakage index, and meteorological factor of each node in each monitoring period of each region; comprehensively analyze the gas mass flow value, gas calorific value, leakage index, and meteorological factor of each node in each monitoring period of each region based on the normalized processing to obtain the initial gas demand correction factor for each monitoring period in each region; comprehensively analyze the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring period of each region to obtain the gas fluidity index for each monitoring period in each region; and perform normalization processing on the initial gas demand value, initial gas demand correction factor, and gas fluidity index for each monitoring period in each region; and comprehensively analyze the initial gas demand value, initial gas demand correction factor, and gas fluidity index for each monitoring period in each region based on the normalized processing to obtain the gas demand index for each monitoring period in each region.

[0069] The specific formulas for calculating the initial gas demand correction factor and the gas demand index for each monitoring period in each region are as follows: ;

[0070] Where, is the initial gas demand correction factor for the th monitoring period in the th region, is the gas mass flow value at the th region, th monitoring period, th node after normalization, is the gas mass flow adjustment coefficient stored in the database, is the calorific value of the gas at the th region, th monitoring period, th node after normalization, is the calorific value adjustment coefficient stored in the database, is the leakage index at the th region, th monitoring period, th node after normalization, is the leakage adjustment coefficient stored in the database, is the meteorological factor at the th region, th monitoring period, th node after normalization, is the meteorological adjustment coefficient stored in the database, is the coupling adjustment coefficient stored in the database, is the gas demand index for the th monitoring period in the th region, is the initial gas demand value for the th region, th monitoring period after normalization, is the initial adjustment coefficient stored in the database, is the initial gas demand correction factor for the th region, th monitoring period after normalization, is the correction factor adjustment coefficient stored in the database, is the gas fluidity index for the th region, th monitoring period, is the fluidity correction coefficient 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.

[0071] It should be noted that the in the formula is used to adjust the superposition risk of leakage under high load conditions.

[0072] , , , , can be obtained through the following steps: Based on historical monitoring data, determine the initial influence weights of each variable (gas mass flow value, gas calorific value, leakage index, meteorological factor) on the initial gas demand correction factor through statistical regression analysis. Then, use the sensitivity analysis method to adjust the value range of the coefficients to evaluate the stability and applicability of these parameters to the formula output. Next, further fit the weights through model optimization (such as machine learning algorithms 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 fine-tune the coefficients based on different regional characteristics to ensure its applicability to specific initial gas demand correction requirements.

[0073] The in the formula is used to regulate the gain effect of liquidity on gas demand, making the demand index more elastic and realistic.

[0074] , , , can be obtained through the following steps: Use historical monitoring data, combine the initial gas demand value, initial gas demand correction factor, and gas liquidity index, conduct statistical regression analysis, quantify the specific impact of each factor on the gas demand index, and thus fit the initial weight values. Secondly, use the sensitivity analysis method to adjust the value range of each coefficient, observe its impact on the evaluation results of the gas demand index, and ensure the stability and rationality of the model. Based on regional characteristics and actual situations, correct and optimize the preliminarily fitted coefficients, and finally determine the coefficient values applicable to specific regions.

[0075] In this implementation plan, through comprehensive analysis and normalization of multiple indicators such as gas flow velocity, gas density, gas mass flow rate, gas calorific value, leakage index, and meteorological factors in each area, the gas demand index of each area at different monitoring time periods is accurately calculated. Especially considering the differences of different nodes (such as meteorological factors and leakage index), it can more precisely reflect the actual gas demand situation. By reasonably adjusting these factors and quantifying the gas demand more accurately according to the regional characteristics, over-scheduling or resource waste can be avoided, and the efficiency and response speed of scheduling are improved. Secondly, by adjusting the weights of factors such as gas mass flow rate, calorific value, leakage index, and meteorological factors, the initial gas demand correction factor better conforms to the actual demand changes. Using sensitivity analysis methods and model optimization techniques, precise adjustment can be made according to the characteristics of different regions, so as to ensure that the demand correction factor of each area highly matches the actual situation, reduce unnecessary scheduling and over-supply caused by high risks, and then ensure the safety and economy of gas supply. Finally, through comprehensive analysis and regulation of the gas fluidity index and flexibly adjusting the gas demand index according to factors such as climate conditions and infrastructure in different regions, the system has strong adaptability, ensuring the rationality and accuracy of scheduling decisions and avoiding resource waste or insufficient supply caused by insufficient fluidity or unreasonable scheduling.

[0076] Specifically, the specific steps to obtain the gas fluidity index of each monitoring time 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 time period of each area and perform standardization processing (i.e., unit removal processing); Based on the gas viscosity value, gas compression index, gas oscillation value, and friction factor of each node in each monitoring time period of each area after standardization processing, comprehensive analysis is carried out to obtain the gas fluidity index of each monitoring time period in each area.

[0077] The specific formula for calculating the gas fluidity index of each monitoring time period in each area is as follows: ; where is the gas fluidity index of the th area at the th monitoring time period, is the gas viscosity value of the th area at the th monitoring time period and the th node after standardization processing, is the viscosity adjustment coefficient stored in the database, is the gas compression index of the th area at the th monitoring time period and the th node after standardization processing, is the compression adjustment coefficient stored in the database, is the th monitoring period of the th node's gas oscillation value in the is the oscillation adjustment coefficient stored in the database, is the th monitoring period of the th node's friction factor in the is the friction adjustment coefficient stored in the database, is the 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.

