Multi-service collaborative gas service system

Abstract

The invention relates to the technical field of gas business, and discloses a multi-business collaborative gas business system, which comprises a data acquisition and processing module, a business module integration and collaboration module, an optimization scheduling module and a real-time monitoring and feedback module, the data acquisition and processing module acquires operation data of gas equipment by using Internet of Things equipment, and performs primary processing on the acquired data through a data processing algorithm; the business module integration and collaboration module realizes data sharing and business collaboration of each business module through a collaboration algorithm; the optimal scheduling module is used for maintaining the gas equipment through an optimal scheduling algorithm according to the state and the cooperation result of each business module; the real-time monitoring and feedback module ensures efficient operation and stability of the system through a real-time monitoring and feedback mechanism, and based on a complex collaborative algorithm and multi-service integration, the gas service system can effectively realize data sharing and optimized collaboration among all service modules, and improve operation efficiency and service quality.

Description

Technical Field

[0001] The present invention relates to the field of gas business technology, and more specifically, to a multi-business collaborative gas business system. Background Art

[0002] As the digital transformation of the global energy industry accelerates, the gas industry is also facing unprecedented opportunities for change. The widespread application of technologies such as the Internet of Things, big data, and cloud computing has provided strong technical support for the intelligent management of the gas industry. At the same time, users' demand for gas services is becoming increasingly diversified and personalized, requiring gas companies to continuously improve service quality to meet market demand.

[0003] In traditional gas business management systems, each business module (such as customer management, equipment maintenance, emergency handling and bill management) usually has information islands, making it difficult to achieve effective collaboration. This situation leads to problems such as low business processing efficiency, slow service response, and waste of resources. In order to improve overall operational efficiency, a gas business system that can achieve efficient collaboration and optimization is needed to solve existing problems through intelligent algorithms and data processing technologies.

[0004] Therefore, the present invention provides a multi-service collaborative gas service system to meet user needs. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-service collaborative gas service system.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-business collaborative gas business system comprises a data acquisition and processing module, a business module integration and collaboration module, an optimization scheduling module and a real-time monitoring and feedback module; the data acquisition and processing module utilizes Internet of Things devices to acquire operating data of gas equipment, and performs preliminary processing on the acquired data through a data processing algorithm; the business module integration and collaboration module realizes data sharing and business collaboration of each business module through a collaboration algorithm; the optimization scheduling module schedules maintenance, emergency treatment, etc. of gas equipment through an optimization scheduling algorithm according to the status of each business module and the collaboration result; the real-time monitoring and feedback module ensures efficient operation and stability of the system through a real-time monitoring and feedback mechanism.

[0008] Furthermore, the data acquisition and processing module uses the Internet of Things device to collect the operating data of the gas equipment, and performs preliminary processing on the collected data through the data processing algorithm. The data acquisition and processing module includes a storage, a processor, a temperature sensor, a pressure sensor and a flow sensor. The temperature is marked as T = {T 1 , T2 ......, T o}, where T o represents the oth data, o is a positive integer, P = {P 1 , P 2 ......, P l}, where P l It is represented as the Ith data, I is a positive integer, and the flow mark is Q = {Q 1 ,Q......,Q i}, where Q i Represents the i-th data, i is a positive integer, and then the data is preprocessed using a processor.

[0009] The data processing module performs preliminary processing on the collected data, including data cleaning, smoothing and normalization. The normalization formula is as follows:

[0010]

[0011] Where x is the original data, namely temperature T, flow Q and pressure P data, x max and x min are the minimum and maximum values ​​of the data respectively, and x′ is the normalized data.

