Intelligent operation and maintenance management platform for waste incineration power plant, information management method and equipment

Abstract

The application relates to the garbage incineration technical field and discloses a garbage incineration power plant intelligent operation management and control platform, an information management method and equipment; a management information system of the platform comprises a data acquisition module, which is used for acquiring to-be-processed production data from a distributed control system, the to-be-processed production data comprising at least one of industrial garbage entering quantity, household garbage entering quantity, urea consumption quantity, activated carbon consumption quantity, calcium hydroxide consumption quantity, power generation quantity, fly ash quantity and slag quantity; a data processing module is used for statistically processing and / or comparing the to-be-processed production data, obtaining a processing result and returning the processing result to a front-end interface. The platform of the embodiment of the application comprises a new management information system, the to-be-processed production data can be secondarily processed, and the to-be-processed production data is displayed to a management personnel in the form of intuitive and high readability charts through the front-end interface, so that the management personnel can quickly master the production efficiency and operation condition of the power plant and make correct decisions according to the charts.

Description

Technical Field

[0001] This disclosure relates to the field of waste incineration technology, and more specifically, to a smart operation and maintenance management platform, information management method, and equipment for waste incineration power plants. Background Technology

[0002] Waste-to-energy incineration technology converts the heat generated from incinerating waste into electricity, which not only reduces the environmental damage caused by municipal solid waste but also enables the resource utilization of waste, playing a vital role in the stable operation of cities. To enable waste-to-energy plant managers to promptly understand the plant's operational status and facilitate informed decision-making, an information management tool is urgently needed.

[0003] In related technologies, management information systems can be used for information management in waste-to-energy plants. However, although such systems can display data obtained from the distributed control system of the power plant, they cannot perform data statistics and comparisons, and the readability of the data is weak. Summary of the Invention

[0004] In view of the above situation, this application provides a smart operation and maintenance management platform, information management method and equipment for waste incineration power plants, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, embodiments of this application provide a smart operation and maintenance management platform for a waste incineration power plant. The platform includes a management information system, which is communicatively connected to a distributed control system. The distributed control system is used to perform production control on the power plant and to store the power plant's production data. The management information system includes a data acquisition module and a data processing module that are interconnected.

[0006] The data acquisition module is used to acquire production data to be processed from the distributed control system. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag.

[0007] The data processing module is used to perform statistics and / or comparison on the production data to be processed, obtain the processing results, and return them to the front-end interface.

[0008] Secondly, embodiments of this application also provide an information management method, which is applied to the management information system described in the first aspect above;

[0009] The method includes:

[0010] The production data to be processed is obtained from the distributed control system. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag.

[0011] The production data to be processed is statistically analyzed and / or compared to obtain the processing results, which are then returned to the front-end interface.

[0012] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the above-described information management method.

[0013] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the above-described information management method.

[0014] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0015] The intelligent operation and maintenance management platform for waste-to-energy plants provided in this application embodiment acquires production data to be processed from the distributed control system through the data acquisition module of the management information system within the platform. It then uses the data processing module of the management information system to statistically analyze and / or compare the production data to be processed, obtaining the processing results and returning them to the front-end interface. As can be seen, this application embodiment proposes a novel management information system that can perform secondary processing on production data to be processed in waste-to-energy plants and display it to managers in an intuitive and highly readable format, such as charts, through the front-end interface. This facilitates managers' quick understanding of the power plant's production efficiency and operational status, enabling them to make informed decisions based on these charts. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This paper shows a schematic diagram of the structure of the intelligent operation and maintenance management platform for waste incineration power plants provided in an embodiment of this application;

[0018] Figure 2 This invention provides a schematic diagram of the structure of a smart operation and maintenance management platform for waste-to-energy plants, according to another embodiment of this application.

