Gas pipe network maintenance method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a gas pipe network maintenance method and device, electronic equipment and a readable storage medium. The method comprises the steps that multiple pieces of target data collected by each piece of target equipment at at least two collection times are obtained; for any preset target device group, based on first target data collected for each first target device in the target device group, obtaining a first analysis result corresponding to the target device group; for any target device, acquiring a second analysis result corresponding to the target device based on second target data acquired for the target device at at least two acquisition times; and determining a target maintenance scheme based on the first analysis result and the second analysis result. Thus, state monitoring and fault detection can be carried out on the equipment and / or the pipe network from different dimensions based on the first analysis result and the second analysis result, the safety and reliability of gas supply are improved, and then the emergency response efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of gas safety technology, and in particular to a gas pipeline network maintenance method, device, electronic equipment and readable storage medium. Background Art

[0002] With the acceleration of urbanization and the popularization of gas use, the existing gas monitoring system has shown technical shortcomings of being incomplete and untimely.

[0003] Traditional monitoring methods often rely on fixed-point installed detectors with limited coverage, making it difficult to achieve global and real-time monitoring of the gas pipeline network. In addition, due to the lag in data transmission and processing speed, gas leaks are often not detected until after they occur, which not only increases safety risks but also reduces the efficiency of emergency response. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present invention provides a maintenance method, device, electronic equipment and readable storage medium for a gas pipeline network.

[0005] In a first aspect, the present invention provides a method for maintaining a gas pipeline network, the method comprising:

[0006] Acquire a plurality of target data collected at at least two collection times for each target device;

[0007] For any preset target device group, based on the first target data collected for each first target device in the target device group, obtaining a first analysis result corresponding to the target device group;

[0008] For any target device, based on the second target data collected for the target device at the at least two collection times, obtaining a second analysis result corresponding to the target device;

[0009] A target maintenance plan is determined based on the first analysis result and the second analysis result.

[0010] Optionally, the acquiring, based on the first target data collected for each first target device in the target device group, a first analysis result corresponding to the target device group includes:

[0011] Selecting first target data from the plurality of target data; the first target data includes target data collected at at least two collection times for each of the first target devices in the target device group;

[0012] The first target data is analyzed to determine the first analysis result.

[0013] Optionally, analyzing the first target data to determine the first analysis result includes:

[0014] When the difference between the target data corresponding to each of the first target devices at any collection time is less than a preset threshold, determining that the first analysis result is a normal result;

[0015] For any collection time, if the difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time is greater than the preset threshold, the first analysis result is determined to be an abnormal result.

[0016] Optionally, the acquiring a second analysis result corresponding to the target device based on the second target data collected for the target device at the at least two collection times includes:

[0017] Acquire second target data corresponding to the target device; the second target data includes at least two target data collected by the target device at the at least two collection times;

[0018] The second target data is analyzed to determine the second analysis result.

[0019] Optionally, analyzing the second target data to determine the second analysis result includes:

[0020] Determine a maximum value and a minimum value of the second target data corresponding to the at least two acquisition times;

[0021] For any collection time, determine a first difference between target data corresponding to the collection time and target data corresponding to a first collection time, and determine a second difference between target data corresponding to the collection time and target data corresponding to a second collection time; wherein the first collection time is an adjacent collection time before the collection time, and the second collection time is an adjacent collection time after the collection time;

[0022] The maximum value, the minimum value, the first difference value, and the second difference value are determined as a second analysis result.

[0023] Optionally, determining a target maintenance plan based on the first analysis result and the second analysis result includes:

[0024] When the first analysis result satisfies a first preset condition and / or the second analysis result satisfies a second preset condition, determining the target maintenance solution as a fault handling solution;

[0025] The first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than a first value, the second difference is greater than the first value, the maximum value is greater than a second value, and / or the minimum value is greater than a third value;

[0026] When the first analysis result does not satisfy the first preset condition and the second analysis result does not satisfy the second preset condition, the target maintenance plan is determined to be an equipment maintenance plan.

[0027] In a second aspect, the present invention provides a maintenance device for a gas pipeline network, the device comprising:

[0028] A first acquisition module is used to acquire a plurality of target data collected at at least two collection times for each target device;

[0029] A second acquisition module, configured to acquire, for any preset target device group, a first analysis result corresponding to the target device group based on first target data collected for each first target device in the target device group;

[0030] A third acquisition module is used to acquire, for any target device, a second analysis result corresponding to the target device based on the second target data collected for the target device at the at least two collection times;

[0031] The first determination module is used to determine a target maintenance plan based on the first analysis result and the second analysis result.