[0012] Furthermore, the business module integration and collaboration module integrates multiple business modules such as customer management, equipment maintenance, emergency handling and bill management, and realizes data sharing and business collaboration among the modules through a collaborative algorithm. The collaborative algorithm includes the following steps:

[0013] S1: Initialize the business module status:

[0014] S 0 ={S cust , S maint , S emerg , S bill}:

[0015] Initialize the initial status of customer management, equipment maintenance, emergency handling and bill management business modules;

[0016] S2: Define the collaborative objective function:

[0017] J(S)=ω 1 f cust (S cust )+ω 2 f ma int (S ma int )+ω 3 f emerg (S emerg )+ω 4 f bill (Sbill )

[0018] J(S)=ω 1 f cust (S cust )+ω 2 f maint (S maint )+ω 3 f emerg (S emerg )+ω 4 f bill (S bill ):

[0019] J(S) is the collaborative objective function, which is used to measure the optimization goal of each business module status. 1 ,ω 2 ,ω 3 ,ω 4 are the weight coefficients of the customer management, equipment maintenance, emergency handling and bill management business modules, respectively, cust (S cust ), f maint (S maint ), f emerg (S emerg ), f bill (S bill ) is the objective function of each business module, which is used to evaluate the efficiency or satisfaction of each module status;

[0020] S3: Iteratively update the business module status:

[0021]

[0022] Update the state of the business module according to the gradient of the collaborative objective function, where η is the learning rate, which controls the step size of each update;

[0023] S4: Iteratively update the business module status:

[0024] If |S k+1 -S k |<∈, then the iteration stops, where ∈ is the convergence threshold, which is used to determine whether the algorithm converges to the optimal solution or a near-optimal solution.

[0025] Furthermore, the optimization scheduling module schedules the maintenance, emergency treatment, etc. of the gas equipment through a multi-objective optimization scheduling algorithm according to the status and coordination results of each business module.

[0026] Furthermore, the optimization scheduling algorithm includes initializing scheduling parameters, defining a multi-objective scheduling function, iteratively updating scheduling parameters, and checking convergence steps, and the multi-objective scheduling function includes a coordination function between scheduling parameters, and the scheduling objective function is as follows:

[0027]

[0028] Where D is the scheduling parameter vector, f maint 、f emerg are the objective functions of equipment maintenance and emergency handling, g is the scheduling function for customer and bill management, α i is the weight coefficient, ψ ij (D i ,D j ) is the scheduling parameter D i and D j The synergy function between ij is the synergy coefficient;

[0029] The iterative update scheduling parameters are as follows:

[0030] Where γ is the learning rate, is the gradient of the objective function.

[0031] Furthermore, the feedback control module adjusts the production process parameters according to the quality assessment results. The specific analysis steps are as follows:

[0032] When ΔQ=QQ 标准 , if it is not within the allowable error range, the lubricant is considered to be of unqualified quality, indicating that the production process data of the lubricant needs to be adjusted. Suppose the production process parameters are W = {W 1 , W 2 ......, W e}, where W e Represents the e-th data, where e is a positive integer;

[0033]

[0034] Where k is the control gain coefficient, γ is the integral control coefficient, and M is the integral time in the historical time period.

[0035] Furthermore, the real-time monitoring and feedback module also includes data recording and feedback: the real-time data monitoring module can provide real-time feedback and warnings to ensure the stability and reliability of the simulation process, and record and monitor data through the following formula:

[0036] Data record = {(T i ,L i ,F i ,E i )i=1,2,3,4......,n};

[0037] Where n is the number of data records, T i ,L i,F i ,E i are the response time, load condition, fault status and gas consumption recorded for the i-th time respectively.

[0038] Furthermore, the iterative optimization and result feedback module includes optimizing and adjusting the model and simulation parameters through optimization steps, including parameter initialization, loss function calculation, parameter update and convergence checking.

[0039] Further, the optimization steps are specifically analyzed as follows:

[0040] S1: Initialize model parameters to θ 0 , these parameters may include model weights, biases, and other variables that control the behavior of the model;

[0041] S2: Calculate the loss function:

[0042] Where L(θ) is the loss function, Represents the model prediction value With the actual value y i The sum of squared errors between represents the mutual influence between data point i and other data points j, where λ ij is the weight coefficient, which measures the correlation between data points i and j.

[0043] S3: Update parameters:

[0044] where θ k is the parameter value of the kth iteration, θ k+1 is the parameter value of the k+1th iteration, η is the learning rate, and the loss function is the parameter θ k The gradient of , which represents the derivative of the loss function at the current parameter value;

[0045] S4: Check convergence: Calculate the difference before and after parameter update |θ k+1 -θ k |, if the difference is less than the set convergence threshold ∈, the iteration is stopped; if |θ k+1 -θ k |<ξ, then stop the iteration. If the difference does not reach the convergence threshold, return to step 2 and continue iterating. ξ is the preset convergence threshold. When the amplitude of parameter update is less than the threshold, the parameter is considered to have converged and the iteration is stopped.

