Intelligent gas work order scheduling optimization method and internet of things system

Abstract

This specification provides a smart gas work order scheduling optimization method and an Internet of Things (IoT) system. The method is implemented by the smart gas work order scheduling optimization IoT system, which includes a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially. The method is executed by the smart gas management platform. In this method, the smart gas service sub-platform can generate work orders to be assigned based on gas processing requests received from the gas user sub-platform. A gas processing request refers to a user's request to handle gas-related issues. Gas-related issues may include fault reporting, service complaints, gas equipment function inquiries, and new gas product inquiries.

Description

Technical Field

[0001] This manual relates to the field of gas IoT, and in particular to intelligent gas work order scheduling optimization methods and IoT systems. Background Technology

[0002] In gas services, the relevant service system generates a gas work order and dispatches staff to handle each user's gas service request. However, there are many uncertainties in dispatching staff, such as varying skill levels among staff and the availability of available staff, which can easily lead to problems such as untimely gas problem handling and prolonged processing times.

[0003] Therefore, we hope to provide a smart gas work order scheduling optimization method and an Internet of Things system that can achieve accurate, timely and efficient work order dispatch. Summary of the Invention

[0004] This specification provides one or more embodiments of a smart gas work order scheduling optimization method. The method is implemented by an IoT system for smart gas work order scheduling optimization. The IoT system includes a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially. The method is executed by the smart gas management platform and includes: obtaining a newly generated work order to be assigned from the smart gas service platform, the work order being generated by the smart gas service platform based on a gas processing request received from the smart gas user platform; determining at least one target scheduling subdomain corresponding to the work order to be assigned from multiple scheduling subdomains based on the gas consultation characteristics and gas user characteristics of the work order to be assigned, using a preset method; the gas consultation characteristics including at least one of consultation type and consultation location information, the gas user characteristics including at least one of user type, terminal type, and usage characteristics; and assigning the work order to be assigned to the target scheduling subdomain corresponding to the work order to be assigned.

[0005] This specification provides one or more embodiments of an IoT system for optimizing smart gas work order scheduling. The IoT system includes a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially. The smart gas management platform is configured to perform the following operations: obtain newly generated work orders to be assigned from the smart gas service platform, which are generated by the smart gas service platform based on gas processing requests received from the smart gas user platform; based on the gas consultation characteristics and gas user characteristics of the work orders to be assigned, determine at least one target scheduling subdomain corresponding to the work orders to be assigned from multiple scheduling subdomains using a preset method, wherein the gas consultation characteristics include at least one of consultation type and consultation location information, and the gas user characteristics include at least one of user type, terminal type, and usage characteristics; and assign the work orders to be assigned to the target scheduling subdomain corresponding to the work orders to be assigned.

[0006] One embodiment of this specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the intelligent gas work order scheduling optimization method as described in any of the above embodiments. Attached Figure Description

[0007] 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:

[0008] Figure 1 This is a schematic diagram of an IoT system for intelligent gas work order scheduling optimization, as shown in some embodiments of this specification.

[0009] Figure 2 This is an exemplary flowchart of a smart gas work order scheduling optimization method according to some embodiments of this specification;

[0010] Figure 3 This is an exemplary flowchart illustrating the determination of multiple scheduling subdomains by clustering according to some embodiments of this specification;

[0011] Figure 4 This is an exemplary schematic diagram illustrating the determination of a target scheduling subdomain via vectors according to some embodiments of this specification;

[0012] Figure 5 This is an exemplary flowchart illustrating the order acceptance and scheduling of work orders to be assigned, according to some embodiments of this specification. Detailed Implementation

[0013] 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.

[0014] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0016] 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.

[0017] Figure 1 This is a schematic diagram of an IoT system for optimizing smart gas work order scheduling, as shown in some embodiments of this specification.

[0018] like Figure 1 As shown, the IoT system 100 for optimizing smart gas work order scheduling includes a smart gas user platform 110, a smart gas service platform 120, a smart gas management platform 130, a smart gas sensor network platform 140, and a smart gas object platform 150. In some embodiments, the IoT system 100 for optimizing smart gas work order scheduling may be part of or implemented by a processing device.

[0019] The smart gas user platform 110 can refer to a user-centric platform. Specifically, the smart gas user platform 110 can be configured as a terminal device.

[0020] In some embodiments, the smart gas user platform 110 may include a gas user sub-platform, a government user sub-platform, and a regulatory user sub-platform. The gas user sub-platform can be used by gas users. The gas user sub-platform can acquire input instructions from gas users. The government user sub-platform can be used by government users. The government user sub-platform can acquire input instructions from government users. The regulatory user sub-platform can be used by regulatory users. For example, regulatory users can include relevant personnel and / or departments involved in ensuring gas safety. The regulatory user sub-platform can acquire input instructions from regulatory users.

[0021] The smart gas service platform 120 refers to a platform that can provide users with both input and output gas services.

[0022] In some embodiments, the smart gas service platform 120 may include a smart gas consumption service sub-platform, a smart operation service sub-platform, and a smart supervision service sub-platform.

[0023] The smart gas service sub-platform can correspond to the gas user sub-platform. In some embodiments, the smart gas service sub-platform can generate work orders to be assigned based on gas processing requests received from the gas user sub-platform. A gas processing request refers to a user's request to handle gas-related issues. Gas-related issues may include fault reporting, service complaints, gas equipment function inquiries, and inquiries about new gas products. For more information on work orders to be assigned and how to generate them, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0024] The intelligent operation service sub-platform can interact with the government user sub-platform. For example, the intelligent operation service sub-platform can receive input instructions (such as gas operation management information query instructions) issued by the government user sub-platform and transmit gas operation management information (such as gas work order scheduling plans) to the government user sub-platform.

