Intelligent gas metering management and analysis platform

Abstract

The present application relates to a kind of wisdom gas metering management and analysis platform, platform includes customer management module, equipment management module, operation and maintenance management module and data analysis module;Realize the information maintenance of multiple customers, and using equipment management module to carry out information maintenance to equipment, operation and maintenance management module can make inspection plan, calibration plan and operation and maintenance plan, realize the continuous dynamic tracking of gas in use, data analysis module can analyze and evaluate the gas consumption of customer, realize the accurate monitoring and early warning when gas is used abnormally, give optimization strategy to existing gas consumption, improve customer experience comfort, realized the rational use of gas data, improve the safety of gas use, give full play to the use advantage of gas.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a smart gas metering management and analysis platform. BACKGROUND

[0002] Natural gas is mainly composed of methane, compared with traditional fossil fuels such as coal and oil, the pollutant emissions generated during the combustion of gas are lower, which reduces the emission of atmospheric pollution and greenhouse gases. Gas combustion almost does not produce solid particles, sulfides and nitrogen oxides and other harmful substances, and has less impact on the environment, and gas can provide higher energy utilization efficiency. This makes gas able to meet large-scale energy demand, suitable for residential, industrial and commercial use, such as gas has a wide range of applications, suitable for heating, cooking, power generation, industrial manufacturing and many other fields.

[0003] The existing gas use process will be equipped with a dedicated gas meter to realize gas metering and gas on-off protection. In the management of gas, since the gas meter only has the functions of meter reading, metering and safety protection, it is impossible to reasonably integrate and sort the gas data used by each customer, and it is impossible to use gas data to realize accurate and optimized management of multiple customers. SUMMARY

[0004] In view of the above problems, the present application provides a smart gas metering management and analysis platform, which solves the problem that the existing gas data cannot be reasonably utilized.

[0005] In order to achieve the above purpose, the present application provides a smart gas metering management and analysis platform, which comprises a customer management module, an equipment management module, an operation and maintenance management module and a data analysis module. The customer management module is used for adding, modifying, deleting and managing customer information of customers, and the customer information comprises at least one of customer name information, customer number information, customer address information, customer telephone information, customer level information and customer state information. The equipment management module is used for adding, modifying, deleting and managing at least one equipment information associated with each customer information, and each equipment information comprises at least one of equipment quantity information, equipment model information, equipment state information and equipment metering value. The operation and maintenance management module is used for obtaining and editing operation and maintenance information corresponding to each equipment information, and the operation and maintenance information comprises inspection task information, operation and maintenance task information and calibration task information. The data analysis module is used for data analysis on equipment information in a preset analysis range, and the preset analysis range comprises a customer analysis range and an equipment analysis range. A first analysis report corresponding to the current customer information is obtained according to the customer analysis range. First evaluation information is generated according to the first analysis report. A second analysis report corresponding to specified equipment information is obtained according to the equipment analysis range. Second evaluation information is generated according to the second analysis report.

[0006] In some embodiments, the first analysis report corresponding to the current customer information according to the customer analysis range comprises:

[0007] Obtaining all device information associated with the current customer information, and sequentially arranging the all device information to obtain a first device information table;

[0008] Drawing the device metering value in the device information according to the first preset time period one by one to obtain a first consumption curve corresponding to the current device information;

[0009] Integrating the plurality of first consumption curves into the same chart to obtain a first consumption curve chart;

[0010] And, dividing the first preset time period according to the minimum time unit, and accumulating the device metering value of all device information associated with the customer information according to the minimum time unit to obtain a plurality of first device metering sum values within the first preset time period;

[0011] Generating a second consumption curve associated with the current customer information according to the plurality of first device metering sum values to obtain a second consumption curve chart;

[0012] Obtaining the first analysis report according to the first device information table, the first consumption curve and the second consumption curve.

[0013] In some embodiments, the first evaluation information is generated according to the first analysis report, comprising:

[0014] Obtaining the first consumption curve and the second consumption curve within the previous first preset time period, denoted as the first historical consumption curve and the second historical consumption curve;

[0015] Matching the first consumption curve within the current first preset time period with the first historical consumption curve one by one to obtain a first matching result;

[0016] Judging whether the first matching result has an abnormal value, if yes, obtaining the number of abnormal values in the first matching result and the time stamp when the abnormal value is generated, denoted as the first abnormal value and the first abnormal time stamp, and mapping and storing the first abnormal value and the first abnormal time stamp corresponding thereto to generate the first abnormal information of the current device information;

[0017] Matching the second consumption curve within the current first preset time period with the second historical consumption curve to obtain a second matching result;

[0018] Judging whether the second matching result has an abnormal value, if yes, obtaining the number of abnormal values in the second matching result and the time stamp when the abnormal value is generated, denoted as the second abnormal value and the second abnormal time stamp, and mapping and storing the second abnormal value and the second abnormal time stamp corresponding thereto to generate the second abnormal information;

[0019] The first inspection task obtains multiple device information based on the first anomaly information, and the first optimization strategy obtains current customer information based on the second anomaly information;

[0020] The first evaluation information is obtained by organizing the first abnormal information, the second abnormal information, the first inspection task, and the first optimization strategy.

[0021] In some embodiments, the first inspection task includes at least one of the following: a first inspection cycle, a first time node for the next inspection, a first inspection list, a first inspection personnel, a first inspection suggestion, and a first inspection location. The first inspection list includes the equipment models that need to be inspected during the next inspection, and the first inspection suggestion includes the inspection suggestion corresponding to each equipment model.

[0022] The first inspection task, which obtains multiple device information based on the first anomaly information, includes:

[0023] First device anomaly information is generated based on the first anomaly information. The first device anomaly information includes at least one of the following: first device model, first device operating location, first anomaly value, and timestamp corresponding to the first anomaly value.

[0024] The first device anomaly information is input into the first neural network model to obtain the first pre-diagnosis result corresponding to the first device anomaly information;

[0025] Based on the first pre-diagnosis result, a first inspection suggestion corresponding to the current first equipment anomaly information is generated, and the first inspection suggestion is mapped and stored with the first equipment anomaly information to obtain the first inspection task.

[0026] In some embodiments, the first optimization strategy includes at least one of multiple first device model replacement information and multiple first device runtime improvement information;

[0027] The first optimization strategy for obtaining current customer information based on the second anomaly information includes:

[0028] The second outlier is obtained to generate the corresponding second device outlier information. The second device outlier information includes at least one of the following: the second device model, the second device operating location, and the second device operating duration.

[0029] The abnormal information of the second device is input into the second neural network model to obtain at least one of the first device model replacement information and the first device running time improvement information corresponding to the abnormal information of the second device.

