Centralized heating network management monitoring method and system based on artificial intelligence

Abstract

The invention relates to the technical field of heat supply, in particular to a centralized heat supply network management monitoring method and system based on artificial intelligence, and aims to solve the problem that an existing centralized heat supply network management monitoring system cannot acquire data in multiple aspects in different regions and cannot accurately judge abnormity of a heat supply network management. And maintenance personnel cannot be arranged in time to maintain the abnormal heat supply network pipe, so that faults are easy to expand, and the operation efficiency and the safety of the heat supply network pipe are influenced. According to the system, key parameter data of the heat supply pipe network are collected in real time, the collected data are processed and analyzed through the artificial intelligence model, the accuracy of the monitoring result is improved, faults in the pipe network can be found in time, an alarm mechanism is automatically triggered, and the safety of the heat supply pipe network is improved. And maintenance personnel are efficiently and reasonably arranged to maintain the abnormal heat supply network pipe, faults in the network pipe can be processed in time, the operation efficiency and safety of the heat supply network pipe can be remarkably improved, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of heating technology, and in particular to a centralized heating network management monitoring method and system based on artificial intelligence. Background Art

[0002] With the acceleration of urbanization, the central heating system has become an important part of urban infrastructure. However, the traditional heating network monitoring method mainly relies on manual inspection and regular inspection, which is not only inefficient, but also difficult to detect and deal with faults in the network in a timely manner. Therefore, the patent with application number CN202311531418.0 discloses an artificial intelligence central heating network management monitoring system, including a control center, a device management module is provided on the control center, a monitoring module is provided on the control center, a data acquisition module is provided on the control center, a terminal module is provided on the control center, an intelligent early warning module is provided on the control center, a data analysis module is electrically connected to the control center, and the data analysis module is used to analyze the collected data, and a fault diagnosis module is electrically connected to the control center, and the fault diagnosis module is used to diagnose faults generated when the heating network is used. The invention relates to an artificial intelligence centralized heating network monitoring system, which has the characteristics of real-time monitoring and intelligent early warning of centralized heating network management, but still has the following shortcomings: it is impossible to collect data in multiple areas and accurately judge the abnormalities of the heating network management, and it is even more impossible to arrange maintenance personnel in time to repair the abnormal heating network management, which makes it easy for faults to expand and affect the operating efficiency and safety of the heating network. Summary of the invention

[0003] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a centralized heating network management monitoring method and system based on artificial intelligence, which solves the problem that the existing centralized heating network management monitoring system is unable to collect data in multiple areas and accurately judge the abnormalities of the heating network management, and is even unable to arrange maintenance personnel in time to repair the abnormal heating network management, which makes it easy for the fault to expand and affect the operating efficiency and safety of the heating network.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A centralized heating network management monitoring system based on artificial intelligence, comprising: The heating monitoring module is used to establish an artificial intelligence model, and use the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, and send the abnormal information of the pipe network to the heating analysis module; wherein the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL; The heating analysis module is used to obtain the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and send the pipe network abnormality coefficient GYi to the monitoring and judgment module; The specific process of the heating analysis module obtaining the pipe network abnormality coefficient GYi is as follows: The temperature information WD, pressure information YL and flow information LL are quantified using the artificial intelligence model, the values ​​of the temperature information WD, pressure information YL and flow information LL are extracted, and substituted into the formula for calculation.

[0005] The abnormal coefficient of the pipe network GYi is obtained, where π and e are mathematical constants, g1, g2 and g3 are preset weight factors corresponding to the set temperature information WD, pressure information YL and flow information LL, respectively, and g1, g2 and g3 satisfy g2>g1>g3>1.693, and g1=2.41, g2=3.03, and g3=1.85; Send the pipe network abnormality coefficient GYi to the monitoring and judgment module; The monitoring and judgment module is used to generate abnormal alarm instructions and personnel management instructions according to the pipeline abnormality coefficient GYi, and send the abnormal alarm instructions to the abnormal alarm module and send the personnel management instructions to the personnel management module.

