Gas repair management method, internet of things system, device and medium of smart gas

Abstract

The embodiment of the present specification provides a kind of gas repair management method of wisdom gas, internet of things system, device and medium, the method is realized by the internet of things system of gas repair management of wisdom gas of wisdom gas, the internet of things system includes wisdom gas management platform, wisdom gas sensing network platform and wisdom gas object platform.The method includes obtaining historical gas use data and historical gas repair data;Based on historical gas use data and historical gas repair data, generate first gas repair area;At least based on the historical gas use data and historical gas repair data in first gas repair area, generate future gas repair data in first gas repair area;Based on first gas repair area and future gas repair data in first gas repair area, generate second gas repair area and its corresponding future gas repair data;Based on the future gas repair data in second gas repair area, generate maintenance personnel arrangement scheme.

Description

Technical Field

[0001] This specification relates to the field of Internet of Things (IoT) technology and gas management systems, and in particular to a smart gas repair management method, IoT system, device, and medium. Background Technology

[0002] As natural gas becomes increasingly widespread, so too do gas-related problems, such as gas leaks, insufficient gas supply, and malfunctioning gas meters. If gas-related repairs are not addressed promptly, people's normal lives and personal safety will be affected.

[0003] To improve problem-solving efficiency, CN113283915A provides a gas call center business processing method. The focus of this application is to identify the business type corresponding to the business requests reported by users from the client, generate business work orders, assign these work orders to the corresponding personnel, and monitor the progress of work order processing. However, because different business types have varying frequencies of occurrence, it is still impossible to improve efficiency while simultaneously reducing overall user waiting costs.

[0004] Therefore, it is hoped that a smart gas repair management method, IoT system, device, and medium can be provided. This will balance the efficiency of the call center with the waiting costs for all users, improve the efficiency of handling repair issues, and thus ensure people's daily gas use and personal safety. Summary of the Invention

[0005] This specification provides one or more embodiments of a smart gas repair management method. The method is executed through a smart gas management platform within a gas call center gas repair management IoT system. The method includes: obtaining historical gas usage data and historical gas repair data through a smart gas object platform via a smart gas sensor network platform; generating multiple first gas repair zones based on the historical gas usage data and historical gas repair data; generating future gas repair data for multiple first gas repair zones based at least on the historical gas usage data and historical gas repair data within the multiple first gas repair zones, wherein the historical gas repair data includes at least historical gas repair volume and historical gas repair level, and the future gas repair data includes at least future gas repair volume and future gas repair level; generating multiple second gas repair zones and their corresponding future gas repair data based on the multiple first gas repair zones and the future gas repair data within the multiple first gas repair zones; and generating a maintenance personnel arrangement plan based on the future gas repair data within the multiple second gas repair zones.

[0006] This specification provides one or more embodiments of a smart gas repair management system, including a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform is used to: acquire historical gas usage data and historical gas repair data through the smart gas sensor network platform based on the smart gas object platform; generate multiple first gas repair areas based on the historical gas usage data and historical gas repair data; generate future gas repair data for multiple first gas repair areas based on at least the historical gas usage data and historical gas repair data for the multiple first gas repair areas, wherein the historical gas repair data includes at least the historical gas repair volume and historical gas repair level, and the future gas repair data includes at least the future gas repair volume and future gas repair level; generate multiple second gas repair areas and their corresponding future gas repair data based on the multiple first gas repair areas and the future gas repair data for the multiple first gas repair areas; and generate a maintenance personnel arrangement plan based on the future gas repair data for the multiple second gas repair areas.

[0007] This specification provides one or more embodiments of a smart gas repair management device, the device including at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least some of the computer instructions to implement any of the smart gas repair management methods described above.

[0008] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a smart gas repair management method.

[0009] This invention addresses the problem of improving the efficiency of handling gas repair requests. By acquiring historical gas usage data and historical gas repair data, a first gas repair area is generated, followed by a second gas repair area and its future repair data. This allows for the creation of a maintenance personnel allocation plan, which can determine the future gas repair data for different areas in real time. This facilitates the generation of accurate and realistic maintenance personnel allocation plans, shortens the time required to determine these plans, saves labor costs, and improves the efficiency of gas repair processing. Furthermore, it enables government gas operation departments and users to promptly obtain and implement maintenance personnel allocation plans. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0011] Figure 1 This is a schematic diagram of the platform structure of a smart gas repair management Internet of Things system according to some embodiments of this specification;

[0012] Figure 2 This is an exemplary flowchart of a smart gas repair management method according to some embodiments of this specification;

[0013] Figure 3 This is a schematic diagram illustrating the process of generating future gas repair data within multiple first gas repair areas based on a gas repair model, according to some embodiments of this specification.

[0014] Figure 4 This is an exemplary flowchart illustrating the generation of multiple second gas repair areas and their corresponding future gas repair data, as shown in some embodiments of this specification.

[0015] Figure 5 This is an exemplary schematic diagram illustrating the generation of multiple second gas repair zones according to some embodiments of this specification. Detailed Implementation

[0016] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] Figure 1 This is a schematic diagram of the platform structure of a smart gas repair management Internet of Things system according to some embodiments of this specification.

[0019] In some embodiments, such as Figure 1 As shown, the smart gas repair management IoT system can include a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform.

[0020] In some embodiments, the smart gas repair management IoT system disclosed in this specification can be implemented to determine the maintenance personnel scheduling plan.

[0021] A smart gas user platform can be a platform for interacting with users. In some embodiments, the smart gas user platform can be configured as a terminal device, such as a mobile device, tablet computer, or any combination thereof. In some embodiments, the smart gas user platform can be used to provide users with reminders related to maintenance personnel scheduling plans.

[0022] In some embodiments, the smart gas user platform includes a gas user sub-platform, a government user sub-platform, and a regulatory user sub-platform. The gas user sub-platform is for gas users, providing them with gas usage-related data and information such as solutions to gas-related problems. A gas user is someone who uses gas. In some embodiments, the gas user sub-platform can interact with and correspond to the smart gas service sub-platform to obtain safe gas usage services. The government user sub-platform is for government users, providing them with gas operation-related data. Government users are those in government departments related to gas operations. In some embodiments, the government user sub-platform can obtain maintenance management information, such as the dispatch of personnel for maintenance projects. The regulatory user sub-platform is for regulatory users, monitoring the operation of the entire gas call center emergency response IoT system. Regulatory users are those in the safety department. In some embodiments, the regulatory user sub-platform can interact with and correspond to the smart regulatory service sub-platform to obtain services related to safety regulatory needs.