[0025] The intelligent regulatory service sub-platform can interact with the regulatory user sub-platform. For example, the intelligent regulatory service sub-platform can transmit safety regulatory information such as pipeline equipment status and gas pressure to the regulatory user sub-platform based on input commands.

[0026] The intelligent gas management platform 130 can provide a platform for sensing and control management functions for the IoT system 100 that optimizes intelligent gas work order scheduling. In some embodiments, the intelligent gas management platform 130 can be a remote platform controlled by managers, artificial intelligence, or preset rules.

[0027] In some embodiments, the intelligent gas management platform 130 may include an intelligent gas data center, an intelligent customer service management sub-platform, and an intelligent operation management sub-platform.

[0028] The smart gas data center can aggregate and store all operational data from the IoT system 100, which optimizes smart gas work order scheduling. The smart gas management platform 130 can interact with the smart gas service platform 120 and the smart gas sensor network platform 140 through the smart gas data center.

[0029] Both the intelligent customer service management sub-platform and the intelligent operation management sub-platform can be independent data usage platforms. Both can acquire relevant data from the intelligent gas data center and send management operation data to it. In some embodiments, the intelligent customer service management sub-platform may include modules for revenue management, business registration management, application management, customer service management, message management, and customer analysis management. In some embodiments, the intelligent operation management sub-platform may include modules for gas procurement management, gas storage management, gas dispatch management, purchase-sales difference management, pipeline engineering management, and comprehensive office management.

[0030] For example, the smart gas data center can receive gas operation management information query instructions issued by the smart operation service sub-platform, as well as work orders to be assigned issued by the smart gas consumption service sub-platform. The smart gas data center can issue instructions to obtain information related to gas-related indoor and / or pipeline equipment to the smart gas sensor network platform. The smart gas data center can receive gas pipeline information uploaded by the smart gas sensor network platform and send the relevant information to the smart operation customer service sub-platform for processing, obtaining information such as gas inquiry characteristics and gas user characteristics. Based on information such as gas inquiry characteristics and gas user characteristics, the smart operation management sub-platform can determine the target scheduling sub-domain and assign work orders, thereby determining solutions to gas operation management information and gas-related issues. For more information on determining the target scheduling sub-domain and assigning work orders, please refer to [link to relevant documentation]. Figure 2 This includes a description of the intelligent operation management sub-platform, which sends gas operation management information and solutions to gas-related issues to the intelligent gas data center. The intelligent gas data center can then send gas operation management information and solutions to gas-related issues to the intelligent gas service platform.

[0031] In some embodiments, the smart gas management platform can obtain newly generated work orders to be assigned from the smart gas service platform; based on the gas inquiry characteristics and gas user characteristics of the work orders to be assigned, it can determine at least one target scheduling subdomain corresponding to the work orders to be assigned from multiple scheduling subdomains using a preset method; and assign the work orders to be assigned to the target scheduling subdomains corresponding to the work orders to be assigned. For further explanation on assigning work orders to target scheduling subdomains, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0032] The intelligent gas sensor network platform 140 can refer to a functional platform that manages the sensor communication of the IoT system 100, which optimizes intelligent gas work order scheduling. Specifically, the intelligent gas sensor network platform 140 can be configured as a communication network and gateway. The intelligent gas sensor network platform 140 can send instructions to obtain relevant data (such as gas indoor and / or pipeline equipment data) to the intelligent gas object platform 150, and receive relevant data uploaded by the intelligent gas object platform 150. The intelligent gas sensor network platform 140 can receive instructions from the intelligent gas data center to obtain relevant data and upload relevant data to the intelligent gas data center.

[0033] In some embodiments, the intelligent gas sensor network platform 140 may include a gas indoor equipment sensor network sub-platform and a gas pipeline equipment sensor network sub-platform. Both the gas indoor equipment sensor network sub-platform and the gas pipeline equipment sensor network sub-platform can realize one or more functions such as network management, protocol management, command management, and data parsing.

[0034] The intelligent gas platform 150 can refer to a functional platform for generating sensing information. The intelligent gas platform 150 can be configured as various types of gas equipment. These gas equipment can include indoor equipment and pipeline equipment. Indoor equipment can include gas users' gas terminals (e.g., gas meters). Pipeline equipment can include gas pressure regulating stations, pipeline monitoring equipment, and pipeline valve control equipment.

[0035] In some embodiments, the smart gas target platform 150 may include a gas indoor equipment target sub-platform and a gas pipeline equipment target sub-platform. In some embodiments, the gas indoor equipment target sub-platform may correspond to the gas indoor equipment sensor network sub-platform, and the gas pipeline equipment target sub-platform may correspond to the gas pipeline equipment sensor network sub-platform.

[0036] Specifically, the indoor gas equipment sensor network sub-platform can transmit the corresponding gas terminal information obtained through the indoor gas equipment object sub-platform to the smart gas data center. Similarly, the gas pipeline equipment sensor network sub-platform can transmit the corresponding gas terminal information obtained through the gas pipeline equipment object sub-platform to the smart gas data center.

[0037] It should be noted that the above description of the IoT system for optimizing intelligent gas work order scheduling is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of this system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles.