[0030] Based on the first equipment model replacement information and the first equipment running time improvement information, a first optimization suggestion corresponding to the current second equipment abnormality information is generated, and the first optimization suggestion is mapped and stored with the second equipment abnormality information to obtain the first optimization strategy.

[0031] In some embodiments, obtaining a second analysis report corresponding to specified device information based on the device analysis scope includes:

[0032] Arrange the specified device information and its associated customer information in sequence to obtain the second device information table;

[0033] Construct a device profile for each piece of equipment information, and map and store the device profile with the equipment information. The device profile includes at least one of the following: equipment model information, equipment status information, equipment metering value, equipment installation record, equipment calibration record, equipment inspection record, and equipment operation and maintenance record corresponding to the current equipment information.

[0034] The second analysis report is obtained based on the second equipment information table and equipment profile.

[0035] In some embodiments, constructing a device profile for each device information includes:

[0036] The equipment image is matched with the equipment model information and recorded as the equipment image information. The equipment model information, equipment image information and equipment status information are then organized in a list to obtain the third equipment information table.

[0037] The equipment timeline is generated based on the equipment installation record, equipment calibration record, equipment inspection record, and equipment operation and maintenance record.

[0038] The equipment calibration records in the equipment information are plotted according to the second preset time period to obtain the calibration record curve corresponding to the current equipment information;

[0039] Obtain the first usage curve associated with the equipment information;

[0040] The equipment profile is obtained based on the third equipment information table, equipment time record line, calibration record curve, and first usage curve.

[0041] In some embodiments, generating second evaluation information based on a second analysis report includes:

[0042] The equipment information in the second equipment information table is classified according to its associated customer information to obtain multiple first category groups, and each first category group contains at least one piece of equipment information;

[0043] Obtain the second usage curve associated with each of the first category groups;

[0044] Cluster analysis was performed on multiple second dosage curves according to their similarity to obtain multiple second category groups that fall within the same similarity threshold range. Each second category group includes at least two second dosage curves.

[0045] Calculate the energy consumption information of the second usage curves in the same second category group, select the second usage curve with the minimum energy consumption information and record it as the reference usage curve, and record the remaining second usage curves as the usage curves to be improved.

[0046] Each usage curve to be improved is compared with the reference usage curve by equipment model to obtain the comparison results. The comparison results include at least one of the differences between the current usage curve to be improved and the reference usage curve: equipment model difference, equipment runtime difference, and equipment energy consumption difference.

[0047] Based on the comparison results, a first weight value is generated corresponding to the difference in device model, a second weight value is generated corresponding to the difference in device runtime, and a third weight value is generated corresponding to the difference in device energy consumption.

[0048] Based on the comparison results and the first weight value, the second weight value, and the third weight value, the improvement value of the usage curve to be improved is calculated. It is determined whether the improvement value is within the adjustment threshold range. If so, a second optimization strategy corresponding to the usage curve to be improved is generated based on the comparison results. The second optimization strategy includes at least one of the following: the second equipment model replacement information of the current customer information and the second equipment running time improvement information.

[0049] Second evaluation information is generated based on the second optimization strategy.

[0050] In some embodiments, the inspection task information further includes a second inspection task, which is obtained through the following steps:

[0051] Obtain regional map information, as well as the first inspection locations and first inspection lists for multiple first inspection tasks in the current regional map information;

[0052] The regional map information and the first inspection location are input into the path planning algorithm model to obtain multiple initial inspection paths within the regional map information;

[0053] Obtain the first inspection list in the initial inspection path one by one, and generate the initial inspection duration in the current initial inspection path;

[0054] Determine whether the current initial inspection duration is within the preset inspection duration range. If not, remove part of the first inspection task in the initial inspection path and record it as the first backup inspection task. Then, take the initial inspection path after removing the first backup inspection task as the first final inspection path.

[0055] Determine whether the number of first backup patrol tasks exceeds the pre-set patrol threshold. If so, input all first backup patrol tasks and area map information into the path planning algorithm model to obtain alternative patrol paths.

[0056] The alternative inspection path will be used as the second and final inspection path.

[0057] The first final inspection path is mapped and stored with the first inspection task, and the second final inspection path is mapped and stored with the first backup inspection task to obtain the second inspection task.

[0058] In some embodiments, verification task information is obtained through the following steps:

[0059] Obtain the replacement cycle from the information of each device;

[0060] The replacement cycle is classified according to time to obtain the equipment information with the same replacement cycle, which is recorded as the equipment information to be inspected. The equipment information to be inspected includes equipment installation location information and equipment location information.

[0061] The regional map information and equipment location information are input into the path planning algorithm model to obtain multiple equipment verification paths within the regional map information;

[0062] Verification task information is generated based on the equipment verification path and the information of the equipment to be verified.

[0063] Unlike existing technologies, the above-mentioned technical solution provides a smart gas metering management and analysis platform, which includes a customer management module, an equipment management module, an operation and maintenance management module, and a data analysis module. This enables the maintenance of information for multiple customers, as well as the use of the equipment management module for equipment information maintenance. The operation and maintenance management module can formulate inspection plans, calibration plans, and operation and maintenance plans, achieving continuous dynamic tracking of gas usage. The data analysis module can analyze and evaluate customer gas usage, enabling precise monitoring and early warning of abnormal gas usage, providing optimization strategies for current gas consumption, improving customer experience and comfort, achieving rational utilization of gas data, enhancing gas safety, and fully leveraging the advantages of gas usage.

[0064] The above description of the invention is merely an overview of the technical solution of the present invention. In order to enable those skilled in the art to better understand the technical solution of the present invention and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of the present invention easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of the present invention. Attached Figure Description

[0065] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on the present invention.

[0066] In the accompanying drawings of the instruction manual:

[0067] Figure 1 A schematic diagram of the platform described in a specific implementation;

[0068] Figure 2 This is a schematic diagram of the first analysis report as a specific implementation method;

[0069] Figure 3 This is a schematic diagram of the first dosage curve for a specific implementation method;

[0070] Figure 4 This is a schematic diagram of the second analysis report as a specific implementation method;

[0071] Figure 5 This is a schematic diagram of the verification record curve described in the specific implementation method;

[0072] Figure 6 A schematic diagram of the operation and maintenance management module described in a specific implementation method;

[0073] The reference numerals used in the above figures are explained as follows:

[0074] 1. Gas metering management and analysis platform;

[0075] 11. Customer Management Module;

[0076] 12. Equipment Management Module;

[0077] 13. Operation and Maintenance Management Module;

[0078] 14. Data Analysis Module. Detailed Implementation

[0079] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this invention in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this invention and are therefore intended only as examples, not as limiting the scope of protection of this invention.