[0019] Figure 3This illustration shows a structural schematic diagram of a smart operation and maintenance management platform for waste-to-energy plants provided in yet another embodiment of this application;

[0020] Figure 4 A schematic diagram illustrating the second statistical results provided in an embodiment of this application is shown;

[0021] Figure 5 A schematic diagram illustrating the fourth statistical result provided in an embodiment of this application is shown;

[0022] Figure 6 A schematic diagram illustrating the first comparison results provided in an embodiment of this application is shown;

[0023] Figure 7 A schematic diagram illustrating the second comparison results provided in the embodiments of this application is shown;

[0024] Figure 8 A flowchart illustrating the information management method provided in an embodiment of this application is shown;

[0025] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0028] Waste-to-energy incineration technology converts the heat energy generated from incinerating waste into electricity, which not only reduces the environmental damage caused by municipal solid waste but also enables the resource utilization of waste, playing a vital role in the stable operation of cities. To enable waste-to-energy plant managers to understand the plant's operational status in a timely manner and facilitate decision-making, an information management tool is urgently needed. Among related technologies, management information systems (MIS) can be used for information management in waste-to-energy plants; however, while these systems can display data obtained from the plant's distributed control system, they lack the ability to perform data statistics and comparisons, and the data readability is relatively weak.

[0029] Based on this, the present invention proposes a smart operation and maintenance management platform for waste-to-energy plants. The smart operation and maintenance management platform for waste-to-energy plants provided in this application embodiment acquires production data to be processed from the distributed control system through the data acquisition module of the management information system within the platform, and uses the data processing module of the management information system to statistically analyze and / or compare the production data to be processed, obtaining the processing results and returning them to the front-end interface. As can be seen, this application embodiment proposes a new management information system that can perform secondary processing on the production data to be processed in waste-to-energy plants and display it to managers in an intuitive and highly readable form, such as charts, through the front-end interface. This facilitates managers in quickly grasping the power plant's production efficiency, operating status, etc., and making correct decisions based on these charts.

[0030] Figure 1 A schematic diagram of the intelligent operation and maintenance management platform for waste-to-energy plants provided in this embodiment is shown. See also... Figure 1 As shown, the intelligent operation and maintenance management platform 100 for waste incineration power plants includes a management information system 101, which is communicatively connected to a distributed control system 200. The distributed control system 200 is used to control the production of the power plant and store the production data of the power plant. The management information system 101 includes a data acquisition module 1011 and a data processing module 1012 that are interconnected.

[0031] The data acquisition module 1011 is used to acquire production data to be processed from the distributed control system 200. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, quicklime consumption, power generation, fly ash, and slag.

[0032] The data processing module 1012 is used to perform statistics and / or comparison on the production data to be processed, obtain the processing results, and return them to the front-end interface.

[0033] The distributed control system 200 can perform production control on the power plant and store real-time production data of the power plant. In some embodiments of this application, the platform 100 also includes a combustion control system and a flue gas control system connected to each other; the distributed control system 200 has a manual control state and an automatic control state; if the distributed control system 200 is connected to the combustion control system and the flue gas control system respectively, the distributed control system 200 is in the automatic control state, used to collect production data, equipment data and environmental data of the power plant and send them to the combustion control system; the combustion control system is used to predict the combustion state of the incinerator in the power plant based on a pre-built parameter prediction model and the received production data, equipment data and environmental data, and obtain the state prediction result; and based on a pre-built control command generation model, generate control commands according to the state prediction result and send them to the distributed control system 200 to realize the automatic control of the power plant; the flue gas control system is used to determine the amount of flue gas absorbent according to the relationship between the flue gas component prediction data in the state prediction result and the amount of flue gas absorbent, and send it to the distributed control system 200 to realize the automatic control of the power plant.

[0034] Figure 2 A schematic diagram of the structure of a smart operation and maintenance management platform for waste-to-energy plants, provided in another embodiment of this application, is shown. See also... Figure 2 As shown, in this embodiment, the distributed control system 200 is connected to both the combustion control system 102 and the flue gas control system 103, indicating that the distributed control system 200 is in automatic control mode. In other embodiments, the distributed control system 200 is not connected to either the combustion control system 102 or the flue gas control system 103, indicating that the distributed control system 200 is in manual control mode.

