Air cooling tower anti-freezing intelligent debugging method and system based on artificial intelligence
Through the intelligent anti-freeze debugging method of air-cooled towers based on artificial intelligence, the characteristic chain is formed using historical air-cooled tower anti-freeze debugging information, which solves the problem of inaccurate anti-freeze debugging of air-cooled towers, and achieves more accurate temperature management and protects equipment safety.
Patent Information
- Application Number
- CN202510243394.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-03
- Publication Date
- 2025-08-01
AI Technical Summary
The anti-freeze debugging of the hollow cooling tower in the prior art cannot be carried out accurately and timely, resulting in damage to the equipment due to excessive temperature.
Using the intelligent anti-freeze debugging method of air-cooling tower based on artificial intelligence, we can obtain and analyze the historical anti-freeze debugging information of multiple reference matters, and form a target air-cooling tower anti-freeze intelligent debugging feature chain, determine the target matters that meet the relationship requirements, and update the debugging results.
Improve the accuracy of anti-freeze debugging of air-cooled towers, protect equipment safety, and ensure equipment anti-freeze treatment under abnormal temperature conditions.
Smart Images
Figure CN120408058A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of intelligent debugging, and in particular to an intelligent debugging method and system for antifreeze of an air-cooling tower based on artificial intelligence. Background Art
[0002] Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence.
[0003] At present, in the antifreeze debugging of air-cooling towers, temperature abnormalities cannot be debugged accurately and timely. As a result, the equipment may be damaged due to excessive temperature. Therefore, a technical solution for intelligent antifreeze debugging of air-cooling towers is urgently needed to improve the above technical problems. Summary of the Invention
[0004] In order to improve the technical problems existing in the related technologies, the present disclosure provides an air cooling tower antifreeze intelligent debugging method and system based on artificial intelligence.
[0005] In a first aspect, an artificial intelligence-based intelligent debugging method for antifreeze of an air cooling tower is provided, the method comprising: Obtaining reference historical air-cooling tower antifreeze debugging information for a set of items that need to be processed and multiple reference items, where the items that need to be processed in the set of items that need to be processed include single items that need to be processed and non-single items that need to be processed, the single items that need to be processed are not included in the multiple reference items, and the non-single items that need to be processed are included in the multiple reference items, and the reference historical air-cooling tower antifreeze debugging information is used to characterize a relationship between at least two reference items in the multiple reference items; For a non-single item that needs to be processed, determining first historical air-cooling tower antifreeze debugging information of the non-single item that needs to be processed based on reference historical air-cooling tower antifreeze debugging information of the non-single item that needs to be processed, wherein the first historical air-cooling tower antifreeze debugging information is used to represent a relationship between the non-single item that needs to be processed and one or more remaining reference items among the multiple reference items except the non-single item that needs to be processed; For a single item that needs to be processed, analyzing second historical air-cooling tower antifreeze debugging information of the single item that needs to be processed based on the multiple reference historical air-cooling tower antifreeze debugging information, wherein the second historical air-cooling tower antifreeze debugging information is used to characterize the relationship between the single item that needs to be processed and one or more reference items in the multiple reference items; Based on the first historical air-cooled tower anti-freezing commissioning information and the second historical air-cooled tower anti-freezing commissioning information, a target air-cooled tower anti-freezing intelligent commissioning feature chain is formed; Using the target air-cooled tower anti-freezing intelligent commissioning feature chain, determine a target matter that meets the relationship requirements with the matters to be processed in the set of matters to be processed, and update the air-cooled tower anti-freezing intelligent commissioning result through the target matter. The target matter belongs to the set of matters to be processed or the multiple reference matters.
[0006] In this application, analyzing the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed according to the multiple reference historical air-cooled tower anti-freezing commissioning information includes: Determine the common matters of the single matter to be processed from the multiple reference matters; Obtain the third historical air-cooled tower anti-freezing commissioning information containing the common matters from the multiple reference historical air-cooled tower anti-freezing commissioning information; Based on the third historical air-cooled tower anti-freezing commissioning information, determine the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed.
[0007] In this application, based on the third historical air-cooled tower anti-freezing commissioning information, determining the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed includes: Obtain the commissioning parameters corresponding to the third historical air-cooled tower anti-freezing commissioning information; Use the third historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the first commissioning threshold as the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed.
[0008] In this application, determining the common matters of the single matter to be processed from the multiple reference matters includes: Obtain the important description factor data of the single matter to be processed and the important description factor data of each reference matter. The important description factor data includes at least one of operation information, scenario category information, and equipment information; Determine the common matters that meet the common parameter requirements with the important description factor data of the single matter to be processed from each reference matter.