[0032] Optionally, the second acquisition module includes:

[0033] A first selection module is used to select first target data from the plurality of target data; the first target data includes target data collected at at least two collection times for each of the first target devices in the target device group;

[0034] The first analysis module is used to analyze the first target data and determine the first analysis result.

[0035] Optionally, the first analysis module includes:

[0036] A first determination submodule, configured to determine that the first analysis result is a normal result when the difference between the target data corresponding to each of the first target devices at any collection time is less than a preset threshold;

[0037] The second determination submodule is used to determine that the first analysis result is an abnormal result for any collection time when the difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time is greater than the preset threshold.

[0038] Optionally, the third acquisition module includes:

[0039] A first acquisition submodule is used to acquire second target data corresponding to the target device; the second target data includes at least two target data collected by the target device at the at least two collection times;

[0040] The second analysis module is used to analyze the second target data and determine the second analysis result.

[0041] Optionally, the second analysis module includes:

[0042] A third determination submodule, used to determine the maximum value and the minimum value of the second target data corresponding to the at least two acquisition times;

[0043] a fourth determination submodule, configured to determine, for any acquisition time, a first difference between target data corresponding to the acquisition time and target data corresponding to a first acquisition time, and to determine a second difference between target data corresponding to the acquisition time and target data corresponding to a second acquisition time; wherein the first acquisition time is an adjacent acquisition time before the acquisition time, and the second acquisition time is an adjacent acquisition time after the acquisition time;

[0044] The fifth determining submodule is used to determine the maximum value, the minimum value, the first difference value and the second difference value as a second analysis result.

[0045] Optionally, the first determining module includes:

[0046] a sixth determination submodule, configured to determine that the target maintenance solution is a fault handling solution when the first analysis result satisfies a first preset condition and / or the second analysis result satisfies a second preset condition;

[0047] The first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than a first value, the second difference is greater than the first value, the maximum value is greater than a second value, and / or the minimum value is greater than a third value;

[0048] The seventh determination submodule is used to determine that the target maintenance plan is an equipment maintenance plan when the first analysis result does not meet the first preset condition and the second analysis result does not meet the second preset condition.

[0049] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the gas pipeline network maintenance method described in any one of the first aspects above is implemented.

[0050] In a fourth aspect, the present invention provides a readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the steps in the gas pipeline network maintenance method in any one of the embodiments of the first aspect above.

[0051] In an embodiment of the present invention, by acquiring multiple target data collected for each target device at at least two collection times; for any preset target device group, based on the first target data collected for each first target device in the target device group, a first analysis result corresponding to the target device group is acquired; for any target device, based on the second target data collected for the target device at at least two collection times, a second analysis result corresponding to the target device is acquired; based on the first analysis result and the second analysis result, a target maintenance plan is determined. In this way, based on the first analysis result and the second analysis result, the equipment and / or pipeline network can be monitored and faults detected from different dimensions, the safety and reliability of gas supply can be improved, and the efficiency of emergency response can be improved. In addition, by pre-analyzing based on the collected target data, an accurate target maintenance plan can be obtained according to the indication of the target data, thereby reducing the equipment failure rate, extending the service life of the equipment, and reducing the maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is a flowchart of the steps of a gas pipe network maintenance method provided by an embodiment of the present invention;

[0054] Figure 2 It is a structural diagram of a gas pipe network maintenance device provided by an embodiment of the present invention;

[0055] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Figure 1 is a flowchart of a method for maintaining a gas pipe network provided by an embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0058] Step 101: Acquire a plurality of target data collected for each target device at at least two collection times.

[0059] In the embodiment of the present invention, the target device can be deployed in scenes such as pipelines, engineering, plants, ubiquitous energy, and indoors. By configuring data acquisition devices such as sensors for the target device, the sensors and data acquisition systems are used to collect the operating data of the target device, including pressure, flow, temperature, etc. After receiving the operating data of the target device, the collected operating data can be transmitted to the central database through the Internet of Things technology, and recorded in the central database according to the corresponding relationship between the collection time, operating data, and the target device. Among them, the target device can be a pressure regulating device, such as a valve, a filter, a pressure regulator, a flow meter, etc.