[0046] Furthermore, a multi-service collaborative gas service method adopts a multi-service collaborative gas service system according to any one of claims 1 to 9, and is characterized in that it includes the following steps:

[0047] Step 1: Use the data collection and processing module to collect the operating data of the gas equipment using the Internet of Things device, and perform preliminary processing on the collected data through the data processing algorithm;

[0048] Step 2: Use the business module integration and collaboration module to achieve data sharing and business collaboration among business modules through collaborative algorithms;

[0049] Step 3: Use the optimization scheduling module to schedule the maintenance and emergency treatment of gas equipment according to the status and coordination results of each business module through the optimization scheduling algorithm;

[0050] Step 4: Real-time monitoring and feedback module ensures the efficient operation and stability of the system through real-time monitoring and feedback mechanism.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present application discloses a multi-business collaborative gas business system. Through the Internet of Things devices and data processing algorithms, the system realizes the efficient collection and processing of gas equipment operation data. The business module integration and collaboration module realizes the efficient collaboration between business modules such as customer management, equipment maintenance, emergency handling and bill management through the collaborative algorithm. The optimization scheduling module uses the optimization scheduling algorithm to optimally schedule the maintenance and emergency handling of gas equipment. The real-time monitoring and feedback module ensures the efficient operation and stability of the system through the real-time monitoring and feedback mechanism.

[0053] 2. The present invention is based on complex collaborative algorithms and multi-business integration. The gas business system can effectively realize data sharing and optimized collaboration among various business modules, thereby improving operational efficiency and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the steps of a multi-business collaborative gas business system provided by the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] A multi-business collaborative gas business system includes a data acquisition and processing module, a business module integration and collaboration module, an optimization scheduling module and a real-time monitoring and feedback module; the data acquisition and processing module uses Internet of Things devices to collect operating data of gas equipment, and preliminarily processes the collected data through a data processing algorithm; the business module integration and collaboration module realizes data sharing and business collaboration of each business module through a collaboration algorithm; the optimization scheduling module schedules maintenance, emergency treatment, etc. of gas equipment through an optimization scheduling algorithm according to the status and collaboration results of each business module; the real-time monitoring and feedback module ensures efficient operation and stability of the system through a real-time monitoring and feedback mechanism.

[0057] The data acquisition and processing module uses the Internet of Things devices to collect the operating data of the gas equipment and performs preliminary processing on the collected data through the data processing algorithm. The data acquisition and processing module includes storage, processor, temperature sensor, pressure sensor and flow sensor. The temperature is marked as T = {T 1 , T 2 ......, T o}, where To represents the oth data, o is a positive integer, P = {P 1 , P 2 ......, P l}, where Pl represents the Ith data, I is a positive integer, and the flow mark is Q = {Q 1 ,Q......,Q i}, where Q i Represents the i-th data, i is a positive integer, and then the data is preprocessed using a processor.

[0058] The data processing module performs preliminary processing on the collected data, including data cleaning, smoothing and normalization. The normalization formula is as follows:

[0059]

[0060] Where x is the original data, namely temperature T, flow Q and pressure P data, x max and x min are the minimum and maximum values ​​of the data respectively, and x′ is the normalized data.

[0061] The business module integration and collaboration module integrates multiple business modules such as customer management, equipment maintenance, emergency handling and bill management, and realizes data sharing and business collaboration among modules through collaborative algorithms. The collaborative algorithm includes the following steps:

[0062] S1: Initialize the business module status:

[0063] S 0 ={S cust , Smaint , S emerg , S bill};

[0064] Initialize the initial status of customer management, equipment maintenance, emergency handling and bill management business modules;

[0065] S2: Define the collaborative objective function:

[0066] J(S)=ω 1 f cust (S cust )+ω 2 f ma int (S ma int )+ω 3 f emerg (S emerg )+ω 4 f bill (S bill )