[0009] In this application, each reference historical air-cooled tower anti-freezing commissioning information corresponds to commissioning parameters. Determining the first historical air-cooled tower anti-freezing commissioning information of the non-single matter to be processed according to the reference historical air-cooled tower anti-freezing commissioning information of the non-single matter to be processed includes: Obtain the debugging parameters corresponding to the reference historical air-cooled tower anti-freezing debugging information for each non-single matter that needs to be processed; Use the reference historical air-cooled tower anti-freezing debugging information of the non-single matter whose debugging parameters are higher than the second debugging threshold as the first historical air-cooled tower anti-freezing debugging information of the non-single matter that needs to be processed.
[0010] In this application, the method of using the target air-cooled tower anti-freezing intelligent debugging feature chain to determine the target matters that meet the relationship requirements with the matters that need to be processed in the set of matters that need to be processed, and updating the air-cooled tower anti-freezing intelligent debugging result through the target matters includes: Obtain the number of historical air-cooled tower anti-freezing debugging information included in the target air-cooled tower anti-freezing intelligent debugging feature chain; When the number of historical air-cooled tower anti-freezing debugging information exceeds the quantity threshold, determine the matching matters of the set of matters that need to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain, where the matching matters are the matters that have a matching relationship with the set of matters to be updated; Determine the target matters that meet the relationship requirements with the matters that need to be processed in the set of matters that need to be processed from the matching matters.
[0011] In this application, the target air-cooled tower anti-freezing intelligent debugging feature chain includes the debugging parameters of the historical air-cooled tower anti-freezing debugging information. Determining the matching matters of the set of matters that need to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain includes: Determine the candidate matters that meet the matching requirements with the set of matters that need to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain; Determine the candidate historical air-cooled tower anti-freezing debugging information that includes the candidate matters; Obtain the debugging parameters of each candidate historical air-cooled tower anti-freezing debugging information from the target air-cooled tower anti-freezing intelligent debugging feature chain; Use the candidate historical air-cooled tower anti-freezing debugging information whose debugging parameters are higher than the third debugging threshold as the selected historical air-cooled tower anti-freezing debugging information; Use the matters included in the selected historical air-cooled tower anti-freezing debugging information as the matching matters of the set of matters that need to be processed.
[0012] In this application, the target air-cooled tower anti-freezing intelligent debugging feature chain includes the matter description vectors of each matter that needs to be processed and the relationship description vectors of the relationship requirements. The method of using the target air-cooled tower anti-freezing intelligent debugging feature chain to determine the target matters that meet the relationship requirements with the matters that need to be processed in the set of matters that need to be processed, and updating the air-cooled tower anti-freezing intelligent debugging result through the target matters further includes: When the number of historical air-cooled tower anti-freezing commissioning information is not greater than the number threshold, obtain the first matter description vector of the matters to be processed, the second matter description vector of the remaining matters to be processed, and the relationship description vector of the relationship requirements, where the remaining matters to be processed are the matters to be processed in the set of matters to be processed except for the matters to be processed; Perform weight processing on the first matter description vector and the relationship description vector of the relationship requirements to obtain a weight vector; Determine the vector differences between the weight vector and each second matter description vector; Use the matters corresponding to the vector differences smaller than the difference threshold as the target matters that meet the relationship requirements with the matters to be processed, and update the intelligent commissioning result of the air-cooled tower anti-freezing through the target matters.
[0013] In this application, the method further includes: Obtain the initial matter description vectors of each reference matter and the initial relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information; Perform training and updating on the initial matter description vectors and the initial relationship description vectors to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information.
[0014] In this application, performing training and updating on the initial matter description vectors and the initial relationship description vectors to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information includes: Obtain an associated example set; Determine a non-associated example set based on the associated example set; Form an analysis solution based on the first differences of each associated example in the associated example set and the second differences of each non-associated example in the non-associated example set; Perform training and updating on each initial matter description vector and each initial relationship description vector based on the analysis solution to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information.
[0015] In this application, each of the associated examples includes a start matter, a relationship, and an end matter. Determining the non-associated example set based on the associated example set includes: Obtain the c-th associated example, where c = 1, 2,..., M, and M is the total number of examples in the associated example set; Replace the c-th start matter in the c-th associated example with a first random matter to obtain the c-th non-associated example, where the first random matter is different from the c-th start matter; or replace the c-th last matter in the c-th associated example with a second random matter to obtain the c-th non-associated example, where the second random matter is different from the c-th last matter.