[0006] As a further solution of the present invention: the specific process of the heating monitoring module obtaining abnormal information of the pipe network is as follows: An artificial intelligence model is established, and several network management monitoring areas are randomly selected in the central heating network management, and they are marked as monitoring areas i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring area, and n is the total number of monitoring areas; The artificial intelligence model is used to obtain the temperature of the conveying medium in the monitoring area i, and it is marked as a temperature value. The difference between the temperature value and the preset standard temperature value is obtained and marked as temperature information WD. The artificial intelligence model is used to obtain the pressure of the conveying medium in the monitoring area i, and it is marked as a pressure value, and the difference between the pressure value and the preset standard pressure value is obtained and marked as pressure information YL; The artificial intelligence model is used to obtain the flow rate of the transport medium in the monitoring area i, and it is marked as a flow value, and the difference between the flow value and the preset standard flow value is obtained and marked as flow information LL; The temperature information WD, pressure information YL and flow information LL are sent to the heating analysis module.

[0007] As a further solution of the present invention: the specific process of the monitoring and judgment module generating abnormal alarm instructions and personnel management instructions is as follows: The pipe network anomaly coefficient GYi is compared with the preset pipe network anomaly threshold GYy, and the comparison results are as follows: If the pipe network abnormality coefficient GYi≥pipe network abnormality threshold GYy, an abnormal alarm instruction and a personnel management instruction are generated, and the abnormal alarm instruction is sent to the abnormal alarm module, and the personnel management instruction is sent to the personnel management module.

[0008] As a further solution of the present invention: the central heating network management monitoring system based on artificial intelligence also includes: The abnormal alarm module is used to control the abnormal alarm bell to ring after receiving the abnormal alarm instruction.

[0009] As a further solution of the present invention: the central heating network management monitoring system based on artificial intelligence also includes: The personnel management module is used to obtain the to-be-assigned personnel j after receiving the personnel management instruction, and obtain the personnel selection information of the to-be-assigned personnel j, and send the personnel selection information to the personnel analysis module; wherein the personnel selection information includes the route length information LC, the order information DD and the work information GZ; it is also used to generate an inspection and repair order, and send the inspection and repair order to the mobile terminal of the selected personnel.

[0010] As a further solution of the present invention: the specific process of the personnel management module obtaining the personnel j to be assigned is as follows: After receiving the personnel management instruction, the monitoring area i corresponding to the pipeline abnormality coefficient GYi is marked as an abnormal area, and the location of the abnormal area is obtained and marked as a maintenance point; Draw a circular area on the map with the maintenance point as the center of the circular area and a preset length as the radius of the circular area, and mark it as a reference area; All the central heating network maintenance personnel in the reference area are obtained, and are marked as personnel to be assigned j in sequence, where j=1, ..., m, m is a positive integer, j is the number of any one of the personnel to be assigned, and m is the total number of personnel to be assigned.

[0011] As a further solution of the present invention: the specific process of the personnel management module obtaining the personnel selection information is as follows: Obtain the route length between the position of the person j to be assigned and the maintenance point, and mark it as the route length information LC; Get the number of repair orders assigned to the assigned personnel j, and mark it as the check value JS, sort them according to the order of the repair orders, and get the full repair route formed by the assigned personnel j arriving at the repair positions in the repair order in turn, get the length of the full repair route, and mark it as the check value JJ, quantify the check value JS and the check value JJ, extract the values ​​of the check value JS and the check value JJ, and substitute them into the formula for calculation. Get order information DD, where d1 and d2 are preset proportional coefficients corresponding to the set detection value JS and the detection distance value JJ, respectively. d1 and d2 satisfy d1+d2=1, 0<d2<d1<1, and d1=0.67 and d2=0.33; Get the total number of inspection and repair orders completed by the assigned personnel j, and mark it as the completion value WS. Get the total time that the assigned personnel j is engaged in the maintenance of the central heating network management, and mark it as the working time value GS. Quantify the completion value WS and the working time value GS, extract the values ​​of the completion value WS and the working time value GS, and substitute them into the formula for calculation. According to the formula Get the work information GZ, where z1 and z2 are the preset proportional coefficients corresponding to the set completion value WS and working time value GS respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.42 and z2=0.58; The route length information LC, order information DD and work information GZ are sent to the personnel analysis module.