[0023] In some embodiments, the smart gas user platform can interact bidirectionally with the smart gas service platform, sending gas user repair information to the smart gas service sub-platform and receiving repair personnel arrangement plans uploaded by the smart gas service sub-platform.

[0024] A smart gas service platform can be a platform for receiving and transmitting data and / or information. For example, a smart gas service platform can receive gas maintenance management information query instructions issued by a government user sub-platform, and send gas maintenance management information to the government user sub-platform. In some embodiments, the smart gas service platform is equipped with a smart gas usage service sub-platform, a smart operation service sub-platform, and a smart supervision service sub-platform.

[0025] In some embodiments, the smart gas service platform can interact with the smart gas management platform, send gas maintenance management information query instructions to the smart gas data center and receive gas maintenance management information uploaded by the smart gas data center; receive gas maintenance management information query instructions sent by the government user sub-platform and upload gas maintenance management information to the government user sub-platform, etc.

[0026] A smart gas management platform can refer to a platform that coordinates and integrates the connections and collaboration between various functional platforms, aggregating all information from the Internet of Things (IoT) and providing sensing, management, and control functions for the IoT operating system. For example, a smart gas management platform can obtain information on gas repair requests.

[0027] In some embodiments, the smart gas management platform includes a smart customer service management sub-platform, a smart operation management sub-platform, and a smart gas data center. Each management sub-platform can interact bidirectionally with the smart gas data center. The smart gas data center aggregates and stores all system operation data, while each management sub-platform can retrieve data from the smart gas data center and provide relevant operational information. For example, the smart gas data center can receive gas maintenance management information query commands from the operation service sub-platform and customer feedback information from the smart gas service sub-platform.

[0028] In some embodiments, the intelligent customer service management sub-platform can be used for revenue management, application management, message management, business owner management, customer service management, and customer analysis management, allowing users to view customer feedback information and provide corresponding responses. In some embodiments, the intelligent operation management sub-platform can be used for gas procurement management, gas usage scheduling management, pipeline engineering management, gas reserve management, sales difference management, and comprehensive office management, allowing users to view pipeline engineering work order information, personnel configuration, and progress, thus achieving pipeline engineering management.

[0029] In some embodiments, the intelligent operation management sub-platform and the intelligent service management sub-platform can interact with the intelligent gas service platform and the intelligent gas sensor network platform through the intelligent gas data center. In some embodiments, the intelligent gas data center can receive customer feedback information issued by the intelligent gas service platform, receive query instructions for gas maintenance management information issued by the intelligent gas service platform and upload gas maintenance management information to the intelligent gas service platform; it can also interact with the intelligent gas sensor network platform, sending instructions to obtain gas equipment-related data to the intelligent gas sensor network platform and receiving gas equipment-related data uploaded by the intelligent gas sensor network platform.

[0030] A smart gas sensor network platform can be a functional platform for managing sensor communication. It can be configured as a communication network and gateway, enabling functions such as network management, protocol management, command management, and data parsing.

[0031] In some embodiments, the smart gas sensor network platform may include a gas indoor equipment sensor network sub-platform and a gas pipeline equipment sensor network sub-platform, which correspond to the gas indoor equipment object sub-platform and the gas pipeline equipment object sub-platform, respectively, and are used to acquire relevant data of indoor equipment and relevant data of pipeline equipment (both of which belong to gas equipment related data).

[0032] In some embodiments, the smart gas sensor network platform can connect the smart gas management platform and the smart gas object platform to realize the functions of sensing and communication of perception information and control information. For example, the smart gas sensor network platform can receive gas equipment-related data uploaded by the smart gas object platform and send instructions to the smart gas object platform to obtain gas equipment-related data; it can also receive instructions from the smart gas data center to obtain gas equipment-related data and upload gas equipment-related data to the smart gas data center.

[0033] A smart gas management platform can be a functional platform for generating and executing sensing and control information, and can include gas equipment and other devices. Gas equipment can include indoor equipment and pipeline equipment. Other devices can include monitoring equipment, temperature sensors, pressure sensors, etc.

[0034] In some embodiments, the smart gas target platform may also include a sub-platform for indoor gas equipment and a sub-platform for gas pipeline equipment. The indoor gas equipment sub-platform may include indoor equipment, such as gas user metering equipment. The gas pipeline equipment sub-platform may include pipeline equipment, such as pressure regulating equipment, gas gate compressors, gas flow meters, valve control equipment, thermometers, and barometers. The indoor gas equipment sub-platform corresponds to the indoor gas equipment sensor network sub-platform; data related to indoor equipment is uploaded to the smart gas data center through the indoor gas equipment sensor network sub-platform. Similarly, the gas pipeline equipment sub-platform corresponds to the gas pipeline equipment sensor network sub-platform; data related to pipeline equipment is uploaded to the smart gas data center through the gas pipeline equipment sensor network sub-platform.

[0035] In some embodiments, the smart gas object platform can interact with the smart gas sensor network platform, receive instructions from the smart gas sensor network platform to obtain relevant data of the gas equipment, and upload the relevant data of the gas equipment to the smart gas sensor network platform.

[0036] One embodiment of this specification achieves informatization and intelligence through a closed-loop management system formed by five platform IoT functional architectures. By clearly defining the roles of each platform, user waiting costs are reduced, problem-solving efficiency is improved, and IoT information processing becomes smoother and more efficient.

[0037] Figure 2 This is an exemplary flowchart of a smart gas repair management method according to some embodiments of this specification. Figure 2 As shown, process 200 includes steps 210-250.

[0038] Step 210: Obtain historical gas usage data and historical gas repair data through the smart gas sensor network platform based on the smart gas object platform.