[0016] In a second aspect, there is provided an air-cooled tower anti-freezing intelligent debugging system based on artificial intelligence, including a processor and a memory that communicate with each other. The processor is configured to read and execute a computer program from the memory to implement the above method.
[0017] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects.
[0018] For non-single matters to be processed in a set of matters to be processed, the first historical air-cooled tower anti-freezing debugging information of the non-single matters to be processed is determined according to the reference historical air-cooled tower anti-freezing debugging information of the non-single matters to be processed, where the non-single matters to be processed are included in multiple reference matters; for a single matter to be processed in a set of matters to be processed, the second historical air-cooled tower anti-freezing debugging information of the single matter to be processed is analyzed according to multiple reference historical air-cooled tower anti-freezing debugging information, where the single matter to be processed is not included in multiple reference matters; then, based on the first historical air-cooled tower anti-freezing debugging information and the second historical air-cooled tower anti-freezing debugging information, a target air-cooled tower anti-freezing intelligent debugging feature chain is formed. The target air-cooled tower anti-freezing intelligent debugging feature chain is the air-cooled tower anti-freezing intelligent debugging feature chain corresponding to the matters to be processed, and the air-cooled tower anti-freezing intelligent debugging feature chain includes single characters with connection relationships; finally, the target matter that meets the relationship requirements with the matters to be processed in the set of matters to be processed is determined by using the target air-cooled tower anti-freezing intelligent debugging feature chain, and the air-cooled tower anti-freezing debugging result is updated through the target matter, so as to improve the debugging accuracy, accurately perform anti-freezing treatment on the air-cooled tower, and protect the safety of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments that meet the present application, and are used together with the specification to explain the principles of the present application.
[0020] Figure 1 It is a flowchart of the air-cooled tower anti-freezing intelligent debugging method provided by the embodiments of the present application. DETAILED DESCRIPTION
[0021] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0022] Based on the above, please refer to Figure 1 , which is a schematic flow chart of the intelligent debugging method for air-cooled tower anti-freezing based on artificial intelligence provided by the embodiment of the present application. Further, the intelligent debugging method for air-cooled tower anti-freezing based on artificial intelligence may specifically include the content described in the following steps S101 - step S105.
[0023] In step S101, obtain a set of matters to be processed and the reference historical air-cooled tower anti-freezing debugging information of multiple reference matters.
[0024] Among them, a matter can be understood as a three-dimensional scene object.
[0025] In this step, the reference historical air-cooled tower anti-freezing debugging information is used to characterize the relationship between at least two reference matters among multiple reference matters. For example, the reference historical air-cooled tower anti-freezing debugging information can represent the reference matters and the relationship between the reference matters. The reference matters can be entity matters, and the reference matters can also be virtual matters.
[0026] Through step S101, a set of matters to be processed and the reference historical air-cooled tower anti-freezing debugging information of multiple reference matters can be obtained, making preparations for subsequent image data processing.
[0027] In step S102, for a non-single matter to be processed, determine the first historical air-cooled tower anti-freezing debugging information of the non-single matter to be processed according to the reference historical air-cooled tower anti-freezing debugging information of the non-single matter to be processed.
[0028] Among them, a single matter to be processed refers to the information in the anti-freezing data being less and being relatively simple and easy to process, such as: temperature data. A non-single matter to be processed refers to the information in the anti-freezing data being more and there may be mutual interference, such as: information such as equipment operating temperature data.
[0029] In this step, since the non-single matter to be processed is a matter to be processed included in multiple reference matters, the reference historical air-cooled tower anti-freezing debugging information of the non-single matter to be processed can be obtained from the reference historical air-cooled tower anti-freezing debugging information.
[0030] For some possible embodiments, each piece of reference historical air-cooled tower anti-freezing commissioning information can be traversed to determine whether the reference historical air-cooled tower anti-freezing commissioning information includes non-single matters that need to be processed. If it includes non-single matters that need to be processed, then this reference information is used as the reference historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed.
[0031] For some possible embodiments, each piece of reference historical air-cooled tower anti-freezing commissioning information corresponds to commissioning parameters. To improve accuracy, when determining the first historical air-cooled tower anti-freezing commissioning information, it is also necessary to continue to determine whether the commissioning parameters meet the requirements. Based on this, "determine the first historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed according to the reference historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed" in step S102 can be implemented in the following manner.
[0032] Obtain the commissioning parameters corresponding to the reference historical air-cooled tower anti-freezing commissioning information for each non-single matter that needs to be processed, and then use the reference historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed with commissioning parameters higher than the second commissioning threshold as the first historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed.