[0080] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this invention, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0081] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit the invention.

[0082] In the description of this invention, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " generally indicates that the preceding and following objects have an "or" logical relationship.

[0083] In this invention, terms such as “first” and “second” are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy, or order between these entities or operations.

[0084] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this invention is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0085] Similar to the understanding in the Examination Guidelines, in this invention, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments of this invention, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0086] Please see Figures 1 to 6This embodiment provides a smart gas metering management and analysis platform 1, including a customer management module 11, an equipment management module 12, an operation and maintenance management module 13, and a data analysis module 14. The customer management module 11 is used to add, modify, delete, and manage customer information, which includes at least one of the following: customer name, customer number, customer address, customer telephone number, customer level, and customer status. The equipment management module 12 is used to add, modify, delete, and manage at least one piece of equipment information associated with each piece of customer information, where each piece of equipment information includes at least one of the following: equipment quantity, equipment model, equipment status, and equipment metering value. The operation and maintenance management module 13 is used to acquire and edit the operation and maintenance information corresponding to each piece of equipment information, which includes inspection task information, operation and maintenance task information, and calibration task information. The data analysis module 14 is used to perform data analysis on equipment information within a preset analysis range, which includes both customer analysis range and equipment analysis range. It obtains a first analysis report corresponding to the current customer information based on the customer analysis range; generates first evaluation information based on the first analysis report; obtains a second analysis report corresponding to the specified equipment information based on the equipment analysis range; and generates second evaluation information based on the second analysis report.

[0087] In this embodiment, the customer management module 11 is used to manage customer information, specifically including adding, modifying, and deleting customer information, as well as other management operations. These operations include classifying customers according to their usage categories, such as grouping residential customers into one group and industrial customers into another, facilitating subsequent information maintenance and manpower allocation. In some embodiments, a customer search operation can be added to the customer management module 11, such as adding a fuzzy search: inputting the customer's geographical location, the customer's initials in pinyin, or the customer's name, etc., to quickly filter a single customer or a group of customers, and then selecting customers based on the filtered information. This method can improve customer search efficiency.

[0088] Furthermore, in the customer management module 11, quick access cards can be set up. Each customer's information creates a quick access card, which is then integrated with the map. Users can then directly select the customer they wish to view on the map and click on their associated quick access card to access the corresponding content. Optionally, the quick access card includes customer information, real-time data, device information, device profiles, etc., and the content of the quick access card can be adjusted according to actual needs.

[0089] Customer information includes customer name, customer ID, customer address, customer phone number, customer level, and customer status. The customer name information records the customer's name; the customer ID information records the customer's number in the current gas system (which can be a gas account number); the customer address information is the customer's specific address; and the customer level information can be generated according to the gas company's internal level classification standards. For example, residential customers can be classified as Level 1, and industrial customers as Level 2; alternatively, monthly or quarterly gas consumption can be used as the basis for customer level classification, depending on actual needs. Customer status information can be understood as the customer's current activity level. For example, if a customer is still using gas appliances normally, their status is recorded as "normal"; if a customer has cancelled their account and all their gas appliances have been recycled or discarded, their status is recorded as "cancelled." Optionally, customer status information can be used for quick customer filtering to help users quickly identify target customers.

[0090] In some embodiments, the customer management module 11 can be further subdivided according to the gas company's business model. For example, if gas company A has multiple subsidiaries B, then gas company A's customer management module 11 has customer management permissions for all subsidiaries B, and each subsidiary B's customer management module 11 has management permissions for its own customers, and so on. The hierarchical structure of the current platform within the gas company can be reasonably adjusted according to actual needs to achieve a comprehensive gas management function.

[0091] In this embodiment, the equipment management module 12 is used to add, modify, delete, and manage the equipment information associated with each customer information. The equipment information includes equipment quantity information, equipment model information, equipment status information, and equipment metering value. Among them, the equipment quantity information is the number of equipment under the same customer information, the equipment model information is the relevant attributes of the equipment, such as equipment model, manufacturing time, maximum gas output, etc., the equipment status information is the current usage status of the equipment, including abnormal status and normal status, and the equipment metering value is the gas output recorded on the equipment in real time.

[0092] In some embodiments, the equipment management module 12 further includes a spare meter management unit, which contains multiple unused gas devices to realize data management of spare gas devices in the warehouse, making it easier for users to select spare gas devices and improve calibration efficiency.

[0093] The operation and maintenance management module 13 is used to obtain and edit the operation and maintenance information corresponding to each device. For details, please refer to [link / reference]. Figure 6As shown, the operation and maintenance information includes inspection task information, operation and maintenance task information, and calibration task information. The inspection task information records the specific details of the inspection, as described later. The calibration task information includes the specific details of the calibration, such as the equipment installation, replacement process, disassembly and repair process, etc. The operation and maintenance task information includes daily gas consumption data records and abnormal problem records. In some embodiments, the operation and maintenance management module 13 also includes a meter reading management unit. Specifically, the meter reading management unit has meter reading query, scan meter reading, and meter number meter reading formats, and the corresponding meter reading method and query method can be selected according to different equipment models. In some optional embodiments, correction parameters can be set in the meter reading query to correct errors in the meter reading data.

[0094] In this embodiment, the data analysis module 14 is used to perform data analysis on equipment information within a preset analysis range to better serve customers. Specifically, the preset analysis range is divided into a customer analysis range and an equipment analysis range. The customer analysis range involves analyzing data on equipment information used by a specific customer, while the equipment analysis range involves analyzing data on specified equipment information. In this embodiment, a first analysis report corresponding to the current customer information can be obtained based on the customer analysis range. Simultaneously, a first evaluation information is obtained based on the first analysis report. The first evaluation information allows for a general monitoring and estimation of the current customer's equipment usage status. A second analysis report corresponding to the specified equipment information is obtained based on the equipment analysis range. Second evaluation information is generated based on the second analysis report. The second analysis report and the second evaluation information facilitate the gas company's accurate tracking and monitoring of the usage status of the current equipment model, enabling the gas company to provide corresponding services to customers.

[0095] This embodiment provides a smart gas metering management and analysis platform 1, which includes a customer management module 11, an equipment management module 12, an operation and maintenance management module 13, and a data analysis module 14. It enables information maintenance for multiple customers, and utilizes the equipment management module 12 to maintain equipment information. The operation and maintenance management module 13 can formulate inspection plans, calibration plans, and operation and maintenance plans, achieving continuous dynamic tracking of gas usage. The data analysis module 14 can analyze and evaluate customer gas usage, achieving accurate monitoring and early warning of abnormal gas usage, providing optimization strategies for existing gas consumption, improving customer experience and comfort, realizing the rational use of gas data, improving gas safety, and fully leveraging the advantages of gas usage.