[0038] Figure 2This is an exemplary flowchart of a smart gas work order scheduling optimization method according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a smart gas management platform.

[0039] Step 210: Obtain the newly generated work order to be assigned from the smart gas service platform.

[0040] A work order is a task or operation related to gas services. For example, a work order may include at least one of the following: gas repair, gas inspection, gas meter installation application, etc.

[0041] A work order to be assigned is a work order that needs to be assigned and is awaiting processing. In some embodiments, a work order to be assigned may be generated by the smart gas service platform based on a gas processing request received from the smart gas user platform. For more information on gas processing requests, please refer to [link to relevant documentation]. Figure 1 And its related descriptions.

[0042] In some embodiments, the smart gas management platform can obtain real-time generated work orders to be assigned from the smart gas service platform. For example, the smart gas management platform can send an instruction to the smart gas service platform to obtain work orders to be assigned. The smart gas management platform can receive work orders to be assigned uploaded by the smart gas service platform.

[0043] Step 220: Based on the gas consultation characteristics and gas user characteristics of the work order to be assigned, at least one target scheduling subdomain corresponding to the gas work order to be assigned is determined from multiple scheduling subdomains using a preset method.

[0044] Gas inquiry features can reflect the inquiry status of a work order. In some embodiments, gas inquiry features include at least one of inquiry type and inquiry location information. In some embodiments, the gas inquiry features of a work order can be obtained by analyzing and processing the work order to be assigned through various methods such as voice recognition, text parsing, and location.

[0045] The type of consultation can refer to the type of consultation content. For example, the type of consultation can include at least one of the following: fault reporting, service complaint, consultation on the function of gas equipment, consultation on new gas products, etc.

[0046] Consultation location information can refer to the geographical location information of the user who made the inquiry. In some embodiments, the consultation location information can be used to locate the user who made the inquiry through the call system of the smart gas user platform.

[0047] Gas user characteristics can reflect the relevant information of the user corresponding to a work order. In some embodiments, gas user characteristics include at least one of user type, terminal type, and usage characteristics. In some embodiments, the smart gas management platform can determine the gas user characteristics corresponding to the work order to be assigned based on gas pipeline network information obtained from the smart gas object platform by the smart gas sensor network platform.

[0048] User type can refer to the type of user who inquires about gas services, such as residential, commercial, or industrial users.

[0049] Terminal type can refer to the type of gas equipment used by the user, such as gas stove, smelting boiler, gas meter, flow meter, etc.

[0050] Usage characteristics can refer to information reflecting a user's gas usage, such as the frequency of gas usage and the average duration of each gas usage session.

[0051] A scheduling subdomain can refer to a predefined sub-area used for processing work orders. Each scheduling subdomain can include at least one order-receiving personnel. An order-receiving personnel can refer to staff members who process work orders to be assigned. Each scheduling subdomain can process one or more work orders of the same type (e.g., with the same terminal characteristics). In some embodiments, work orders to be assigned within each scheduling subdomain can only be received by order-receiving personnel within that scheduling subdomain, and order-receiving personnel within each scheduling subdomain can only receive work orders to be assigned within that scheduling subdomain.

[0052] For example, the smart gas management platform can receive user-input classification criteria (e.g., based on user characteristics) and classify work orders to be assigned based on these criteria. Based on the number of different types of work orders to be assigned after classification, the smart gas management platform can determine the corresponding number of scheduling subdomains. Each type can be considered as a type of work order to be assigned that a scheduling subdomain can handle. The historical order-receiving personnel corresponding to each type of work order can be designated as the order-receiving personnel for the scheduling subdomain corresponding to that type.

[0053] In some embodiments, multiple scheduling subdomains can be determined based on historical work orders to be assigned within a preset time period. The preset time period can be a preset historical time period.

[0054] In some embodiments, the intelligent gas management platform can determine a preset number of cluster centers; cluster historical work orders to be assigned within a preset time period to determine a preset number of clusters; and define each cluster as a scheduling subdomain. For further explanation on determining scheduling subdomains through clustering, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0055] The target scheduling subdomain refers to the scheduling subdomain that can be used to process work orders to be assigned.

[0056] A preset method refers to a method used to determine the target scheduling subdomain. In some embodiments, the preset method may be to pre-establish a mapping table. For example, a smart gas management platform may pre-use historical gas consultation characteristics and historical gas user characteristics of multiple work orders from historical periods as first reference data to generate a first mapping relationship table between the first reference data and the corresponding historical scheduling subdomains. The smart gas management platform can query the first reference data in the first mapping relationship table that is the same as or similar to the gas consultation characteristics and gas user characteristics of the current work order, and use the corresponding historical scheduling subdomain as the target scheduling subdomain of the current work order to be assigned.

[0057] In some embodiments, the intelligent gas management platform can determine at least one target scheduling subdomain corresponding to a gas work order to be assigned, based on the gas inquiry characteristics, gas user characteristics, and gas work order characteristics of the work order to be assigned. For further explanation of gas work order characteristics, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0058] For example, the aforementioned first reference data may include not only historical gas consultation characteristics and historical gas user characteristics, but also gas work order characteristics of the work order. The smart gas management platform can query the first reference data in the first mapping relationship table that is the same as or similar to the gas consultation characteristics, gas user characteristics, and gas work order characteristics of the current work order, and use the corresponding historical scheduling subdomain as the target scheduling subdomain of the current work order to be assigned.