[0039] Historical gas usage data refers to data related to gas usage over a historical period. For example, historical gas usage data may include the amount of gas used during a specific historical period, the number of gas users during a specific historical period, etc.

[0040] Historical gas repair data can refer to data related to gas repairs over a historical period. In some embodiments, historical gas repair data may include at least the historical gas repair volume and the historical gas repair level. Further details regarding historical gas repair data can be found in step 230 and will not be repeated here.

[0041] In some embodiments, the smart gas management platform can obtain historical gas usage data and historical gas repair data through gas operator data, government gas operation platforms, etc.

[0042] Step 220: Based on historical gas usage data and historical gas repair data, generate multiple first gas repair areas.

[0043] The primary gas repair reporting area can refer to at least two sub-areas within the target area that have similar historical gas usage data and / or historical gas repair data. For example, the primary gas repair reporting area could be two residential communities with similar numbers of gas users or three residential communities with similar historical gas repair frequencies within the target area. Here, the target area refers to the management scope corresponding to the smart gas management platform. For example, the target area could be a city. Sub-areas refer to independent areas. For example, sub-areas can include residential communities, office buildings, etc.

[0044] In some embodiments, the smart gas management platform can directly classify sub-regions within a target area that have identical or similar historical gas usage data and historical gas repair data, and are geographically adjacent, into the same first gas repair area. Here, a sub-region with similar historical gas usage data and historical gas repair data refers to a sub-region where the difference between the historical gas usage data and historical gas repair data is within a first threshold, which can be set based on experience.

[0045] In some embodiments, by generating multiple first gas repair zones, regions with similar historical gas usage data and historical gas repair data, and those geographically adjacent, can be merged. This facilitates the subsequent scheduling and allocation of maintenance personnel, improves maintenance efficiency, reduces non-maintenance-related costs, and saves human and material resources.

[0046] Step 230: Based at least on historical gas usage data and historical gas repair data within multiple first gas repair areas, generate future gas repair data within multiple first gas repair areas.

[0047] In some embodiments, historical gas repair data may include at least the historical gas repair volume and the historical gas repair level.

[0048] Historical gas repair reports refer to the number of gas repair incidents that occurred at a specific location within a certain historical period. For more information on historical gas repair reports, please refer to the foregoing explanation and related descriptions.

[0049] Historical gas repair ratings refer to the severity of past gas repair incidents; the more severe the incident, the higher the rating. For example, historical gas repair ratings can be categorized as ordinary repair, serious repair, and emergency repair. An ordinary repair indicates that the gas appliance is malfunctioning but still usable; a serious repair indicates that the gas appliance is unusable; and an emergency repair indicates that the malfunction may endanger the user's life.

[0050] In some embodiments, historical gas repair ratings can be represented in other ways. For example, they can be represented by a number between 1 and 5, with a higher number indicating a more severe repair issue.

[0051] Future gas repair data can refer to data related to gas repairs in the predicted future time. In some embodiments, future gas repair data may include at least the future gas repair volume and the future gas repair level.

[0052] Future gas repair volume refers to the number of gas repair incidents occurring at a specific location within a future time period, while future gas repair level refers to the severity of future gas repair faults. Specific examples can be found in the aforementioned sections on historical gas repair volume and historical gas repair level, and will not be repeated here.

[0053] In some embodiments, the smart gas management platform can use the average of historical gas repair data for each first gas repair area across multiple historical time periods of equal length as the future gas repair data for that first gas repair area. For example, if the current time is the 20th week of 2025, the smart gas management platform can use the average of the gas repair data for the first day of the 19th week of 2025, the first day of the 18th week of 2025, and the first day of the 17th week of 2025 for the first gas repair area 1 as the gas repair data for the first day of the 21st week of 2025 for the first gas repair area 1. And so on, the gas repair data for the 21st week of 2025 for the first gas repair area 1 can be obtained (i.e., the future gas repair data).

[0054] In some embodiments, the intelligent gas management platform can input historical gas usage data and historical gas repair data from multiple first gas repair areas into a gas repair model. The gas repair model then processes this data to output future gas repair data for multiple first gas repair areas. The gas repair model is a machine learning model. For more information on generating future gas repair data for multiple first gas repair areas based on a gas repair model, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0055] Step 240: Based on multiple first gas repair areas and future gas repair data within the multiple first gas repair areas, generate multiple second gas repair areas and their corresponding future gas repair data.

[0056] A second gas repair area can refer to an area formed by the merger of multiple first gas repair areas. For example, second gas repair area A can be an area formed by the merger of first gas repair areas a and b, which have the same future gas repair data and are geographically adjacent.

[0057] In some embodiments, the smart gas management platform can divide a first gas repair area into a second gas repair area where the difference between multiple future gas repair data is less than a second threshold and they are geographically adjacent. The corresponding future gas repair data is the average of the future gas repair data of the multiple first gas repair areas.

[0058] In some embodiments, the intelligent gas management platform can generate regional attributes for multiple first gas repair areas based on multiple first gas repair areas and future gas repair data within those areas; and then merge these first gas repair areas based on the regional attributes to generate multiple second gas repair areas. More information on generating multiple second gas repair areas based on regional attributes can be found in [link to relevant documentation]. Figure 4 And its related descriptions.

[0059] Step 250: Generate a maintenance personnel arrangement plan based on future gas repair data from multiple second gas repair areas.

[0060] A maintenance personnel arrangement plan refers to a plan for arranging maintenance personnel to handle gas repair requests. For example, a maintenance personnel arrangement plan may include various information such as the number of maintenance personnel, repair time, and repair location. For instance, a maintenance personnel arrangement plan could be to arrange 3 maintenance personnel to perform repairs in the second gas repair area 1, 5 maintenance personnel to perform repairs in the second gas repair area 2, and 10 maintenance personnel to perform repairs in the second gas repair area 3 on January 1, 2025.

[0061] In some embodiments, the smart gas management platform can organize future gas repair requests and maintenance personnel arrangement plans into a first data lookup table, and determine the maintenance personnel arrangement plan based on this first data lookup table. For example, if the first data lookup table shows that the future gas repair requests are 0-10 and the future gas repair level is "normal," the maintenance personnel arrangement plan is to arrange 3 maintenance personnel to go to the repair location within 24 hours. Then, if the future gas repair requests for the second gas repair area 1 are 5 and the future gas repair level is "normal," the maintenance personnel arrangement plan is to arrange 3 maintenance personnel to go to the second gas repair area 1 within 24 hours.