[0096] Please see Figure 2 and Figure 3 In some embodiments, obtaining the first analysis report corresponding to the current customer information based on the customer analysis scope includes:

[0097] Retrieve all device information associated with the current customer information, and arrange all device information in order to obtain the first device information table;

[0098] The equipment metering values ​​in the equipment information are plotted one by one according to the first preset time period to obtain the first usage curve corresponding to the current equipment information;

[0099] Multiple first dosage curves are integrated into a single chart to obtain the first dosage curve chart;

[0100] Furthermore, the first preset time period is divided according to the smallest time unit, and the device metering values ​​of all device information associated with the customer information are accumulated according to the smallest time unit to obtain multiple first device metering values ​​within the first preset time period;

[0101] A second usage curve is generated based on the metering and values ​​of multiple first devices, which is associated with the current customer information, and a second usage curve graph is obtained.

[0102] The first analysis report is obtained based on the first equipment information table, the first usage curve, and the second usage curve.

[0103] In this embodiment, all device information associated with the current customer information can be understood as the device information of all devices currently used by the customer. All device information is arranged sequentially and displayed in a list format, making it convenient for users to view the current customer's device usage status in a timely manner. As a preferred embodiment, in the first device information table, multiple devices are arranged vertically, and the horizontal components are the specific items of each device's device information.

[0104] Furthermore, the device metering values ​​for each device are plotted one by one to obtain the first usage curve. It should be noted that the device metering values ​​are the instantaneous or average flow rates corresponding to the devices automatically collected by the platform at regular intervals. For example, if device metering values ​​are collected at a frequency of one minute per time, then when the first preset time period is one day, the first usage curve is plotted from the device metering data for the current device within the past 24 hours. This can be further explained in conjunction with... Figure 2 as well as Figure 3 The curve shown is for interpretation. The first preset time period can be set according to requirements, such as seven days, thirty days, one year, one quarter, or one day. Within the first preset time period, appropriate time intervals can be selected to plot the equipment's measurement values, such as... Figure 2The graph shows the curves obtained by plotting the equipment metering values ​​at hourly intervals over the past seven days. Multiple first usage curves are integrated into a single chart to obtain the first usage curve graph. Alternatively, the equipment metering values ​​for a specific device can be plotted according to a first preset time period of thirty days, with one day as the minimum time interval, to obtain the thirty-day usage curve for the current device.

[0105] The minimum time unit is the time interval set in the first preset time period. The minimum time unit can be seconds, minutes, hours, days, etc., and can be set according to actual needs. Further, the first preset time period is divided according to the minimum time unit, and the metering values ​​of all equipment information associated with the customer information are accumulated one by one according to the minimum time unit to obtain multiple first equipment metering sums. These multiple first equipment metering sums are then plotted according to the first preset time period to obtain the second consumption curve, i.e., the second consumption curve graph. This method can provide a direct representation of the customer's total gas consumption within the first preset time period.

[0106] The first analysis report is obtained based on the first equipment information table, the first usage curve, and the second usage curve.

[0107] In some embodiments, a customer persona is constructed, which includes a first analysis report, such as... Figure 2 As shown, the customer profile can also display customer information. For example, when the customer is an industrial customer, the factory floor plan and gas equipment layout can be displayed as small images. Double-clicking or clicking the small image will bring up the original image and enlarge it, facilitating initial troubleshooting and detection by the user. Optionally, the customer profile can also include early warning records, which record unresolved issues, such as abnormal data values ​​generated by a gas device requiring investigation results. This method allows users to promptly notice abnormal states when reviewing the customer profile, improving the efficiency of anomaly troubleshooting.

[0108] Furthermore, the device profiles described later can be integrated into the customer profiles to achieve partial data association between the customer management module and the device management module. This facilitates the data analysis module in filtering and viewing customer and device information, improves the efficiency of gas metering management, and makes the whole process easier to operate.

[0109] In some embodiments, generating first evaluation information based on a first analysis report includes:

[0110] Obtain the first usage curve and the second usage curve within the previous first preset time period, and record them as the first historical usage curve and the second historical usage curve.

[0111] The first usage curve within the current first preset time period is matched one by one with the first historical usage curve to obtain the first matching result;

[0112] Determine whether there are any outliers in the first matching result. If so, obtain the number of outliers in the first matching result and the timestamp of the outlier, and record them as the first outlier and the first outlier timestamp. Map and store the first outlier and its corresponding first outlier timestamp to generate the first outlier information of the current device information.

[0113] The second usage curve within the current first preset time period is matched with the second historical usage curve to obtain the second matching result;

[0114] Determine whether there are outliers in the second matching result. If so, obtain the number of outliers in the second matching result and the timestamp of the outlier generation, and record them as the second outlier and the second outlier timestamp. Map and store the second outlier and its corresponding second outlier timestamp to generate the second outlier information.

[0115] The first inspection task obtains multiple device information based on the first anomaly information, and the first optimization strategy obtains current customer information based on the second anomaly information;

[0116] The first evaluation information is obtained by organizing the first abnormal information, the second abnormal information, the first inspection task, and the first optimization strategy.

[0117] In this embodiment, the first and second consumption curves within the previous first preset time period are acquired and recorded as the first historical consumption curve and the second historical consumption curve. It should be noted that customers' gas usage exhibits relatively obvious peak and trough cycles. By selecting equipment metering data from the previous first preset time period, it is convenient to dynamically monitor the customer's actual usage. For example, through multiple comparisons, the trend of the user's gas consumption over multiple first preset time periods can be obtained; the gas consumption may gradually increase or gradually decrease, thus reflecting the user's dynamic gas demand variables and providing a reference for the user when providing gas equipment services to customers.

[0118] Furthermore, the first historical consumption curve is matched with the first consumption curve to obtain the first matching result. Specifically, the matching process can be to compare the historical equipment metering value in the first historical consumption curve with the equipment metering value in the first consumption curve one by one, obtain the difference between the historical equipment metering value and the current equipment metering value, and record it as the first gas consumption fluctuation value. It is then determined whether the first gas consumption fluctuation value is within the first preset fluctuation range. If it is, it means that there is no outlier in the current first matching result. If the first gas consumption fluctuation value exceeds the first preset fluctuation range, it means that there is an outlier in the current first matching result. The outlier is the equipment metering value corresponding to the first gas consumption fluctuation value that exceeds the first preset fluctuation range, and the timestamp corresponding to the equipment metering value is obtained and recorded as the first outlier and the first outlier timestamp. It should be noted that if there are multiple first usage curves, each first usage curve needs to be matched with its corresponding first historical usage curve; furthermore, the same first usage curve can have multiple first outliers. When multiple first outliers occur continuously, the duration of the first outlier can be calculated based on the first outlier timestamp to facilitate subsequent operations; furthermore, each device information corresponds to one first outlier information. When there is no first outlier, the first outlier information for that device information is an empty set.