[0012] As a further solution of the present invention: the central heating network management monitoring system based on artificial intelligence also includes: The personnel analysis module is used to obtain the personnel selection coefficient RXj according to the personnel selection information, obtain the selected personnel according to the personnel selection coefficient RXj, and send the selected personnel to the personnel management module.

[0013] As a further solution of the present invention: the specific process of the personnel analysis module obtaining the personnel selection coefficient RXj is as follows: Quantify the route length information LC, order information DD and work information GZ, extract the values ​​of route length information LC, order information DD and work information GZ, and substitute them into the formula for calculation. Get the personnel selection coefficient RXj, where e is a mathematical constant, r1, r2 and r3 are the preset weight factors corresponding to the set route length information LC, order information DD and work information GZ, r1, r2 and r3 satisfy r3>r2>r1>2.011, and take r1=2.37, r2=2.92, r3=3.51; Sort all the personnel j to be assigned according to the personnel selection coefficient RXj from large to small, and mark the personnel selection coefficient RXj at the top as the selected personnel; Send the selected personnel to the personnel management module.

[0014] As a further solution of the present invention: a centralized heating network management monitoring method based on artificial intelligence comprises the following steps: Step 1: The heating monitoring module establishes an artificial intelligence model and uses the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, where the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL, and sends the abnormal information of the pipe network to the heating analysis module; Step 2: The heating analysis module obtains the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and sends the pipe network abnormality coefficient GYi to the monitoring and judgment module; Step 3: The monitoring and judgment module generates an abnormal alarm instruction and a personnel management instruction according to the abnormal coefficient GYi of the pipeline network, and sends the abnormal alarm instruction to the abnormal alarm module and sends the personnel management instruction to the personnel management module; Step 4: After receiving the abnormal alarm instruction, the abnormal alarm module controls the abnormal alarm bell to ring; Step 5: After receiving the personnel management instruction, the personnel management module obtains the personnel j to be assigned, and obtains the personnel selection information of the personnel j to be assigned, wherein the personnel selection information includes the route length information LC, the order information DD and the work information GZ, and sends the personnel selection information to the personnel analysis module; Step 6: The personnel analysis module obtains the personnel selection coefficient RXj according to the personnel selection information, obtains the selected personnel according to the personnel selection coefficient RXj, and sends the selected personnel to the personnel management module; Step 7: The personnel management module generates a repair form and sends the repair form to the mobile phone terminal of the selected personnel.

[0015] Beneficial effects of the present invention: The present invention provides a method and system for monitoring a centralized heating network management based on artificial intelligence. The system first monitors the centralized heating network management to obtain abnormal information of the network. The network abnormality coefficient obtained according to the network abnormality information can comprehensively measure the abnormality degree of the centralized heating network management. The larger the network abnormality coefficient is, the higher the abnormality degree is. Therefore, it is necessary to promptly alarm and arrange personnel for maintenance. Then, data collection and analysis are performed on the maintenance personnel to obtain personnel selection information. The personnel selection coefficient obtained according to the personnel selection information can comprehensively measure the excellence of the maintenance personnel. The higher the personnel selection coefficient is, the higher the excellence is. Then, priority is given to the selection. Finally, the selected personnel are obtained and the selected personnel are arranged to perform maintenance on the monitoring area where the abnormality occurs. The present invention provides a centralized heating network monitoring method and system based on artificial intelligence, which improves the accuracy of the monitoring results by collecting key parameter data of the heating network in real time and uses the artificial intelligence model to process and analyze the collected data, can promptly discover faults in the network and automatically trigger the alarm mechanism, reduce manual intervention, improve monitoring efficiency, and efficiently and reasonably arrange maintenance personnel to repair abnormal heating network pipes, can promptly handle faults in the network, can significantly improve the operating efficiency and safety of the heating network, reduce operation and maintenance costs, and provide high-quality monitoring and management services for urban heating systems. It has broad application prospects and market potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 It is a principle block diagram of a centralized heating network management monitoring system based on artificial intelligence in the present invention; Figure 2 It is a flow chart of a centralized heating network management monitoring method based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with 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.