[0059] The methods described in some embodiments of this specification can fully combine multiple features such as gas consultation characteristics, gas user characteristics, and gas work order characteristics to determine the appropriate target scheduling subdomain for the current work order to be assigned, thereby improving the efficiency of subsequent work order allocation and processing.

[0060] In some embodiments, the intelligent gas management platform can determine a work order vector based on the gas inquiry characteristics and gas user characteristics of the work order to be assigned; and determine the target scheduling subdomain corresponding to the work order to be assigned based on the work order vector and the subdomain feature vectors of multiple scheduling subdomains. For further explanation on how to determine the target scheduling subdomain using the work order vector and subdomain feature vectors, please refer to [link to relevant documentation]. Figure 4 And its related descriptions.

[0061] In some embodiments, the preset method may also include model building, regression analysis, etc., without limitation.

[0062] Step 230: Assign the work orders to be assigned to the target scheduling subdomain.

[0063] In some embodiments, the intelligent gas management platform can assign work orders to a target scheduling subdomain. Work orders to be assigned in the target scheduling subdomain can be updated. The personnel receiving the orders in that subdomain can then process the assigned work orders.

[0064] In some instances, the intelligent gas management platform can perform order acceptance and scheduling for work orders awaiting assignment within each scheduling subdomain. Order acceptance and scheduling refers to assigning work orders awaiting assignment within the target scheduling subdomain to the order acceptance personnel within that subdomain.

[0065] In some embodiments, the intelligent gas management platform can determine the urgency of work orders to be assigned in the current scheduling subdomain based on the consultation type, terminal characteristics, and usage characteristics of the work orders; determine the order-taking generalization value of the order takers in the current scheduling subdomain based on the historical order-taking distribution of the order takers; and perform order-taking scheduling for work orders to be assigned in the scheduling subdomain based on the urgency of the work orders to be assigned and the order-taking generalization value of the order takers. For more information on order-taking scheduling based on urgency and order-taking generalization value, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0066] In some embodiments, the smart gas management platform can also schedule orders in other ways, such as assigning them to available personnel in sequence according to the generation time of the work orders to be assigned, without limitation.

[0067] The methods described in some embodiments of this specification can allocate work orders to suitable personnel in various ways, thereby achieving scientific order scheduling and improving the efficiency of work order acceptance and processing.

[0068] The methods described in some embodiments of this specification, by comprehensively analyzing multiple characteristics of work orders and establishing multiple scheduling subdomains, enable scientific allocation and scheduling optimization of work orders when the gas platform has a large number of different work orders, thereby improving the efficiency of work order acceptance and processing, and ensuring the efficient and orderly operation of the gas platform.

[0069] Figure 3 This is an exemplary flowchart illustrating the determination of multiple scheduling subdomains through clustering, according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by a smart gas management platform.

[0070] Step 310: Determine the preset number of cluster centers.

[0071] The intelligent gas management platform can set preset quantities based on experience values, system default values, or any combination thereof. In some embodiments, the preset quantity does not exceed the total number of personnel accepting orders.

[0072] Step 320: Cluster the historical work orders to be assigned within the preset time period to determine the preset number of clusters.

[0073] A cluster can refer to a set of one or more historical work orders awaiting assignment. The intelligent gas management platform can use clustering algorithms to cluster historical work orders awaiting assignment within a preset time period based on clustering characteristics, thus determining a preset number of clusters. For more information on preset time periods, please refer to [link to relevant documentation]. Figure 2 This includes a description of the historical work orders to be assigned. Specifically, there can be multiple historical work orders to be assigned. Based on this set of historical work orders, the smart gas management platform can construct a set of historical feature vectors. These historical feature vectors can be vector representations of the historical work orders to be assigned. The elements of the historical feature vectors can correspond to the features included in the clustering features (e.g., gas consultation features, gas user features, etc.). The smart gas management platform can use a clustering algorithm to cluster the set of historical feature vectors, obtaining a set of a predetermined number of clusters. Each cluster can have a corresponding cluster center. The clustering algorithm can include, but is not limited to, K-Means clustering algorithm, DBSCAN algorithm, etc.

[0074] Clustering features can refer to the characteristics of historical work orders awaiting clustering. In some embodiments, clustering features may include at least gas consultation features and gas user features of historical work orders awaiting clustering. Further instructions on how to obtain the gas consultation features and gas user features of work orders can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0075] In some embodiments, in addition to gas consultation features and gas user features, clustering features may also include gas work order features of historical pending work orders. Gas work order features can reflect the service communication status of work orders. In some embodiments, gas work order features include at least one of call consultation duration and information interaction degree.

[0076] Call duration refers to the duration of the service call corresponding to the work order. Service calls can be initiated by the user.

[0077] Information interaction level can be used to measure the degree of information interaction between a user and customer service. In some embodiments, information interaction level can be determined based on speech recognition technology. Specifically, the smart gas user platform can identify the duration of each speech by the customer service representative and the user, and determine the communication frequency between the customer service representative and the customer, thus determining the information interaction level based on the communication frequency. For example, the higher the communication frequency, the higher the information interaction level. Here, communication frequency = number of communications / call duration. A single communication is counted as one response from the customer service representative after the user finishes speaking.

[0078] The methods described in some embodiments of this specification can cluster the work orders to be assigned by combining the gas work order characteristics of historical work orders to be assigned, so that the service communication of the scheduling subdomains obtained after clustering is similar, thereby facilitating the subsequent determination of the target scheduling subdomain that matches the fault status of the work orders to be assigned.

[0079] In some embodiments, in addition to gas consultation features and gas user features, clustering features also include historical gas fault distribution and fault inquiry data for historical work orders pending assignment.