[0119] In this embodiment, the second usage curve within the current first preset time period is matched with the second historical usage curve to obtain a second matching result. The specific matching process can be similar to the matching process of the first matching result. The difference between the first device metering sum of the second usage curve and the first historical device metering sum of the second historical usage curve is calculated one by one and recorded as the second gas consumption fluctuation value. It is then determined whether the second gas consumption fluctuation value is within the second preset fluctuation range. If not, it indicates that the first device metering sum corresponding to the current second gas consumption fluctuation value is an abnormal value, i.e., the second abnormal value. The second abnormal value is mapped and stored with the second abnormal timestamp to obtain the second abnormal information.

[0120] The first inspection task obtains multiple device information based on the first anomaly information, and the first optimization strategy obtains current customer information based on the second anomaly information. The first evaluation information is then compiled based on the first anomaly information, the second anomaly information, the first inspection task, and the first optimization strategy.

[0121] In this embodiment, the first analysis report allows users to gain a preliminary understanding of the customer and view any anomalies, facilitating the provision of convenient services. Furthermore, in this embodiment, first evaluation information is generated based on the first analysis report. This first evaluation information allows users to gain a more in-depth and detailed understanding of the customer's current service needs, enabling comprehensive monitoring and management of the customer's gas equipment and enhancing the customer experience.

[0122] Specifically, in some embodiments, the first inspection task includes at least one of the following: a first inspection cycle, a first time node for the next inspection, a first inspection list, a first inspection personnel, a first inspection suggestion, and a first inspection location. The first inspection list includes the equipment models that need to be inspected during the next inspection, and the first inspection suggestion includes the inspection suggestion corresponding to each equipment model.

[0123] The first inspection task, which obtains multiple device information based on the first anomaly information, includes:

[0124] First device anomaly information is generated based on the first anomaly information. The first device anomaly information includes at least one of the following: first device model, first device operating location, first anomaly value, and timestamp corresponding to the first anomaly value.

[0125] The first device anomaly information is input into the first neural network model to obtain the first pre-diagnosis result corresponding to the first device anomaly information;

[0126] Based on the first pre-diagnosis result, a first inspection suggestion corresponding to the current first equipment anomaly information is generated, and the first inspection suggestion is mapped and stored with the first equipment anomaly information to obtain the first inspection task.

[0127] In this embodiment, the first inspection cycle is the inspection cycle corresponding to the current equipment, the first inspection list is the equipment models that the current customer needs to inspect, and the first inspection suggestion includes inspection suggestions corresponding to each equipment model. The generation of the first inspection task allows users to refer to the current customer's inspection plan.

[0128] The first anomaly information is integrated with the data in the equipment information to form the first equipment anomaly information. This first equipment anomaly information is then input into a first neural network model. Optionally, the first neural network model can be a convolutional neural network model, which is trained multiple times. The first neural network model obtains the first pre-diagnosis result corresponding to the current first equipment anomaly information. This first pre-diagnosis result includes possible factors that could cause the same problem as the current first equipment anomaly information. Based on the first pre-diagnosis result, a first inspection suggestion corresponding to the current equipment information is generated, and the first inspection suggestion is mapped and stored with the first equipment anomaly information. This process is repeated until all first equipment anomaly information and their corresponding first inspection suggestions are generated. These suggestions are then organized into the first inspection task corresponding to the current customer information, facilitating unified handling of anomalies in the current customer information by the user.

[0129] This embodiment utilizes a first neural network model based on deep learning to perform pre-diagnosis of abnormal equipment, allowing users to make necessary preparations before performing the first inspection task, including checking spare parts inventory, selecting tools, and equipping repair materials, thus saving inspection time and improving inspection efficiency.

[0130] Furthermore, in some embodiments, the inspection task information also includes a second inspection task, which is obtained through the following steps:

[0131] Obtain regional map information, as well as the first inspection locations and first inspection lists for multiple first inspection tasks in the current regional map information;

[0132] The regional map information and the first inspection location are input into the path planning algorithm model to obtain multiple initial inspection paths within the regional map information;

[0133] Obtain the first inspection list in the initial inspection path one by one, and generate the initial inspection duration in the current initial inspection path;

[0134] Determine whether the current initial inspection duration is within the preset inspection duration range. If not, remove part of the first inspection task in the initial inspection path and record it as the first backup inspection task. Then, take the initial inspection path after removing the first backup inspection task as the first final inspection path.

[0135] Determine whether the number of first backup patrol tasks exceeds the pre-set patrol threshold. If so, input all first backup patrol tasks and area map information into the path planning algorithm model to obtain alternative patrol paths.

[0136] The alternative inspection path will be used as the second and final inspection path.

[0137] The first final inspection path is mapped and stored with the first inspection task, and the second final inspection path is mapped and stored with the first backup inspection task to obtain the second inspection task.

[0138] In this embodiment, when there are multiple first inspection tasks, the first inspection tasks can be classified and planned based on geographical location to obtain second inspection tasks. That is, the second inspection tasks contain multiple first inspection tasks.

[0139] Specifically, the system acquires regional map information, which includes map data of the area where the current user's customers are located, encompassing traffic routes, residential areas, industrial zones, and other distribution information. Each customer's information corresponds to a first inspection task containing a first inspection location and a first inspection list. The regional map information and the first inspection locations are then input into a path planning algorithm model, which can be either a D* or A* path planning algorithm model. This model connects multiple first inspection locations to generate an initial inspection path. It should be noted that this initial inspection path is the preliminary planned path when the user executes a second inspection task within a preset inspection timeframe.

[0140] Furthermore, the initial inspection path needs to be adjusted based on the initial inspection duration. During the inspection process, different first inspection tasks correspond to different inspection durations. The inspection durations of the first inspection tasks on the same initial inspection path are accumulated to obtain the initial inspection duration.

[0141] Determine if the initial inspection duration falls within the preset inspection duration range. If so, designate the initial inspection path as the first final inspection path. If not, it indicates that there are too many first inspection tasks on the current initial inspection path, and the user cannot execute all of them within the preset inspection duration range. Then, remove the first inspection tasks located at the end of the initial inspection path. The number of tasks removed can be set according to actual needs, until the initial inspection duration of the remaining first inspection tasks falls within the preset inspection duration range. At this point, the removed first inspection tasks are recorded as the first backup inspection tasks.