[0062] In some embodiments, the smart gas management platform can generate maintenance personnel demand data based on future gas repair data within multiple second gas repair areas; and generate maintenance personnel arrangement plans based on the maintenance personnel demand data.

[0063] Maintenance personnel demand data can refer to data related to the demand for maintenance personnel, such as the demand for the number of maintenance personnel, maintenance time demand, and maintenance location demand.

[0064] In some embodiments, the smart gas management platform can organize future gas repair requests and maintenance personnel demand data into a second data lookup table, and determine the maintenance personnel demand data based on this second data lookup table. For example, in the second data lookup table, if the future gas repair requests are 0-10, the future gas repair level is severe, and the maintenance personnel demand data is a repair time requirement of within 6 hours and a maintenance personnel requirement of 2 per incident, then if the future gas repair requests for the second gas repair area 1 are 3 and the future gas repair level is severe, the maintenance personnel arrangement plan is to dispatch 6 maintenance personnel to the second gas repair area 1 for repairs within 6 hours.

[0065] In some embodiments, the smart gas management platform can aggregate the obtained maintenance personnel demand data and directly generate a maintenance personnel arrangement plan. In some embodiments, the smart gas management platform can manually adjust the maintenance personnel demand data obtained from the smart gas user platform through the smart gas service platform, and then aggregate the manually adjusted maintenance personnel demand data to generate a maintenance personnel arrangement plan.

[0066] In some embodiments of this specification, maintenance personnel demand data is generated based on future gas repair data within multiple second gas repair areas, thereby generating a maintenance personnel arrangement plan. This makes the process of determining the maintenance personnel arrangement plan more reasonable, more in line with the actual situation when repairs occur in different areas, more efficient, and saves labor costs.

[0067] In some embodiments of this specification, by acquiring historical gas usage data and historical gas repair data, a first gas repair area is generated, followed by a second gas repair area and its future gas repair data. This generates a maintenance personnel arrangement plan, which can determine the future gas repair data for different areas in real time and dynamically. This facilitates the generation of accurate and realistic maintenance personnel arrangement plans, shortens the time required to determine maintenance personnel arrangement plans, saves labor costs, and improves the processing efficiency of gas repairs. It also allows government gas operation departments and users to obtain and implement maintenance personnel arrangement plans in a timely manner.

[0068] Figure 3 This is a schematic diagram illustrating the process of generating future gas repair data within multiple first gas repair areas based on a gas repair model, according to some embodiments of this specification.

[0069] In some embodiments, the intelligent gas management platform can input historical gas usage data and historical gas repair data from multiple first gas repair areas into the gas repair model, process the historical gas usage data and historical gas repair data from multiple first gas repair areas using the gas repair model, and output future gas repair data from multiple first gas repair areas.

[0070] In some embodiments, the gas repair reporting model can be a model for determining future gas repair data within multiple first gas repair reporting areas. In some embodiments, the gas repair reporting model can be a machine learning model. For example, the gas repair reporting model can be a neural network (NN), deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), Transformer model, or any combination thereof.

[0071] In some embodiments, the input to the gas repair model may include historical gas usage data and historical gas repair data within multiple first gas repair areas, and the output of the gas repair model may include future gas repair data within multiple first gas repair areas.

[0072] In some embodiments, such as Figure 3 As shown, the gas repair reporting model 320 may include a feature extraction layer 320-1 and a prediction layer 320-2.

[0073] The feature extraction layer 320-1 takes as input historical gas usage data 310-1 and historical gas repair data 310-2 from multiple first gas repair areas, and outputs as regional features 330 from multiple first gas repair areas. The prediction layer 320-2 takes as input regional features 330 from multiple first gas repair areas, and outputs as future gas repair data 340 from multiple first gas repair areas.

[0074] In some embodiments, the feature extraction layer 320-1 can be a Transformer model.

[0075] The feature extraction layer 320-1 can be used to determine the regional features 330 within multiple first gas repair areas. The input to the feature extraction layer 320-1 may include historical gas usage data 310-1 and historical gas repair data 310-2 within multiple first gas repair areas, and the output may include the regional features 330 within multiple first gas repair areas.

[0076] For example, historical gas usage data 310-1 within multiple gas repair reporting areas may include a sequence of daily gas usage and the number of gas users for a historical week within the gas repair reporting area 1. The historical data of gas usage and number of gas users for one week in Gas Repair Area 2. The first row represents the amount of gas used, and the second row represents the number of gas users.

[0077] Historical gas repair data 310-2 from multiple first gas repair areas may include a sequence consisting of the historical daily gas repair volume for one week in first gas repair area 1 and the corresponding number of historical gas repairs at each level. The sequence of historical gas repair volume and the corresponding number of each gas repair level for one week in Gas Repair Area 2. The first row represents the historical number of gas repair requests, and the second row represents the number of gas repair requests of each level (the three numbers represent the number of ordinary repairs, the number of serious repairs, and the number of emergency repairs, respectively).

[0078] The regional characteristics 330 within multiple first gas repair areas can refer to characteristic information related to gas usage and gas repairs within the first gas repair area, such as usage characteristics and repair characteristics. In some embodiments, usage characteristics may include the number of gas users corresponding to the first gas repair area, gas consumption, and usage frequency; repair characteristics may include the number of repairs corresponding to the first gas repair area, repair frequency, duplicate repair rate, and the number of each repair level. The duplicate repair rate refers to the proportion of the number of the same gas repair event occurring twice or more at a certain location within a certain time period to the total number of repairs. The duplicate repair rate is used to predict the number of potential recurrences.

[0079] In some embodiments, prediction layer 320-2 can be a CNN model.

[0080] The prediction layer 320-2 can be used to determine future gas repair data 340 within multiple first gas repair areas. The input to the prediction layer 320-2 may include regional features 330 within multiple first gas repair areas, and the output may include future gas repair data 340 within multiple first gas repair areas.

