Equipment joint control Internet of Things system, method and medium for gas safety pressure regulation

Abstract

The present invention provides an Internet of Things (IoT) system, method, and medium for joint control of equipment for gas safety pressure regulation. The IoT system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform. The method is executed by the gas company management platform and includes: obtaining historical sensor data of a sensor device; determining multiple sets of sensor statistics based on the historical sensor data; and determining adjustment instructions based on current sensor data and sensor statistics, wherein the adjustment instructions are configured to adjust cooling parameters and pressure regulation parameters. The method can also be executed after being read by computer instructions stored in a computer-readable storage medium. The present invention relates to the field of gas pressure regulation technology and can achieve equipment joint control by timely adjusting pressure regulation parameters and cooling parameters to ensure efficient operation of the gas pressure regulating station.

Description

Technical Field

[0001] This specification relates to the field of gas pressure regulation technology, and in particular to an Internet of Things system, method, and medium for equipment joint control of gas safety pressure regulation. Background Art

[0002] To ensure gas compliance and transportation safety, the delivered gas needs to be pressure-regulated by a gas pressure regulating station before entering the downstream pipeline. During the pressure regulation process, changes in gas pressure often lead to changes in the temperature of the equipment and gas. By jointly monitoring gas pressure, gas temperature, and equipment temperature, joint control of the gas pressure regulating station's pressure regulating equipment and cold energy recovery equipment can be achieved, thereby realizing cold energy recovery and utilization, which is beneficial for energy conservation. However, there is currently a lack of effective means for adjusting the pressure regulating parameters of the pressure regulating equipment and the cooling parameters of the cold energy recovery equipment in real time during the gas pressure regulation process, as well as for achieving joint control of the pressure regulating equipment and cold energy recovery equipment.

[0003] Therefore, it is necessary to provide an IoT system, method and medium for equipment joint control of gas safety pressure regulation, which can ensure the efficient operation of the gas pressure regulating station and the safe and stable transportation of gas by timely adjusting the pressure regulating parameters of the pressure regulating equipment and the cooling parameters of the cold energy recovery equipment. Summary of the Invention

[0004] In order to solve the problem of how to adjust the working parameters of the pressure regulating equipment and the cold energy recovery equipment in real time during the gas pressure regulation process, the present invention provides an Internet of Things system, method and medium for equipment joint control of gas safety pressure regulation.

[0005] The invention includes an IoT system for device-linked control of gas pressure safety regulation, comprising a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision target platform, a gas company sensor network platform, and a gas equipment target platform. The government safety supervision target platform includes a gas company management platform; the gas equipment target platform includes pressure regulating equipment and cold energy recovery equipment, each equipped with a sensor device, and each installed at a gas pressure regulating station; and the gas company management platform is configured to execute a method for device-linked control of gas pressure safety regulation.

[0006] The invention content includes a device joint control method for safe gas pressure regulation, which is executed by a gas company management platform, and the method includes: obtaining historical sensor data of a sensor device, the historical sensor data including sensor data at multiple historical moments, the sensor data including a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, the gas temperature and the device temperature of the pressure regulating device; based on the historical sensor data, determining multiple groups of sensor statistical data, the sensor statistical data including pressure difference statistics and temperature difference statistics; and based on current sensor data and the sensor statistical data, determining an adjustment instruction, the adjustment instruction being configured to adjust the cooling parameters of the cold energy recovery device and the pressure regulation parameters of the pressure regulating device.

[0007] The invention includes a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the medium, the computer executes a device joint control method for safe gas pressure regulation.

[0008] The beneficial effects brought about by the above invention include but are not limited to: (1) by statistically analyzing historical sensor data, the sensor statistical data can be determined, and then the changing patterns of gas pressure, equipment temperature and gas temperature can be evaluated; by grouping the sensor statistical data, the gas can be divided into different intervals with reference, which is convenient for performing different degrees of pressure regulation and temperature reduction operations on gas with different pressures, so that the pressure regulating equipment and cold energy recovery equipment can maintain good working efficiency; (2) by adjusting the first time interval according to the number of input pipelines, it can flexibly adapt to gas pressure regulating stations of different sizes and complexities, and significantly improve the overall efficiency and stability of the gas pressure regulating system; (3) according to the first pressure, target output pressure and target gas temperature of the current gas pressure regulating station, it can determine the appropriate second time interval, so as to timely adjust the working parameters of the equipment at the appropriate time and reduce the risk of gas pipeline network failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0010] Figure 1 This is a schematic diagram of the platform structure of an IoT system for joint control of equipment for safe gas pressure regulation according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flow chart of a method for joint control of equipment for safe gas pressure regulation according to some embodiments of this specification;

[0012] Figure 3is an exemplary flow chart of determining multiple sets of sensory statistics according to some embodiments of this specification;

[0013] Figure 4 is an exemplary flow chart for determining a voltage regulation parameter at a first moment according to some embodiments of this specification;

[0014] Figure 5 is an exemplary schematic diagram of a pressure difference model according to some embodiments of this specification. DETAILED DESCRIPTION

[0015] The following is a brief introduction to the drawings required for describing the embodiments. The drawings do not represent all embodiments. As used herein, the terms "system," "device," "unit," and / or "module" are used to distinguish between different components, elements, parts, portions, or assemblies at different levels. Other expressions may be used to replace the aforementioned terms if they can achieve the same purpose.

[0016] Unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0017] When operations are described in steps in the present invention, unless otherwise specified, the order of the steps is interchangeable, steps may be omitted, and other steps may be included in the operation process.

[0018] Figure 1 This is a schematic diagram of the platform structure of an equipment joint control Internet of Things system for gas safety pressure regulation according to some embodiments of this specification.

[0019] In some embodiments, as Figure 1 As shown, the equipment joint control Internet of Things system 100 for gas safety pressure regulation may include a government safety supervision management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140 and a gas equipment object platform 150.

[0020] The government safety supervision and management platform 110 refers to a platform for supervision and safety management of gas pipeline networks.

[0021] The government security supervision sensor network platform 120 refers to a functional platform for managing government sensor communications, and can be configured as a communication network or a gateway.

[0022] In some embodiments, the government safety supervision sensor network platform 120 can interact with the government safety supervision management platform 110 and interact with the government safety supervision object platform 130. For example, the government safety supervision object platform 130 can send a reporting instruction to the government safety supervision management platform 110 through the government safety supervision sensor network platform 120.

[0023] The government security supervision object platform 130 refers to an object platform for generating perception information and executing control information.

[0024] In some embodiments, the government safety supervision object platform 130 may include a gas company management platform 131 .

[0025] The gas company management platform 131 is a comprehensive management platform for relevant information of the gas company.

[0026] In some embodiments, the gas company management platform 131 may include a processor. In some embodiments, the processor may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core processing device). By way of example only, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), or any combination thereof.

[0027] The gas company sensor network platform 140 refers to a comprehensive management platform for the gas company's sensor information, and can be configured as a communication network or a gateway, etc.

[0028] In some embodiments, the gas company sensor network platform 140 can interact with the government safety supervision platform 130 and with the gas equipment object platform 150. For example, the government safety supervision platform 130 can send the acquired adjustment instructions to the gas equipment object platform 150 through the gas company sensor network platform 140.

[0029] The gas equipment object platform 150 is a functional platform that performs temperature and pressure monitoring and equipment parameter adjustment. In some embodiments, the gas equipment object platform 150 may include pressure regulating equipment and cold energy recovery equipment, each equipped with a sensor device, and the pressure regulating equipment is located in a gas pressure regulating station.

[0030] Pressure regulating equipment refers to equipment that can regulate gas pressure. In some embodiments, the gas equipment object platform may further include multiple input pipelines of a gas pressure regulating station, and the pressure regulating equipment may include distribution equipment, boosting equipment, and / or pressure reducing equipment.

[0031] A gas pressure regulating station is a station for regulating the pressure of gas. In some embodiments, the gas pressure regulating station may include an input pipeline, pressure regulating equipment, and the like.

[0032] The input pipeline refers to the pipeline that transports gas into the gas pressure regulating station.

[0033] Distribution equipment refers to devices that distribute gas to different pathways. These devices perform multiple functions, including flow control, pressure balancing, and path selection. They distribute gas from different input pipelines to different processing pathways (such as boosting, reducing, or direct output) according to specific rules or proportions. In some embodiments, the distribution equipment can be a distribution valve that distributes gas from multiple input pipelines to boosting and reducing devices.

[0034] Booster equipment is a device used to increase gas pressure. For example, a compressor is used. When the gas pressure in the input pipeline is lower than the target output pressure, the booster equipment is activated to increase the gas pressure by adding energy (e.g., compressing the gas volume) to meet subsequent gas delivery or usage requirements.