[0019] Example 1: Please refer to Figure 1 As shown, this embodiment is a centralized heating network management monitoring system based on artificial intelligence, including the following modules: a heating monitoring module, a heating analysis module, a monitoring and judgment module, an abnormal alarm module, a personnel management module and a personnel analysis module; The heating monitoring module is used to establish an artificial intelligence model, and use the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, and send the abnormal information of the pipe network to the heating analysis module; wherein the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL; The heat supply analysis module is used to obtain the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and send the pipe network abnormality coefficient GYi to the monitoring and judgment module; The monitoring and judgment module is used to generate an abnormal alarm instruction and a personnel management instruction according to the abnormal coefficient GYi of the pipeline network, and send the abnormal alarm instruction to the abnormal alarm module and send the personnel management instruction to the personnel management module; Wherein, the abnormal alarm module is used to control the abnormal alarm bell to ring after receiving the abnormal alarm instruction; The personnel management module is used to obtain the personnel j to be assigned after receiving the personnel management instruction, obtain the personnel selection information of the personnel j to be assigned, and send the personnel selection information to the personnel analysis module; the personnel selection information includes the route length information LC, the order information DD and the work information GZ; and is also used to generate the inspection form and send the inspection form to the mobile terminal of the selected personnel; The personnel analysis module is used to obtain the personnel selection coefficient RXj according to the personnel selection information, obtain the selected personnel according to the personnel selection coefficient RXj, and send the selected personnel to the personnel management module.

[0020] Example 2: Please refer to Figure 2 As shown, this embodiment is a centralized heating network management monitoring method based on artificial intelligence, comprising the following steps: Step 1: The heating monitoring module establishes an artificial intelligence model and uses the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, where the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL, and sends the abnormal information of the pipe network to the heating analysis module; Step 2: The heating analysis module obtains the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and sends the pipe network abnormality coefficient GYi to the monitoring and judgment module; Step 3: The monitoring and judgment module generates an abnormal alarm instruction and a personnel management instruction according to the abnormal coefficient GYi of the pipeline network, and sends the abnormal alarm instruction to the abnormal alarm module and sends the personnel management instruction to the personnel management module; Step 4: After receiving the abnormal alarm instruction, the abnormal alarm module controls the abnormal alarm bell to ring; Step 5: After receiving the personnel management instruction, the personnel management module obtains the personnel j to be assigned, and obtains the personnel selection information of the personnel j to be assigned, wherein the personnel selection information includes the route length information LC, the order information DD and the work information GZ, and sends the personnel selection information to the personnel analysis module; Step 6: The personnel analysis module obtains the personnel selection coefficient RXj according to the personnel selection information, obtains the selected personnel according to the personnel selection coefficient RXj, and sends the selected personnel to the personnel management module; Step 7: The personnel management module generates a repair form and sends the repair form to the mobile phone terminal of the selected personnel.