[0081] For example, the future gas repair data 340 for multiple first gas repair areas may include a sequence consisting of the future daily gas repair volume for the first gas repair area 1 for the next week and the corresponding number of future gas repair levels. A sequence consisting of the daily future gas repair volume and the corresponding number of gas repair requests for each gas repair level for the next week in Gas Repair Area 2, for the first gas repair area. The first row represents the future number of gas repair requests, and the second row represents the number of gas repair requests of each level (the three numbers represent the number of ordinary repairs, the number of serious repairs, and the number of emergency repairs, respectively).

[0082] In some embodiments, the input to the prediction layer may also include time data 310-3.

[0083] Time data 310-3 can refer to time information including peak and off-peak times. Peak times refer to times when gas consumption is high, such as weekends and holidays; off-peak times refer to times when gas consumption is low, such as weekdays.

[0084] In some embodiments, time data can be represented by a vector. For example, time data can be a vector [0, 0, 0, 0, 0, 1, 1] corresponding to a historical week, where 1 represents peak time (such as weekends) and 0 represents off-peak time (such as midweek).

[0085] In some embodiments, time data 310-3 can be determined based on usage characteristics in the area characteristics 330 of multiple first gas repair areas. For example, if the number of gas users, gas consumption, and usage frequency in the usage characteristics of a certain first gas repair area all exceed the corresponding preset thresholds, then the time corresponding to that usage characteristic is determined to be a peak time; otherwise, the time corresponding to that usage characteristic is determined to be an off-peak time. The preset thresholds can be set based on experience. In some embodiments, time data 310-3 can be merged into one element of the usage characteristics in the area characteristics 330 of multiple first gas repair areas.

[0086] In some embodiments of this specification, by incorporating time data into the input of the prediction layer, the impact of time factors on future gas repair data can be considered, thereby determining more accurate future gas repair data.

[0087] In some embodiments, the feature extraction layer 320-1 and the prediction layer 320-2 can be obtained through joint training. For example, the historical gas usage data and historical gas repair data of the sample first gas repair area corresponding to the sample time are input into the feature extraction layer 320-1 to obtain the regional features of the sample first gas repair area corresponding to the sample time output by the feature extraction layer 320-1; the regional features of the sample first gas repair area corresponding to the sample time output by the feature extraction layer 320-1 are input into the prediction layer 320-2 to obtain the future gas repair data of the first gas repair area corresponding to the sample time output by the prediction layer 320-2.

[0088] The labels for training samples can be obtained based on future gas repair data within the first gas repair reporting area corresponding to the sample time in historical data. The future time period corresponding to the future gas repair data is the time period in the historical data. During training, the gas repair model 320 can construct a loss function based on the labels and the output of the prediction layer 320-2. Simultaneously, the parameters of the feature extraction layer 320-1 and the prediction layer 320-2 are updated until preset conditions are met, at which point training is complete. These preset conditions can be one or more of the following: the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0089] When the input to the prediction layer 320-2 includes time data 310-3, the training samples also include sample time data. The regional features and time data 310-3 of the sample time corresponding to the first gas repair area output by the feature extraction layer 320-1 can be input together into the prediction layer 320-2.

[0090] In some embodiments of this specification, a gas repair reporting model comprising a feature extraction layer and a prediction layer processes historical gas usage data, historical gas repair data, and time data within multiple first gas repair reporting areas to obtain future gas repair data for multiple first gas repair reporting areas. This helps to solve the problem of difficulty in obtaining labels when training the feature extraction layer alone. Furthermore, jointly training the feature extraction layer and the prediction layer not only reduces the number of samples required but also improves training efficiency.

[0091] In some embodiments of this specification, by generating future gas repair data within multiple first gas repair areas based on a gas repair model, it is possible to determine future gas repair data more accurately by combining actual conditions, thereby reducing the manpower costs and resource waste required for manual assessment and determination.

[0092] Figure 4 This is an exemplary flowchart illustrating the generation of multiple second gas repair reporting areas and their corresponding future gas repair reporting data, according to some embodiments of this specification. Figure 4 As shown, process 400 includes steps 410-420.

[0093] Step 410: Based on multiple first gas repair areas and future gas repair data within multiple first gas repair areas, generate regional attributes for multiple first gas repair areas.

[0094] Regional attributes can refer to information related to the geographical location of a region and gas repair requests. For example, regional attributes may include the center coordinates of multiple primary gas repair areas, the future gas repair volume in multiple primary gas repair areas, the future gas repair frequency in multiple primary gas repair areas, and the future gas repair level in multiple primary gas repair areas.

[0095] The center location coordinates can refer to the location coordinates of the geometric center of a certain area. For example, the center location coordinates of the first gas repair area 1 can be the location coordinates (x1, y1) of the geometric center point A.

[0096] Future gas repair frequency refers to the frequency of gas repair incidents occurring at a specific location within a future time period. For example, future gas repair frequency could mean that the frequency of gas repair incidents in Gas Repair Area 1 (the first gas repair area) is 10 incidents per day over the next two days. More information on future gas repair volume and future gas repair levels across multiple Gas Repair Areas (the first gas repair area) can be found here. Figure 1 And its related descriptions.

[0097] In some embodiments, the smart gas management platform can generate regional attributes in various ways. For example, the smart gas management platform can obtain the center coordinates of the first gas repair area through web crawling, third-party platforms, or internal or external storage devices of the smart gas repair management IoT system. As another example, the smart gas management platform can obtain the future gas repair volume, future gas repair frequency, and future gas repair level within multiple first gas repair areas through a gas repair model.

[0098] In some embodiments, the frequency of determining a region attribute is related to time data. The frequency of determining a region attribute can refer to the number of times a region attribute is determined per unit of time; for example, the frequency of determining a region attribute could be once per day. More information on time data can be found at [link to relevant documentation]. Figure 3 And its related descriptions.

[0099] In some embodiments, the smart gas management platform can determine the frequency of regional attributes based on time data. For example, during peak hours (such as weekends), the frequency of determining regional attributes can be 5 times / day; during off-peak hours (such as midweek), the frequency of determining regional attributes can be 1 time / day.