[0035] Pressure-reducing equipment is used to reduce gas pressure. For example, a pressure regulating valve is used. When the gas pressure in the input pipeline exceeds the target output pressure, or when pressure reduction is necessary for safety or efficiency reasons, the pressure-reducing equipment is activated. It reduces the gas pressure by reducing its energy (e.g., by increasing its volume) to ensure safe and stable delivery of gas to the gas network.

[0036] Cold energy recovery equipment refers to equipment used to recover refrigerant. Refrigerant refers to a medium capable of lowering temperature, such as propane or cooling water. In some embodiments, the cold energy recovery equipment may include a refrigerant circulation pipeline. The refrigerant circulation pipeline refers to a pipe through which the refrigerant circulates or passes.

[0037] A sensing device is a device used to monitor sensor data from gas or equipment, and may include pressure sensors and temperature sensors. In some embodiments, the sensing devices can be installed at appropriate locations in gas pipelines, pressure regulating equipment, and cold energy recovery equipment based on monitoring requirements. For example, pressure sensors and temperature sensors can be installed near the inlet or outlet of the pressure regulating equipment, and temperature sensors can be installed on the outer surface of the pressure regulating equipment.

[0038] In some embodiments, the cold energy recovery device may further include an expansion refrigeration device.

[0039] Expansion refrigeration equipment refers to equipment that uses the cold energy released by gas during its expansion during decompression to provide cooling. Examples include turbine expanders and piston expanders.

[0040] In some embodiments, the expansion refrigeration device can be connected to the pressure reducing device. The expansion refrigeration device directly converts the gas pressure energy generated by the pressure reducing device into cold energy without consuming additional energy, thereby achieving energy-saving and efficient refrigeration.

[0041] In some embodiments of this specification, based on the equipment joint control Internet of Things system for gas safety pressure regulation, an information operation closed loop can be formed between various functional platforms, and coordinated and regularly operated under the unified management of the gas company management platform, realizing the informatization and intelligence of smart gas pressure regulation and temperature regulation.

[0042] Figure 2 This is an exemplary flow chart of a device joint control method for gas safety pressure regulation according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps S210 to S230.

[0043] In some embodiments, the process 200 may be executed by the gas company management platform 131 , for example, by a processor in the gas company management platform 131 .

[0044] Step S210: Acquire historical sensing data of the sensing device.

[0045] Sensor data refers to data related to gas pressure regulation acquired by a sensor device. In some embodiments, the sensor data may include a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, the gas temperature, and the device temperature of the pressure regulating device.

[0046] The first pressure and the second pressure refer to the gas pressure before and after pressure regulation by the pressure regulating equipment respectively.

[0047] Gas temperature refers to the temperature of the gas after it passes through the pressure regulating equipment.

[0048] Equipment temperature refers to the temperature of the voltage regulating equipment itself.

[0049] In some embodiments, the sensor data can be acquired in real time by a sensor device. For example, a pressure sensor deployed at or near the inlet of the pressure regulating device can acquire a first pressure, a pressure sensor and a temperature sensor deployed at or near the outlet of the pressure regulating device can acquire a second pressure and gas temperature, respectively, and a temperature sensor deployed on the outer surface of the pressure regulating device can acquire the device temperature.

[0050] In some embodiments, the historical sensor data may include sensor data from multiple historical moments. A historical moment refers to a moment that is farther away than the current moment, for example, multiple moments within a day a week ago.

[0051] For example, historical sensor data can be represented in the following table (1):

[0052] Table (1)

[0053]

[0054] in, represents the nth historical moment, 、 、 、 Representing historical moments The historical first pressure, historical second pressure, historical gas temperature, and historical equipment temperature at that time.

[0055] In some embodiments, the sensor data is monitored and acquired by the sensor device and uploaded to the gas company management platform through the gas company sensor network platform. The processor can directly retrieve the historical sensor data.

[0056] Step S220 : determining multiple sets of sensor statistics based on the historical sensor data.

[0057] Sensory statistics refer to statistics related to changes in historical sensory data. In some embodiments, the sensory statistics may include pressure difference statistics and temperature difference statistics. A set of sensory statistics includes pressure difference statistics and temperature difference statistics corresponding to the historical sensory data of the set.

[0058] In some embodiments, the processor may divide the historical sensor data into intervals to determine multiple groups of historical sensor data, and for each group of historical sensor data, calculate sensor statistics for that group. For more details on this, see the following.

[0059] Pressure difference statistics refer to statistical data related to gas pressure changes. In some embodiments, the pressure difference statistics may include extreme pressure change rates, median pressure change rates, and the like.

[0060] The term "pressure change" refers to the speed of pressure change. In some embodiments, the pressure change can be represented by the ratio of the pressure change to the time corresponding to the pressure change. The pressure change extreme value can include multiple maximum and / or minimum values ​​of the pressure change. The pressure change median value can refer to the median of multiple pressure change values.

[0061] Temperature difference statistics refer to statistical data related to changes in gas temperature and / or device temperature. In some embodiments, the temperature difference statistics may include extreme values ​​of gas temperature variation, median values ​​of gas temperature variation, extreme values ​​of device temperature variation, median values ​​of device temperature variation, etc.

[0062] Among them, temperature variation refers to the speed of temperature change. Gas temperature variation and equipment temperature variation refer to the temperature variation of gas temperature and the temperature variation of equipment temperature, respectively. In some embodiments, the temperature variation can be represented by the ratio of the temperature variation to the time corresponding to the temperature variation, the temperature variation extreme value can include multiple maximum values ​​and / or minimum values ​​of the temperature variation, and the temperature variation median can refer to the median of multiple temperature variations. The temperature variation can be positive or negative, and the corresponding temperature variation can be positive or negative. A positive temperature variation indicates that the equipment temperature and / or gas temperature has increased; a negative temperature variation indicates that the equipment temperature and / or gas temperature has decreased. When the equipment temperature and / or gas temperature decreases, the cold energy recovery device does not work.

[0063] In some embodiments, the processor may determine multiple sets of sensory statistics based on historical sensory data in a variety of ways.

[0064] For example, the processor may determine multiple sets of sensory statistical data through statistical analysis based on historical sensory data, which may specifically include the following steps S11 to S13:

[0065] Step S11 , dividing the historical first pressure into intervals, and dividing the historical sensing data into multiple groups.

[0066] In some embodiments, the processor may divide the multiple historical first pressures at multiple historical moments into different intervals according to a preset pressure step size, and divide the multiple historical sensor data corresponding to each historical first pressure interval into a cluster. The preset pressure step size may be set by default by the processor or pre-set by a technician based on experience.

[0067] For example, the range of the historical first pressure is 1MPa~5MPa, and the preset pressure step can be 1MPa. Then the historical first pressure can be divided into multiple intervals [1MPa,2MPa), [2MPa,3MPa), [3MPa,4MPa), [4MPa,5MPa], and the historical sensor data corresponding to each historical first pressure interval is divided into different groups.

[0068] Step S12 : for each group of historical sensor data, calculate multiple historical pressure changes in the group, divide the multiple historical pressure changes in the group into intervals, and divide the group of historical sensor data into multiple groups.

[0069] In some embodiments, for each group of historical sensor data, two adjacent historical moments constitute a historical time period. Each group of historical sensor data may include multiple historical time periods, and multiple historical pressure speed changes may be calculated accordingly. For a historical time period, the processor may calculate a corresponding historical pressure speed change based on the historical first pressure at the start historical moment and the historical second pressure at the end historical moment. For example, in combination with Table (1), The first pressure in history at this historical moment is , The second historical pressure of the historical moment is ,but The historical pressure change in the historical time period is , the corresponding historical pressure change is .

[0070] In some embodiments, the processor may divide the multiple historical pressure speed changes corresponding to each group of historical sensor data into different intervals according to a preset speed change step length, and divide the multiple historical sensor data corresponding to each historical pressure speed change interval into a group. The preset speed change step length may be set by default by the processor or pre-set by a technician based on experience.

[0071] For example, for a family of historical sensor data whose historical first pressure interval is [1MPa, 2MPa), the corresponding historical pressure speed change range is [0.1MPa / s, 0.4MPa / s], and the preset speed change step can be 0.1MPa / s. The historical pressure speed change can be divided into multiple intervals of [0.1MPa / s, 0.2MPa / s), [0.2MPa / s, 0.3MPa / s), and [0.3MPa / s, 0.4MPa / s). The historical sensor data corresponding to each historical pressure speed change interval are divided into a group, that is, the family of historical sensor data whose historical first pressure interval is [1MPa, 2MPa) is subdivided into 3 groups according to the historical pressure speed change.

[0072] Just as an example, combined with the following table (2), for the first historical pressure range The corresponding historical sensor data has a range of historical pressure changes of , the historical pressure speed change is divided into , 2 intervals, the historical sensing data corresponding to the 2 historical pressure change intervals are divided into 2 groups, namely the historical first pressure interval The historical sensing data of this group is subdivided into two groups according to the historical pressure change.