[0035] The distributed control system 200 can collect production data, equipment data, and environmental data from the power plant and send them to the combustion control system 102. Production data includes, but is not limited to, the amount of industrial waste entering the plant, the amount of municipal solid waste entering the furnace, urea consumption, activated carbon consumption, quicklime consumption, power generation, fly ash volume, slag volume, main steam temperature, main steam flow rate, and furnace temperature. Equipment data includes, but is not limited to, feed rate, grate speed, primary air volume, and secondary air volume. Environmental data can be pixel data of real-time images captured by high-temperature cameras installed inside the incinerator. This application does not limit the types of parameters included in the production and equipment data.

[0036] The combustion control system 102 is deployed in the field production network and internally contains a pre-trained parameter prediction model and a control command generation model. It can utilize machine learning algorithms, such as deep learning-based convolutional neural networks, multilayer perceptrons, and decision tree models, to learn from historical production data, equipment data, and environmental data of the power plant to generate the parameter prediction model and control command generation model. During implementation, based on the pre-built parameter prediction model, it can perform inference calculations on the received production data, equipment data, and environmental data to predict the combustion state of the power plant's incinerator. The resulting state prediction results include, but are not limited to, flue gas composition prediction data and production data prediction data. Then, based on the pre-built control command generation model, it performs inference calculations on the state prediction results to generate control commands, which are sent to the distributed control system 200. These control commands include, but are not limited to, parameters such as feed rate, grate speed, primary air volume, and secondary air volume; this application does not limit these parameters.

[0037] The flue gas control system 103 can determine the dosage of flue gas absorbent based on the relationship between the flue gas composition prediction data in the state prediction results and the dosage of flue gas absorbent, and send this information to the distributed control system 200 to achieve automatic control of the power plant. For example, if SO2 is present in the incinerator A of the power plant, according to the chemical reaction formula for SO2 absorption in the semi-dry purification process, SO2 + Ca(OH)2 → CaSO3 + H2O, the dosage relationship between SO2 and Ca(OH)2 is 1:1. If the flue gas composition prediction data is: the SO2 content is 1 in the next 1 second, and the current flue gas absorbent Ca(OH)2 content is 0.5, then it can be determined that the next amount of flue gas absorbent added should be at least 0.5.

[0038] In this embodiment, the production data to be processed can be the power plant production data stored in the distributed control system 200, including at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag. For example, the production data to be processed includes real-time data of two indicators: urea consumption and power generation. Specifically, the production data to be processed is as follows: during the time period 0:00-0:05, urea consumption is A1 and power generation is B1; during the time period 0:05-0:10, urea consumption is A2 and power generation is B2; ...; during the time period 23:55-24:00, urea consumption is A288 and power generation is B288.

[0039] The data processing module 1012 can perform statistics and / or comparison on the received production data to be processed, obtain the processing results, and return them to the front-end interface. The processing results can be various forms of charts, including but not limited to bar charts, line charts, and pie charts.

[0040] In some embodiments, if the production data to be processed includes the amount of industrial waste entering the site, the total amount of industrial waste entering the site in a day can be calculated, and this statistical result can be used as the processing result and returned to the front-end interface. Similarly, the total amount of industrial waste entering the site in a year can be calculated, and this statistical result can also be used as the processing result and returned to the front-end interface. In other embodiments, if the production data to be processed includes electricity generation, the total electricity generation in two months can be calculated, resulting in Total Electricity Generation 1 and Total Electricity Generation 2. Total Electricity Generation 1 and Total Electricity Generation 2 can be compared, and this comparison result can be used as the processing result and returned to the front-end interface. In still other embodiments, the electricity generation in different time periods over two days can be compared, and this comparison result can be used as the processing result and returned to the front-end interface.

[0041] from Figure 1 As shown in the platform, the intelligent operation and maintenance management platform for waste-to-energy plants provided in this application embodiment acquires production data to be processed from the distributed control system through the data acquisition module of the management information system within the platform. It then uses the data processing module of the management information system to statistically analyze and / or compare the production data to be processed, obtaining the processing results and returning them to the front-end interface. This application embodiment proposes a new management information system that can perform secondary processing on production data to be processed in waste-to-energy plants and display it to managers in an intuitive and readable format, such as charts, through the front-end interface. This facilitates managers' quick understanding of the power plant's production efficiency and operating status, enabling them to make correct decisions based on these charts.