[0033] In the embodiments of the present application, the commissioning parameter is a differential description used to reflect the actual relationship between matters in the historical air-cooled tower anti-freezing commissioning information. Among them, the higher the commissioning parameter, the greater the differential description; conversely, the lower the commissioning parameter, the smaller the differential description.
[0034] For some possible embodiments, the commissioning parameters of the reference historical air-cooled tower anti-freezing commissioning information for each non-single matter that needs to be processed can be obtained.
[0035] In the embodiments of the present application, the second commissioning threshold can be a pre-set value. Exemplarily, the second commissioning threshold can be 10, 15, 20, etc.
[0036] For some possible embodiments, the historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the second commissioning threshold in the reference historical air-cooled tower anti-freezing commissioning information for the non-single matters that need to be processed is used as the first historical air-cooled tower anti-freezing commissioning information.
[0037] For some possible embodiments, the reference historical air-cooled tower anti-freezing commissioning information for matters that need to be processed other than a single one may first be sorted in descending order of the commissioning parameters to obtain the sorted reference historical air-cooled tower anti-freezing commissioning information; then, the first preset number of historical air-cooled tower anti-freezing commissioning information in the sorted reference historical air-cooled tower anti-freezing commissioning information is used as the first historical air-cooled tower anti-freezing commissioning information. Among them, the first preset number is less than the number of the second historical air-cooled tower anti-freezing commissioning information. Exemplarily, the first preset number may be 50, 60, 100, etc.
[0038] Through step S102, for matters that need to be processed other than a single one, according to the reference historical air-cooled tower anti-freezing commissioning information of the matters that need to be processed other than a single one, the historical air-cooled tower anti-freezing commissioning information that includes any matter that needs to be processed other than a single one and whose commissioning parameters are greater than the second commissioning threshold is used as the first historical air-cooled tower anti-freezing commissioning information of the matters that need to be processed other than a single one, so as to determine highly accurate historical air-cooled tower anti-freezing commissioning information for the matters that need to be processed other than a single one in the matters that need to be processed.
[0039] Continuing the description of step S102 above.
[0040] In step S103, for a matter that needs to be processed singly, the second historical air-cooled tower anti-freezing commissioning information of the matter that needs to be processed singly is analyzed based on multiple reference historical air-cooled tower anti-freezing commissioning information.
[0041] In this step, the second historical air-cooled tower anti-freezing commissioning information is used to characterize the relationship between a matter that needs to be processed singly and one or more of the multiple reference matters. Since the matter that needs to be processed singly is not included in the multiple reference matters, that is, the historical air-cooled tower anti-freezing commissioning information that includes the matter that needs to be processed singly cannot be determined from the reference historical air-cooled tower anti-freezing commissioning information, the second historical air-cooled tower anti-freezing commissioning information of the matter that needs to be processed singly is determined based on the reference historical air-cooled tower anti-freezing commissioning information to establish the relationship between the matter that needs to be processed singly and the remaining matters that need to be processed, so that when the intelligent commissioning feature chain of the air-cooled tower anti-freezing is trained and updated subsequently, the single character corresponding to the matter that needs to be processed singly can also be updated, so that the single character can be applied to the prediction and analysis of the relationship, improving the accuracy of the prediction and analysis.
[0042] For some possible embodiments, "analyzing the second historical air-cooled tower anti-freezing commissioning information of the matter that needs to be processed singly based on multiple reference historical air-cooled tower anti-freezing commissioning information" in step S103 can be implemented through the following steps S1031 to S1033, which are specifically described below.
[0043] In step S1031, a common item of a single item to be processed is determined from multiple reference items.
[0044] For some possible embodiments, important description factor data of a single item to be processed and important description factor data of each reference item can be obtained first. The important description factor data includes at least one of operation information, scenario category information, and device information. Then, a common item that meets the common parameter requirements with the important description factor data of the single item to be processed is determined from each reference item.
[0045] For some possible embodiments, a reference item that meets the common requirements for the operation information, scenario category information, and device information of a single item to be processed can be used as a common item. In implementation, first, a first reference item with the same operation information as the single item to be processed is determined from each reference item; then, a second reference item with the same scenario category information as the single item to be processed is determined from each first reference item; finally, the reference item with the same device information as the single item to be processed among each second reference item is used as the common item.