[0067] J(S)=ω 1 f cust (S cust )+ω 2 f ma int (S ma int )+ω 3 f emerg (S emerg )+ω 4 f bill (S bill );

[0068] J(S) is the collaborative objective function, which is used to measure the optimization goal of each business module status. 1 ,ω 2 ,ω 3 ,ω 4 are the weight coefficients of the customer management, equipment maintenance, emergency handling and bill management business modules, respectively, cust (S cust ), f maint (S ma int ), f emerg (S emerg ), f bill (S bill ) is the objective function of each business module, which is used to evaluate the efficiency or satisfaction of each module status;

[0069] S3: Iteratively update the business module status:

[0070]

[0071] Update the state of the business module according to the gradient of the collaborative objective function, where η is the learning rate, which controls the step size of each update;

[0072] S4: Iteratively update the business module status:

[0073] If |S k+1 -S k |<∈, then the iteration stops, where ∈ is the convergence threshold, which is used to determine whether the algorithm converges to the optimal solution or a near-optimal solution.

[0074] The optimization scheduling module schedules the maintenance and emergency response of gas equipment based on the status and coordination results of each business module through a multi-objective optimization scheduling algorithm.

[0075] This module uses the status information and coordination results of each business module obtained in real time, and through a multi-objective optimization scheduling algorithm, comprehensively considers the following aspects for precise scheduling: Business priority scheduling: Dynamically adjust the maintenance and emergency processing order of gas equipment according to the urgency and priority of each business module to ensure that key business needs are given priority. Optimal resource allocation: Through the optimization algorithm, gas equipment and human resources are allocated to maximize resource utilization efficiency while meeting the needs of multiple business modules. For example, the idle state and maintenance history of the equipment are considered at the same time. Dynamic adjustment strategy: According to real-time data and environmental changes, the scheduling strategy is adjusted, including equipment adjustment and task dispatch. and time arrangements to respond to emergencies and changing business needs, real-time monitoring and feedback: through real-time monitoring of the execution of each business module and the operating status of the equipment, timely feedback to the optimized scheduling algorithm, dynamic adjustment and optimization of the scheduling process can be achieved, and the synergistic benefits can be maximized. Real-time monitoring includes the business blueprint at the same time. The business blueprint includes core business capabilities such as user management, order processing, payment settlement, work order management, data analysis, and related business processes and data models. At the same time, through collaborative algorithms, the interaction between different business modules is considered, the scheduling plan is optimized, and the various business activities are coordinated with each other to achieve the goal of maximizing the overall benefits.

[0076] The optimization scheduling algorithm includes initializing scheduling parameters, defining multi-objective scheduling functions, iteratively updating scheduling parameters, and checking convergence steps. The multi-objective scheduling function contains the coordination function between scheduling parameters. The scheduling objective function is as follows:

[0077]

[0078] Where D is the scheduling parameter vector, f maint 、f emerg are the objective functions of equipment maintenance and emergency handling, g is the scheduling function for customer and bill management, α i is the weight coefficient, ψ ij (D i ,D j ) is the scheduling parameter D iand D j The synergy function between ij is the synergy coefficient;

[0079] The iterative update scheduling parameters are as follows:

[0080] Where γ is the learning rate, is the gradient of the objective function.

[0081] The feedback control module adjusts the production process parameters according to the quality evaluation results. The specific analysis steps are as follows: When ΔQ = QQ 标准 , if it is not within the allowable error range, the lubricant is considered to be of unqualified quality, indicating that the production process data of the lubricant needs to be adjusted. Suppose the production process parameters are W = {W 1 , W 2 ......, W e}, where W e Represents the e-th data, where e is a positive integer;

[0082]

[0083] Where k is the control gain coefficient, γ is the integral control coefficient, and M is the integral time in the historical time period.

[0084] The real-time monitoring and feedback module also includes data recording and feedback: The real-time data monitoring module can provide real-time feedback and warnings to ensure the stability and reliability of the simulation process, and record and monitor data through the following formula:

[0085] Data record = {(T i ,L i ,F i ,E i )i=1,2,3,4......,n};

[0086] Where n is the number of data records, T i ,L i ,F i ,E i are the response time, load condition, fault status and gas consumption recorded for the i-th time respectively.