[0042] Figure 3 A schematic diagram of the structure of a smart operation and maintenance management platform for waste-to-energy plants, provided in yet another embodiment of this application, is shown. See also... Figure 3 As shown, in some embodiments of this application, in Figure 1 Based on the platform shown, the data processing module 102 includes a statistics unit 10121 and a display unit 10122; the statistics unit 10121 is used to generate multiple first-day statistical results based on the production data to be processed, the multiple first-day statistical results include a first statistical result and / or a second statistical result, wherein the data in the first statistical result comes from the distributed control system in manual control mode, and the data in the second statistical result comes from the distributed control system in automatic control mode; the display unit 10122 is used to return the first statistical result and / or the second statistical result as processing results to the front-end interface.

[0043] In this embodiment, the statistics unit 10121 can generate multiple first-day statistical results based on the production data to be processed. These multiple first-day statistical results include first statistical results and / or second statistical results. For example, if the distributed control system 200 is in manual control mode every day in January, 31 first statistical results can be generated. Similarly, if the distributed control system 200 is in automatic control mode every day in February, 28 second statistical results can be generated. Furthermore, if the distributed control system 200 is in manual control mode on March 1st and in automatic control mode at other times in March, 1 first statistical result and 30 second statistical results can be generated.

[0044] Figure 4 A schematic diagram illustrating the second statistical results provided in an embodiment of this application is shown. See also... Figure 4 As shown, in some embodiments, the distributed control system 200 is assumed to be in automatic control mode. The production data to be processed includes real-time data of three indicators: During the period 0:00-0:05, urea consumption is 10, activated carbon consumption is 6, and power generation is 5; during the period 0:05-0:10, urea consumption is 9, activated carbon consumption is 6, and power generation is 4; ...; during the period 23:55-24:00, urea consumption is 10, activated carbon consumption is 5, and power generation is 6. The urea consumption in all time periods is summed to obtain a total urea consumption of 2890; the activated carbon consumption in all time periods is summed to obtain a total activated carbon consumption of 1728; and the power generation in all time periods is summed to obtain a total power generation of 1445. Then, based on the totals of the above three indicators, a data set is generated as follows: Figure 4 The second statistical result is shown.

[0045] Then, the display unit 10122 can return the first statistical result and / or the second statistical result as the processing result to the front-end interface.

[0046] In some embodiments of this application, the production data to be processed comes from a distributed control system in the automatic control state; the statistics unit 10121 is further configured to generate multiple second-day statistical results based on the production data to be processed, the multiple second-day statistical results including a third statistical result and / or a fourth statistical result, wherein, during the generation period of the data in the third statistical result, no industrial waste was put into the incinerator of the power plant, and during the generation period of the data in the fourth statistical result, industrial waste was put into the incinerator of the power plant; the display unit 10122 is further configured to return the third statistical result and / or the fourth statistical result as processing results to the front-end interface.

[0047] In this embodiment, the production data to be processed comes from the distributed control system 200 in automatic control mode. The statistics unit 10121 can generate multiple second-day statistical results based on the production data to be processed. These multiple second-day statistical results include third and / or fourth statistical results. For example, if no industrial waste is fed into the power plant's incinerator in January, 31 third statistical results can be generated. Similarly, if industrial waste is fed into the power plant's incinerator every day in February, 28 fourth statistical results can be generated. And if industrial waste is fed into the power plant's incinerator on March 1st and 2nd in March, 29 third statistical results and 2 fourth statistical results can be generated.