[0100] In some embodiments of this specification, by determining the frequency of regional attributes relative to time data, the frequency of determining regional attributes can be flexibly adjusted according to the actual use of gas, so that the determined regional attributes are real-time and accurate.

[0101] Step 420: Merge multiple first gas repair areas based on regional attributes to generate multiple second gas repair areas.

[0102] In some embodiments, the smart gas management platform can construct a regional attribute vector based on the regional attributes of multiple first gas repair areas. For example, the smart gas management platform can construct a regional attribute vector ((x1, y1), 5, 5, (4, 1, 0)) for the first gas repair area 1 based on its center coordinates (x1, y1), the number of gas repairs in the next day (5), the frequency of gas repairs in the next day (5) / day, and the future gas repair level (4 for normal, 1 for severe, and 0 for emergency). The smart gas management platform can calculate the distance between the regional attribute vectors of multiple first gas repair areas. If the distance is less than a third threshold, multiple first gas repair areas that are geographically adjacent and whose regional attribute vectors are less than the third threshold can be merged to generate multiple second gas repair areas. The third threshold can be set empirically. For more information on constructing regional attribute vectors, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0103] In some embodiments, the intelligent gas management platform can perform cluster analysis based on regional attributes to generate multiple secondary gas repair reporting areas. For more information on generating multiple secondary gas repair reporting areas based on regional attributes, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0104] In some embodiments of this specification, multiple first gas repair areas and their future gas repair data are used to generate area attributes, which are then further integrated to generate multiple second gas repair areas. This allows for the determination of second gas repair areas based on various factors, making the determination process more accurate and efficient.

[0105] Figure 5 This is an exemplary schematic diagram illustrating the generation of multiple second gas repair zones according to some embodiments of this specification.

[0106] In some embodiments, the intelligent gas management platform can perform cluster analysis based on regional attributes to generate multiple second gas repair reporting areas. Each cluster obtained from the cluster analysis is a second gas repair reporting area, and the cluster centers of the cluster analysis are determined based on the future gas repair frequency and future gas repair level within the multiple first gas repair reporting areas.

[0107] The number of basic cluster centers can refer to the pre-set number of cluster centers that need to be adjusted. In some embodiments, the smart gas management platform can obtain the manually set number of basic cluster centers through the government user sub-platform.

[0108] In some embodiments, the number of cluster centers may be related to the average future gas repair volume of multiple first gas repair areas. The higher the average future gas repair volume, the more maintenance personnel are needed, and the target area composed of the aforementioned multiple first gas repair areas can be divided into more second gas repair areas, and the number of cluster centers set accordingly is greater.

[0109] In some embodiments, the smart gas management platform can adjust the number of basic cluster centers based on the average future gas repair requests in the target area to determine the total number of cluster centers. If the average future gas repair requests in the target area are high, the smart gas management platform can increase the number of basic cluster centers, and vice versa. For example, if the average future gas repair requests are p, the corresponding number of basic cluster centers is q. If the average future gas repair requests in the target area increase by a relative to the average future gas repair requests, then the number of cluster centers in the target area increases by n relative to the number of basic cluster centers.

[0110] In some embodiments, the smart gas management platform can determine the cluster center locations for cluster analysis based on the future gas repair frequency and the future gas repair level within multiple first gas repair areas. For example, the smart gas management platform can perform a weighted calculation by combining the number of future gas repairs at the emergency level with the future gas repair frequency, sort the calculated values ​​in descending order, and select the first gas repair area corresponding to the value that satisfies the number of cluster centers as the cluster center. The weight can be determined based on experience. For example, if the number of cluster centers is 3, the number of future gas repairs at the emergency level in first gas repair areas 1-5 are 10, 8, 9, 4, and 3 respectively, with a weight of 0.6 for the number of future gas repairs at the emergency level, and the future gas repair frequencies are 18, 20, 17, 6, and 8 per day respectively, with a weight of 0.4 for the future gas repair frequency, then the calculated values ​​are 13.2, 12.8, 12.2, 4.8, and 5 respectively. The calculated values ​​were arranged in descending order as 13.2, 12.8, 12.2, 5, 4.8. The first three gas repair areas corresponding to the top three values ​​were selected as cluster centers.

[0111] By determining the cluster centers in cluster analysis based on the future gas repair frequency and future gas repair level within multiple first-level gas repair areas, the number of emergency repairs and the first-level gas repair areas with higher future repair frequencies can be more evenly distributed across the second-level gas repair areas. This avoids situations where a particular area experiences a concentration of emergency repairs or a high frequency of repairs that cannot be processed in a timely manner, thus impacting user experience.

[0112] In some embodiments, the intelligent gas management platform can cluster multiple first gas repair areas based on the distance between the center location of each first gas repair area and the center location of the cluster center to determine the second gas repair area. In some embodiments, the clustering criterion used by the intelligent gas management platform is the distance from the center location of the first gas repair area to the center location of the cluster center, which can be determined based on the center location coordinates in the area attributes. In some embodiments, the clustering frequency is related to the frequency of determining area attributes; the higher the frequency of determining area attributes, the higher the clustering frequency. The clustering frequency can refer to the number of times clustering is performed per unit time; for example, the clustering frequency could be 1 time per day. More information on the frequency of determining area attributes can be found in [link to relevant documentation]. Figure 4 And its related descriptions.

[0113] like Figure 5 As shown, the first gas repair area can be represented by a small hexagonal region. In some embodiments, the first gas repair area can also be represented by other shapes, as long as the overall area composed of multiple first gas repair area shapes has no gaps. (x1, y1) to (x n y n ) are the center coordinates of the first gas repair area 1-n, (x6, y6) and (x 12 y 12 ), (x 15 y 15 The cluster centers are determined based on the aforementioned content. The specific steps of clustering are as follows:

[0114] S510. Based on the center coordinates of the first gas repair area to be assigned (hereinafter referred to as the assigned area) and the location coordinates of the cluster centers, calculate the distance between the assigned area and multiple cluster centers.