[0073] Among them, dividing a family of historical sensor data into multiple groups can be represented by the following table (2), where multiple historical sensor data corresponding to each historical first pressure interval are divided into a family, and multiple historical sensor data corresponding to each historical pressure change interval in each family are divided into a group.

[0074] Table (2)

[0075]

[0076] in, 、 、…、 are the minimum values ​​of the historical first pressure in different historical first pressure intervals, 、 、…、 are the maximum values ​​of the historical first pressure in different historical first pressure intervals, Indicates the The first pressure range in history, 、 、…、 They are 、 、…、 The first historical pressure corresponding to the historical moment, 、 …、 They are 、 、…、 The second historical pressure corresponding to the historical moment; 、 、…、 They are 、 、…、 Historical pressure changes in historical time periods, Represents the historical pressure speed change range corresponding to the first group of historical sensor data of the first family, Representative Clan House The historical pressure speed change range corresponding to the set of historical sensor data.

[0077] Step S13: For each set of historical sensing data, calculate the sensing statistics of the set.

[0078] In some embodiments, for each set of historical sensor data, the processor may determine multiple historical pressure speed changes, historical gas temperature speed changes, and historical device temperature speed changes corresponding to the set of historical sensor data. The calculation process for the historical gas temperature speed changes and historical device temperature speed changes can be found in the calculation process for the historical pressure speed changes in step S12 and will not be further described here.

[0079] In some embodiments, the processor can determine the median and extreme values ​​of multiple historical pressure changes, the median and extreme values ​​of multiple historical gas temperature changes, and the median and extreme values ​​of multiple historical equipment temperature changes through statistical analysis, thereby determining a set of sensor statistics for the set of historical sensor data.

[0080] In some embodiments, the processor may further determine sensing speed data based on historical sensing data; classify the sensing speed data into clusters based on historical sensing data or sensing speed data to determine one or more clusters of sensing speed data; and group the sensing speed data of each cluster into clusters to determine multiple groups of sensing statistics data. Figure 3 and its related descriptions.

[0081] Step S230: determining an adjustment instruction based on the current sensor data and the sensor statistical data.

[0082] Current sensor data refers to sensor data for the current time period. The current time period refers to a shorter period before the current moment, such as 5 minutes or 10 minutes before the current moment. Current sensor data can include sensor data from two or more time points within the current time period.

[0083] In some embodiments, the current sensor data can be monitored and acquired by the sensor device and uploaded to the gas company management platform through the gas company sensor network platform, and the processor can directly retrieve the current sensor data.

[0084] Adjustment instructions refer to instructions for adjusting relevant equipment parameters within the gas equipment object platform.

[0085] In some embodiments, the adjustment instruction may be sent by the gas company management platform to the government safety supervision management platform, and may be configured to adjust the cooling parameters of the cold energy recovery device and the pressure regulating parameters of the pressure regulating device.

[0086] In some embodiments, the adjustment instruction may include a target cooling parameter and a target voltage regulation parameter.

[0087] Cooling parameters refer to the working parameters of the cold energy recovery equipment, which may include the cooling working power and refrigerant circulation speed of the cold energy recovery equipment.

[0088] The refrigerant circulation rate refers to the speed at which the refrigerant passes through the circulation system, which can be expressed by the volume or mass of the fluid passing through the circulation system per unit time.

[0089] The target cooling parameters refer to the cooling parameters that the cold energy recovery equipment needs to execute, including the target cooling working power and the target refrigerant circulation speed.

[0090] Pressure regulating parameters refer to the working parameters of the pressure regulating equipment, which may include the pressure regulating working power and pressure valve opening of the pressure regulating equipment.

[0091] The pressure regulating working power refers to the working power of the pressure regulating equipment when regulating the gas pressure.

[0092] The pressure valve opening refers to the degree to which the pressure regulating valve in the pressure regulating device is open. The larger the pressure valve opening, the greater the pressure regulating valve is open, the larger the passage for gas to pass through the valve, and the greater the degree of gas pressure reduction.

[0093] The target pressure regulation parameters refer to the pressure regulation parameters that the pressure regulating equipment needs to execute, including the target pressure regulation working power and target pressure valve opening, etc.

[0094] In some embodiments, the processor may determine an adjustment instruction based on the current sensor data and the sensor statistics data, which may specifically include the following steps S21-S25:

[0095] Step S21, based on the current sensor data, determine the current pressure speed change and the current temperature speed change.

[0096] In some embodiments, the processor may calculate the current pressure speed change and the current temperature speed change for the current time period based on the current sensor data at two time points within the current time period. The calculation method for the current pressure speed change and the current temperature speed change is similar to the calculation method for the historical pressure speed change and the historical temperature speed change. For details, please refer to the relevant description of steps S12 and S13 above and will not be repeated here.

[0097] Step S22: determining current statistical data based on the current first pressure, the current pressure change and the historical sensor data.

[0098] Current statistical data refers to statistical data of current sensor data. In some embodiments, the processor may determine the historical first pressure interval to which the current first pressure belongs, and determine a corresponding set of historical sensor data; within this set of historical sensor data, determine the historical pressure change interval to which the current pressure change belongs, and determine a corresponding set of historical sensor data; and determine the sensor statistical data corresponding to this set of historical sensor data as the current statistical data.

[0099] Step S23, compare the current temperature change rate with the current statistical data, and in response to meeting the preset conditions, determine the target cooling parameter based on the current temperature change rate, the preset conditions being that the current gas temperature change rate is greater than the median value of the gas temperature change rate in the current statistical data and / or the current device temperature change rate is greater than the median value of the device temperature change rate in the current statistical data.

[0100] It can be understood that the current temperature change rate exceeds the median value of the historical temperature change rate, indicating that the current temperature rises too quickly and the pressure regulating device needs to be cooled by adjusting the cooling parameters of the cold energy recovery device.

[0101] In some embodiments, in response to the current temperature change satisfying a preset condition, the processor may determine a target cooling parameter based on the current temperature change by querying a first preset comparison table. The first preset comparison table may include a correspondence between the temperature change and the cooling parameter. The first preset comparison table may be constructed by a technician based on historical data or experience.

[0102] In some embodiments, the processor may determine the temperature change rate in a first preset comparison table that is most similar to the current temperature change rate, and determine the corresponding cooling parameter as the target cooling parameter. The similarity may be represented by the absolute value of the difference between the current temperature change rate and the temperature change rate in the table, with the smaller the absolute value of the difference, the higher the similarity. In some embodiments, the processor may prioritize adjusting the cooling parameters of the cold energy recovery device based on the difference between the current cooling parameter and the target cooling parameter.

[0103] Step S24 , in response to the temperature change satisfying a preset condition after cooling for a preset time period based on the target cooling parameter, a target voltage regulation parameter is determined based on the device temperature after the preset time period.

[0104] The preset time period refers to a period of time after the current moment during which the cold energy recovery equipment operates based on the target cooling parameters. The preset time period can be set by a technician based on historical experience. In some embodiments, the processor can determine the pressure regulation efficiency by querying the second preset comparison table based on the equipment temperature after the preset time period; and determine the target pressure regulation parameters based on the current pressure regulation parameters and the pressure regulation efficiency. The second preset comparison table may include a correspondence between the equipment temperature and the pressure regulation efficiency. The pressure regulation efficiency refers to the efficiency of the pressure regulating equipment in regulating the pressure of the gas, which can be represented by the ratio of the actual working capacity of the pressure regulating equipment to the theoretical working capacity. For example, a pressure regulation efficiency of 80% means that the ratio of the output power to the input power of the pressure regulating equipment is 80%. In some embodiments, the pressure regulation efficiency is negatively correlated with the equipment temperature.

[0105] For example, the processor may search the second preset comparison table for the voltage regulation efficiency corresponding to the device temperature after a preset time period, and determine the ratio of the current voltage regulation working power to the voltage regulation working efficiency as the target voltage regulation working power.

[0106] Step S25: Determine a pressure regulation instruction based on the target cooling parameter and the target pressure regulation parameter.

[0107] In some embodiments, the processor may input the target cooling parameter and the target voltage regulation parameter into a preset template of the voltage regulation instruction to generate the voltage regulation instruction.

[0108] In some embodiments of the present specification, by performing statistical analysis on historical sensor data, sensor statistical data can be determined, and then the changing patterns of gas pressure, equipment temperature and gas temperature can be evaluated; by grouping the sensor statistical data, the gas can be divided into different intervals for reference, which facilitates different degrees of pressure regulation and temperature reduction operations on gases with different pressures, so that the pressure regulating equipment and cold energy recovery equipment can maintain good working efficiency.

[0109] Figure 3 FIG. 1 is an exemplary flow chart of determining multiple sets of sensory statistics according to some embodiments of this specification. Figure 3 As shown, the process 300 may include the following steps. In some embodiments, the process 300 may be executed by the gas company management platform 131. For example, it may be executed by a processor.