[0048] Figure 5 A schematic diagram illustrating the fourth statistical result provided in an embodiment of this application is shown. See also... Figure 5 As shown, in some embodiments, the production data to be processed includes real-time data for four indicators: During the period 0:00-0:05, urea consumption is 9, activated carbon consumption is 5, power generation is 6, and slag quantity is 3; during the period 0:05-0:10, urea consumption is 10, activated carbon consumption is 6, power generation is 4, and slag quantity is 4; ...; during the period 23:55-24:00, urea consumption is 10, activated carbon consumption is 5, power generation is 6, and slag quantity is 3. The total urea consumption for all time periods is summed to obtain a total urea consumption of 2800; the total activated carbon consumption for all time periods is summed to obtain a total activated carbon consumption of 1750; the total power generation for all time periods is summed to obtain a total power generation of 1480; and the total slag quantity for all time periods is summed to obtain a total slag quantity of 864. Then, based on the totals of the above four indicators, a data set is generated as follows: Figure 5 The fourth statistical result is shown.

[0049] Then, the display unit 10122 can return the third statistical result and / or the fourth statistical result as the processing result to the front-end interface.

[0050] See Figure 3 As shown, in some embodiments of this application, in Figure 1Based on the platform shown, the data processing module 1012 includes a comparison unit 10123, which is connected to the statistics unit 10121 and the display unit 10122 respectively. The comparison unit 10123 is used to generate a first comparison result based on the average value of each target element in each second statistical result and the corresponding preset average value, wherein the target element includes at least one of urea consumption, activated carbon consumption, quicklime consumption, power generation, fly ash, and slag. The comparison unit 10123 is also used to generate a second comparison result based on the average value of each target element in each third statistical result and the average value of each target element in each fourth statistical result. The display unit 10122 is used to return the first comparison result and / or the second comparison result as the processing result to the front-end interface.

[0051] In this embodiment, the distributed control system 200 corresponding to the preset average value is in manual control mode.

[0052] The comparison unit 10123 can generate a first comparison result based on the average value of each target element in each second statistical result and the corresponding preset average value. Figure 6 A schematic diagram illustrating the first comparison results provided by an embodiment of this application is shown. In some embodiments, see [link to relevant documentation]. Figure 6 As shown, for example, during January, there is a 31st second statistical result. The target elements include urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag. The average values ​​for urea consumption are 8; activated carbon consumption is 10; hydrated lime consumption is 15; power generation is 10; fly ash is 5; and slag is 4. If the preset average values ​​for urea consumption are 10; activated carbon consumption is 12; hydrated lime consumption is 18; power generation is 8; fly ash is 10; and slag is 5, then based on the aforementioned average values ​​and preset average values, the following can be generated: Figure 6 The first comparison result is shown.

[0053] As can be seen from the above embodiments, a first comparison result can be generated based on the average value of each target element in each second statistical result and the corresponding preset average value. The second statistical result is generated when the distributed control system is in automatic control mode, and the preset average value is generated when the distributed control system is in manual control mode. Power plant managers can quickly know through the first comparison result whether allowing the combustion control system and flue gas control system to replace manual operation to control the power plant's production activities can improve production efficiency and the extent of the improvement, and then make corresponding decisions.

[0054] The comparison unit 10123 can also generate a second comparison result based on the average value of each target element in each third statistical result and the average value of each target element in each fourth statistical result. Figure 7 A schematic diagram illustrating the second comparison results provided by an embodiment of this application is shown. In some embodiments, see [link to relevant documentation]. Figure 7 As shown, during February, there were 8 third statistical results and 20 fourth statistical results. The target elements included urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag. In the 8 third statistical results, the average urea consumption was 5; the average activated carbon consumption was 9; the average hydrated lime consumption was 12; the average power generation was 9; the average fly ash was 6; and the average slag was 5. In the 20 fourth statistical results, the average urea consumption was 9; the average activated carbon consumption was 11; the average hydrated lime consumption was 13; the average power generation was 12; the average fly ash was 6; and the average slag was 5. Based on these averages, the following can be generated: Figure 7 The second comparison result is shown.

[0055] As can be seen from the above embodiments, a second comparison result is generated based on the average value of each target element in each third statistical result and the average value of each target element in each fourth statistical result. Through the second comparison result, it is easy for managers to see the degree of impact of feeding industrial waste into the incinerator of the power plant on the power plant's production efficiency when the distributed control system is in automatic control mode.

[0056] Then, the display unit 10122 can return the first comparison result and / or the second comparison result as the processing result to the front-end interface.