[0021] Embodiment 3: Based on any of the above embodiments, Embodiment 3 of the present invention is a heat supply monitoring module. The function of the heat supply monitoring module is to obtain abnormal information of the pipe network, wherein the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL. The specific process is as follows: The heating monitoring module establishes an artificial intelligence model and randomly selects several network management monitoring areas in the centralized heating network management, and marks them as monitoring areas i in sequence, i=1, ..., n, n is a positive integer, i is the number of any monitoring area, and n is the total number of monitoring areas; The heating monitoring module uses the artificial intelligence model to obtain the temperature of the conveying medium in the monitoring area i, and marks it as a temperature value, obtains the difference between the temperature value and the preset standard temperature value, and marks it as temperature information WD; The heating monitoring module uses the artificial intelligence model to obtain the pressure of the conveying medium in the monitoring area i, and marks it as a pressure value, obtains the difference between the pressure value and the preset standard pressure value, and marks it as pressure information YL; The heating monitoring module uses the artificial intelligence model to obtain the flow rate of the transport medium in the monitoring area i, and marks it as a flow value, obtains the difference between the flow value and the preset standard flow value, and marks it as flow information LL; The heat supply monitoring module sends the temperature information WD, the pressure information YL and the flow information LL to the heat supply analysis module.

[0022] Embodiment 4: Based on any of the above embodiments, Embodiment 4 of the present invention is a heat supply analysis module. The function of the heat supply analysis module is to obtain the abnormal coefficient GYi of the pipe network. The specific process is as follows: The heating analysis module uses an artificial intelligence model to quantify the temperature information WD, pressure information YL and flow information LL, extract the values ​​of the temperature information WD, pressure information YL and flow information LL, and substitute them into the formula for calculation. The abnormal coefficient of the pipe network GYi is obtained, where π and e are mathematical constants, g1, g2 and g3 are preset weight factors corresponding to the set temperature information WD, pressure information YL and flow information LL, respectively, and g1, g2 and g3 satisfy g2>g1>g3>1.693, and g1=2.41, g2=3.03, and g3=1.85; The heating analysis module sends the pipe network abnormality coefficient GYi to the monitoring and judgment module.

[0023] Embodiment 5: Based on any of the above embodiments, Embodiment 5 of the present invention is a monitoring and judgment module. The function of the monitoring and judgment module is to generate abnormal alarm instructions and personnel management instructions. The specific process is as follows: The monitoring and judgment module compares the pipe network abnormality coefficient GYi with the preset pipe network abnormality threshold GYy. The comparison results are as follows: If the pipe network abnormality coefficient GYi≥pipe network abnormality threshold GYy, an abnormal alarm instruction and a personnel management instruction are generated, and the abnormal alarm instruction is sent to the abnormal alarm module, and the personnel management instruction is sent to the personnel management module.

[0024] Embodiment 6: Based on any of the above embodiments, Embodiment 6 of the present invention is an abnormal alarm module. The function of the abnormal alarm module is to control the abnormal alarm bell to ring after receiving the abnormal alarm instruction.

[0025] Embodiment 7: Based on any of the above embodiments, Embodiment 7 of the present invention is a personnel management module, which has two functions: One of the functions is to obtain personnel selection information, where the personnel selection information includes route length information LC, order information DD and work information GZ. The specific process is as follows: After receiving the personnel management instruction, the personnel management module marks the monitoring area i corresponding to the pipe network abnormality coefficient GYi as an abnormal area, obtains the location of the abnormal area, and marks it as a maintenance point; The personnel management module draws a circular area on the map with the maintenance point as the center of the circular area and a preset length as the radius of the circular area, and marks it as a reference area; The personnel management module obtains all the central heating network management and maintenance personnel in the reference area, and marks them as personnel to be assigned j in sequence, where j=1, ..., m, m is a positive integer, j is the number of any one of the personnel to be assigned, and m is the total number of personnel to be assigned; The personnel management module obtains the route length between the position of the personnel j to be assigned and the maintenance point, and marks it as the route length information LC; The personnel management module obtains the number of repair orders assigned to the assigned personnel j, and marks it as the check value JS, sorts the repair orders according to the order of placement, and obtains the entire repair route formed by the assigned personnel j arriving at the repair positions in the repair order in turn, obtains the length of the entire repair route, and marks it as the check value JJ, quantifies the check value JS and the check value JJ, extracts the values ​​of the check value JS and the check value JJ, and substitutes them into the formula for calculation. Get order information DD, where d1 and d2 are preset proportional coefficients corresponding to the set detection value JS and the detection distance value JJ, respectively. d1 and d2 satisfy d1+d2=1, 0<d2<d1<1, and d1=0.67 and d2=0.33; The personnel management module obtains the total number of inspection and repair orders completed by the to-be-assigned personnel j, and marks it as the completion value WS, obtains the total time that the to-be-assigned personnel j is engaged in the maintenance of the central heating network, and marks it as the working time value GS, quantifies the completion value WS and the working time value GS, extracts the values ​​of the completion value WS and the working time value GS, and substitutes them into the formula for calculation. Get the work information GZ, where z1 and z2 are the preset proportional coefficients corresponding to the set completion value WS and working time value GS respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.42 and z2=0.58; The personnel management module sends the route length information LC, order information DD and work information GZ to the personnel analysis module; The second function is to generate a repair order. The specific process is as follows: The personnel management module generates a maintenance order based on the maintenance points and sends the maintenance order to the mobile terminal of the selected personnel.