[0110] Step S310: Determine sensing speed data based on historical sensing data.

[0111] Sensor velocity data refers to data related to the rate of change of historical sensor data. Sensor velocity data can include pressure and temperature changes over multiple historical time periods.

[0112] A historical time period is defined as the period between two adjacent historical moments. For more information about historical sensor data, historical moments, pressure change, and temperature change, please refer to the above descriptions.

[0113] In some embodiments, the processor may determine the pressure and temperature speeds for multiple historical time periods based on the historical sensor data, i.e., determine sensor speed data. For more information on determining the pressure and temperature speeds for multiple historical time periods based on the historical sensor data, see the description of steps S12 and S13.

[0114] Step S320 : Based on the historical sensing data or the sensing speed data, the sensing speed data is classified into clusters to determine one or more clusters of sensing speed data.

[0115] For example, following Table (2), one or more groups of sensor speed data can be represented by the following Table (3), where the multiple sensor speed data corresponding to each historical first pressure interval are divided into one group.

[0116] Table (3)

[0117]

[0118] in, 、 、…、 They are 、 、…、 Historical gas temperature changes during historical time periods, 、 、…、 They are 、 、…、 The historical gas temperature corresponding to the historical moment; 、 、…、 They are 、 、…、 Historical device temperature changes over a historical time period, 、 、…、 They are 、 、…、 The historical device temperature corresponding to the historical moment. For explanations of other parameters, please refer to Table (1) and Table (2).

[0119] A group or groups of sensor speed data refers to the sensor speed data corresponding to each group of historical sensor data after grouping. A group of historical sensor data corresponds to a group of sensor speed data.

[0120] In some embodiments, the processor may classify the sensed speed data into clusters based on the historical first pressure in the historical sensed data to determine one or more clusters of sensed speed data. For more information on this, please refer to steps S11-S13 and their related descriptions.

[0121] In some embodiments, the processor may determine cluster features based on the sensory speed data, cluster the cluster features, and determine one or more clusters of sensory speed data.

[0122] Among them, clustering features refer to the features based on which sensor speed data is clustered. One sensor speed data in a historical time period corresponds to one clustering feature. Taking the sensor speed data of the historical time period as an example, the processor can determine its corresponding clustering features through the following formula (1):

[0123] (1)

[0124] in, 、 、 Respectively Historical pressure changes, historical gas temperature changes, and historical equipment temperature changes over a historical period of time, express clustering features.

[0125] In some embodiments, the processor may cluster multiple clustering features, and determine the sensor speed data corresponding to the clustering features of the same category in the clustering results as a family of sensor speed data, and the sensor speed data corresponding to multiple categories of clustering features as multiple families of sensor speed data. Clustering methods include, but are not limited to, mean shift clustering.

[0126] Step S330 , grouping the sensing speed data of each group within the group to determine multiple groups of sensing statistical data.

[0127] In some embodiments, the processor may divide the multiple historical pressure changes within each family of sensor speed data into intervals, divide the family of sensor speed data into multiple groups, and then calculate the sensor statistics data for each group. For the specific process of this part, please refer to the relevant content of steps S12 and S13.

[0128] For example, following Table (2) and Table (3), after one or more families of sensor speed data are grouped within the family, the corresponding multiple groups of sensor statistics can be represented by the following Table (4):

[0129] Table (4)

[0130]

[0131] in, Representative Clan House The pressure difference statistics corresponding to the group sensor speed data, Represent the maximum, minimum and median values ​​of historical pressure change, Representing the Clan House Gas temperature statistics and equipment temperature statistics corresponding to group sensor speed data, Represent the maximum, minimum and median values ​​of the historical gas temperature variation, They represent the maximum, minimum and median values ​​of the historical equipment temperature change respectively. For explanations of other parameters, please refer to Table (1), Table (2) and Table (3).

[0132] In some embodiments, for each group of sensor speed data, the processor can group the sensor speed data within the group based on the historical adjustment points of the voltage regulation parameters and cooling parameters to determine multiple groups of sensor speed data; and determine multiple groups of sensor statistical data based on the multiple groups of sensor speed data.

[0133] For more information about voltage regulation parameters and cooling parameters, please refer to the relevant description above.

[0134] The historical adjustment time point refers to a historical time point when the voltage regulation parameter and / or cooling parameter changes. In some embodiments, the historical adjustment time point is a historical time point within the collection period corresponding to the historical sensor data.

[0135] In some embodiments, the historical adjustment time point can be obtained by the processor or technician based on historical data. For example, the historical sensor data includes the historical time point. To the historical moment The sensor data of the historical sensor data is collected during the period of ,exist During the collection period and At this historical moment, the voltage regulation parameters changed. During the collection period and At historical moments, if the cooling parameters change, the historical adjustment points include 、 、 and .

[0136] In some embodiments, for a group of sensor speed data, the processor may group the group of sensor speed data based on historical adjustment time points within a collection period corresponding to the group of sensor speed data to determine multiple groups of sensor speed data.

[0137] For example, combined with Table (3), the acquisition period corresponding to the first family of sensor speed data is ,exist During the period, the voltage regulation parameters are The cooling parameters change at all times. If the time changes, adjust the time point according to history 、 , the first family of sensor speed data is divided into 、 、 These three time periods correspond to three sets of sensor speed data.

[0138] In some embodiments, the processor determines multiple sets of sensing statistical data through statistical analysis and calculation based on the multiple sets of sensing speed data. For the specific calculation process, please refer to the relevant content of step S13.

[0139] In some embodiments of the present specification, based on the historical adjustment time points of the voltage regulation parameters and the cooling parameters, the sensor speed data is further reasonably divided into intra-family groups, so that the statistical results of the sensor speed data are more accurate.

[0140] In some embodiments, the processor may determine multiple fluctuation results corresponding to multiple sets of sensor speed data; determine multiple confidence levels for the multiple sets of sensor speed data based on the multiple fluctuation results; and determine multiple sets of filtered sensor statistical data based on the multiple confidence levels.

[0141] The fluctuation result refers to data that can characterize the fluctuation of a set of sensor speed data. In some embodiments, a set of sensor speed data corresponds to a fluctuation result, and the fluctuation result of a set of sensor speed data can be represented by the variance or standard deviation of the set of sensor speed data.

[0142] For example, in combination with Table (4), if the first family of sensor speed data is determined after grouping within the family, The sensor speed data of the time period is divided into the first group of the first group, and the sensor speed data of this group includes 、 、 、 、 、 , calculate the historical pressure change 、 Standard deviation of historical gas temperature variation 、 Standard deviation, historical equipment temperature change 、 The standard deviation of the three standard deviations is weighted and summed, and the result of the weighted sum is used as the fluctuation result of the set of sensor speed data.

[0143] Confidence refers to a measure that can characterize the reliability of data. In some embodiments, confidence can be represented by a numerical value.

[0144] In some embodiments, the confidence level may be negatively correlated with the fluctuation result, where the greater the fluctuation result, the lower the confidence level. For example, the processor may determine the inverse of the fluctuation result of a set of sensory speed data as the confidence level of the set of sensory speed data.

[0145] In some embodiments, for each group of sensor speed data, the processor may filter out one or more groups of sensor speed data whose group-built-in reliability is less than a preset reliability threshold, and determine multiple filtered groups of sensor statistics based on one or more groups of sensor speed data whose group-built-in reliability is not less than the preset reliability threshold. The process of determining sensor statistics based on the sensor speed data can be found in the relevant content of step S13.

[0146] In some embodiments, the preset confidence threshold may be a fixed value set by the processor by default or by a technician based on experience, or may be determined by the processor based on confidence. For example, for a family of sensory velocity data, the processor may determine the average confidence level of all groups of sensory velocity data in the family as the preset confidence threshold for the family.

[0147] In some embodiments of the present specification, the fluctuation of the sensor speed data is reflected by determining the fluctuation result corresponding to the sensor speed data, determining the confidence level based on the fluctuation result, and further filtering the sensor speed data based on the confidence level. This helps to eliminate abnormal data or unreliable data caused by accidental factors such as equipment failure and environmental interference, retain sensor statistical data with higher reliability, and thus improve the accuracy of subsequent analysis and decision-making.

[0148] In some embodiments of the present specification, sensing speed data is determined based on historical sensing data and then based on the historical sensing data or the sensing speed data, the sensing speed data is divided into families and grouped within families to accurately determine multiple groups of sensing statistical data of multiple groups of sensing speed data, which can reflect the statistical conditions of each group of historical sensing data and is conducive to the accurate determination of adjustment instructions.

[0149] Figure 4 FIG. 1 is an exemplary flow chart for determining the voltage regulation parameters at the first moment according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by the gas company management platform 131. For example, it may be executed by a processor.