[0057] In some embodiments of this application, the comparison unit 10123 is used to add the element values ​​of each target element in the second statistical result of a first quantity for any target element to obtain a first sum, and use the ratio of the first sum to the first quantity as the first average value of the target element; and generate a first comparison result based on the first average value of each target element and the corresponding preset average value.

[0058] In this embodiment, the comparison unit 10123 can, for any target element, add the element values ​​of each target element in the second statistical result of the first quantity to obtain a first sum, and use the ratio of the first sum to the first quantity as the first average value of the target element, and generate a first comparison result based on the first average value of each target element and the corresponding preset average value.

[0059] For example, in some embodiments, the target elements include activated carbon consumption and power generation, with a first quantity of 25. For the target element 1, activated carbon consumption, the element value of target element 1 in the second statistical result 1 is 11, the element value of target element 1 in the second statistical result 2 is 11, ..., the element value of target element 1 in the second statistical result 25 is 12. Adding these element values ​​yields a first sum of 1, which is 300. Dividing the first sum of 1 by the first quantity 25 gives 12, which is then used as the first average value of target element 1. Similarly, for the target element 2, power generation, the element values ​​of target element 2 in the second statistical result 1, second statistical result 2, ..., second statistical result 25 are added together to obtain a first sum of 2. Dividing the first sum of 2 by the first quantity 25 gives 15, which is then used as the first average value of target element 2. If the preset average value corresponding to target element 1 is 10, and the preset average value corresponding to target element 2 is 13, then based on the first average values ​​of target elements 1 and 2, and their corresponding preset average values, similar calculations can be performed. Figure 6 The first comparison result is shown.

[0060] In some embodiments of this application, the comparison unit 10123 is further configured to add the element values ​​of each target element in the third statistical result of the second quantity to obtain a second sum, and use the ratio of the second sum to the second quantity as the second average value of the target element; and to add the element values ​​of each target element in the fourth statistical result of the third quantity to obtain a third sum, and use the ratio of the third sum to the third quantity as the third average value of the target element; and to generate a second comparison result based on each second average value and each third average value.

[0061] In this embodiment, the comparison unit 10123 can also add the element values ​​of each target element in the third statistical result of the second quantity to obtain a second sum, and use the ratio of the second sum to the second quantity as the second average value of the target element; and add the element values ​​of each target element in the fourth statistical result of the third quantity to obtain a third sum, and use the ratio of the third sum to the third quantity as the third average value of the target element; and generate a second comparison result based on each second average value and each third average value.

[0062] For example, in some embodiments, the target elements are urea consumption and slag quantity. During January, the second quantity is 10, and the third quantity is 21. For the target element 3, urea consumption, the element value of target element 3 in the third statistical result 1 is 8, the element value of target element 3 in the third statistical result 2 is 9, ..., the element value of target element 3 in the third statistical result 10 is 8. Adding these element values ​​gives a second sum of 80 for target element 3. Dividing this second sum by the second quantity 10 gives 8, which is used as the second average value of target element 3. Similarly, for the target element 4, slag quantity, the element values ​​of target element 4 in the fourth statistical result 1, fourth statistical result 2, ..., fourth statistical result 21 are added together to obtain a third sum for target element 4. Dividing this third sum by the third quantity 21 gives 10, which is used as the third average value of target element 4. Based on the aforementioned second and third average values, similar... Figure 7 The second comparison result is shown.

[0063] This application also discloses an information management method applicable to the above-mentioned intelligent operation and maintenance management platform for waste incineration power plants. Specifically, this method can be applied to the management information system of any of the above embodiments. For the structure of the intelligent operation and maintenance management platform for waste incineration power plants, please refer to... Figure 1 , Figure 2 or Figure 3 . Figure 8 This illustration shows a flowchart of the information management method provided in an embodiment of this application. Figure 8 It can be seen that this embodiment includes at least steps S801 to S802:

[0064] Step S801: Obtain the production data to be processed from the distributed control system. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, quicklime consumption, power generation, fly ash, and slag.