[0080] Historical gas fault distribution reflects the fault status of the gas terminal for the user corresponding to the work order over a historical period. The historical gas fault distribution can include historical fault types and their corresponding frequencies. Fault types can be insufficient gas pressure, insufficient battery power in the gas terminal, etc. Fault frequency reflects the number of times a particular fault occurs within a certain period (e.g., the aforementioned preset time period).

[0081] In some embodiments, the distribution of historical gas faults can be represented as a vector ( , , ……, ),in, This can represent the frequency of occurrence of faults of type A. This can represent the frequency of occurrence of faults of type B, and so on.

[0082] Fault inquiry data refers to data related to customer service inquiries about fault conditions. This data can include questions asked by customer service representatives and user responses. Understandably, customer service representatives can understand the basic situation of a fault by asking users questions related to it; for example, a fault-related question could be, "Can the gas ignite normally?"

[0083] In some embodiments, in addition to gas consultation features, gas user features, historical gas fault distribution of historical work orders pending assignment, and fault inquiry data, clustering features may also include the fault prediction difficulty of historical work orders pending assignment. Fault prediction difficulty can be used to measure the ease or difficulty of determining the type of gas fault.

[0084] In some embodiments, the difficulty of fault prediction can be determined based on historical gas fault distribution and / or gas work order characteristics.

[0085] In some embodiments, the intelligent gas management platform can determine the difficulty of fault prediction based on the frequency of fault occurrence in the historical gas fault distribution.

[0086] In some embodiments, if the occurrence frequencies of various types of faults in the historical gas fault distribution are closer, it indicates that it is more difficult to determine the most likely type of fault in the current situation, and the difficulty of fault prediction is greater. For example, if the difference between the minimum and maximum values ​​of the fault occurrence frequencies in the historical gas fault distribution of a work order to be assigned is less than a set threshold, it indicates that the occurrence frequencies of various types of faults are closer.

[0087] In some embodiments, the intelligent gas management platform can calculate the difference between the maximum value and other values ​​of the fault occurrence frequency in the historical gas fault distribution, and determine the sum of all differences, using the sum of all differences as the fault prediction difficulty. Alternatively, the intelligent gas management platform can calculate the variance of the fault occurrence frequency before removing the maximum value from the historical gas fault distribution, and the variance of the fault occurrence frequency after removing the maximum value from the historical gas fault distribution, using the difference between the two variances as the fault prediction difficulty.

[0088] In some embodiments, the longer the call duration and the greater the level of information interaction, the easier it is to predict the fault. It should be understood that the longer the call duration and the greater the level of information interaction, the more detailed the fault-related information that customer service can obtain, and the easier it is to determine the fault type.

[0089] In some embodiments, the difficulty of fault prediction can also be determined by the following formula (1):

[0090]

[0091] Where t is the call consultation duration. i is the information interaction degree. s is the sum of the differences between the maximum frequency of the distribution in the historical gas fault distribution and other values. k is a preset coefficient, which can be set based on empirical values, system default values, or any combination thereof.

[0092] The methods described in some embodiments of this specification can be combined with the fault prediction difficulty to cluster the work orders to be assigned, so that the fault prediction difficulty of the scheduling subdomains obtained after clustering is similar, thereby facilitating the subsequent determination of the target scheduling subdomain that matches the fault prediction difficulty of the work orders to be assigned.

[0093] The methods described in some embodiments of this specification can be combined with historical fault information, such as historical gas fault distribution and fault inquiry data, to cluster the work orders to be assigned, so that the historical fault information of the scheduling subdomains obtained after clustering is similar, thereby facilitating the subsequent determination of the target scheduling subdomain that matches the fault information of the work orders to be assigned.

[0094] Step 330: Each cluster is identified as a scheduling subdomain.

[0095] The intelligent gas management platform can treat each of the preset number of clusters as a scheduling subdomain. In some embodiments, work orders to be assigned that have a vector distance (e.g., Euclidean distance, cosine distance, etc.) to the cluster center of a cluster corresponding to a certain scheduling subdomain that is less than a certain threshold can be considered as work orders to be assigned that can be processed by that scheduling subdomain.

[0096] In some embodiments, the smart gas management platform determines the order takers for each dispatch subdomain based on their historical order taker data. For example, the smart gas management platform can determine the corresponding historical order taker vector set based on the historical order taker data of all order takers (e.g., gas consultation characteristics and gas user characteristics of historical work orders). The smart gas management platform can calculate the vector distance between the cluster center of the cluster corresponding to a certain dispatch subdomain and each vector in the aforementioned historical order taker vector set, and select the order taker corresponding to the historical order taker vector with the smallest vector distance as the order taker for that dispatch subdomain. This process is repeated to determine the order takers for all dispatch subdomains.

[0097] The methods described in some embodiments of this specification, by comprehensively analyzing multiple characteristics of work orders and establishing multiple scheduling subdomains, scientifically allocate work orders when the gas platform has a large number of different work orders, improve the efficiency of work order acceptance and processing, and ensure the efficient and orderly operation of the gas platform.

[0098] Figure 4 This is an exemplary schematic diagram illustrating the determination of a target scheduling subdomain by vector according to some embodiments of this specification.

[0099] like Figure 4 As shown, the intelligent gas management platform can determine the work order vector 420 to be assigned based on the gas consultation feature 411 and gas user feature 412 of the work order to be assigned; and determine the target scheduling subdomain 440 corresponding to the work order to be assigned based on the work order vector 420 to be assigned and the subdomain feature vector 430 of multiple scheduling subdomains.