[0150] In some embodiments, the gas equipment object platform also includes multiple input pipelines of the gas pressure regulating station, the pressure regulating equipment includes a distribution equipment, a boosting equipment and / or a pressure reducing equipment, and the pressure regulating parameters include the distribution parameters of the distribution equipment, the boosting parameters of the boosting equipment and / or the pressure reducing parameters of the pressure reducing equipment; the processor can determine the initial pressure regulating parameters based on the multiple first pressures and target output pressures corresponding to the multiple input pipelines, and control the pressure regulating equipment to operate based on the initial pressure regulating parameters; obtain the sensor data after the first time interval; and determine the pressure regulating parameters at the first moment based on the sensor data.

[0151] For more information about the above-mentioned equipment and pressure regulation parameters, first pressure, and sensor data, please refer to the relevant description above.

[0152] Distribution parameters refer to the operating parameters of the distribution device that control the flow of gas. For example, the gases from input pipelines L1 and L2 are mixed and then distributed to the booster. Another example is to allocate input pipeline L1 to the depressurization device and input pipeline L2 to the booster. For another example, if the gas pressures of multiple input pipelines differ significantly, making direct gas mixing impossible, input pipeline L1 can be allocated to the depressurization device and input pipeline L2 to the booster. After reducing and boosting the pressures separately, the gases are then mixed. The mixed gas then flows through a multi-stage circulation boosting pipeline and re-enters the booster.

[0153] Boost parameters refer to the operating parameters of the boost device. For example, they include compressor power, motor speed, boost valve opening, and boost inlet flow rate. The boost device may include multiple compressors, with different compressors used to meet different boost requirements. Compressor power refers to the operating power of the compressor. The greater the compressor power, the faster the boost speed of the boost device. The faster the motor speed, the faster the boost speed of the boost device. The boost valve opening refers to the degree of opening of the pressure regulating valve within the boost device. The greater the boost valve opening, the greater the degree of opening of the pressure regulating valve, and the faster the boost speed. The boost inlet flow rate refers to the flow rate of gas entering the boost device. The faster the boost inlet flow rate, the slower the boost speed of the boost device.

[0154] Pressure reduction parameters refer to the operating parameters of the pressure-reducing device. Examples include pressure reducing valve opening and pressure-reducing inlet flow rate. The pressure-reducing inlet flow rate refers to the flow rate of gas entering the pressure-reducing device. The faster the pressure-reducing inlet flow rate, the slower the pressure reduction rate of the pressure-reducing device. The pressure-reducing valve opening refers to the degree of opening of the pressure regulating valve within the pressure-reducing device. A larger pressure-reducing valve opening, the greater the pressure regulating valve opening, and the faster the pressure reduction rate.

[0155] Step S410: determining initial pressure regulation parameters based on a plurality of first pressures corresponding to a plurality of input pipelines and a target output pressure, and controlling the pressure regulation device to operate based on the initial pressure regulation parameters.

[0156] The target output pressure refers to the expected value of the gas pressure after the gas passes through the pressure regulating equipment.

[0157] In some embodiments, the target output pressure may be pre-set by a technician based on experience.

[0158] In some embodiments, the target output pressure may also be determined by the processor based on the gas demand of the output pipeline.

[0159] In some embodiments, an input pipeline corresponds to a first pressure, and the first pressure corresponding to each input pipeline may refer to the gas pressure of each input pipeline before the gas is regulated by the pressure regulating device. For instructions on obtaining the first pressure, please refer to the relevant description of step S210.

[0160] Initial pressure regulation parameters refer to the initial values ​​of the pressure regulation parameters during system startup or adjustment. In some embodiments, the initial pressure regulation parameters may include initial allocation parameters, initial boost parameters, and initial depressurization parameters. For example, the initial pressure regulation parameters may be expressed as: [(input pipeline L1 allocated to the depressurization device, input pipeline L2 allocated to the boost device), (compressor power P1 of compressor 1, motor speed R, boost valve opening K1, boost inlet flow rate E1), (reducing valve opening K2, depressurization inlet flow rate E2)].

[0161] In some embodiments, the processor may determine initial distribution parameters through a first preset rule based on multiple first pressures and target gas pressures; and determine initial pressure increase parameters and initial pressure reduction parameters through a first preset algorithm based on the initial distribution parameters.

[0162] The first preset rule and the first preset algorithm can be pre-set by a technician. For example, the first preset rule may be that when the first pressure of the input pipeline is greater than the target gas pressure, the gas in the input pipeline is allocated to the pressure-reducing device; and when the first pressure of the input pipeline is less than the target gas pressure, the gas in the input pipeline is allocated to the pressure-boosting device. The first preset algorithm may be to determine the ratio of the first pressure of the input pipeline allocated to the pressure-reducing device to the target gas pressure as the compression ratio and calculate the compressor power based on the compression ratio, or to determine the pressure-reducing valve opening based on the first pressure of the input pipeline allocated to the pressure-reducing device and the target gas pressure. The pressure-reducing valve opening is positively correlated with the difference between the first pressure and the target gas pressure; the greater the difference between the first pressure and the target gas pressure, the greater the pressure-reducing valve opening.

[0163] In some embodiments, the processor may control the operation of the voltage regulating device according to the initial voltage regulating parameters.

[0164] Step S420: Acquire sensing data after a first time interval.

[0165] The first time interval refers to a time interval used to determine whether a parameter needs to be significantly adjusted.

[0166] In some embodiments, the first time interval may be related to a difference between the first pressure and the target output pressure. The greater the difference between the first pressure and the target output pressure, the longer the first time interval.

[0167] In some embodiments, the first time intervals corresponding to different gas pressure regulating stations are the same or different, and the first time interval is related to the number of input pipelines of the gas pressure regulating station.

[0168] In some embodiments, the processor can determine the first time interval by querying a first preset table. The first preset table includes a correspondence between the number of input pipelines of the gas pressure regulating station and the first time interval. For example, the greater the number of input pipelines of the gas pressure regulating station, the longer the first time interval. The first preset table can be constructed by a technician based on historical data and prior experience.

[0169] In some embodiments of this specification, adjusting the first time interval according to the number of input pipelines can flexibly adapt to gas pressure regulating stations of different sizes and complexities, significantly improving the overall efficiency and stability of the gas pressure regulating system.

[0170] In some embodiments, the first time interval may also be related to the second time interval, the first time interval being longer than the second time interval.

[0171] The second time interval refers to a time interval used to determine whether a parameter requires a minor adjustment.

[0172] In some embodiments, whether the pressure regulation parameters and cooling parameters need to be adjusted can be determined based on the sensor data after a first time interval, and whether the refrigerant circulation speed in the cooling parameters needs to be adjusted can be determined based on the sensor data after a second time interval. The first time interval is longer than the second time interval.

[0173] There is no causal relationship between the first time interval and the second time interval. For example, the first time interval may be before the second time interval. For another example, the second time interval may be included in the first time interval.

[0174] In some embodiments, the second time interval may be determined based on querying a second preset table. For details about this part, please refer to the relevant description below.

[0175] In some embodiments of the present specification, only the refrigerant circulation speed is adjusted based on the second time interval, and the pressure regulation parameters and cooling parameters are adjusted as a whole based on the first time interval. Therefore, the second time interval is considered when determining the first time interval, and the first time interval is set longer than the second time interval, so that the first time interval is more reasonable and reliable.

[0176] In some embodiments, after a first time interval passes at the current moment, the sensor device monitors and obtains sensor data, and uploads it to the gas company management platform through the gas company sensor network platform. The processor can directly retrieve the sensor data after the first time interval.

[0177] Step S430: Determine the voltage regulation parameters at the first moment based on the sensing data.

[0178] The first moment refers to a time point after the first time interval of the current moment.

[0179] In some embodiments, the processor may determine the voltage regulation parameter at the first moment based on the sensor data after the first time interval. The process of determining the voltage regulation parameter based on the sensor data can be referred to the description of step S230. The process is similar and will not be repeated here.

[0180] In some embodiments of the present specification, the gas pressure regulating station incorporates multiple devices into the Internet of Things, so that the pressure regulating equipment and the cold energy recovery equipment can be precisely controlled. The gas company management platform determines the initial pressure regulating parameters based on the first pressures and target output pressures of multiple input pipelines, and by acquiring and analyzing the sensor data after the first time interval, it can continuously judge system changes and thus determine the pressure regulating parameters again to ensure that the pressure regulating parameters meet real-time needs, thereby improving the stability and efficiency of system operation.

[0181] In some embodiments, the cold energy recovery device may further include an expansion refrigeration device connected to the pressure-reducing device; the cooling parameter may also include a refrigerant valve opening of the expansion refrigeration device; and the gas company management platform may be further configured to determine the cooling parameter at the first moment based on the sensor data after the first time interval and the initial pressure regulation parameter. For more information about the initial pressure regulation parameter, see the description of step S410.

[0182] For more information about cooling parameters, please refer to the relevant description above.