[0046] For some possible embodiments, any one of the operation information, scenario category information, or device information can be used as the common parameter requirement. Exemplarily, a reference item determined to have the same operation information (scenario category information or device information) as the single item to be processed among each reference item can be used as the common item, that is, the same operation information can be used as the common item. In some other embodiments, any two of the operation information, scenario category information, or device information can also be used as the common parameter requirement. Exemplarily, a reference item determined to have the same operation information and scenario category information as the single item to be processed among each reference item can be used as the common item.
[0047] In step S1032, third historical air-cooled tower anti-freezing commissioning information containing the common item is obtained from multiple reference historical air-cooled tower anti-freezing commissioning information.
[0048] In this step, each reference historical air-cooled tower anti-freezing commissioning information can be traversed, and it is judged whether the common item is included in each reference historical air-cooled tower anti-freezing commissioning information. If it is included, the reference historical air-cooled tower anti-freezing commissioning information is used as the third historical air-cooled tower anti-freezing commissioning information; if not, the reference historical air-cooled tower anti-freezing commissioning information is deleted until the traversal and judgment of all reference historical air-cooled tower anti-freezing commissioning information are completed.
[0049] In step S1033, second historical air-cooled tower anti-freezing commissioning information of a single item to be processed is determined based on the third historical air-cooled tower anti-freezing commissioning information.
[0050] In this step, first obtain the debugging parameters corresponding to the third historical air-cooled tower anti-freezing debugging information, and then use the third historical air-cooled tower anti-freezing debugging information with debugging parameters higher than the first debugging threshold as the second historical air-cooled tower anti-freezing debugging information for a single matter to be processed.
[0051] In the embodiment of the present application, the first debugging threshold can be a pre-set value. The first debugging threshold can be the same as the above-mentioned second debugging threshold or different from the above-mentioned second debugging threshold. Exemplarily, the second debugging threshold can be 10, 16, 25, etc.
[0052] For some possible embodiments, the historical air-cooled tower anti-freezing debugging information with debugging parameters higher than the first debugging threshold in the third historical air-cooled tower anti-freezing debugging information is used as the second historical air-cooled tower anti-freezing debugging information.
[0053] For some possible embodiments, the third historical air-cooled tower anti-freezing debugging information can also be sorted first in descending order of debugging parameters to obtain the sorted third historical air-cooled tower anti-freezing debugging information; then the first second preset number of historical air-cooled tower anti-freezing debugging information in the sorted third historical air-cooled tower anti-freezing debugging information is used as the second historical air-cooled tower anti-freezing debugging information. Among them, the second preset number is less than the number of the first historical air-cooled tower anti-freezing debugging information. The second preset number can be the same as the first preset number or different from the first preset number. Exemplarily, the second preset number can be 50, 65, 100, etc.
[0054] Through step S103, for a single matter to be processed, first determine the common matter of the single matter to be processed from the reference matters, then obtain the third historical air-cooled tower anti-freezing debugging information containing the common matter from multiple reference historical air-cooled tower anti-freezing debugging information, and at the same time obtain the debugging parameters of the third historical air-cooled tower anti-freezing debugging information; finally, use the third historical air-cooled tower anti-freezing debugging information with debugging parameters higher than the first debugging threshold as the second historical air-cooled tower anti-freezing debugging information for a single matter to be processed. To establish the relationship between the single matter to be processed and the remaining matters to be processed, based on this, a complete intelligent debugging feature chain for air-cooled tower anti-freezing can be established. It also enables the single character corresponding to the single matter to be processed to be updated when training and updating the intelligent debugging feature chain for air-cooled tower anti-freezing subsequently, so that the single character can be applied to the prediction and analysis of the relationship, improving the accuracy of prediction and analysis.
[0055] In step S104, based on the first historical air-cooled tower anti-freezing debugging information and the second historical air-cooled tower anti-freezing debugging information, a target intelligent debugging feature chain for air-cooled tower anti-freezing is established.
[0056] The target air-cooled tower anti-freezing intelligent debugging feature chain can be understood as the database in cIGC.
[0057] In step S105, use the target air-cooled tower anti-freezing intelligent debugging feature chain to determine the target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed, and update the air-cooled tower anti-freezing intelligent debugging result through the target matters.
[0058] In this step, the matters to be processed can be determined from the set of matters to be processed according to actual needs, and the matters to be processed can be any random matter in the set.
[0059] In the embodiment of the present application, the target matters belong to the set of matters to be processed or multiple reference matters.
[0060] For some possible embodiments, the "using the target air-cooled tower anti-freezing intelligent debugging feature chain to determine the target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed, and updating the air-cooled tower anti-freezing intelligent debugging result" in step S105 can be implemented through the following steps S1051 to S1058, which are specifically described below.