[0100] In some embodiments, the work order vector to be assigned can be obtained by directly concatenating gas consultation features and gas user features. For example, the work order vector to be assigned can be represented as ( , ),in, This can represent the characteristics of gas consulting. This can represent the characteristics of gas users. In some embodiments, the work order vector to be assigned can be obtained through an embedding model based on gas consultation features and gas user features. Specifically, the gas consultation features and gas user features can be input into a pre-trained embedding model, and the embedding vector output by the embedding model can be used as the work order vector to be assigned.

[0101] In some embodiments, the subdomain feature vector can be determined based on the clustering features of the cluster centers. Each cluster has a cluster center, and each cluster center has a corresponding clustering feature. Further explanation of clusters and clustering features can be found in [link to relevant documentation]. Figure 3 And its related descriptions.

[0102] In some embodiments, when the work order vector to be assigned is determined by direct concatenation, the subdomain feature vector of the scheduling subdomain can be obtained by directly concatenating the cluster features corresponding to the cluster center of its cluster.

[0103] In some embodiments, when determining the work order vector to be assigned through the embedding model, the subdomain feature vector of the scheduling subdomain can be the embedding vector obtained by inputting the cluster features corresponding to the cluster center of its cluster into the trained embedding model.

[0104] In some embodiments, the subdomain feature vector can be determined based on the clustering features of all work orders to be assigned currently included in the corresponding cluster.

[0105] For example, after clustering, three clusters o, p, and q are obtained. These three clusters can correspond to three scheduling subdomains O, P, and Q, respectively. Taking the determination of the subdomain feature vector of scheduling subdomain O as an example, when determining the work order vector to be assigned by direct concatenation, the smart gas management platform can obtain the subdomain feature vector of scheduling subdomain O by taking the average of the cluster features of the same type of multiple gas work orders to be assigned currently included in cluster o, and then concatenating the cluster features after taking the average of each type.

[0106] For example, when determining the work order vector to be assigned through the embedding model, the smart gas management platform can input the clustered features (after averaging the clustered features of each type) into the trained embedding model. The embedding vector output by the embedding model can then be used as the subdomain feature vector of the scheduling subdomain O.

[0107] The method described in some embodiments of this specification determines the corresponding subdomain feature vector by using the clustering features of each work order to be assigned in the cluster, so that the subdomain feature vector can more comprehensively reflect the characteristics of each work order to be assigned.

[0108] In some embodiments, the smart gas management platform can perform vector matching between the work order vector to be assigned and the subdomain feature vectors of each scheduling subdomain to determine the similarity between the work order vector to be assigned and the subdomain feature vectors of each scheduling subdomain. The similarity can be determined based on the vector distance (e.g., Euclidean distance, cosine distance, etc.) between the work order vector to be assigned and the subdomain feature vectors. Understandably, the smaller the vector distance, the greater the similarity between the vectors. The smart gas management platform can then determine the target scheduling subdomain based on the similarity. For example, the smart gas management platform can determine the scheduling subdomain corresponding to the subdomain feature vector with the highest similarity as the target scheduling subdomain.

[0109] In some embodiments, the intelligent gas management platform can determine the similarity between the work order vector to be assigned and the feature vector of each subdomain; determine the subdomain busyness of each scheduling subdomain; and determine the target scheduling subdomain based on the similarity and subdomain busyness.

[0110] For further explanation on how to determine similarity, please refer to the preceding content.

[0111] Subdomain busyness can be used to measure the current busyness of a scheduled subdomain.

[0112] In some embodiments, the intelligent gas management platform can determine the busyness of a subdomain based on the number of work orders to be assigned and the number of personnel accepting those orders. For example, subdomain busyness = number of work orders to be assigned / number of personnel accepting those orders.

[0113] In some embodiments, subdomain busyness can be related to the urgency of work orders to be assigned in the scheduling subdomain and the order acceptance generality value of order takers in the entire scheduling subdomain. Further explanation of urgency and order acceptance generality value can be found in [link to relevant documentation]. Figure 5 And related explanations.

[0114] In some embodiments, the subdomain busyness of a scheduling subdomain can be positively correlated with (Σ urgency of work orders to be assigned) / (Σ order-taking generalization value of order takers). Σ urgency of work orders to be assigned represents the sum of the urgency of all work orders to be assigned in that scheduling subdomain. Σ order-taking generalization value of order takers represents the sum of the order-taking generalization values ​​of all order takers in that scheduling subdomain. The larger the ratio of (Σ urgency of work orders to be assigned) / (Σ order-taking generalization value of order takers) in a scheduling subdomain, the greater the subdomain busyness.

[0115] The method described in some embodiments of this specification determines the corresponding subdomain feature vector by using the clustering features of each work order to be assigned in the cluster, so that the subdomain feature vector can more comprehensively reflect the characteristics of each work order to be assigned.

[0116] In some embodiments, the intelligent gas management platform can determine the subdomain feature vector with the highest similarity to the work order vector to be assigned from the subdomain feature vectors corresponding to the scheduling subdomains whose subdomain busyness meets preset conditions (e.g., the subdomain busyness is less than a preset threshold), and determine the scheduling subdomain corresponding to the subdomain feature vector with the highest similarity as the target scheduling subdomain.