[0087] The real-time monitoring and feedback module also includes data recording and feedback: the real-time data monitoring module can provide real-time feedback and warnings to ensure the stability and reliability of the simulation process, detailed data recording: the monitoring module records the changes in key parameters in real time, including the operating status of the equipment, the execution of maintenance operations, the response speed of emergency processing, etc., to ensure the integrity and accuracy of the data, real-time alarm mechanism: set up preset thresholds and anomaly detection algorithms, once the equipment operation is abnormal or the business module execution timeout is detected, the alarm is triggered immediately to notify the relevant personnel so that timely measures can be taken and further losses can be prevented, automated decision support: based on real-time data analysis, the monitoring module can generate decision support reports and suggestions, provide analysis charts and trend charts of the equipment operation status, and help managers make correct decisions quickly, fault diagnosis and processing: the monitoring module supports the fault diagnosis process through real-time feedback and data recording, analyzes the causes of equipment abnormalities and provides Repair suggestions, shorten fault recovery time, reduce downtime losses, remote monitoring and operation: through remote monitoring technology, the monitoring module allows remote access to the equipment's operating status and real-time data, realize remote operation and adjust equipment parameters, improve response speed and operating efficiency, data analysis and optimization: the monitoring module analyzes the collected data in real time, detects potential problems in equipment operation and puts forward improvement suggestions, optimizes the equipment's operating strategy and maintenance plan, and improves the overall performance of the system. The cloud platform monitoring tools, operation and maintenance monitoring platform, log service and other tools used by the real-time monitoring and feedback module monitor and visualize various indicators of the business middle-end system in real time; through the relevant monitoring and alarm system, abnormal situations can be discovered and handled in time, and log management tools such as Elasticsearch and Logstash are used to collect, store, analyze and display system logs to provide data support for troubleshooting and performance optimization.

[0088] The iterative optimization and result feedback module includes optimizing the model and simulation parameters through optimization steps, including parameter initialization, calculating loss function, updating parameters and checking convergence.

[0089] The specific analysis through optimization steps is as follows:

[0090] S1: Initialize model parameters to θ 0 , these parameters may include model weights, biases, and other variables that control the behavior of the model.

[0091] S2: Calculate the loss function:

[0092] Where L(θ) is the loss function, Represents the model prediction value With the actual value y i The sum of squared errors between represents the mutual influence between data point i and other data points j, where λ ij is the weight coefficient, which measures the correlation between data points i and j.

[0093] S3: Update parameters:

[0094] where θ k is the parameter value of the kth iteration, θ k+1 is the parameter value of the k+1th iteration, η is the learning rate, and the loss function is the parameter θ k The gradient of represents the derivative of the loss function at the current parameter value.

[0095] S4: Check convergence: Calculate the difference before and after parameter update |θ k+1 -θ k |, if the difference is less than the set convergence threshold ∈, the iteration is stopped; if |θ k+1 -θ k |<ξ, then stop the iteration. If the difference does not reach the convergence threshold, return to step 2 and continue iterating. ξ is the preset convergence threshold. When the amplitude of parameter update is less than the threshold, the parameter is considered to have converged and the iteration is stopped.

[0096] Furthermore, a multi-service collaborative gas service method adopts a multi-service collaborative gas service system according to any one of claims 1 to 9, characterized in that it includes the following steps:

[0097] Step 1: Use the data collection and processing module to collect the operating data of the gas equipment using the Internet of Things device, and perform preliminary processing on the collected data through the data processing algorithm;

[0098] Step 2: Use the business module integration and collaboration module to achieve data sharing and business collaboration among business modules through collaborative algorithms;

[0099] Step 3: Use the optimization scheduling module to schedule the maintenance and emergency treatment of gas equipment according to the status and coordination results of each business module through the optimization scheduling algorithm;

[0100] Step 4: Real-time monitoring and feedback module ensures the efficient operation and stability of the system through real-time monitoring and feedback mechanism.