[0061] In step S1051, obtain the number of historical air-cooled tower anti-freezing debugging information included in the target air-cooled tower anti-freezing intelligent debugging feature chain.
[0062] In this step, what is obtained is the total number of all historical air-cooled tower anti-freezing debugging information included in the target air-cooled tower anti-freezing intelligent debugging feature chain, which can be recorded as the number of historical air-cooled tower anti-freezing debugging information.
[0063] In step S1052, determine whether the number of historical air-cooled tower anti-freezing debugging information is greater than the quantity threshold.
[0064] In this step, the quantity threshold can be a value set in advance.
[0065] In the embodiments of the present application, the relationship between the number of historical air-cooled tower anti-freezing commissioning information and the quantity threshold can be judged by the method of size comparison. When the number of historical air-cooled tower anti-freezing commissioning information is not greater than the quantity threshold, it indicates that the anti-freezing intelligent commissioning feature chain of the target air-cooled tower is not very large, and the analysis duration required for relationship analysis using the anti-freezing intelligent commissioning feature chain of the target air-cooled tower is acceptable, and the computing resources of the computer device can also support the analysis based on the anti-freezing intelligent commissioning feature chain of the target air-cooled tower, then step S1053 is entered. If the number of historical air-cooled tower anti-freezing commissioning information is greater than the quantity threshold, it indicates that the anti-freezing intelligent commissioning feature chain of the target air-cooled tower is very large, and the analysis duration required for relationship analysis using the anti-freezing intelligent commissioning feature chain of the target air-cooled tower may be several days or even weeks, and it is difficult for the computing resources of the computer device to support the analysis based on the anti-freezing intelligent commissioning feature chain of the target air-cooled tower, then step S1057 is entered.
[0066] In step S1053, obtain the first matter description vector of the matter to be processed, the second matter description vector of the remaining matters to be processed, and the relationship description vector of the relationship requirement.
[0067] In step S1054, perform weight processing on the first matter description vector and the relationship description vector to obtain a weight vector.
[0068] In this step, the weight vector can be determined by the method of vector summation.
[0069] In step S1055, determine the vector difference between the weight vector and each second matter description vector.
[0070] In this step, the vector difference between the weight vector and each second matter description vector can be determined by means such as Euclidean difference, Manhattan difference, and Chebyshev difference.
[0071] In step S1056, take the matter corresponding to the vector difference less than the difference threshold as the target matter that meets the relationship requirement with the matter to be processed, and update the anti-freezing intelligent commissioning result of the air-cooled tower through the target matter. And end the process.
[0072] [[ID=2I]]In this step, the difference threshold is a difference set in advance. Exemplarily, the difference threshold can be 0.1, 0.2, etc.
[0073] For some possible embodiments, the size relationship between each vector difference and the difference threshold can be judged by the method of size comparison. If the vector difference is less than the difference threshold, take the matter corresponding to the vector difference as the target matter that meets the relationship requirement with the matter to be processed, and update the anti-freezing intelligent commissioning result of the air-cooled tower through the target matter. The number of such target matters can be multiple.
[0074] In step S1057, determine the matching matters of the matters to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain.
[0075] At this time, when the number of historical air-cooled tower anti-freezing debugging information is greater than the quantity threshold, it indicates that the target air-cooled tower anti-freezing intelligent debugging feature chain is very large. The analysis duration required for relationship analysis using the target air-cooled tower anti-freezing intelligent debugging feature chain may be several days or even weeks, and it is difficult for the computing resources of the computer device to support the analysis based on the target air-cooled tower anti-freezing intelligent debugging feature chain. Then, instead of directly using the target air-cooled tower anti-freezing intelligent debugging feature chain for analysis, determine the matching matters, where the matching matters are the matters having a matching relationship with the matter set to be updated.
[0076] For some possible embodiments, the target air-cooled tower anti-freezing intelligent debugging feature chain includes the debugging parameters of the historical air-cooled tower anti-freezing debugging information. Step S1057, "determine the matching matters of the matter set to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain", can be implemented through the following steps S571 to S575, which are specifically described below.
[0077] In step S571, determine the candidate matters that meet the matching requirements with the matter set to be processed from the target air-cooled tower anti-freezing intelligent debugging feature chain.