[0117] The method described in some embodiments of this specification determines the appropriate target scheduling sub-region by determining the busyness of the sub-domain, thereby avoiding the assignment of work orders to be assigned to a busy scheduling sub-domain, which would cause long waiting times for the work orders to be assigned, and thus improving the processing efficiency of subsequent work orders to be assigned.

[0118] The method described in some embodiments of this specification obtains the work order vector and the subdomain feature vector by quantizing the work order to be assigned and the target scheduling subdomain, thereby more accurately determining the target scheduling subdomain corresponding to the work order to be assigned.

[0119] Figure 5 This is an exemplary flowchart illustrating the order acceptance and scheduling of work orders to be assigned, according to some embodiments of this specification. Figure 5 As shown, process 500 includes the following steps. In some embodiments, process 500 may be executed by a smart gas management platform.

[0120] Step 510: Determine the urgency of the work orders to be assigned in the current scheduling subdomain based on the consultation type, terminal characteristics, and usage characteristics of the work orders to be assigned.

[0121] Urgency level can be used to assess how urgent work orders need to be processed.

[0122] In some embodiments, the smart gas management platform can pre-define the correspondence between different consultation types, terminal characteristics, usage characteristics, and different levels of urgency. For example, the urgency level for consultation types such as fault reporting can be the highest, followed by other consultation types (e.g., service complaints, gas equipment function inquiries, gas new product inquiries), and so on. As another example, in terms of terminal characteristics, larger terminals (e.g., large boilers) can have a higher urgency level than smaller terminals (e.g., gas stoves). Furthermore, in terms of usage characteristics, the higher the frequency of gas use and the longer the average duration of gas use, the higher the urgency level should be. Accordingly, the smart gas management platform can determine the urgency level corresponding to the consultation type, terminal characteristics, and usage characteristics of the currently pending work order based on the correspondence between these factors.

[0123] In some embodiments, in response to a fault report, the urgency level is related to the historical distribution of gas faults and the accuracy of fault location. Further information on historical gas faults and the difficulty of fault prediction can be found in [link to relevant documentation]. Figure 3 And its related descriptions.

[0124] In some embodiments, the more severe the fault type in the historical gas fault distribution, the higher its frequency of occurrence, and the higher the urgency of the work order to be assigned. The severity of the fault type can be determined based on historical experience data, manual judgment, etc.

[0125] In some embodiments, the lower the difficulty of fault prediction, the higher the urgency of the work order to be assigned. Lower fault prediction difficulty means that the fault type is easier to identify, and the shorter the processing time for the work order to be assigned. Therefore, prioritizing the processing of the work order to be assigned can improve the efficiency of work order processing.

[0126] The methods described in some embodiments of this specification determine the urgency of work orders to be assigned by analyzing the historical distribution of gas faults and the accuracy of fault location. This facilitates prioritizing work orders with higher urgency and ensures the safe operation of the gas pipeline network.

[0127] Step 520: Based on the historical order acceptance distribution of the order accepters, determine the general order acceptance value of the order accepters in the current scheduling subdomain.

[0128] For more information about order takers, please refer to [link / reference]. Figure 2 And its related descriptions.

[0129] Historical order distribution reflects the order history of each order taker. Each order taker can have a corresponding historical order distribution. This distribution can include the number of orders taken, average completion time, and average satisfaction rate for each type of order. Orders of the same type can have similar characteristics (e.g., the same gas consultation characteristics and gas user characteristics, or the same gas consultation characteristics, gas user characteristics, and gas order characteristics). Average completion time can be determined based on the average completion time of orders of the same type. Average satisfaction rate can be determined based on the average user satisfaction rate of orders of the same type.

[0130] In some embodiments, the historical order distribution can be represented, for example, in the form of Table 1:

[0131]

[0132] Table 1 - Exemplary representation of historical order distribution

[0133] The order acceptance generality value can be used to measure the quality of order processing by the order taker. The higher the order acceptance generality value, the higher the quality of order processing by the order taker.

[0134] In some embodiments, the difficulty of fault prediction can be determined by the following formula (2):

[0135] (2)

[0136] Where U is the general value for order acceptance. K is the set of the number of times work orders with different characteristics are accepted in the historical order acceptance distribution (that is, K can include multiple number of times of order acceptance). This represents the variance of the number of times different types of work orders are accepted. This represents the maximum number of orders received in the set of order receipts. The minimum number of orders received is given in the set of order receipts. i can represent the row number in Table 1, where i ≥ 1. Let i be the number of times the work order in the i-th row of Table 1 is accepted. Let be the average completion satisfaction rate of the work orders in the i-th row of Table 1. Let be the average completion time of the work orders in the i-th row of Table 1.

[0137] It should be understood that the more balanced the number of orders a person accepts across different types of work orders, the shorter the average completion time of each work order, and the higher the average satisfaction level with the completion of each work order, the greater the person's general applicability value.

[0138] Step 530: Based on the urgency of the work orders to be assigned and the general acceptance value of the personnel accepting the orders, schedule the work orders to be assigned in the scheduling subdomain.

[0139] In some embodiments, the intelligent gas management platform can prioritize order takers with low order acceptance generality values ​​to accept the most urgent work orders within their corresponding available order range. Understandably, by prioritizing order takers with low order acceptance generality values, the order takers in the scheduling subdomain can be fully scheduled and arranged, avoiding situations where an order taker has no orders to accept, thus improving the order acceptance fault tolerance rate of the scheduling subdomain.