[0142] To determine whether the number of the first backup patrol tasks exceeds the pre-set patrol threshold, it should be noted that the pre-set patrol threshold can be obtained based on the user's historical patrol data. Specifically, it can be obtained by calculating the patrol delay time of several customers in the historical patrol process, assessing the probability of the current customer causing a patrol delay in this patrol process based on the patrol delay time, and estimating the actual patrol time based on the probability of the patrol delay. The estimated patrol time is then calculated by comparing the estimated patrol time with the initial patrol time to obtain the pre-set patrol time. Finally, the pre-set patrol time is matched with the patrol time corresponding to the first backup patrol task to obtain the pre-set patrol threshold. During the inspection process, there are issues where inspections cannot be carried out due to customer factors. Also, during the actual inspection, the actual inspection time may be shorter than the initial inspection time due to the final condition of the equipment. Based on this, when the actual inspection time is shorter than the initial inspection time, a pre-set equipment inspection threshold can be introduced. That is, users can perform an additional first backup inspection task on top of completing the predetermined inspection task. This method can enable users to dynamically adjust the first inspection task during the actual inspection process.

[0143] When the number of first backup patrol tasks exceeds the preset patrol threshold, it indicates that there are too many first backup patrol tasks, and the current user cannot complete the predetermined first patrol task and first backup patrol task within the preset patrol time. In this case, additional patrol times or additional manpower are needed to complete all the above patrol tasks. Therefore, a separate path planning is required for all first backup patrol tasks to obtain alternative patrol paths, which will then be used as the second final patrol path.

[0144] Based on actual needs, you can choose to increase the number of inspections or increase the number of inspection personnel to complete the first backup inspection task. Finally, map and store the first final inspection path with the first inspection task, and map and store the second final inspection path with the first backup inspection task to obtain the second inspection task.

[0145] This embodiment integrates multiple first inspection tasks in an orderly manner to form a second inspection task containing inspection path information, thereby achieving standardized management of inspection tasks and making full use of users' functions.

[0146] Furthermore, in some embodiments, the first optimization strategy includes at least one of multiple first device model replacement information and multiple first device runtime improvement information;

[0147] The first optimization strategy for obtaining current customer information based on the second anomaly information includes:

[0148] The second outlier is obtained to generate the corresponding second device outlier information. The second device outlier information includes at least one of the following: the second device model, the second device operating location, and the second device operating duration.

[0149] The abnormal information of the second device is input into the second neural network model to obtain at least one of the first device model replacement information and the first device running time improvement information corresponding to the abnormal information of the second device.

[0150] Based on the first equipment model replacement information and the first equipment running time improvement information, a first optimization suggestion corresponding to the current second equipment abnormality information is generated, and the first optimization suggestion is mapped and stored with the second equipment abnormality information to obtain the first optimization strategy.

[0151] It should be noted that the second usage curve reflects the current gas consumption of the customer within the first preset time period. When the second usage curve shows an anomaly, it indicates that the customer's current gas usage plan is unreasonable, requiring optimization of the customer's gas equipment. Optimization methods include replacing equipment models and adjusting equipment operating time. Based on this, in this embodiment, the second neural network model can use a convolutional neural network model as the base neural network model, obtained through multiple training iterations. The second neural network model can generate at least one of the following based on the second equipment anomaly information: first equipment model replacement information and first equipment operating time improvement information. Users can provide appropriate improvement suggestions to customers based on the first optimization strategy to improve customer service efficiency, thereby achieving higher customer satisfaction and maintaining customer loyalty.

[0152] Please see Figure 4 and Figure 5 In some embodiments, obtaining a second analysis report corresponding to specified device information based on the device analysis scope includes:

[0153] Arrange the specified device information and its associated customer information in sequence to obtain the second device information table;

[0154] Construct a device profile for each piece of equipment information, and map and store the device profile with the equipment information. The device profile includes at least one of the following: equipment model information, equipment status information, equipment metering value, equipment installation record, equipment calibration record, equipment inspection record, and equipment operation and maintenance record corresponding to the current equipment information.

[0155] The second analysis report is obtained based on the second equipment information table and equipment profile.

[0156] In this embodiment, the second device information table can be understood with reference to the first device information table. Furthermore, the second device information table can be associated with corresponding customer profiles or customer information. When a user clicks on a customer's name, they can be directly redirected to the customer information or customer profile, allowing the user to gain a more detailed understanding of the customer information.

[0157] In this embodiment, a device profile is constructed for each device information, and the device profile and device information are mapped and stored to facilitate users to understand the current device more intuitively and comprehensively.

[0158] For details, please refer to Figure 4 In some embodiments, constructing a device profile for each device includes:

[0159] The equipment image is matched with the equipment model information and recorded as the equipment image information. The equipment model information, equipment image information and equipment status information are then organized in a list to obtain the third equipment information table.

[0160] The equipment timeline is generated based on the equipment installation record, equipment calibration record, equipment inspection record, and equipment operation and maintenance record.

[0161] The equipment calibration records in the equipment information are plotted according to the second preset time period to obtain the calibration record curve corresponding to the current equipment information;

[0162] Obtain the first usage curve associated with the equipment information;

[0163] The equipment profile is obtained based on the third equipment information table, equipment time record line, calibration record curve, and first usage curve.

[0164] Figure 4 and Figure 5 The system displays the specific details of the equipment profile. Through this profile, users can stay informed about the current status of the equipment. Furthermore, by acquiring real-time metering data, users can infer the actual operating status of the equipment, enabling them to better control equipment information and provide customers with precise gas management services.

[0165] In some embodiments, generating second evaluation information based on a second analysis report includes:

[0166] The equipment information in the second equipment information table is classified according to its associated customer information to obtain multiple first category groups, and each first category group contains at least one piece of equipment information;

[0167] Obtain the second usage curve associated with each of the first category groups;

[0168] Cluster analysis was performed on multiple second dosage curves according to their similarity to obtain multiple second category groups that fall within the same similarity threshold range. Each second category group includes at least two second dosage curves.

[0169] Calculate the energy consumption information of the second usage curves in the same second category group, select the second usage curve with the minimum energy consumption information and record it as the reference usage curve, and record the remaining second usage curves as the usage curves to be improved.

[0170] Each usage curve to be improved is compared with the reference usage curve by equipment model to obtain the comparison results. The comparison results include at least one of the differences between the current usage curve to be improved and the reference usage curve: equipment model difference, equipment runtime difference, and equipment energy consumption difference.

[0171] Based on the comparison results, a first weight value is generated corresponding to the difference in device model, a second weight value is generated corresponding to the difference in device runtime, and a third weight value is generated corresponding to the difference in device energy consumption.