[0026] Embodiment 8: Based on any of the above embodiments, Embodiment 8 of the present invention is a personnel analysis module. The function of the personnel analysis module is to obtain the personnel selection coefficient RXj. The specific process is as follows: The personnel analysis module quantifies the route length information LC, order information DD and work information GZ, extracts the values ​​of the route length information LC, order information DD and work information GZ, and substitutes them into the formula for calculation. Get the personnel selection coefficient RXj, where e is a mathematical constant, r1, r2 and r3 are the preset weight factors corresponding to the set route length information LC, order information DD and work information GZ, r1, r2 and r3 satisfy r3>r2>r1>2.011, and take r1=2.37, r2=2.92, r3=3.51; The personnel analysis module sorts all the personnel j to be assigned according to the personnel selection coefficient RXj from large to small, and marks the personnel selection coefficient RXj at the top as the selected personnel; The personnel analysis module sends the selected personnel to the personnel management module Based on the above embodiments 1-8, the working principle of the present invention is as follows: The invention discloses a centralized heating network management monitoring method and system based on artificial intelligence. The artificial intelligence model is established through a heating monitoring module, and the abnormal information of the pipe network in the monitoring area is obtained by using the artificial intelligence model, wherein the abnormal information of the pipe network includes temperature information, pressure information and flow information. The abnormal coefficient of the pipe network is obtained according to the abnormal information of the pipe network through the heating analysis module. The abnormal alarm instruction and the personnel management instruction are generated according to the abnormal coefficient of the pipe network through the monitoring judgment module. After the abnormal alarm instruction is received by the abnormal alarm module, the abnormal alarm bell is controlled to ring. After the personnel management instruction is received by the personnel management module, the personnel to be assigned are obtained, and the personnel selection information of the personnel to be assigned is obtained, wherein the personnel selection information includes the route length information, order information and work information. The personnel selection coefficient is obtained according to the personnel selection information through the personnel analysis module, and the selected personnel is obtained according to the personnel selection coefficient. The inspection and repair form is generated by the personnel management module, and the inspection and repair form is sent to the mobile phone terminal of the selected personnel. The system first monitors the centralized heating network management, obtains the abnormal information of the pipe network, and the abnormal coefficient of the pipe network obtained according to the abnormal information of the pipe network can be comprehensively The system can comprehensively measure the degree of abnormality of the centralized heating network, and the larger the abnormality coefficient of the network, the higher the degree of abnormality. Therefore, it is necessary to promptly alarm and arrange personnel for maintenance, and then collect and analyze data on the maintenance personnel to obtain personnel selection information. The personnel selection coefficient obtained according to the personnel selection information can comprehensively measure the excellence of the maintenance personnel, and the higher the personnel selection coefficient, the higher the excellence. The personnel are selected first, and finally the selected personnel are arranged to perform maintenance on the abnormal monitoring area. The system collects key parameter data of the heating network in real time, and uses artificial intelligence models to process and analyze the collected data, thereby improving the accuracy of the monitoring results, being able to promptly detect faults in the network, and automatically triggering the alarm mechanism, reducing manual intervention, and improving monitoring efficiency. It can also efficiently and reasonably arrange maintenance personnel to perform maintenance on abnormal heating network pipes, and can promptly handle faults in the network, which can significantly improve the operating efficiency and safety of the heating network, reduce operation and maintenance costs, and provide high-quality monitoring and management services for urban heating systems. It has broad application prospects and market potential.