[0060] Specifically, the operation data of the target device can be collected in real time, or the operation data of the target device can be collected periodically at a preset interval. For example, the operation data of each target device can be collected once every hour to obtain the target data collected at least two collection times. It can be understood that for each deployed target device, the operation data of the target device at at least two collection times is collected to obtain the target data.

[0061] Step 102: For any preset target device group, based on first target data collected for each first target device in the target device group, obtain a first analysis result corresponding to the target device group.

[0062] In the embodiment of the present invention, multiple target device groups are divided in advance according to the device functions and the device deployment corresponding to the specific application scenarios and requirements. The target device group may include multiple first target devices, and the first target device may be a target device selected from the multiple target devices according to a certain division standard. The operation states of the first target devices in the target device group have a certain correlation, and the corresponding operation data also have a certain correlation relationship. Exemplarily, the device models of multiple first target devices may be the same and the operating status may be the same, and accordingly, the size relationship of their operating data may be the same or similar, such as for multiple pipelines with the same layout status and arranged in parallel in a machine room, the first target devices deployed at similar positions of each pipe network, the size relationship of the target data collected is the same or similar; it may also be that the operating status of each first target device has a cascade relationship, and accordingly, the change of its operating data also has a certain rule, such as gradually decreasing, gradually increasing, or there is a certain fixed data change requirement, for example, in a large gas storage and distribution station or a pressure regulating station group, multiple pressure regulating devices work together, and the gas pressure needs to be adjusted to different pressure levels. Therefore, the size relationship of the target data collected by the corresponding multiple first target devices is a cascade relationship, which can be determined according to the actual gas control requirements and the equipment deployment method. Exemplarily, the target device group may include multiple first target devices deployed upstream and downstream of the same pipe network, and may also include multiple first target devices deployed in the same machine room for different parallel pipe networks.

[0063] The target data of each first target device in the target device group is obtained as the first target data. Based on the first target data, the key features of the first target data are extracted, such as the change characteristics and conditions of different indicators, such as whether the indicators continue to change steadily or suddenly change or are abnormal, and then the first analysis result corresponding to the target device group is obtained. The first analysis result is determined based on the status comparison of each first target device in the time horizontal dimension, that is, based on the device operation data of each first target device corresponding to the same collection time, it is determined whether the device status is normal. The first analysis result is used to characterize the device status of multiple first target devices that have a certain correlation at the same collection time.

[0064] Optionally, step 102 may include the following steps:

[0065] Step 201: Select first target data from the plurality of target data; the first target data includes target data collected for each of the first target devices in the target device group at at least two collection times.

[0066] In an embodiment of the present invention, a first target device included in a target device group is predetermined. Based on the correspondence between the target data and each target device recorded in a central database, multiple groups of target data corresponding to the target device group are selected from multiple target data as first target data. The first target data includes target data collected for each first target device at at least two collection times.

[0067] Step 202: Analyze the first target data to determine the first analysis result.

[0068] In an embodiment of the present invention, the first target data is analyzed to obtain the first analysis result corresponding to the target device group. According to the division criteria of different target device groups, the way of analyzing the first target data can be different. For example, for multiple first target devices deployed in the same computer room with different parallel pipe networks, it can be judged whether the target data corresponding to each first target device is in a reasonable fluctuation range at the same collection time, such as whether the difference between each target data is less than a preset threshold. Exemplarily, assuming that the normal pressure value collected by multiple first target devices in the same computer room is 100, and the reasonable fluctuation range of the pressure value is 90-110, correspondingly, the preset threshold is 20, then it can be judged whether the difference between each target data is less than 20. Or, for multiple first target devices deployed upstream and downstream of the same pipe network, it can be judged whether the target data corresponding to each first target device meets the reasonable data change law at the same collection time, such as the target data of the upstream first target device is twice the target data of the downstream first target device. It can be understood that the above-mentioned way of analyzing the first target data is determined based on the association relationship between the first target devices, and the embodiment of the present invention does not limit this.

[0069] Optionally, step 202 may include the following steps:

[0070] Step 301: When the difference between the target data corresponding to each of the first target devices at any collection time is less than a preset threshold, determine that the first analysis result is a normal result.