[0172] Based on the comparison results and the first weight value, the second weight value, and the third weight value, the improvement value of the usage curve to be improved is calculated. It is determined whether the improvement value is within the adjustment threshold range. If so, a second optimization strategy corresponding to the usage curve to be improved is generated based on the comparison results. The second optimization strategy includes at least one of the following: the second equipment model replacement information of the current customer information and the second equipment running time improvement information.

[0173] Second evaluation information is generated based on the second optimization strategy.

[0174] In this embodiment, K-means clustering analysis can be used to cluster multiple second consumption curves, resulting in multiple clusters. The second consumption curves in each cluster have a certain degree of similarity, and each cluster is denoted as a second category group. This method allows customers with similar gas consumption patterns to be placed in the same second category group.

[0175] Furthermore, the energy consumption information of the second usage curves within the same second category group is calculated, and the second usage curve with the minimum energy consumption is used as the reference usage curve, while the remaining second usage curves are recorded as usage curves to be improved. The usage curves to be improved are then compared with the reference usage curves based on equipment model. Specifically, during the comparison process, the installation location and runtime of the same equipment model can be matched and judged to obtain at least one of the following: equipment model difference, equipment runtime difference, and equipment energy consumption difference.

[0176] It should be noted that the first weight value, the second weight value, and the third weight value can be obtained in the following ways:

[0177] A first weighted score table is constructed, and equipment performance scores are divided according to the performance advantages and disadvantages of different equipment models. The equipment models corresponding to the reference usage curves in the equipment model differences are assigned values ​​in the first weighted score table and accumulated to obtain the final first weighted value. A second weighted score table is constructed, and scores are divided based on equipment runtime. The equipment runtime corresponding to the usage curves to be improved in the equipment runtime differences is assigned values ​​in the second weighted score table and accumulated to obtain the final second weighted value. A third weighted score table is constructed, and scores are divided based on equipment energy consumption. The equipment energy consumption corresponding to the usage curves to be improved in the equipment energy consumption differences is assigned values ​​in the second weighted score table and accumulated to obtain the final third weighted value.

[0178] The aforementioned comparison results are used to calculate the improvement value using weights. The improvement value is then judged. When the improvement value exceeds the adjustment threshold range, it indicates that the current customer can carry out further optimization and improvement to reduce energy consumption. In other words, a second optimization strategy corresponding to the usage curve to be improved is generated based on the comparison results. The second optimization strategy is then integrated to form the second evaluation information.

[0179] This embodiment performs cluster analysis on multiple customers and calculates the differences among customers in the same second category group to obtain a second optimization strategy for some customers. Based on the second optimization strategy, a second evaluation information is obtained. This approach can fully absorb the improvement methods of other customers to provide corresponding optimization solutions for the current customers and improve customer satisfaction.

[0180] In some embodiments, verification task information is obtained through the following steps:

[0181] Obtain the replacement cycle from the information of each device;

[0182] The replacement cycle is classified according to time to obtain the equipment information with the same replacement cycle, which is recorded as the equipment information to be inspected. The equipment information to be inspected includes equipment installation location information and equipment location information.

[0183] The regional map information and equipment location information are input into the path planning algorithm model to obtain multiple equipment verification paths within the regional map information;

[0184] Verification task information is generated based on the equipment verification path and the information of the equipment to be verified.

[0185] This embodiment can organize equipment with the same replacement cycle to generate verification task information. When the replacement stage is approaching, the verification task information can be published so that users can perform verification tasks based on the verification task information, thereby improving efficiency.

[0186] The aforementioned technical solution provides a smart gas metering management and analysis platform, which includes a customer management module, an equipment management module, an operation and maintenance management module, and a data analysis module. This enables information maintenance for multiple customers, as well as equipment information maintenance via the equipment management module. The operation and maintenance management module can formulate inspection plans, calibration plans, and operation and maintenance plans, achieving continuous dynamic tracking of gas usage. The data analysis module can analyze and evaluate customer gas usage, enabling precise monitoring and early warning of abnormal gas usage, providing optimization strategies for existing gas consumption, improving customer experience and comfort, achieving rational utilization of gas data, enhancing gas safety, and fully leveraging the advantages of gas usage.

[0187] Finally, it should be noted that although the above embodiments have been described in the description and drawings of this invention, this should not limit the scope of patent protection of this invention. Any technical solutions that are based on the essential concept of this invention, utilize the content described in the description and drawings of this invention to make equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this invention.

Claims

1. A smart gas metering management and analysis platform, characterized in that, It includes a customer management module, an equipment management module, an operation and maintenance management module, and a data analysis module; The customer management module is used to add, modify, delete, and manage customer information, which includes at least one of the following: customer name, customer number, customer address, customer telephone number, customer level, and customer status. The device management module is used to add, modify, delete, and manage at least one device information associated with each customer information. Each device information includes at least one of the following: device quantity information, device model information, device status information, and device metering value. The operation and maintenance management module is used to acquire and edit the operation and maintenance information corresponding to each piece of equipment information. The operation and maintenance information includes inspection task information, operation and maintenance task information, and calibration task information. The data analysis module is used to perform data analysis on equipment information within a preset analysis range, which includes both customer analysis range and equipment analysis range. Based on the customer analysis scope, a first analysis report corresponding to the current customer information is obtained, including: Obtain all device information associated with the current customer information, and arrange all device information in order to obtain a first device information table; The device metering values ​​in the device information are plotted one by one according to the first preset time period to obtain the first usage curve corresponding to the current device information; Multiple first dosage curves are integrated into the same chart to obtain the first dosage curve chart; Furthermore, the first preset time period is divided according to the minimum time unit, and the device metering values ​​of all device information associated with the customer information are accumulated according to the minimum time unit to obtain multiple first device metering sums within the first preset time period; A second usage curve associated with the current customer information is generated based on the metering and values ​​of multiple first devices, resulting in a second usage curve graph. The first analysis report is obtained based on the first equipment information table, the first usage curve, and the second usage curve. First evaluation information is generated based on the first analysis report; A second analysis report corresponding to the specified device information is obtained based on the device analysis scope, including: The specified device information and its associated customer information are arranged sequentially to obtain a second device information table; Second assessment information is generated based on the second analysis report, including: The device information in the second device information table is classified according to its associated customer information to obtain multiple first category groups, and each first category group contains at least one device information. Obtain the second usage curve associated with each of the first category groups; Cluster analysis is performed on multiple second dosage curves according to their similarity to obtain multiple second category groups that are placed within the same similarity threshold range, and each second category group includes at least two second dosage curves; Calculate the energy consumption information of the second usage curves in the same second category group, select the second usage curve with the minimum energy consumption information and record it as the reference usage curve, and record the remaining second usage curves as the usage curves to be improved; The equipment model of each of the usage curves to be improved is compared with that of the reference usage curve to obtain the comparison results. The comparison results include at least one of the differences in equipment model, equipment runtime, and equipment energy consumption between the current usage curve to be improved and the reference usage curve. Based on the comparison results, a first weight value corresponding to the difference in device model is generated, a second weight value corresponding to the difference in device runtime is generated, and a third weight value corresponding to the difference in device energy consumption is generated. Based on the comparison results and the first weight value, the second weight value, and the third weight value, the improvement value of the usage curve to be improved is calculated. It is determined whether the improvement value is within the adjustment threshold range. If so, a second optimization strategy corresponding to the usage curve to be improved is generated based on the comparison results. The second optimization strategy includes at least one of the second equipment model replacement information and the second equipment running time improvement information of the current customer information. The second evaluation information is generated based on the second optimization strategy.