[0101] Working principle: The data acquisition and processing module uses the Internet of Things devices to collect the operating data of the gas equipment, and performs preliminary processing on the collected data through the data processing algorithm. The data acquisition and processing module includes storage, processor, temperature sensor, pressure sensor and flow sensor. The temperature is marked as T = {T 1 , T 2 ......, To}, where T o represents the oth data, o is a positive integer, P = {P 1 , P 2 ......, P l}, where P l It is represented as the lth data, l is a positive integer, and the flow mark is Q = {Q 1 ,Q......,Q i}, where Q i Represents the i-th data, i is a positive integer, and then the data is preprocessed by the processor, and then the data processing module is used to perform preliminary processing on the collected data, including data cleaning, smoothing and normalization. The normalization formula is as follows:

[0102]

[0103] Where x is the original data, namely temperature T, flow Q and pressure P data, x max and x min are the minimum and maximum values ​​of the data respectively, and x' is the normalized data; then the business module integration and collaboration module is used to realize data sharing and business collaboration of each business module through the collaborative algorithm, and then the optimization scheduling module is used to schedule the maintenance and emergency treatment of gas equipment according to the status and collaboration results of each business module through the optimization scheduling algorithm, that is, the data sharing and business collaboration between each module are realized through the collaborative algorithm, and the collaborative algorithm includes the following steps:

[0104] S1: Initialize the business module status:

[0105] S 0 ={S cust ,S maint ,S emerg ,S bill};

[0106] Initialize the initial status of customer management, equipment maintenance, emergency handling and bill management business modules

[0107] S2: Define the collaborative objective function:

[0108] J(S)=ω 1 f cust (S cust )+ω 2 f maint (S maint )+ω 3 f emerg (S emerg )+ω 4 f bill (S bill )

[0109] J(S)=ω 1 f cust (S cust )+ω 2 f maint (S maint )+ω 3 f emerg (S emerg )+ω 4 f bill (S bill );

[0110] J(S) is the collaborative objective function, which is used to measure the optimization goal of each business module status. 1 ,ω 2 ,ω 3 ,ω 4 are the weight coefficients of the customer management, equipment maintenance, emergency handling and bill management business modules, respectively, cust (S cust ), f maint (S maint ), f emerg (S emerg ), f bill (S bill ) is the objective function of each business module, which is used to evaluate the efficiency or satisfaction of each module status.

[0111] S3: Iteratively update the business module status:

[0112]

[0113] Update the state of the business module according to the gradient of the collaborative objective function, where η is the learning rate, which controls the step size of each update;

[0114] S4: Iteratively update the business module status:

[0115] If |S k+1 -S k |<∈, then the iteration is stopped, where ∈ is the convergence threshold, which is used to determine whether the algorithm converges to the optimal solution or the approximate optimal solution. At the same time, the optimization scheduling algorithm includes initializing scheduling parameters, defining multi-objective scheduling functions, iteratively updating scheduling parameters and checking convergence steps, and the multi-objective scheduling function includes the coordination function between scheduling parameters. The scheduling objective function is as follows:

[0116]

[0117] Where D is the scheduling parameter vector, f maint 、f emerg are the objective functions of equipment maintenance and emergency handling, g is the scheduling function for customer and bill management, αi is the weight coefficient, ψ ij (D i ,D j ) is the scheduling parameter D i and D j The synergy function between ij is the synergy coefficient;

[0118] The iterative update scheduling parameters are as follows:

[0119] Where γ is the learning rate, is the gradient of the objective function;

[0120] At the same time through the optimization steps:

[0121] S1: Initialize model parameters to θ 0 , these parameters may include model weights, biases, and other variables that control the behavior of the model.

[0122] S2: Calculate the loss function:

[0123] Where L(θ) is the loss function, Represents the model prediction value With the actual value y i The sum of squared errors between represents the mutual influence between data point i and other data points j, where λ ij is the weight coefficient, which measures the correlation between data points i and j;

[0124] S3: Update parameters:

[0125] where θ k is the parameter value of the kth iteration, θ k+1 is the parameter value of the k+1th iteration, η is the learning rate, and the loss function is the parameter θ k The gradient of , which represents the derivative of the loss function at the current parameter value;

[0126] S4: Check convergence: Calculate the difference before and after parameter update |θ k+1 -θ k |, if the difference is less than the set convergence threshold ∈, the iteration is stopped; if |θ k+1 -θ k |<ξ, then stop the iteration. If the difference does not reach the convergence threshold, return to step 2 and continue iterating. ξ is the preset convergence threshold. When the parameter update amplitude is less than the threshold, the parameter is considered to have converged and the iteration is stopped.