[0115] In some embodiments, the intelligent gas management platform can calculate the distance between the area to be allocated and multiple cluster centers using various methods, including but not limited to Euclidean distance, cosine distance, Mahalanobis distance, Chebyshev distance, and / or Manhattan distance. For example, Figure 5As shown, the intelligent gas management platform can calculate the center coordinates (x1, y1) of the first gas repair area 1 to be assigned to multiple cluster centers, namely the first gas repair area 6 (x6, y6) and the first gas repair area 12 (x1, y1). 12 y 12 ), First Gas Repair Area 15 (x 15 y 15 The distance.

[0116] S520. Compare the distances between the determined region to be assigned and multiple cluster centers to determine the shortest distance.

[0117] In some embodiments, the intelligent gas management platform can sort the distances between a determined area to be allocated and multiple cluster centers from shortest to longest, and determine the shortest distance based on the sorting results. For example, Figure 5 As shown, the intelligent gas management platform can map the central location coordinates (x1, y1) of the first gas repair area 1 to multiple cluster centers (x6, y6), (x... 12 y 12 ), (x 15 y 15 Sort the distances l1, l2, and l3 from shortest to longest. <l3<l2。

[0118] S530. In response to the fact that there is only one shortest distance, the region to be assigned is assigned to the cluster center corresponding to the shortest distance.

[0119] In some embodiments, if only one shortest distance exists, the smart gas management platform can directly assign the area to be allocated to the cluster center corresponding to that shortest distance. For example, such as Figure 5 As shown, the intelligent gas management platform can assign the first gas repair area 1 to the first gas repair area 6 corresponding to the cluster center (x6, y6).

[0120] S540. In response to the existence of multiple shortest distances, the region to be allocated and the cluster centers corresponding to its multiple shortest distances are stored as a set of data in the secondary allocation set.

[0121] In some embodiments, if multiple shortest distances exist, the smart gas management platform can store the area to be allocated and the cluster centers corresponding to its multiple shortest distances as a set of data in a secondary allocation set. For example, such as Figure 5 As shown, if the first gas repair area 9 (x9, y9) is far from the cluster center, the first gas repair area 6 (x6, y6) and the first gas repair area 15 (x9, y9) are... 15 y 15 If the distances between the elements are all shortest distances, then these elements are stored as a set of data in the secondary allocation set.

[0122] S550: Select the next region to be allocated, and repeat S510 to S540 until all regions to be allocated have been traversed.

[0123] S560. For each set of data in the secondary allocation set, determine the current future gas repair requests of the clusters corresponding to the multiple cluster centers in each set of data, and assign the unallocated area in that set of data to the cluster with the fewest current future gas repair requests among the multiple nearest clusters. If there are clusters with the same current future gas repair requests, the area can be randomly assigned to any cluster with the fewest current future gas repair requests, or to a cluster center with fewer future gas repair requests at the emergency level and a lower future gas repair frequency, until all unallocated areas in the set have been allocated. The method for determining the cluster centers with fewer future gas repair requests at the emergency level and a lower future gas repair frequency can refer to the method for determining the location of the cluster centers described above.

[0124] The current future gas repair volume can refer to the sum of future gas repair requests from all first gas repair areas currently assigned to this cluster. For example... Figure 5 As shown, the distance between the first gas repair reporting area 9 (x9, y9) and the cluster center, the first gas repair reporting area 6 (x6, y6), and the first gas repair reporting area 15 (x9, y9) are... 15 y 15 The distances between clusters are all shortest distances. If the current future gas repair requests for cluster 1 (corresponding to cluster center (x6, y6)) are 500, then the current future gas repair requests for cluster 2 (corresponding to cluster center (x6, y6)) are also shortest distances. 15 y 15 If the current future gas repair volume is 300, then the smart gas management platform can assign the first gas repair area 9 (x9, y9) to cluster 2.

[0125] By performing secondary allocation during the clustering process, the areas to be allocated are assigned to the cluster with the fewest future gas repair requests. This makes the future gas repair requests in each secondary gas repair area as balanced as possible, which facilitates the balanced allocation of maintenance personnel, improves maintenance efficiency, and enhances user experience.

[0126] In some embodiments of this specification, by relating the number of cluster centers to the average future gas repair volume of multiple first gas repair areas, a second gas repair area can be divided according to actual maintenance needs, so as to arrange personnel more precisely.

[0127] In some embodiments, the smart gas repair management device includes a processor and a memory; the memory is used to store instructions, which, when executed by the processor, cause the device to implement the smart gas repair management method.

[0128] This specification also provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the smart gas repair management method as described in any of the above embodiments.

[0129] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart gas repair management method, characterized in that, The method is implemented by a smart gas repair management IoT system, which includes a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The method is executed by a processor in the smart gas management platform and includes: The intelligent gas sensor network platform acquires historical gas usage data and historical gas repair data based on the intelligent gas object platform. Based on the historical gas usage data and the historical gas repair data, multiple first gas repair areas are generated. Based at least on historical gas usage data and historical gas repair data within the plurality of first gas repair areas, future gas repair data within the plurality of first gas repair areas is generated. The historical gas repair data includes at least historical gas repair volume and historical gas repair level, and the future gas repair data includes at least future gas repair volume and future gas repair level. Based on the plurality of first gas repair reporting areas and the future gas repair reporting data within the plurality of first gas repair reporting areas, a plurality of second gas repair reporting areas and their corresponding future gas repair reporting data are generated, including: Based on the plurality of first gas repair areas and the future gas repair data within the plurality of first gas repair areas, regional attributes are generated for the plurality of first gas repair areas. The regional attributes include the center coordinates of the plurality of first gas repair areas, the future gas repair volume within the plurality of first gas repair areas, the future gas repair frequency within the plurality of first gas repair areas, and the future gas repair level within the plurality of first gas repair areas. Based on the aforementioned regional attributes, the plurality of first gas repair reporting regions are merged to generate the plurality of second gas repair reporting regions, including: Based on the aforementioned regional attributes, cluster analysis is performed to generate the plurality of second gas repair reporting regions. Each cluster obtained from the cluster analysis is a second gas repair reporting region. The cluster centers of the cluster analysis are determined based on the future gas repair frequency and the future gas repair level within the plurality of first gas repair reporting regions. The cluster analysis further includes: Based on the center coordinates of the first gas repair area to be assigned and the location coordinates of the cluster centers, the distance between the first gas repair area to be assigned and the multiple cluster centers is calculated; The distances between the first gas repair area to be assigned and the multiple cluster centers are compared to determine the shortest distance; When the number of the shortest distances is one, the first gas repair area to be assigned is assigned to the cluster center corresponding to the shortest distance; When there are multiple shortest distances, the first gas repair area to be allocated and the cluster centers corresponding to the multiple shortest distances are stored as a set of data in the secondary allocation set; For each set of data in the secondary allocation set, determine the current and future gas repair requests of the corresponding clusters of multiple cluster centers in each set of data, and assign the first gas repair request area to be allocated in that set of data to the cluster with the fewest current and future gas repair requests among the multiple nearest clusters; and Based on the future gas repair data in the multiple second gas repair areas, a maintenance personnel arrangement plan is generated.