[0140] In some embodiments, the order-accepting range for each order taker can be preset based on historical experience data and system default ranges. The order-accepting range can include user types, etc. For example, the order-accepting range can include gas faults occurring to residential users, gas faults occurring to commercial users, gas faults occurring to industrial users, etc.

[0141] In some embodiments, the smart gas management platform may also prioritize order takers with high order acceptance generality values ​​to accept the order with the highest urgency among all pending work orders in the current scheduling subdomain.

[0142] The methods described in some embodiments of this specification, by comprehensively analyzing multiple characteristics of work orders and establishing multiple scheduling subdomains, scientifically allocate work orders when the gas platform has a large number of different work orders, improve the efficiency of work order acceptance and processing, and ensure the efficient and orderly operation of the gas platform.

[0143] This specification also provides a computer-readable storage medium that can store computer instructions. When a computer reads the computer instructions from the storage medium, the computer runs any intelligent gas work order scheduling optimization method as provided in this specification.

[0144] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0145] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0146] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0147] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0148] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0149] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0150] 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 are 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 work order scheduling optimization method, wherein the method is implemented by a smart gas work order scheduling optimization Internet of Things (IoT) system, the IoT system comprising a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially, and the method is executed by the smart gas management platform, comprising: Obtain newly generated work orders to be assigned from the smart gas service platform, which are generated by the smart gas service platform based on gas processing requests received from the smart gas user platform; Multiple scheduling subdomains are determined based on historical work orders to be assigned within a preset time period, including: Determine the preset number of cluster centers; Clustering is performed on historical work orders to be assigned within a preset time period to determine the preset number of clusters, wherein the clustering features used for the clustering include at least the gas consultation features, gas user features, and gas work order features of the historical work orders to be assigned. Each cluster is defined as a scheduling subdomain; the gas consultation features include at least one of consultation type and consultation location information; the gas user features include at least one of user type, terminal type and usage features; the gas work order features include at least one of call consultation duration and information interaction degree. Based on the gas consultation characteristics and gas user characteristics of the work orders to be assigned, a vector of work orders to be assigned is determined. Based on the work order vector to be assigned and the subdomain feature vectors of the multiple scheduling subdomains, the target scheduling subdomain corresponding to the work order to be assigned is determined, wherein the subdomain feature vectors are determined based on the clustering features of the cluster centers of the clusters; wherein determining the target scheduling subdomain corresponding to the work order to be assigned based on the work order vector to be assigned and the subdomain feature vectors of the multiple scheduling subdomains includes: determining the similarity between the work order vector to be assigned and each subdomain feature vector; and determining the subdomain busyness of each scheduling subdomain; determining the target scheduling subdomain based on the similarity and the subdomain busyness; and The work orders to be assigned are assigned to the corresponding target scheduling subdomains.

2. The intelligent gas work order scheduling optimization method as described in claim 1, characterized in that, The method further includes: The work orders to be assigned in each of the scheduling subdomains are scheduled for acceptance.

3. The intelligent gas work order scheduling optimization method as described in claim 1, characterized in that, The Internet of Things system also includes a smart gas sensor network platform and a smart gas object platform; The intelligent gas management platform includes an intelligent management sub-platform and an intelligent gas data center; The method is executed by the intelligent management sub-platform and also includes: The work orders to be assigned are obtained from the smart gas service platform based on the smart gas data center.

4. An IoT system for optimizing intelligent gas work order scheduling, characterized in that, The Internet of Things (IoT) system includes a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially. The smart gas management platform is configured to perform the following operations: Obtain newly generated work orders to be assigned from the smart gas service platform, which are generated by the smart gas service platform based on gas processing requests received from the smart gas user platform; Multiple scheduling subdomains are determined based on historical work orders to be assigned within a preset time period, including: Determine the preset number of cluster centers; Clustering is performed on historical work orders to be assigned within a preset time period to determine the preset number of clusters, wherein the clustering features used for the clustering include at least the gas consultation features, gas user features, and gas work order features of the historical work orders to be assigned. Each cluster is defined as a scheduling subdomain; the gas consultation features include at least one of consultation type and consultation location information; the gas user features include at least one of user type, terminal type and usage features; the gas work order features include at least one of call consultation duration and information interaction degree. Based on the gas consultation characteristics and gas user characteristics of the work orders to be assigned, a vector of work orders to be assigned is determined. Based on the work order vector to be assigned and the subdomain feature vectors of the multiple scheduling subdomains, the target scheduling subdomain corresponding to the work order to be assigned is determined, wherein the subdomain feature vectors are determined based on the clustering features of the cluster centers of the clusters; wherein determining the target scheduling subdomain corresponding to the work order to be assigned based on the work order vector to be assigned and the subdomain feature vectors of the multiple scheduling subdomains includes: determining the similarity between the work order vector to be assigned and each subdomain feature vector; and determining the subdomain busyness of each scheduling subdomain; determining the target scheduling subdomain based on the similarity and the subdomain busyness; and The work orders to be assigned are assigned to the corresponding target scheduling subdomains.

5. The IoT system for intelligent gas work order scheduling optimization as described in claim 4, characterized in that, The intelligent gas management platform is also configured to: The work orders to be assigned in each of the scheduling subdomains are scheduled for acceptance.

6. The IoT system for optimizing intelligent gas work order scheduling as described in claim 4, characterized in that, The Internet of Things system also includes a smart gas sensor network platform and a smart gas object platform; The intelligent gas management platform includes an intelligent management sub-platform and an intelligent gas data center; The work orders to be assigned are obtained from the smart gas service platform based on the smart gas data center.

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