[0183] Refrigerant valve opening refers to the degree of opening of the valve that controls refrigerant flow. Refrigerant valve opening can be expressed as a percentage. For example, a refrigerant valve opening of 100% or 50% indicates the valve is fully open and halfway open, respectively. The greater the refrigerant valve opening, the faster the expansion refrigeration system cools.

[0184] In some embodiments, the processor can determine the cooling parameters at the first moment through a second preset algorithm based on the sensor data and the initial pressure regulation parameters after the first time interval. The second preset algorithm can be preset by a technician. For example, the second preset algorithm is based on the pressure reduction inlet flow rate of the pressure reduction device, the first pressure of the input pipeline assigned to the pressure reduction device, and the gas temperature, and calculates the temperature reduction value through mathematical models such as the ideal gas equation. The temperature reduction value is positively correlated with the refrigerant valve opening. The temperature reduction value refers to the temperature drop of the gas after passing through the gas pressure regulating station. The larger the temperature reduction value, the more cold energy is released by the gas pressure reduction, and a larger refrigerant flow rate is required to absorb the cold energy, so the refrigerant valve opening should also be larger.

[0185] In some embodiments of the present specification, the expansion refrigeration equipment can utilize the cold energy released by the pressure reduction for refrigeration, and energy saving can be achieved by directly connecting the expansion refrigeration equipment to the pressure reduction equipment; the cooling parameters include the opening of the refrigerant valve, which can accurately control the refrigerant flow to control the progress of the cooling work; by analyzing the sensor data and the initial pressure regulation parameters after the first time interval, the cooling parameters at the first moment can be accurately determined, which is conducive to timely and accurate parameter adjustment of the cold energy recovery equipment.

[0186] In some embodiments, the processor can determine the target output pressure based on the gas demand of the output pipeline; based on multiple first pressures corresponding to multiple input pipelines, the target output pressure, and the gas temperature, the pressure regulation parameters and cooling parameters for the future time period are determined through a pressure difference model, and the pressure difference model is a machine learning model.

[0187] Gas demand refers to data related to the gas demand of downstream users connected to the output pipeline, for example, the gas pressure of downstream users at different time periods.

[0188] Gas transmission pressure refers to the gas input pressure when used by downstream users. Downstream users are the users to whom the gas from the output pipeline flows, such as residences and office buildings. Time periods can be divided into various categories, such as every 24 hours of every day in every season.

[0189] In some embodiments, the processor may directly read the gas demand uploaded by the gas equipment object platform.

[0190] In some embodiments, the target output pressure may include target output pressures at multiple future times. For example, if the current time is 12:00 on September 20, 2024, the target output pressure may include target output pressures at multiple future times such as 13:00, 14:00, and 15:00 on September 20, 2024. Correspondingly, the target output pressure may be expressed in a sequence form. For example, the target output pressure may be expressed as … ],in, 、 、…、 Indicates the 1st, 2nd, ..., A future moment, 、 、…、 Indicates the 1st, 2nd, ..., The target output pressure corresponding to a future moment.

[0191] In some embodiments, the processor may determine the target output pressure at a future time through statistical analysis based on the gas demand of the output pipeline. For example, the processor may calculate the gas pressure between 2:00 PM and 3:00 PM each day in the autumn of 2023 (which meteorologically refers to August 23rd to November 20th) based on the gas demand of the output pipeline, and determine the average of these multiple gas pressures as the target output pressure at 3:00 PM on September 20th, 2024.

[0192] The pressure differential model is a model used to determine pressure regulation parameters and cooling parameters for future time periods. In some embodiments, the pressure differential model can be a machine learning model, such as a recurrent neural network (RNN).

[0193] Figure 5 is an exemplary schematic diagram of a pressure difference model according to some embodiments of this specification.

[0194] In some embodiments, as Figure 5 As shown, the input of the pressure difference model 520 includes one or more first pressures 511 corresponding to one or more input pipelines at the current moment, the target output pressure 512, the gas temperature 513, and the equipment temperature 514, and the output is the pressure regulation parameter 531 and the cooling parameter 532 of the future time period.

[0195] The pressure regulation parameters 531 include the pressure regulation working power 531 - 1 and the pressure valve opening 531 - 2 , and the cooling parameters 532 include the cooling working power 532 - 1 and the refrigerant circulation speed 532 - 2 .

[0196] For example, the current time is , the target output pressure is … ], the pressure regulation parameters and cooling parameters of the future period output by the pressure difference model can be 、 .in, Representative A future moment, Representative A future period, ( , ) represents the Voltage regulation parameters for future periods, and Representing the The pressure regulating working power and pressure valve opening of the future period; ( , ) represents the Cooling parameters for future periods, and Representing the The cooling power and refrigerant circulation speed for a future period.

[0197] For more information about the above parameters (such as the first pressure, the target output pressure, the refrigerant circulation speed, etc.), please refer to the relevant description above.

[0198] In some embodiments, the pressure differential model can be trained using multiple training samples. A training sample includes multiple sample first pressures, sample gas temperatures, sample equipment temperatures, and sample target output pressures corresponding to multiple input pipelines at a sample time. The training labels corresponding to the training sample are the actual pressure regulation parameters and actual cooling parameters during the sample period. The sample period consists of multiple future moments after the sample time.

[0199] In some embodiments, the training samples and training labels can be obtained by the processor from the historical data according to the second preset rule. The second preset rule can be pre-set by a technician. For example, the second preset rule can include the following steps S31-S32:

[0200] Step S31: Acquire multiple candidate training samples and corresponding multiple candidate training labels based on historical data.

[0201] In some embodiments, each candidate training sample includes a first pressure, a second pressure, a gas temperature, and an equipment temperature at two candidate moments, and the corresponding candidate training labels are a pressure regulation parameter and a cooling parameter of the candidate time period.

[0202] The candidate moment can be a historical moment, and the candidate period is the period beginning and ending with the two candidate moments. During the candidate period formed by the two candidate moments, the voltage regulation parameters and cooling parameters remain unchanged. That is, the candidate period does not include historical adjustment points. For more information about historical adjustment points, please refer to the relevant description above.

[0203] Step S32: determining the efficiency scores of the candidate training samples, and selecting the candidate training samples whose efficiency scores are higher than a preset score threshold as training samples.

[0204] The efficiency score refers to the ratio of energy consumption to pressure change. Energy consumption refers to the total energy consumption of the pressure regulating equipment and the cold energy recovery equipment. Energy consumption can include equipment power consumption and refrigerant replenishment. In some embodiments, energy consumption can be obtained based on monitoring data from relevant monitoring equipment. Relevant monitoring equipment may include electricity meters, refrigerant replenishment monitoring devices, etc.

[0205] The pressure change refers to the difference between the second pressure at the end candidate time and the first pressure at the start candidate time in the candidate time period corresponding to the candidate training sample.

[0206] The preset score threshold may be set by the processor or by a technician based on experience.

[0207] In some embodiments, the processor may train the pressure difference model based on a plurality of training samples with training labels.

[0208] In some embodiments, the processor may input training samples into the initial pressure difference model, construct a loss function based on the output of the initial pressure difference model and the training labels, and iteratively update the initial pressure difference model based on the loss function. When preset training conditions are met, the initial pressure difference model training is completed, resulting in a trained pressure difference model. The preset training conditions may include convergence of the loss function, a number of iterations reaching a threshold, etc.

[0209] In some embodiments, different training sample sets of the pressure difference model have different learning rates, and the learning rates are determined based on the confidence of the training sample sets.

[0210] In some embodiments, the processor may divide the plurality of training samples into a plurality of groups based on the adjustment time point. For example, the plurality of training samples may be based on the following: , , ...., ) is obtained by historical sensor data at multiple historical moments, among which, The historical adjustment time point of the voltage regulation parameters, is the historical adjustment time point of the cooling parameter, then the training samples can be divided into 3 groups according to the historical moments, namely, the sample moment belongs to ( , , ...., )、( , , ...., )、( , , ...., ) of 3 groups of training samples.

[0211] In some embodiments, the processor may determine multiple fluctuation results corresponding to multiple sets of training samples; and based on the multiple fluctuation results, determine multiple confidence levels for the multiple sets of training samples. For details on this, refer to the determination of the confidence level of the sensed velocity data in step S330. The process is similar and will not be further described here.

[0212] In some embodiments, the processor and / or technicians may divide the confidence intervals of the multiple sets of training samples into intervals, for example, into confidence intervals of (0, 10%), (10%, 20%), and so on.

[0213] In some embodiments, the processor may divide multiple groups of training samples whose confidence levels are in the same confidence interval into a training sample set, and multiple confidence intervals correspond to multiple training sample sets.

[0214] In some embodiments, the learning rates of different training sample sets of the pressure difference model are different. One training sample set may correspond to one learning rate, and the learning rate of a training sample set may be determined based on the confidence level of the training sample set. For example, the learning rate of a training sample set may be positively correlated to the mean confidence level of all groups of training samples in the training sample set. Exemplarily, the learning rate may be calculated based on the following formula (2):

[0215] (2)

[0216] in, is the learning rate, is a positive correlation coefficient, is the mean of the confidence levels.