[0065] Step S802: Perform statistics and / or comparison on the production data to be processed, obtain the processing results, and return them to the front-end interface.

[0066] In some embodiments of this application, the platform further includes an interconnected combustion control system and a flue gas control system in the above method; the distributed control system has a manual control state and an automatic control state; if the distributed control system is connected to both the combustion control system and the flue gas control system, the distributed control system is in an automatic control state, used to collect production data, equipment data, and environmental data of the power plant and send them to the combustion control system; the combustion control system is used to predict the combustion state of the incinerator in the power plant based on a pre-built parameter prediction model and the received production data, equipment data, and environmental data, obtaining a state prediction result; and based on a pre-built control command generation model, generates control commands according to the state prediction result and sends them to the distributed control system to achieve automatic control of the power plant; the flue gas control system is used to determine the amount of flue gas absorbent based on the relationship between the flue gas component prediction data in the state prediction result and the amount of flue gas absorbent, and sends it to the distributed control system to achieve automatic control of the power plant.

[0067] In some embodiments of this application, in the above method, the step of statistically analyzing and / or comparing the production data to be processed, obtaining processing results, and returning them to the front-end interface includes: generating multiple first-day statistical results based on the production data to be processed, wherein the multiple first-day statistical results include a first statistical result and / or a second statistical result, wherein the data in the first statistical result comes from a distributed control system in manual control mode, and the data in the second statistical result comes from a distributed control system in automatic control mode; and returning the first statistical result and / or the second statistical result as processing results to the front-end interface.

[0068] In some embodiments of this application, in the above method, the production data to be processed comes from a distributed control system in an automatic control state; the step of statistically analyzing and / or comparing the production data to be processed, obtaining processing results, and returning them to the front-end interface includes: generating multiple second-day statistical results based on the production data to be processed, the multiple second-day statistical results including third statistical results and / or fourth statistical results, wherein during the generation period of the data in the third statistical results, no industrial waste was put into the incinerator of the power plant, and during the generation period of the data in the fourth statistical results, industrial waste was put into the incinerator of the power plant; and returning the third statistical results and / or the fourth statistical results as processing results to the front-end interface.

[0069] In some embodiments of this application, in the above method, the step of statistically analyzing and / or comparing the production data to be processed, obtaining processing results, and returning them to the front-end interface includes: generating a first comparison result based on the average value of each target element in each second statistical result and the corresponding preset average value, wherein the target element includes at least one of urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag; the step of statistically analyzing and / or comparing the production data to be processed, obtaining processing results, and returning them to the front-end interface includes: generating a second comparison result based on the average value of each target element in each third statistical result and the average value of each target element in each fourth statistical result; and returning the first comparison result and / or the second comparison result as the processing result to the front-end interface.

[0070] In some embodiments of this application, in the above method, generating a first comparison result based on the average value of each target element in each second statistical result and the corresponding preset average value includes: for any target element, adding the element values ​​of each target element in a first number of second statistical results to obtain a first sum, and using the ratio of the first sum to the first number as the first average value of the target element; and generating a first comparison result based on the first average value of each target element and the corresponding preset average value.

[0071] In some embodiments of this application, in the above method, generating a second comparison result based on the average value of each target element in each third statistical result and the average value of each target element in each fourth statistical result includes: adding the element values ​​of each target element in a second number of third statistical results to obtain a second sum, and using the ratio of the second sum to the second number as the second average value of the target element; adding the element values ​​of each target element in a third number of fourth statistical results to obtain a third sum, and using the ratio of the third sum to the third number as the third average value of the target element; and generating a second comparison result based on each second average value and each third average value.

[0072] It should be noted that any of the aforementioned management information systems can implement the above information management methods one by one, which will not be elaborated here.

[0073] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 9As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0074] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0075] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0076] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a management information system at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0077] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0078] The electronic device can execute the information management methods provided in several embodiments of this application and be implemented as a management information system. Figure 1 or Figure 3 The functions of the embodiments shown are not described in detail here.

[0079] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the information management methods provided in various embodiments of this application.