[0078] For some possible embodiments, the matters belonging to the same region as the matter set to be processed can be determined from the matters of the target air-cooled tower anti-freezing intelligent debugging feature chain, and the matters belonging to the same region are used as the first candidate matters. The matters belonging to the same industry as the matter set to be processed can also be determined from the matters of the target air-cooled tower anti-freezing intelligent debugging feature chain, and the matters belonging to the same industry are used as the second candidate matters. The matters belonging to the same community as the matter set to be processed can also be determined from the matters of the target air-cooled tower anti-freezing intelligent debugging feature chain, and the matters belonging to the same community are used as the third candidate matters. The matters having a holding relationship with the matter set to be processed can also be determined from the matters of the target air-cooled tower anti-freezing intelligent debugging feature chain, and the matters having a holding relationship are used as the fourth candidate matters. Finally, the first candidate matters, the second candidate matters, the third candidate matters, and the fourth candidate matters are used as the candidate matters.
[0079] In step S572, determine the candidate historical air-cooled tower anti-freezing debugging information including the candidate matters.
[0080] In this step, each piece of historical air-cooled tower anti-freezing commissioning information in the target intelligent commissioning feature chain for air-cooled tower anti-freezing is obtained one by one, and it is determined whether each piece of historical air-cooled tower anti-freezing commissioning information contains a candidate item. If it contains a candidate item, the historical air-cooled tower anti-freezing commissioning information containing the candidate item is used as the candidate historical air-cooled tower anti-freezing commissioning information; if it does not contain a candidate item, the historical air-cooled tower anti-freezing commissioning information that does not contain the candidate item is deleted.
[0081] In step S573, the commissioning parameters of each candidate historical air-cooled tower anti-freezing commissioning information are obtained from the target intelligent commissioning feature chain for air-cooled tower anti-freezing.
[0082] In this step, since the commissioning parameters of each piece of historical air-cooled tower anti-freezing commissioning information are included in the target intelligent commissioning feature chain for air-cooled tower anti-freezing, the commissioning parameters of each candidate historical air-cooled tower anti-freezing commissioning information are obtained from the target intelligent commissioning feature chain for air-cooled tower anti-freezing.
[0083] In step S574, the candidate historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the third commissioning threshold is used as the selected historical air-cooled tower anti-freezing commissioning information.
[0084] In this step, the third commissioning threshold can be a pre-set value, which can be the same as the second commissioning threshold or different from the second commissioning threshold. Exemplarily, the third commissioning threshold can be 15, 20, 30, etc.
[0085] In the embodiment of the present application, the historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the third commissioning threshold in the candidate historical air-cooled tower anti-freezing commissioning information is used as the selected historical air-cooled tower anti-freezing commissioning information.
[0086] In step S575, the items included in the selected historical air-cooled tower anti-freezing commissioning information are used as the matching items of the item set to be processed.
[0087] In this step, the items included in the selected historical air-cooled tower anti-freezing commissioning information are first obtained, and then the items included in the selected historical air-cooled tower anti-freezing commissioning information are used as the matching items, where the number of the matching items can be multiple.
[0088] In step S1058, from the matching items, the target items that meet the relationship requirements with the items to be processed in the item set to be processed are determined, and the intelligent commissioning result for air-cooled tower anti-freezing is updated through the target items.
[0089] In this step, the matching items are used as the analysis scope to determine the target items that meet the relationship requirements with the items to be processed, and the intelligent commissioning result for air-cooled tower anti-freezing is updated through the target items.
[0090] For some possible embodiments, the method for determining the target matter may refer to the above steps S1053 to S10546, but it is necessary to replace the "second matter description vector of each matter in the target air-cooled tower anti-freezing intelligent debugging feature chain" in step S1053 with the "third matter description vector of the matching matter".
[0091] Through step S105, first determine the matters to be processed from the set of matters to be processed; then, if the number of historical air-cooled tower anti-freezing debugging information included in the target air-cooled tower anti-freezing intelligent debugging feature chain does not exceed the quantity threshold, based on all the matters in the target air-cooled tower anti-freezing intelligent debugging feature chain, determine the target matter that meets the relationship requirements with the matters to be processed, and update the air-cooled tower anti-freezing intelligent debugging result through the target matter; and if the number of historical air-cooled tower anti-freezing debugging information included in the target air-cooled tower anti-freezing intelligent debugging feature chain exceeds the quantity threshold, determine the matching matters, and determine the target matter that meets the relationship requirements with the matters to be processed from the matching matters, and update the air-cooled tower anti-freezing intelligent debugging result through the target matter, where the matching matters are very likely to be the target matter. In this way, there is no need for full-scale analysis, which can reduce the complexity of the analysis and improve the relationship analysis efficiency.