2. The method according to claim 1, characterized in that, The Internet of Things system also includes a smart gas user platform and a smart gas service platform that interact sequentially. The intelligent gas user platform includes a gas user sub-platform, a government user sub-platform, and a regulatory user sub-platform, wherein the gas user sub-platform corresponds to gas users, the government user sub-platform corresponds to government users, and the regulatory user sub-platform corresponds to regulatory users; and The smart gas service platform includes a smart user service sub-platform, a smart operation service sub-platform, and a smart supervision service sub-platform. The smart user service sub-platform corresponds to the gas user sub-platform, the smart operation service sub-platform corresponds to the government user sub-platform, and the smart supervision service sub-platform corresponds to the supervision user sub-platform.

3. The method according to claim 1, characterized in that, The smart gas management platform includes a smart customer service management sub-platform, a smart operation management sub-platform, and a smart gas data center. The smart customer service management sub-platform interacts bidirectionally with the smart gas data center, and the smart operation management sub-platform also interacts bidirectionally with the smart gas data center. The smart customer service management sub-platform and the smart operation management sub-platform obtain data from the smart gas data center and provide corresponding operational information. The smart gas target platform includes a sub-platform for indoor gas equipment and a sub-platform for gas pipeline equipment. The indoor gas equipment sub-platform corresponds to the indoor equipment of the gas user, and the gas pipeline equipment sub-platform corresponds to the pipeline equipment corresponding to the gas user. The intelligent gas sensing network platform includes a gas indoor equipment sensing network sub-platform and a gas pipeline equipment sensing network sub-platform, wherein the gas indoor equipment sensing network sub-platform corresponds to the gas indoor equipment object sub-platform, and the gas pipeline equipment sensing network sub-platform corresponds to the gas pipeline equipment object sub-platform.

4. The method according to claim 1, characterized in that, The step of generating future gas repair data for the plurality of first gas repair areas based at least on historical gas usage data and historical gas repair data within the plurality of first gas repair areas includes: The historical gas usage data and historical gas repair data in the plurality of first gas repair areas are input into the gas repair model. The gas repair model is used to process the historical gas usage data and historical gas repair data in the plurality of first gas repair areas, and outputs the future gas repair data in the plurality of first gas repair areas. The gas repair model is a machine learning model.

5. The method according to claim 1, characterized in that, The process of generating a maintenance personnel arrangement plan based on the future gas repair data within the multiple second gas repair areas includes: Based on the future gas repair data in the multiple second gas repair areas, generate maintenance personnel demand data; Based on the maintenance personnel demand data, the maintenance personnel arrangement plan is generated.

6. A smart gas repair management IoT system, characterized in that, It includes a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform is used for: The intelligent gas sensor network platform acquires historical gas usage data and historical gas repair data based on the intelligent gas object platform. Based on the historical gas usage data and the historical gas repair data, multiple first gas repair areas are generated. Based at least on historical gas usage data and historical gas repair data within the plurality of first gas repair areas, future gas repair data within the plurality of first gas repair areas is generated. The historical gas repair data includes at least historical gas repair volume and historical gas repair level, and the future gas repair data includes at least future gas repair volume and future gas repair level. Based on the plurality of first gas repair reporting areas and the future gas repair reporting data within the plurality of first gas repair reporting areas, a plurality of second gas repair reporting areas and their corresponding future gas repair reporting data are generated, including: Based on the plurality of first gas repair areas and the future gas repair data within the plurality of first gas repair areas, regional attributes are generated for the plurality of first gas repair areas. The regional attributes include the center coordinates of the plurality of first gas repair areas, the future gas repair volume within the plurality of first gas repair areas, the future gas repair frequency within the plurality of first gas repair areas, and the future gas repair level within the plurality of first gas repair areas. Based on the aforementioned regional attributes, the plurality of first gas repair reporting regions are merged to generate the plurality of second gas repair reporting regions, including: Based on the aforementioned regional attributes, cluster analysis is performed to generate the plurality of second gas repair reporting regions. Each cluster obtained from the cluster analysis is a second gas repair reporting region. The cluster centers of the cluster analysis are determined based on the future gas repair frequency and the future gas repair level within the plurality of first gas repair reporting regions. The cluster analysis further includes: Based on the center coordinates of the first gas repair area to be assigned and the location coordinates of the cluster centers, the distance between the first gas repair area to be assigned and the multiple cluster centers is calculated; The distances between the first gas repair area to be assigned and the multiple cluster centers are compared to determine the shortest distance; When the number of the shortest distances is one, the first gas repair area to be assigned is assigned to the cluster center corresponding to the shortest distance; When there are multiple shortest distances, the first gas repair area to be allocated and the cluster centers corresponding to the multiple shortest distances are stored as a set of data in the secondary allocation set; For each set of data in the secondary allocation set, determine the current and future gas repair requests of the corresponding clusters of multiple cluster centers in each set of data, and assign the first gas repair request area to be allocated in that set of data to the cluster with the fewest current and future gas repair requests among the multiple nearest clusters; and Based on the future gas repair data in the multiple second gas repair areas, a maintenance personnel arrangement plan is generated.

7. A smart gas repair management device, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the smart gas repair management method as described in any one of claims 1 to 5.

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