[0078] It should be noted that , , , can be obtained through the following steps: Based on historical monitoring data, determine the initial influence weights of each variable (gas viscosity value, gas compression index, gas oscillation value, friction factor) on the gas flowability index through statistical regression analysis. Then, use the sensitivity analysis method to adjust the value range of the coefficients to evaluate the stability and applicability of these parameters to the formula output. Next, further fit the weights through model optimization (such as machine learning algorithms) to ensure that the formula can accurately reflect the actual gas flow state, and fine-tune the coefficients based on different regional characteristics to ensure its applicability to specific gas flow assessment requirements.

[0079] In this implementation plan, 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, ensuring that data in each region and monitoring period can be compared under the same standard. Thus, 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. This provides effective data support for gas dispatching and ensures the smooth operation of the pipeline network. Secondly, based on historical monitoring data and statistical regression analysis, the contribution degree of each influencing factor (such as gas viscosity, compression index, oscillation value, etc.) to the gas fluidity index can be quantified, and fine-tuning can be carried out according to the characteristics of different regions to ensure that the fluidity assessment results are more in line with the actual situation. Through sensitivity analysis and model optimization (such as machine learning algorithms), the weights of each parameter are adjusted to ensure the stability and applicability of the formula output. Moreover, sensitivity analysis can evaluate the impact of changes in each variable on the gas fluidity index, thereby optimizing the value range of the coefficient to ensure the accuracy of the model under different conditions, and then avoiding dispatching errors caused by inappropriate parameter adjustment, thus improving reliability.

[0080] Specifically, the specific steps to obtain the gas demand index of the next monitoring period for each region are as follows: Conduct a trend analysis on the gas demand index of each monitoring period in each region (that is, the difference result of subtracting the gas demand index of the previous period from the next period, divided by the period time), to obtain several groups of gas demand index change rates for each region; and conduct 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 the gas demand prediction index for each region, which is regarded as the gas demand index of the next monitoring period for each region.

[0081] Among them, the specific formula for calculating the gas demand prediction index of each region is as follows: ; where is the gas demand prediction index of the th region, is the gas demand index of the th monitoring period of the th region, is the th group of gas demand index change rates of the th region, is the weighting coefficient of the th group of gas demand index change rates of the th region, is the first change adjustment coefficient stored in the database, is the th group of gas demand index change rates of the is the weighting coefficient of the th group of gas demand index change rates for the th area, 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 areas, 1, 2, 3, …, , is the number of monitoring time periods, 1, 2, 3, …, , is the number of groups of gas demand index change rates, and = - 1.

[0082] It should be noted that , can be obtained through the following steps: Summarize and analyze the , th group of gas demand index change rates for each area to obtain the th group of gas demand index change sum for each area, and then perform a proportion analysis of the , th group of gas demand index change rates for each area with the th group of gas demand index change sum for each area respectively. The proportion analysis result is the corresponding weighting coefficient.

[0083] , , can be obtained through the following steps: Obtain historical data, analyze the dynamic changes of adjacent two groups of gas demand index change rates, and use the statistical regression method to quantify the initial influence degree of adjacent two groups of gas demand index change rates on the gas demand prediction index, so as to obtain the initial coefficient value. Then, based on the sensitivity analysis technology, adjust the value range of these coefficients in different scenarios to ensure the applicability of the formula to the gas demand prediction index.

[0084] In this implementation solution, through the comprehensive analysis of trend analysis and the change rate of the gas demand index, it is possible to dynamically predict the gas demand in each region for the next time 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 period characteristics, so as to avoid gas supply shortages or surpluses. At the same time, by using the sensitivity analysis method to adjust the coefficients, the prediction model can have strong adaptability and flexibility, ensuring stable demand prediction in various scenarios, thereby reducing the response time of the system and the error of scheduling. And by refining the calculation steps of the gas demand prediction index and combining the change rate and various adjustment coefficients, it is helpful to generate more accurate demand prediction values, and then effectively prevent overloading or gas waste. Finally, through the comprehensive analysis of multiple groups of gas demand change rates, the optimal allocation of resources can be achieved, so as to ensure that the gas demand in different time periods and different regions can be fully met, thereby improving the operation efficiency of scheduling.

[0085] Please refer to Figure 3 , an embodiment of the present invention provides a technical solution: a method for cross-regional data interaction of intelligent gas, including the following steps: In the data acquisition module, obtain the gas time-series monitoring data of several regions; in the data transmission module, transmit the gas time-series monitoring data of each region obtained 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; in the data receiving module, receive the gas time-series monitoring data of each region transmitted by the data transmission module, and perform verification processing; in the data interaction module, perform data analysis on the gas time-series monitoring data of each region after verification processing to obtain the gas demand index for each monitoring time period in each region, and perform prediction analysis to obtain the gas demand index for the next monitoring time period in each region; in the gas scheduling module, sort the gas demand indexes for the next monitoring time period of each region in descending order to generate a gas demand scheduling table, and perform priority scheduling based on the gas demand scheduling table.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0087] 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 equivalent technologies, the present invention also intends 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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