[0127] Then the real-time monitoring and feedback module ensures the efficient operation and stability of the system through real-time monitoring and feedback mechanism. That is, the real-time data monitoring module can provide real-time feedback and warnings to ensure the stability and reliability of the simulation process. The data is recorded and monitored through the following formula:

[0128] Data record = {(T i ,L i ,F i ,E i )|i=1,2,3,4......,n};

[0129] Where n is the number of data records, T i ,L i ,F i ,E i They are respectively the response time, load condition, fault status and gas consumption recorded for the i-th time. Based on complex collaborative algorithms and multi-business integration, the gas business system can effectively realize data sharing and optimized collaboration among business modules, thereby improving operational efficiency and service quality.

[0130] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, modules and methods can be implemented in other ways. For example, the module embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of modules or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0135] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0136] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-service collaborative gas service system, characterized in that: It includes a data acquisition and processing module, a business module integration and collaboration module, an optimization scheduling module and a real-time monitoring and feedback module; the data acquisition and processing module uses Internet of Things devices to collect the operating data of the gas equipment, and performs preliminary processing on the collected data through a data processing algorithm; the business module integration and collaboration module realizes data sharing and business collaboration of each business module through a collaborative algorithm; the optimization scheduling module schedules the maintenance and emergency handling of the gas equipment through an optimization scheduling algorithm according to the status and collaboration results of each business module; the real-time monitoring and feedback module ensures the efficient operation and stability of the system through a real-time monitoring and feedback mechanism.

2. A multi-service collaborative gas service system according to claim 1, characterized in that: The data acquisition and processing module uses the Internet of Things device to collect the operating data of the gas equipment, and performs preliminary processing on the collected data through the data processing algorithm. The data acquisition and processing module includes a storage, a processor, a temperature sensor, a pressure sensor and a flow sensor. The temperature is marked as T = [T1, T2..., T o }, where T o represents the oth data, o is a positive integer, P = {P1, P2..., P l }, where P l It is represented as the lth data, l is a positive integer, and the flow mark is Q = {Q1, Q..., Q i }, where Q i represents the i-th data, i is a positive integer, and then the data is preprocessed by the processor; The data processing module performs preliminary processing on the collected data, including data cleaning, smoothing and normalization. The normalization formula is as follows: Where x is the original data, namely temperature T, flow Q and pressure P data, x max and x min are the minimum and maximum values ​​of the data respectively, and x' is the normalized data.

3. A multi-service collaborative gas service system according to claim 2, characterized in that: The business module integration and collaboration module integrates multiple business modules such as customer management, equipment maintenance, emergency handling and bill management, and realizes data sharing and business collaboration among the modules through a collaborative algorithm. The collaborative algorithm includes the following steps: S1: Initialize the business module status: S0={S cust ,S maint ,S emerg ,S bill }; Initialize the initial status of customer management, equipment maintenance, emergency handling and bill management business modules; S2: Define the collaborative objective function: J(S)=ω1f cust (S cust )+ω2f maint (S maint )+ω3f emerg (S emerg )+ω4f bill (S bill ) J(S)=ω1f cust (S cust )+ω2f maint (S maint )+ω3f emerg (S emerg )+ω4f bill (S bill ); J(S) is the collaborative objective function, which is used to measure the optimization target of each business module status. ω1, ω2, ω3, and ω4 are the weight coefficients of the customer management, equipment maintenance, emergency handling, and bill management business modules, respectively. cust (S cust ), f maint (S maint ), f emerg (S emerg ), f bill (S bill ) is the objective function of each business module, which is used to evaluate the efficiency or satisfaction of each module status; S3: Iteratively update the business module status: S k+1 =S k -η▽L(S k ); Update the state of the business module according to the gradient of the collaborative objective function, where η is the learning rate, which controls the step size of each update; S4: Iteratively update the business module status: If S k+1 -S k |<∈, then the iteration stops, where ∈ is the convergence threshold, which is used to determine whether the algorithm converges to the optimal solution or a near-optimal solution.