[0027] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0028] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0029] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. A centralized heating network management monitoring system based on artificial intelligence, characterized in that: include: The heating monitoring module is used to establish an artificial intelligence model, and use the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, and send the abnormal information of the pipe network to the heating analysis module; wherein the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL; The heating analysis module is used to obtain the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and send the pipe network abnormality coefficient GYi to the monitoring and judgment module; The specific process of the heating analysis module obtaining the pipe network abnormality coefficient GYi is as follows: The temperature information WD, pressure information YL and flow information LL are quantified using the artificial intelligence model. Obtain the pipe network abnormality coefficient GYi, where π and e are mathematical constants, and g1, g2 and g3 are preset weight factors corresponding to the set temperature information WD, pressure information YL and flow information LL respectively; Send the pipe network abnormality coefficient GYi to the monitoring and judgment module; The monitoring and judgment module is used to generate abnormal alarm instructions and personnel management instructions according to the pipeline abnormality coefficient GYi, and send the abnormal alarm instructions to the abnormal alarm module and send the personnel management instructions to the personnel management module.

2. According to claim 1, a centralized heating network management monitoring system based on artificial intelligence is characterized in that: The specific process of the heating monitoring module obtaining abnormal information of the pipe network is as follows: An artificial intelligence model is established, and several network management monitoring areas are randomly selected in the central heating network management, and they are marked as management and monitoring area i in turn; The artificial intelligence model is used to obtain the temperature of the conveying medium in the monitoring area i, and it is marked as a temperature value. The difference between the temperature value and the preset standard temperature value is obtained and marked as temperature information WD. The artificial intelligence model is used to obtain the pressure of the conveying medium in the monitoring area i, and it is marked as a pressure value, and the difference between the pressure value and the preset standard pressure value is obtained and marked as pressure information YL; The artificial intelligence model is used to obtain the flow rate of the transport medium in the monitoring area i, and it is marked as a flow value, and the difference between the flow value and the preset standard flow value is obtained and marked as flow information LL; The temperature information WD, pressure information YL and flow information LL are sent to the heating analysis module.

3. The central heating network management monitoring system based on artificial intelligence according to claim 1 is characterized in that: The specific process of the monitoring and judgment module generating abnormal alarm instructions and personnel management instructions is as follows: The pipe network anomaly coefficient GYi is compared with the preset pipe network anomaly threshold GYy, and the comparison results are as follows: If the pipe network abnormality coefficient GYi≥pipe network abnormality threshold GYy, an abnormal alarm instruction and a personnel management instruction are generated, and the abnormal alarm instruction is sent to the abnormal alarm module, and the personnel management instruction is sent to the personnel management module.

4. The central heating network management monitoring system based on artificial intelligence according to claim 1 is characterized in that: Also includes: The abnormal alarm module is used to control the abnormal alarm bell to ring after receiving the abnormal alarm instruction.

5. The centralized heating network management monitoring system based on artificial intelligence according to claim 1 is characterized in that: Also includes: The personnel management module is used to obtain the to-be-assigned personnel j after receiving the personnel management instruction, and obtain the personnel selection information of the to-be-assigned personnel j, and send the personnel selection information to the personnel analysis module; wherein the personnel selection information includes the route length information LC, the order information DD and the work information GZ; it is also used to generate an inspection and repair order, and send the inspection and repair order to the mobile terminal of the selected personnel.