[0071] In an embodiment of the present invention, the first target devices included in the target device group may be multiple target devices with similar operating states. When the devices are operating normally, the target data corresponding to each first target device should be the same or similar, that is, each target data should be within a reasonable fluctuation range. When the devices are operating abnormally, the target data corresponding to at least one first target device should be different from the target data of other first target devices, that is, the target data corresponding to at least one first target device is outside the reasonable fluctuation range.

[0072] Since the first target data includes target data collected by multiple first target devices at multiple collection times, the size of the target data corresponding to each first target device collected at the collection time can be compared for each collection time in at least two collection times to determine whether the operation status of each first target device is normal at the same collection time. Specifically, for any collection time, if the difference between the target data corresponding to each first target device at the collection time is less than a preset threshold, the first analysis result is determined to be a normal result. Among them, the value of the preset threshold can be set according to actual conditions, for example, it can be determined according to the reasonable fluctuation range allowed by the parameter data, and the embodiment of the present invention does not limit this. Exemplarily, the target data corresponding to each first target device collected at the collection time can be calculated pairwise, and the calculated difference result can be compared with the preset threshold. If all the differences calculated pairwise are greater than the preset threshold, it indicates that at the collection time, the operation status of each associated first target device is similar, and each operation data is in a stable fluctuation range, so it can be determined that each first target device corresponding to the collection time is in a normal operating state.

[0073] When the target data of each first target device collected at at least two collection times are judged to be in a normal state, that is, the difference between the target data corresponding to each first target device at at least two collection times is less than a preset threshold, the first analysis result can be determined as a normal result.

[0074] Step 302: for any collection time, if the difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time is greater than the preset threshold, determine that the first analysis result is an abnormal result.

[0075] In an embodiment of the present invention, for any collection time, if there is a difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time, which is greater than or less than a preset threshold, it indicates that the operating state of the first target device is different from the operating state of the other first target devices, and the first target device may be abnormal, so the first analysis result is determined to be an abnormal result, and the first target device is recorded, for example, the first target device is marked as an abnormal device.

[0076] Exemplarily, assuming that the pressure difference between any first target device and the other two first target devices among multiple first target devices in the same computer room is greater than a preset threshold, it can be determined that the first target device is in an abnormal operating state, and the first analysis result is determined to be an abnormal result.

[0077] In an embodiment of the present invention, by analyzing the target data corresponding to each first target device at any collection time, the operating status of each first target device at the same collection time is determined, and through the association relationship between the first target devices, the first target devices are made to refer to each other, and the device operating status is compared from the horizontal dimension of time, thereby improving the comprehensiveness and accuracy of device status monitoring.

[0078] Step 103: For any target device, based on the second target data collected for the target device at the at least two collection times, obtain a second analysis result corresponding to the target device.

[0079] In an embodiment of the present invention, for any target device, target data collected for the target device at at least two collection times is obtained as the second target data. Based on the second target data, key features of the second target data are extracted, such as the change characteristics and conditions of different indicators, such as whether the indicators continue to change steadily or suddenly change or are abnormal, and then the second analysis result corresponding to the target device is obtained. Among them, the second analysis result is obtained by comparing the status of the target device based on the time longitudinal dimension, that is, whether the device status is normal is determined based on the performance of the device operation data of the same target device at different collection times. The second analysis result is used to characterize the device status of the target device at different collection times. It can be understood that the time longitudinal dimension can refer to the performance of the same device at different times, and the time horizontal dimension can refer to the data change performance of multiple device quality inspections with an associated relationship.

[0080] Optionally, step 103 may include the following steps:

[0081] Step 401: Acquire second target data corresponding to the target device at the at least two acquisition times.

[0082] In an embodiment of the present invention, based on the correspondence between the target data and each target device recorded in the central database, target data corresponding to the target device at at least two acquisition times are selected from multiple target data as second target data, wherein the second target data includes at least two target data acquired by the target device at at least two acquisition times.

[0083] Step 402: Analyze the second target data to determine the second analysis result.

[0084] In an embodiment of the present invention, the second target data is analyzed to determine a second analysis result. The method for analyzing the second target data may include, but is not limited to, determining the maximum value and minimum value of the second target data; determining parameters such as the slope based on a broken line obtained from the second target data corresponding to at least two acquisition times or a curve obtained by fitting.

[0085] Optionally, step 402 may include the following steps:

[0086] Step 501: Determine the maximum value and the minimum value of the second target data corresponding to the at least two acquisition times.