[0080] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0085] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0086] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0087] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A smart operation and maintenance management platform for waste incineration power plants, characterized in that, The platform includes a management information system, which is communicatively connected to a distributed control system. The distributed control system is used to control the production of the power plant and store the power plant's production data. The management information system includes interconnected data acquisition and data processing modules. The data acquisition module is used to acquire production data to be processed from the distributed control system. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag. The data processing module is used to perform statistics and / or comparison on the production data to be processed, obtain the processing results, and return them to the front-end interface. The platform also includes an interconnected combustion control system and a flue gas control system; The distributed control system has both manual control and automatic control modes. If the distributed control system is connected to the combustion control system and the flue gas control system respectively, the distributed control system is in the automatic control state, used to collect the power plant's production data, equipment data and environmental data and send them to the combustion control system; The combustion control system is used to predict the combustion state of the incinerator in the power plant based on a pre-built parameter prediction model and the received production data, equipment data, and environmental data, and obtain the state prediction result; and based on a pre-built control command generation model, to generate control commands according to the state prediction result and send them to the distributed control system to realize automatic control of the power plant. The flue gas control system is used to determine the amount of flue gas absorbent based on the relationship between the flue gas composition prediction data in the state prediction results and the amount of flue gas absorbent, and send it to the distributed control system to realize automatic control of the power plant. The data processing module includes a statistics unit and a display unit; The statistical unit is used to generate multiple first-day statistical results based on the production data to be processed. The multiple first-day statistical results include a first statistical result and / or a second statistical result, wherein the data in the first statistical result comes from the distributed control system in the manual control state, and the data in the second statistical result comes from the distributed control system in the automatic control state. The display unit is used to return the first statistical result and / or the second statistical result as the processing result to the front-end interface.

2. The platform according to claim 1, characterized in that, The production data to be processed comes from the distributed control system in the automatic control state. The statistical unit is used to generate multiple second-day statistical results based on the production data to be processed. The multiple second-day statistical results include a third statistical result and / or a fourth statistical result. During the generation period of the data in the third statistical result, no industrial waste was put into the incinerator of the power plant. During the generation period of the data in the fourth statistical result, industrial waste was put into the incinerator of the power plant. The display unit is also used to return the third statistical result and / or the fourth statistical result as the processing result to the front-end interface.

3. The platform according to claim 2, characterized in that, The data processing module includes a comparison unit, which is connected to the statistics unit and the display unit respectively. The comparison unit is used to generate a first comparison result based on the average value of each target element in each of the second statistical results and the corresponding preset average value, wherein the target element includes at least one of urea consumption, activated carbon consumption, quicklime consumption, power generation, fly ash, and slag. The comparison unit is used to generate a second comparison result based on the average value of each target element in each of the third statistical results and the average value of each target element in each of the fourth statistical results; The display unit is used to return the first comparison result and / or the second comparison result as the processing result to the front-end interface.

4. The platform according to claim 3, characterized in that, The comparison unit is used to add the element values ​​of each target element in the first number of second statistical results for any target element to obtain a first sum, and to use the ratio of the first sum to the first number as the first average value of the target element. And the first comparison result is generated based on the first average value of each target element and the corresponding preset average value.

5. The platform according to claim 3, characterized in that, The comparison unit is used to add the element values ​​of each target element in the third statistical result of the second quantity to obtain a second sum, and to use the ratio of the second sum to the second quantity as the second average value of the target elements; And sum the element values ​​of each target element in the fourth statistical result of the third quantity to obtain a third sum, and take the ratio of the third sum to the third quantity as the third average value of the target element; and generate the second comparison result based on each of the second average values ​​and each of the third average values.

6. An information management method, characterized in that, The method is applied to the management information system described in any one of claims 1 to 5; The method includes: The production data to be processed is obtained from the distributed control system. The production data to be processed includes at least one of the following: industrial waste input, domestic waste input, urea consumption, activated carbon consumption, hydrated lime consumption, power generation, fly ash, and slag. The production data to be processed is statistically analyzed and / or compared to obtain the processing results, which are then returned to the front-end interface.

7. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the information management method as described in claim 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the steps of the information management method of claim 6.

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