[0217] In some embodiments of this specification, different training sample sets are selected for training the pressure difference model, and the learning rates of different training sets are different. The learning rate is one of the key factors affecting the training speed and stability of the model. The learning rate is adjusted according to the confidence level so that the model can learn quickly in the training sample sets with high confidence, thereby improving the reliability of the model.

[0218] In some embodiments, as Figure 5 As shown, the cooling parameter 532 for the future period output by the pressure difference model 520 also includes a refrigerant valve opening 532 - 3 of the expansion refrigeration device.

[0219] For more information about the refrigerant valve opening, please refer to the relevant description above.

[0220] In some embodiments, the training labels corresponding to the training samples of the pressure differential model may include the actual pressure regulation parameters, actual cooling parameters, and actual refrigerant valve opening during the sample period. The instructions for obtaining the training labels can be found in the relevant description above. The process is similar and is not repeated here.

[0221] In some embodiments of this specification, the refrigerant valve opening directly controls the refrigerant flow and pressure within the refrigeration system, thereby affecting cooling efficiency and energy consumption. By accurately calculating and outputting the optimal refrigerant valve opening using a pressure differential model, the refrigeration system can operate efficiently under different conditions, meeting cooling requirements while reducing energy consumption.

[0222] In some embodiments, as Figure 5As shown, the input of the pressure difference model 520 may further include the target gas temperature 515 , and the cooling parameter for the future period output by the pressure difference model may further include the refrigerant circulation speed 532 - 2 corresponding to the target gas temperature 515 .

[0223] The target gas temperature is the desired gas temperature after the gas passes through the pressure regulating device. For more information about the refrigerant circulation speed, see the relevant description above.

[0224] In some embodiments, the training samples for the pressure differential model also include sample target gas temperatures, and the training labels also include the actual refrigerant circulation speed at the sample target gas temperatures corresponding to the training samples. In some embodiments, the processor can train the pressure differential model based on multiple training samples including sample target gas temperatures. The training process is described above and is not further described here.

[0225] In some embodiments of the present specification, the target gas temperature is used as an input of the pressure difference model, so that the corresponding refrigerant circulation speed can be accurately determined through the pressure difference model, avoiding problems such as increased system pressure loss due to excessively high circulation speed and poor cooling effect due to excessively low circulation speed, thereby optimizing the system's cooling performance as a whole and enhancing the system's stability.

[0226] In some embodiments of this specification, by comprehensively considering multiple data (such as the first pressure of multiple input pipelines, gas temperature, etc.), a machine learning model is used to accurately predict the pressure regulation parameters and cooling parameters of multiple future time periods at one time, so as to prepare for parameter adjustment in advance, which is conducive to reducing energy waste and improving gas transportation efficiency and economic benefits.

[0227] In some embodiments, the cooling parameters may also include a refrigerant circulation speed, and the processor may obtain sensor data at a second time interval; in response to the sensor data satisfying an adjustment condition, the refrigerant circulation speed at the second moment is adjusted, and the adjustment condition is at least one of the equipment temperature continues to rise, the fluctuation value of the second pressure exceeds a preset fluctuation threshold, and the gas temperature is higher than a preset gas temperature.

[0228] In some embodiments, the second time interval is different for different gas pressure regulating stations. In some embodiments, the second time interval may be related to the first pressure, target output pressure, and target gas temperature corresponding to the gas pressure regulating station.

[0229] For more information about the above-mentioned multiple data and parameters (such as sensor data, first pressure, second pressure, refrigerant circulation speed, second time interval, target output pressure, etc.), please refer to the relevant description above.

[0230] In some embodiments, different gas pressure regulating stations have different target output pressures. For example, a gas pressure regulating station located at the beginning of the pressure regulation process may have a relatively higher target output pressure, while a gas pressure regulating station located at the end of the pressure regulation process may have a relatively lower target output pressure.

[0231] In some embodiments, different target output pressures at gas pressure regulating stations correspond to different second time intervals. The processor can determine the second time interval by querying a second preset table based on the current first pressure, target output pressure, and target gas temperature at the gas pressure regulating station. The second preset table may include multiple first pressures, target output pressures, and target gas temperatures for multiple gas pressure regulating stations, along with corresponding second time intervals. The smaller the difference between the first pressure and the target output pressure, and the higher the target gas temperature, the longer the second time interval. In some embodiments, the second preset table can be constructed by a technician based on historical data or experience.

[0232] In some embodiments of this specification, a suitable second time interval can be determined based on the first pressure, target output pressure and target gas temperature of the current gas pressure regulating station, so as to adjust the operating parameters of the equipment in a timely manner at an appropriate time to reduce the risk of failure of the gas pipeline network.

[0233] In some embodiments, the sensor data of the second time interval is monitored and acquired by the sensor device and uploaded to the gas company management platform through the gas company sensor network platform. The processor can directly retrieve the sensor data of the second time interval.

[0234] The adjustment condition refers to a condition requiring adjustment of the refrigerant circulation speed. In some embodiments, the adjustment condition may include at least one of a continuous increase in device temperature, a fluctuation value of the second pressure exceeding a preset fluctuation threshold, and a gas temperature exceeding a preset gas temperature.

[0235] In some embodiments, the second time interval may include multiple time periods, each time period corresponding to a device temperature change. A continuous rise in device temperature may mean that, within the second time interval, the proportion of time periods in which the device temperature change exceeds an average device temperature change is greater than a proportional threshold. The average device temperature change refers to the average value of the device temperature change across all time periods within the second time interval. The proportional threshold may be set by default by the processor or by a technician based on historical experience. For example, 80%.

[0236] In some embodiments, the second time interval may include multiple time points, each time point corresponding to a second pressure. The fluctuation value of the second pressure may be represented by a standard deviation or variance of multiple second pressures at multiple time points within the second time interval.

[0237] In some embodiments, the processor may determine a preset fluctuation threshold based on historical second pressures in the historical sensor data. For example, the processor may select a historical time period of the same duration as the second time interval, determine historical sensor data for that historical time period, calculate the standard deviation of multiple historical second pressures in the historical sensor data, and determine the standard deviation as the preset fluctuation threshold. If the fluctuation value of the second pressure exceeds the preset fluctuation threshold, it indicates that the second pressure fluctuates significantly, requiring timely adjustment of the refrigerant circulation speed.

[0238] The preset gas temperature is the gas temperature required for the output pipeline to deliver gas to downstream users. A gas temperature higher than the preset gas temperature indicates that the gas temperature passing through the gas pressure regulating station is too high, requiring timely adjustment of the refrigerant circulation rate.

[0239] For more information about equipment temperature variation, second pressure, gas temperature, output pipeline and downstream users, please refer to the relevant description above.

[0240] In some embodiments, when one or more of the following occurs: the device temperature continues to rise, the fluctuation value of the second pressure exceeds a preset fluctuation threshold, or the gas temperature is higher than a preset gas temperature, the processor can determine that the sensor data of the second time interval meets the adjustment conditions.

[0241] The second moment refers to a time point after the second time interval of the current moment.

[0242] In some embodiments, in response to the sensing data at the second time interval satisfying the adjustment condition, the processor may determine the refrigerant circulation speed at the second moment by searching the vector database.

[0243] The vector database includes multiple reference vectors and corresponding vector labels. The reference vectors consist of reference sensor data, reference voltage regulation parameters, and reference cooling power for a reference time period. The vector label is the reference refrigerant circulation speed corresponding to the reference vector. For more information about cooling power, see the description above.

[0244] In some embodiments, the vector database can be constructed based on historical data. For example, the processor can calculate the effect scores of historical sensor data from multiple historical time periods, select historical sensor data with an effect score greater than a score threshold as reference sensor data, and determine the corresponding historical voltage regulation parameters and historical cooling operating power as reference voltage regulation parameters and reference cooling operating power, thereby determining a reference vector. The vector label corresponding to the reference vector is the historical refrigerant circulation speed. The historical time period is the same length as the second time interval.

[0245] The effect score refers to the score of the operating effect of the gas pressure regulating station. In some embodiments, the processor can determine the effect score based on the equipment temperature rise score, the fluctuation score, the gas temperature score, and the pressure regulation score.

[0246] The device temperature rise score refers to the score of the device temperature variation over a historical period. In some embodiments, the device temperature rise score is negatively correlated with the device temperature variation, which can be both positive and negative. A greater device temperature variation corresponds to a lower device temperature rise score. For more information on device temperature variation, please refer to the above description.

[0247] The Fluctuation Score is a score of the fluctuation value of the second pressure over a historical period. In some embodiments, the Fluctuation Score is negatively correlated with the fluctuation value of the second pressure; a greater fluctuation value of the second pressure corresponds to a lower Fluctuation Score. For more information on the fluctuation value of the second pressure, please refer to the relevant description above.