6. The central heating network management monitoring system based on artificial intelligence according to claim 5 is characterized in that: The specific process of the personnel management module obtaining the personnel j to be assigned is as follows: After receiving the personnel management instruction, the monitoring area i corresponding to the pipeline abnormality coefficient GYi is marked as an abnormal area, and the location of the abnormal area is obtained and marked as a maintenance point; Draw a circular area on the map with the maintenance point as the center of the circular area and a preset length as the radius of the circular area, and mark it as a reference area; Obtain all the central heating network maintenance personnel in the reference area and mark them as personnel to be assigned j in turn.

7. The central heating network management monitoring system based on artificial intelligence according to claim 5 is characterized in that: The specific process of the personnel management module obtaining personnel selection information is as follows: Obtain the route length between the position of the person j to be assigned and the maintenance point, and mark it as the route length information LC; Get the number of repair orders assigned to the assigned personnel j, and mark it as the check value JS, sort the repair orders according to the order of placement, and get the full repair route formed by the assigned personnel j arriving at the repair positions in the repair order in turn, get the length of the full repair route, and mark it as the check value JJ, quantify the check value JS and the check value JJ, and use the formula Get order information DD, where d1 and d2 are preset proportional coefficients corresponding to the set detection value JS and the detection distance value JJ respectively; Get the total number of inspection and repair orders completed by the assigned personnel j, and mark it as the completion value WS. Get the total time that the assigned personnel j is engaged in the maintenance of the central heating network, and mark it as the working time value GS. Quantify the completion value WS and the working time value GS according to the formula Obtain work information GZ, where z1 and z2 are preset proportional coefficients corresponding to the set completion value WS and working time value GS respectively; The route length information LC, order information DD and work information GZ are sent to the personnel analysis module.

8. The central heating network management monitoring system based on artificial intelligence according to claim 1 is characterized in that: Also includes: The personnel analysis module is used to obtain the personnel selection coefficient RXj according to the personnel selection information, obtain the selected personnel according to the personnel selection coefficient RXj, and send the selected personnel to the personnel management module.

9. The central heating network management monitoring system based on artificial intelligence according to claim 8 is characterized in that: The specific process of the personnel analysis module obtaining the personnel selection coefficient RXj is as follows: Quantify the route length information LC, order information DD and work information GZ according to the formula The personnel selection coefficient RXj is obtained, where e is a mathematical constant, r1, r2 and r3 are respectively the preset weight factors corresponding to the set route length information LC, order information DD and work information GZ; Sort all the personnel j to be assigned according to the personnel selection coefficient RXj from large to small, and mark the personnel selection coefficient RXj at the top as the selected personnel; Send the selected personnel to the personnel management module.

10. A method for monitoring central heating network management based on artificial intelligence, characterized in that: The following steps are involved: Step 1: The heating monitoring module establishes an artificial intelligence model and uses the artificial intelligence model to obtain the abnormal information of the pipe network in the monitoring area i, where the abnormal information of the pipe network includes temperature information WD, pressure information YL and flow information LL, and sends the abnormal information of the pipe network to the heating analysis module; Step 2: The heating analysis module obtains the pipe network abnormality coefficient GYi according to the pipe network abnormality information, and sends the pipe network abnormality coefficient GYi to the monitoring and judgment module; Step 3: The monitoring and judgment module generates an abnormal alarm instruction and a personnel management instruction according to the abnormal coefficient GYi of the pipeline network, and sends the abnormal alarm instruction to the abnormal alarm module and sends the personnel management instruction to the personnel management module; Step 4: After receiving the abnormal alarm instruction, the abnormal alarm module controls the abnormal alarm bell to ring; Step 5: After receiving the personnel management instruction, the personnel management module obtains the personnel j to be assigned, and obtains the personnel selection information of the personnel j to be assigned, wherein the personnel selection information includes the route length information LC, the order information DD and the work information GZ, and sends the personnel selection information to the personnel analysis module; Step 6: The personnel analysis module obtains the personnel selection coefficient RXj according to the personnel selection information, obtains the selected personnel according to the personnel selection coefficient RXj, and sends the selected personnel to the personnel management module; Step 7: The personnel management module generates a repair form and sends the repair form to the mobile phone terminal of the selected personnel.

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