[0087] In the embodiment of the present invention, a maximum value and a minimum value among a plurality of second target data are determined from the second target data corresponding to at least two acquisition times.

[0088] Step 502: for any acquisition time, determine a first difference between target data corresponding to the acquisition time and target data corresponding to a first acquisition time, and determine a second difference between target data corresponding to the acquisition time and target data corresponding to a second acquisition time; wherein the first acquisition time is an adjacent acquisition time before the acquisition time, and the second acquisition time is an adjacent acquisition time after the acquisition time.

[0089] In the embodiment of the present invention, for any collection time, the target data collected for the target device at the collection time is obtained, the target data collected for the target device at the adjacent collection time before the collection time is determined as the target data corresponding to the first collection time, and the target data collected for the target device at the adjacent collection time after the collection time is determined as the target data corresponding to the second collection time. The difference between the target data corresponding to the collection time and the target data corresponding to the first collection time is determined as the first difference, and the difference between the target data corresponding to the collection time and the target data corresponding to the second collection time is determined as the second difference.

[0090] Step 503: Determine the maximum value, the minimum value, the first difference value, and the second difference value as a second analysis result.

[0091] In the embodiment of the present invention, the maximum value, the minimum value, the first difference value and the second difference value are determined as the second analysis result.

[0092] In an embodiment of the present invention, by performing data analysis on the target data of the target device collected at at least two collection times for any target device, a maximum value, a minimum value, a first difference and a second difference are obtained. The periodicity or mutation of the data can be analyzed through the data features of multiple data to determine whether the target device has an abnormality, thereby improving the comprehensiveness and accuracy of device status monitoring.

[0093] Step 104: Determine a target maintenance plan based on the first analysis result and the second analysis result.

[0094] In an embodiment of the present invention, a target maintenance plan can be determined based on the first analysis result and the second analysis result. Specifically, the target maintenance plan can be determined based on the first analysis result; the target maintenance plan can be determined based on the second analysis result; and the target maintenance plan can be determined based on the first analysis result and the second analysis result. Among them, the target maintenance plan can include plan types such as equipment maintenance plan, equipment fault handling plan, fault troubleshooting plan, and fault handling plan. It can be understood that the specific type of the target maintenance plan can be determined according to the risk level corresponding to the first analysis result and the second analysis result, and different plan contents can be determined based on the specific operating status of the equipment. The embodiment of the present invention does not limit the plan content of the target maintenance plan, and it can be set according to actual needs. Exemplarily, the equipment maintenance plan can include periodic maintenance and maintenance of the target equipment, the equipment fault handling plan can include fault handling of abnormal equipment, the fault troubleshooting plan can include inspection of pipelines and comprehensive fault troubleshooting, and the fault handling plan can include fault troubleshooting of pipelines and target equipment and fault handling of equipment or pipeline faults.

[0095] Optionally, step 104 may include the following steps:

[0096] Step 601, when the first analysis result satisfies a first preset condition and / or the second analysis result satisfies a second preset condition, determining that the target maintenance plan is a fault handling plan; wherein the first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than a first value, the second difference is greater than the first value, the maximum value is greater than a second value, and / or the minimum value is greater than a third value.

[0097] In the embodiment of the present invention, when the first analysis result satisfies the first preset condition, the second analysis result satisfies the second preset condition, or the first analysis result satisfies the first preset condition and the second analysis result satisfies the second preset condition, it is characterized that there is an equipment abnormality in the target equipment and the risk level is high, and troubleshooting and fault handling are required. Therefore, the target maintenance plan is determined to be a fault handling plan. Among them, the first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than the first value, the second difference is greater than the first value, the maximum value is greater than the second value, and / or the minimum value is greater than the third value. The values ​​of the first value, the second value, and the third value can be set according to actual needs. For example, they can be determined in combination with the historical operating rules of each acquisition time. For example, assuming that according to the historical operating state, the pressure change will only change in the three fixed gas consumption peaks of morning, middle, and evening, then the gas consumption peak periods corresponding to morning, middle, and evening can be recorded, and then the first value can be determined according to the historical data of the gas consumption peak periods corresponding to morning, middle, and evening, and the historical maximum value and the historical minimum value in the normal operating state of the historical equipment can be counted, and the historical maximum value is determined as the second value and the historical minimum value is determined as the third value.