4. A multi-service collaborative gas service system according to claim 3, characterized in that: The optimization scheduling module schedules the maintenance and emergency treatment of gas equipment through a multi-objective optimization scheduling algorithm according to the status and coordination results of each business module.

5. A multi-service collaborative gas service system according to claim 4, characterized in that: The optimization scheduling algorithm includes initializing scheduling parameters, defining a multi-objective scheduling function, iteratively updating scheduling parameters, and checking convergence steps, and the multi-objective scheduling function includes a coordination function between scheduling parameters. The scheduling objective function is as follows: Where D is the scheduling parameter vector, f maint 、f emerg are the objective functions of equipment maintenance and emergency handling, g is the scheduling function for customer and bill management, α i is the weight coefficient, ψ ij (D i ,D j ) is the scheduling parameter D i and D j The synergy function between ij is the synergy coefficient; The iterative update scheduling parameters are as follows: k+1 =D k -γ▽L(D k ) Where γ is the learning rate, ▽L(D k ) is the gradient of the objective function.

6. A multi-service collaborative gas service system according to claim 5, characterized in that: The real-time monitoring and feedback module includes real-time monitoring and data recording and feedback: The real-time monitoring: monitors the changes of key parameters in real time during the simulation process, and records detailed simulation data, including equipment response time, load conditions, fault conditions, and gas consumption.

7. A multi-service collaborative gas service system according to claim 6, characterized in that: The real-time monitoring and feedback module also includes data recording and feedback: The real-time data monitoring module can provide real-time feedback and warnings to ensure the stability and reliability of the simulation process, and record and monitor data through the following formula: Data record = {(T i ,L i ,F i ,E i )|i=1,2,3,4......,n}; Where n is the number of data records, T i ,L i ,F i ,E i are the response time, load condition, fault status and gas consumption recorded for the i-th time respectively.

8. A multi-service collaborative gas service system according to claim 7, characterized in that: The iterative optimization and result feedback module includes optimizing and adjusting the model and simulation parameters through optimization steps, including parameter initialization, loss function calculation, parameter update and convergence checking.

9. A multi-service collaborative gas service system according to claim 8, characterized in that: The optimization steps are specifically analyzed as follows: S1: Initialize the model parameters to θ0, which may include model weights, biases, and other variables that control the model behavior; S2: Calculate the loss function: Where L(θ) is the loss function, Represents the model prediction value With the actual value y i The sum of squared errors between represents the mutual influence between data point i and other data points j, where λ ij is the weight coefficient, which measures the correlation between data points i and j; S3: Update parameters: θ k+1 =θ k -η▽L(θ k ); where θ k is the parameter value of the kth iteration, θ k+1 is the parameter value of the k+1th iteration, η is the learning rate, and the loss function ▽L(θ k ) is the parameter θ k The gradient of , which represents the derivative of the loss function at the current parameter value; S4: Check convergence: Calculate the difference before and after parameter update |θ k+1 -θ k |, if the difference is less than the set convergence threshold ∈, the iteration is stopped; if |θ k+1 -θ k |<ξ, then stop the iteration. If the difference does not reach the convergence threshold, return to step 2 and continue iterating. ξ is the preset convergence threshold. When the amplitude of parameter update is less than the threshold, the parameter is considered to have converged and the iteration is stopped.

10. A multi-service collaborative gas service method, using a multi-service collaborative gas service system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Use the data collection and processing module to collect the operating data of the gas equipment using the Internet of Things device, and perform preliminary processing on the collected data through the data processing algorithm; Step 2: Use the business module integration and collaboration module to achieve data sharing and business collaboration among business modules through collaborative algorithms; Step 3: Use the optimization scheduling module to schedule the maintenance and emergency treatment of gas equipment according to the status and coordination results of each business module through the optimization scheduling algorithm; Step 4: Real-time monitoring and feedback module ensures the efficient operation and stability of the system through real-time monitoring and feedback mechanism.

Images