[0248] The gas temperature score is a score of the gas temperature variability over a historical period. In some embodiments, the gas temperature score is negatively correlated with the gas temperature variability, which can be both positive and negative. A greater gas temperature variability corresponds to a lower gas temperature score. For more information on gas temperature variability, see the description above.

[0249] The pressure regulation score refers to the score of the pressure variation rate during a historical period. In some embodiments, the pressure regulation score is positively correlated with the pressure variation rate. The larger the absolute value of the pressure variation rate, the higher the corresponding pressure regulation score.

[0250] In some embodiments, the processor may perform a weighted summation of the device temperature rise score, the fluctuation score, the gas temperature score, and the pressure regulation score, and use the weighted summation result as the effect score. In the weighted summation, the pressure regulation score has the highest weight.

[0251] In some embodiments, the processor can construct a vector to be matched based on the sensor data, voltage regulation parameters and cooling working power of the second time interval, calculate multiple similarities between the vector to be matched and multiple reference vectors, and determine the vector label corresponding to the reference vector with the highest similarity as the refrigerant circulation speed at the second moment.

[0252] In some embodiments, the processor may adjust the adjustment condition based on the maintenance frequency of the gas pressure regulating station.

[0253] Maintenance frequency refers to the frequency of maintenance on equipment in a gas pressure regulating station. For example, this includes the frequency of maintenance on pipelines, cold energy recovery equipment, and pressure regulating equipment in a gas pressure regulating station.

[0254] In some embodiments, the maintenance frequency can be obtained by the gas equipment object platform, uploaded to the gas company management platform through the gas company sensor network platform, and directly retrieved by the processor.

[0255] In some embodiments, for a gas pressure regulating station with a high maintenance frequency, the processor may determine the percentage reduction of the adjustment condition by querying a third preset table. The third preset table may include the maintenance frequency and the corresponding percentage reduction of the adjustment condition.

[0256] The percentage reduction of the adjustment condition may include a percentage reduction of the ratio threshold, a percentage reduction of the preset fluctuation threshold, and a percentage reduction of the preset gas temperature. For example, if the ratio threshold is 80% and the percentage reduction of the ratio threshold is 20%, the adjusted ratio threshold is 64%. The third preset table may be constructed by the processor and / or technicians based on historical data or historical experience.

[0257] For gas pressure regulating stations with high maintenance frequency, the processor can lower the proportional threshold, lower the preset fluctuation threshold, lower the preset gas temperature, lower the adjustment conditions, and improve the adjustment accuracy to ensure that the refrigerant circulation speed is adjusted in time when abnormal data occurs.

[0258] In the embodiments of this specification, the adjustment conditions are adjusted based on the maintenance frequency of the gas pressure regulating station. The gas company management platform can determine more stringent adjustment conditions when there are many problems with the equipment. By adjusting the refrigerant circulation speed, the equipment temperature change, the second pressure and the gas temperature are maintained in a relatively stable state, thereby more accurately controlling the output temperature and output pressure of the gas and reducing abnormal gas transmission conditions.

[0259] In the embodiments of this specification, after adjusting the pressure regulating parameters and cooling parameters, the operating effects of the pressure regulating equipment and the cold energy recovery equipment under the adjusted pressure regulating parameters and cooling parameters can be evaluated by monitoring the sensor data at the second time interval again; and based on the equipment temperature change, the fluctuation value of the second pressure and the gas temperature change in the sensor data, the refrigerant circulation speed is adjusted, which is conducive to timely response to abnormal situations and avoid abnormalities in the gas pressure regulation work.

[0260] In some embodiments, a computer-readable storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a device joint control method for safe gas pressure regulation.

[0261] The embodiments of the present invention are only for illustration and description, and do not limit the scope of application of the present invention. For those skilled in the art, various modifications and changes that can be made under the guidance of the present invention are still within the scope of the present invention.

[0262] Furthermore, certain features, structures, or characteristics of one or more embodiments of the present invention may be appropriately combined.

Claims

1. An IoT system for equipment joint control of gas safety pressure regulation, characterized in that: The Internet of Things system includes a gas company management platform and a gas equipment object platform; The gas equipment object platform includes a pressure regulating device and a cold energy recovery device, the pressure regulating device and the cold energy recovery device are equipped with a sensor device, and the pressure regulating device is installed in a gas pressure regulating station; the gas equipment object platform also includes multiple input pipelines of the gas pressure regulating station, and the pressure regulating device includes a distribution device, a boosting device and / or a pressure reducing device; The gas company management platform is configured to: Acquiring historical sensing data of the sensing device, the historical sensing data including sensing data at multiple historical moments, the sensing data including a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, a gas temperature, and a device temperature of the pressure regulating device; Determining multiple sets of sensory statistical data based on the historical sensory data, the sensory statistical data including pressure difference statistics and temperature difference statistics; as well as determining, based on the current sensor data and the sensor statistical data, an adjustment instruction, wherein the adjustment instruction is configured to adjust a cooling parameter of the cold energy recovery device and a pressure regulating parameter of the pressure regulating device; the pressure regulating parameters include a distribution parameter of the distribution device, a boost parameter of the boost device, and / or a pressure reducing parameter of the pressure reducing device; The gas company management platform is further configured to: determining an initial pressure regulation parameter based on a plurality of first pressures corresponding to the plurality of input pipelines and a target output pressure, and controlling the pressure regulating device to operate based on the initial pressure regulation parameter; Acquiring the sensing data after a first time interval; The voltage regulation parameter at a first moment is determined based on the sensor data.

2. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The gas company management platform is further configured to: Determine the target output pressure based on the gas demand of the output pipeline; Based on the multiple first pressures corresponding to the multiple input pipelines, the target output pressures, and the gas temperature, the pressure regulation parameters and the cooling parameters for the future time period are determined through a pressure difference model, and the pressure difference model is a machine learning model.

3. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The cooling parameters also include a refrigerant circulation speed, and the gas company management platform is further configured as follows: acquiring the sensing data at a second time interval; In response to the sensor data satisfying the adjustment conditions, the refrigerant circulation speed at the second moment is adjusted, and the adjustment conditions are at least one of the following: the equipment temperature continues to rise, the fluctuation value of the second pressure exceeds the preset fluctuation threshold, and the gas temperature is higher than the preset gas temperature.

4. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The Internet of Things system also includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, and a gas company sensor network platform; the government safety supervision object platform includes the gas company management platform.

5. A device joint control method for gas safety pressure regulation, characterized in that: The method is executed by a gas company management platform of an equipment joint control Internet of Things system for gas safety pressure regulation, and the method includes: Acquiring historical sensing data of the sensing device, the historical sensing data including sensing data at multiple historical moments, the sensing data including a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, a gas temperature, and a device temperature of the pressure regulating device; Determining multiple sets of sensory statistical data based on the historical sensory data, the sensory statistical data including pressure difference statistics and temperature difference statistics; and determining an adjustment instruction based on current sensor data and the sensor statistical data, wherein the adjustment instruction is configured to adjust a cooling parameter of a cold energy recovery device and a voltage regulating parameter of the voltage regulating device; The Internet of Things system further includes a plurality of input pipelines of a gas pressure regulating station, the pressure regulating equipment includes a distribution device, a boosting device, and a pressure reducing device, and the pressure regulating parameters include a distribution parameter of the distribution device, a boosting parameter of the boosting device, and a pressure reducing parameter of the pressure reducing device; The method further comprises: determining an initial pressure regulation parameter based on a plurality of first pressures corresponding to the plurality of input pipelines and a target output pressure, and controlling the pressure regulating device to operate based on the initial pressure regulation parameter; Acquiring the sensing data after a first time interval; The voltage regulation parameter at a first moment is determined based on the sensor data.

6. The method according to claim 5, characterized in that The method further comprises: Determine the target output pressure based on the gas demand of the output pipeline; Based on the multiple first pressures corresponding to the multiple input pipelines, the target output pressures, and the gas temperature, the pressure regulation parameters and the cooling parameters for the future time period are determined through a pressure difference model, and the pressure difference model is a machine learning model.

7. The method according to claim 5, characterized in that The cooling parameter further includes a refrigerant circulation speed, and the method further includes: acquiring the sensing data at a second time interval; In response to the sensor data satisfying the adjustment conditions, the refrigerant circulation speed at the second moment is adjusted, and the adjustment conditions are at least one of the following: the equipment temperature continues to rise, the fluctuation value of the second pressure exceeds the preset fluctuation threshold, and the gas temperature is higher than the preset gas temperature.

8. A computer-readable storage medium, characterized in that The medium stores computer instructions. When the computer reads the computer instructions in the medium, the computer executes the equipment joint control method for gas safety pressure regulation according to any one of claims 5 to 7.

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