[0098] That is, if the first analysis result is an abnormal result, the target maintenance plan is determined to be a fault handling plan; if the first difference in the second analysis result is greater than the first value, the second difference is greater than the first value, the maximum value is greater than the second value and / or the minimum value is greater than the third value, the target maintenance plan is determined to be a fault handling plan.

[0099] In a possible implementation, the operation state of the device can also be determined in combination with the first analysis result and the second analysis result. Based on the second analysis result, it is determined that the device state of a target device is abnormal. The risk level corresponding to the current abnormal situation can be further determined based on the first analysis result corresponding to the target device group to which the target device belongs. Exemplarily, assuming that the flow data collected by the target device A at the collection time a is greater than the second value, the operation state of the target device A is determined to be abnormal. However, there may be different situations in which the operation state of the target device A is abnormal, for example, the device of the target device A itself has an abnormality, or the pipeline monitored by the target device A has an abnormality, etc. Since the risk level corresponding to the abnormal situation of target device A cannot be determined at this time, it can be further judged based on the first analysis result corresponding to the target device group where the target device A is located: assuming that the first analysis result is a normal result, that is, the difference between the target data corresponding to the target device A at any collection time and the target data corresponding to the other first target devices in the target device group at any collection time is less than the preset threshold, indicating that the data collected by the target device A and the other first target devices in the target device group are the same or similar. In this way, when the target data collected by the target device A is determined to be abnormal, the current situation is that the pipeline monitored by the target device A is likely to be abnormal, and the corresponding risk level is high; if it is assumed that the first analysis result is an abnormal result, that is, the difference between the target data corresponding to the target device A at the collection time a and the target data corresponding to the other first target devices at the collection time a is greater than the preset threshold, then the current situation is that the device of the target device A itself is likely to be abnormal, and the corresponding risk level is low. It can be understood that different types of target maintenance plans can be determined for different risk levels. For example, when the risk level is high, the target maintenance plan can be determined as a fault handling plan, and when the risk level is low, the target maintenance plan can be determined as an equipment fault handling plan. The embodiment of the present invention does not limit this.

[0100] Step 602: When the first analysis result does not satisfy the first preset condition and the second analysis result does not satisfy the second preset condition, determine that the target maintenance plan is an equipment maintenance plan.

[0101] In the embodiment of the present invention, when the first analysis result does not satisfy the first preset condition and the second analysis result does not satisfy the second preset condition, it indicates that the target equipment is operating normally, and the target maintenance plan can be determined to be the equipment maintenance plan.

[0102] In the embodiment of the present invention, the first preset condition and the second preset condition are used as the basis for determining the target maintenance plan, which can comprehensively analyze the target equipment from the time horizontal dimension and the time vertical dimension, so that the determined target maintenance plan is more accurate and practical.

[0103] In summary, in the embodiment of the present invention, by acquiring multiple target data collected for each target device at at least two collection times; for any preset target device group, based on the first target data collected for each first target device in the target device group, the first analysis result corresponding to the target device group is acquired; for any target device, based on the second target data collected for the target device at at least two collection times, the second analysis result corresponding to the target device is acquired; based on the first analysis result and the second analysis result, the target maintenance plan is determined. In this way, based on the first analysis result and the second analysis result, the status monitoring and fault detection of the equipment and / or pipeline network can be performed from different dimensions, the safety and reliability of the gas supply can be improved, and the efficiency of the emergency response can be improved. In addition, by pre-analyzing based on the collected target data, an accurate target maintenance plan can be obtained according to the indication of the target data, which reduces the equipment failure rate, extends the service life of the equipment, and reduces the maintenance cost.