2. The intelligent gas metering management and analysis platform according to claim 1, characterized in that, The first evaluation information generated based on the first analysis report includes: Obtain the first usage curve and the second usage curve within the previous first preset time period, and record them as the first historical usage curve and the second historical usage curve. The first usage curve within the current first preset time period is matched one by one with the first historical usage curve to obtain the first matching result; Determine whether there are any outliers in the first matching result. If so, obtain the number of outliers in the first matching result and the timestamp of the outlier, and record them as the first outlier and the first outlier timestamp. Map and store the first outlier and its corresponding first outlier timestamp to generate the first outlier information of the current device information. The second usage curve within the current first preset time period is matched with the second historical usage curve to obtain the second matching result; Determine whether there are any outliers in the second matching result. If so, obtain the number of outliers in the second matching result and the timestamp of the outlier generation, and record them as the second outlier and the second outlier timestamp. Map and store the second outlier and its corresponding second outlier timestamp to generate the second outlier information. A first inspection task is obtained based on the first anomaly information to obtain multiple device information, and a first optimization strategy is obtained based on the second anomaly information to obtain the current customer information; The first evaluation information is obtained by organizing the first abnormal information, the second abnormal information, the first inspection task, and the first optimization strategy.

3. The intelligent gas metering management and analysis platform according to claim 2, characterized in that, The first inspection task includes at least one of the following: first inspection cycle, first time node of the next inspection, first inspection list, first inspection personnel, first inspection suggestion, and first inspection location. The first inspection list includes the equipment models that need to be inspected in the next inspection process, and the first inspection suggestion includes the inspection suggestion corresponding to each of the equipment models. The first inspection task, which obtains multiple device information based on the first anomaly information, includes: Based on the first abnormal information, generate the first device abnormal information for the corresponding device. The first device abnormal information includes at least one of the following: first device model, first device operating location, first abnormal value, and timestamp corresponding to the first abnormal value. The first device anomaly information is input into the first neural network model to obtain the first pre-diagnosis result corresponding to the first device anomaly information; Based on the first pre-diagnosis result, a first inspection suggestion corresponding to the current abnormal information of the first device is generated, and the first inspection suggestion is mapped and stored with the abnormal information of the first device to obtain the first inspection task.

4. The intelligent gas metering management and analysis platform according to claim 2 or 3, characterized in that, The first optimization strategy includes at least one of multiple first device model replacement information and multiple first device runtime improvement information; The first optimization strategy for obtaining the current customer information based on the second anomaly information includes: The second abnormal value is obtained to generate the corresponding second device abnormal information. The second device abnormal information includes at least one of the second device model, the second device operating location, and the second device operating time. The second device anomaly information is input into the second neural network model to obtain at least one of the first device model replacement information and the first device running time improvement information corresponding to the second device anomaly information; Based on the first device model replacement information and the first device running time improvement information, a first optimization suggestion corresponding to the current second device abnormal information is generated, and the first optimization suggestion is mapped and stored with the second device abnormal information to obtain the first optimization strategy.

5. The intelligent gas metering management and analysis platform according to claim 4, characterized in that, The second analysis report corresponding to the specified equipment information obtained based on the equipment analysis scope includes: Construct a device profile for each of the aforementioned device information, and map and store the device profile with the device information. The device profile includes at least one of the following: device model information, device status information, device metering value, device installation record, device calibration record, device inspection record, and device operation and maintenance record corresponding to the current device information. The second analysis report is obtained based on the second device information table and the device profile.

6. The intelligent gas metering management and analysis platform according to claim 5, characterized in that, The device profile for each of the aforementioned device information includes: The image of the device is matched according to the device model information and recorded as the device image information. The device model information, device image information and device status information are then organized in a list to obtain the third device information table. The equipment timeline is generated based on the equipment installation record, equipment calibration record, equipment inspection record, and equipment operation and maintenance record. The equipment calibration records in the equipment information are plotted according to the second preset time period to obtain the calibration record curve corresponding to the current equipment information; Obtain the first usage curve associated with the device information; The equipment profile is obtained based on the third equipment information table, the equipment time record line, the calibration record curve, and the first usage curve.

7. The intelligent gas metering management and analysis platform according to claim 2, characterized in that, The inspection task information also includes a second inspection task, which is obtained through the following steps: Obtain regional map information, and obtain the first inspection locations and first inspection lists of multiple first inspection tasks in the current regional map information; The area map information and the first inspection location are input into the path planning algorithm model to obtain multiple initial inspection paths within the area map information; The first inspection list in the initial inspection path is obtained one by one, and the initial inspection duration in the current initial inspection path is generated. Determine whether the current initial inspection duration is within the preset inspection duration range. If not, remove part of the first inspection task in the initial inspection path and record it as the first backup inspection task. Then, take the initial inspection path after removing the first backup inspection task as the first final inspection path. Determine whether the number of the first backup patrol tasks exceeds the pre-set patrol threshold. If so, input all the first backup patrol tasks and the area map information into the path planning algorithm model to obtain alternative patrol paths. The alternative inspection path is used as the second final inspection path; The first final inspection path is mapped and stored with the first inspection task, and the second final inspection path is mapped and stored with the first backup inspection task to obtain the second inspection task.

8. The intelligent gas metering management and analysis platform according to claim 7, characterized in that, The verification task information is obtained through the following steps: Obtain the replacement cycle from the information of each of the aforementioned devices; The replacement cycles are classified according to time to obtain equipment information with the same replacement cycle, which is recorded as equipment information to be inspected. The equipment information to be inspected includes equipment installation location information and equipment location information. The regional map information and the device location information are input into the path planning algorithm model to obtain multiple device verification paths within the regional map information; The calibration task information is generated based on the equipment calibration path and the equipment information to be calibrated.

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