[0104] In a possible implementation, the embodiment of the present invention can analyze the target data through the model to obtain the first analysis result and the second analysis result. Exemplarily, the operating data of the target device, including pressure, flow, temperature, etc., can be collected in real time by using sensors and data acquisition systems. Through the Internet of Things technology, the collected operating data is stored in a central database according to the corresponding relationship between the target data, the target device and the collection time. The collected data is cleaned, denoised and standardized to ensure data quality, and the data is converted into a format suitable for large model analysis. A large model is constructed using machine learning and deep learning technology to analyze the spatiotemporal characteristics of the target device. Based on historical data, the large model is trained so that it can determine the first analysis result and the second analysis result and identify the normal operating state and abnormal operating state of the target device according to the first analysis result and the second analysis result. The large model is used to analyze the performance changes of the target device at different times and spaces based on the target data, and identify the key features of the operation of the target device. Through time longitudinal dimension analysis and time horizontal dimension analysis, the regularity and anomaly of the operation of the target device are discovered. Based on the analysis results of the large model (including the first analysis result and the second analysis result), the possible fault type and time of the voltage regulating device are predicted, and the target maintenance plan is determined. According to different target maintenance plans, we can optimize the maintenance strategy of target equipment and / or pipeline network, formulate more reasonable equipment replacement and upgrade plans, and notify maintenance personnel to handle faults through early warning systems, so as to carry out preventive maintenance in advance. Furthermore, we can integrate large model analysis technology into the existing monitoring system of target equipment to realize automatic monitoring and analysis, and provide a user interface so that maintenance personnel can intuitively view equipment status and early warning information.

[0105] Figure 2is a schematic diagram of the structure of a gas pipe network maintenance device provided by an embodiment of the present invention, such as Figure 2 As shown, the device may specifically include:

[0106] The first acquisition module 701 is used to acquire a plurality of target data collected at at least two collection times for each target device;

[0107] A second acquisition module 702 is used to acquire, for any preset target device group, a first analysis result corresponding to the target device group based on first target data collected for each first target device in the target device group;

[0108] A third acquisition module 703 is used to acquire, for any target device, a second analysis result corresponding to the target device based on the second target data collected for the target device at the at least two collection times;

[0109] The first determination module 704 is configured to determine a target maintenance plan based on the first analysis result and the second analysis result.

[0110] In an embodiment of the present invention, by acquiring multiple target data collected for each target device at at least two collection times; for any preset target device group, based on the first target data collected for each first target device in the target device group, a first analysis result corresponding to the target device group is acquired; for any target device, based on the second target data collected for the target device at at least two collection times, a second analysis result corresponding to the target device is acquired; based on the first analysis result and the second analysis result, a target maintenance plan is determined. In this way, based on the first analysis result and the second analysis result, the equipment and / or pipeline network can be monitored and faults detected from different dimensions, the safety and reliability of gas supply can be improved, and the efficiency of emergency response can be improved. In addition, by pre-analyzing based on the collected target data, an accurate target maintenance plan can be obtained according to the indication of the target data, thereby reducing the equipment failure rate, extending the service life of the equipment, and reducing the maintenance cost.

[0111] Optionally, the second acquisition module 702 includes:

[0112] A first selection module is used to select first target data from the plurality of target data; the first target data includes target data collected at at least two collection times for each of the first target devices in the target device group;

[0113] The first analysis module is used to analyze the first target data and determine the first analysis result.

[0114] Optionally, the first analysis module includes:

[0115] A first determination submodule, configured to determine that the first analysis result is a normal result when the difference between the target data corresponding to each of the first target devices at any collection time is less than a preset threshold;

[0116] The second determination submodule is used to determine that the first analysis result is an abnormal result for any collection time when the difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time is greater than the preset threshold.

[0117] Optionally, the third acquisition module 703 includes:

[0118] A first acquisition submodule is used to acquire second target data corresponding to the target device; the second target data includes at least two target data collected by the target device at the at least two collection times;

[0119] The second analysis module is used to analyze the second target data and determine the second analysis result.

[0120] Optionally, the second analysis module includes:

[0121] A third determination submodule, used to determine the maximum value and the minimum value of the second target data corresponding to the at least two acquisition times;

[0122] a fourth determination submodule, configured to determine, for any acquisition time, a first difference between target data corresponding to the acquisition time and target data corresponding to a first acquisition time, and to determine a second difference between target data corresponding to the acquisition time and target data corresponding to a second acquisition time; wherein the first acquisition time is an adjacent acquisition time before the acquisition time, and the second acquisition time is an adjacent acquisition time after the acquisition time;

[0123] The fifth determining submodule is used to determine the maximum value, the minimum value, the first difference value and the second difference value as a second analysis result.

[0124] Optionally, the first determining module 704 includes:

[0125] a sixth determination submodule, configured to determine that the target maintenance solution is a fault handling solution when the first analysis result satisfies a first preset condition and / or the second analysis result satisfies a second preset condition;

[0126] The first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than a first value, the second difference is greater than the first value, the maximum value is greater than a second value, and / or the minimum value is greater than a third value;

[0127] The seventh determination submodule is used to determine that the target maintenance plan is an equipment maintenance plan when the first analysis result does not meet the first preset condition and the second analysis result does not meet the second preset condition.

[0128] The present invention also provides an electronic device, see Figure 3 , including: a processor 801, a memory 802, and a computer program 8021 stored in the memory and executable on the processor, wherein when the processor executes the program, the gas pipeline network maintenance method of the aforementioned embodiment is implemented.

[0129] The present invention also provides a readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the gas pipeline network maintenance method of the aforementioned embodiment.

[0130] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0131] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0132] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0133] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0134] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0135] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the sorting device according to the present invention. The present invention may also be implemented as a device or apparatus program for executing part or all of the methods described herein. Such a program for implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0136] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0137] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0138] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0140] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for maintaining a gas pipeline network, characterized in that: The method comprises: Acquire a plurality of target data collected at at least two collection times for each target device; For any preset target device group, based on the first target data collected for each first target device in the target device group, obtaining a first analysis result corresponding to the target device group; For any target device, based on the second target data collected for the target device at the at least two collection times, obtaining a second analysis result corresponding to the target device; A target maintenance plan is determined based on the first analysis result and the second analysis result.

2. The method according to claim 1, characterized in that: The acquiring, based on the first target data collected for each first target device in the target device group, a first analysis result corresponding to the target device group includes: Selecting first target data from the plurality of target data; the first target data includes target data collected at at least two collection times for each of the first target devices in the target device group; The first target data is analyzed to determine the first analysis result.

3. The method according to claim 2, characterized in that The analyzing the first target data to determine the first analysis result includes: When the difference between the target data corresponding to each of the first target devices at any collection time is less than a preset threshold, determining that the first analysis result is a normal result; For any collection time, if the difference between the target data corresponding to any first target device at the collection time and the target data corresponding to other first target devices at the collection time is greater than the preset threshold, the first analysis result is determined to be an abnormal result.

4. The method according to claim 1, characterized in that The acquiring, based on the second target data collected for the target device at the at least two collection times, a second analysis result corresponding to the target device includes: Acquire second target data corresponding to the target device; the second target data includes at least two target data collected by the target device at the at least two collection times; The second target data is analyzed to determine the second analysis result.

5. The method according to claim 4, characterized in that The analyzing the second target data to determine the second analysis result includes: Determine a maximum value and a minimum value of the second target data corresponding to the at least two acquisition times; For any collection time, determine a first difference between target data corresponding to the collection time and target data corresponding to a first collection time, and determine a second difference between target data corresponding to the collection time and target data corresponding to a second collection time; wherein the first collection time is an adjacent collection time before the collection time, and the second collection time is an adjacent collection time after the collection time; The maximum value, the minimum value, the first difference value, and the second difference value are determined as a second analysis result.

6. The method according to claim 1, characterized in that The determining a target maintenance plan based on the first analysis result and the second analysis result includes: When the first analysis result satisfies a first preset condition and / or the second analysis result satisfies a second preset condition, determining the target maintenance solution as a fault handling solution; The first preset condition is that the first analysis result is an abnormal result, and the second preset condition includes that the first difference is greater than a first value, the second difference is greater than the first value, the maximum value is greater than a second value, and / or the minimum value is greater than a third value; When the first analysis result does not satisfy the first preset condition and the second analysis result does not satisfy the second preset condition, the target maintenance plan is determined to be an equipment maintenance plan.

7. A gas pipe network maintenance device, characterized in that: The device comprises: A first acquisition module is used to acquire a plurality of target data collected at at least two collection times for each target device; A second acquisition module, configured to acquire, for any preset target device group, a first analysis result corresponding to the target device group based on first target data collected for each first target device in the target device group; A third acquisition module is used to acquire, for any target device, a second analysis result corresponding to the target device based on the second target data collected for the target device at the at least two collection times; The first determination module is used to determine a target maintenance plan based on the first analysis result and the second analysis result.

8. The device according to claim 7, characterized in that The second acquisition module includes: A first selection module is used to select first target data from the plurality of target data; the first target data includes target data collected at at least two collection times for each of the first target devices in the target device group; The first analysis module is used to analyze the first target data and determine the first analysis result.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a gas pipeline network maintenance method as described in any one of claims 1 to 6 is implemented.

10. A readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the gas pipeline network maintenance method described in any one of claims 1-6.

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