[0092] For some possible embodiments, before executing step S101, the initial description vectors of each reference matter and the initial relationship description vectors of each reference historical air-cooled tower anti-freezing debugging information will also be obtained, and then the initial description vectors and the initial relationship description vectors will be trained and updated to obtain the updated matter description vectors and the updated relationship description vectors. Based on this, when determining the first historical air-cooled tower anti-freezing debugging information and the second historical air-cooled tower anti-freezing debugging information based on the reference historical air-cooled tower anti-freezing debugging information, the updated relationship description vector corresponding to the first historical air-cooled tower anti-freezing debugging information will also be used as the relationship representation of the first historical air-cooled tower anti-freezing debugging information, and the updated relationship description vector corresponding to the second historical air-cooled tower anti-freezing debugging information will be used as the relationship representation of the second historical air-cooled tower anti-freezing debugging information. Based on this, before executing step S101, the initial matter description vectors of each reference matter and the initial relationship description vectors of each reference historical air-cooled tower anti-freezing debugging information can also be obtained; and the initial matter description vectors and the initial relationship description vectors will be trained and updated to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing debugging information.
[0093] For some possible embodiments, the above "training and updating the initial matter description vector and the initial relationship description vector to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information" can be implemented through the following steps S001 to S004, which are specifically described below.
[0094] In step S001, an associated example set is obtained.
[0095] In this step, the associated example set can be obtained from a public example library, and the associated example set is an actually existing example.
[0096] In step S002, a non-associated example set is determined based on the associated example set.
[0097] For some possible embodiments, each associated example includes a start matter, a relationship, and an end matter. Step S002, "determining a non-associated example set based on the associated example set", can be implemented through the following method 1 and / or method 2, which are specifically described below.
[0098] Method 1: Obtain the c-th associated example; replace the c-th start matter in the c-th associated example with a first random matter to obtain the c-th non-associated example.
[0099] In method 1, c = 1, 2,..., M, where M is the total number of associated examples. That is, each associated example in the associated example set is obtained one by one.
[0100] In the embodiments of the present application, the first random matter and the c-th start matter are different matters.
[0101] Method 2: Obtain the c-th associated example; replace the c-th end matter in the c-th associated example with a second random matter to obtain the c-th non-associated example.
[0102] In method 2, the method of obtaining the c-th positive example is the same as that in method 1 above. Then, a second random matter different from the c-th end matter in the reference matter can be used to replace the c-th end matter in the c-th associated example, so as to obtain the c-th non-associated example. That is, the c-th non-associated example is composed of the c-th start matter, the c-th relationship, and the second random matter.
[0103] In the embodiments of the present application, the second random matter and the c-th end matter are different matters.
[0104] For some possible embodiments, the various uncorrelated examples can be obtained through the above-mentioned Method 1 to obtain an uncorrelated example set; or the various uncorrelated examples can be obtained through the above-mentioned Method 2 to obtain an uncorrelated example set; or a part of the uncorrelated examples can be obtained through the above-mentioned Method 1, while another part of the uncorrelated examples can be obtained through the above-mentioned Method 2 to obtain an uncorrelated example set, that is, the uncorrelated example set is obtained through the two methods together.
[0105] In step S003, an analysis solution is formed based on the first differences of the various correlated examples in the correlated example set and the second differences of the various uncorrelated examples in the uncorrelated example set.
[0106] In this step, it is necessary to first determine the first differences of the various correlated examples in the correlated example set, and it is also necessary to determine the second differences of the various uncorrelated examples in the uncorrelated example set. Among them, the methods for determining the first differences and the second differences are the same.
[0107] Here, taking the determination of the first difference of the c-th correlated example as an example for illustration. First, obtain the description vector of the c-th starting event, the description vector of the c-th relationship, and the description vector of the c-th final event, where the c-th relationship is the relationship between the c-th starting event and the c-th final event; then perform weight processing on the description vector of the c-th starting event and the description vector of the c-th relationship to obtain the c-th weight vector; finally, determine the c-th vector difference between the c-th weight vector and the description vector of the c-th final event, and use this c-th vector difference as the first difference of the c-th correlated example.
[0108] For some possible embodiments, the methods exemplified above can be used to determine the first differences of the various correlated examples and the second differences of the various uncorrelated examples.
[0109] In step S004, the various initial event description vectors and the various initial relationship description vectors are trained and updated based on the analysis solution to obtain a target intelligent debugging feature chain for air-cooled tower anti-freezing.
[0110] For some possible embodiments, preset training times of training and updating can be performed to obtain a target intelligent debugging feature chain for air-cooled tower anti-freezing.
[0111] Through the above steps S001 to S004, the initial event description vectors and the initial relationship description vectors are obtained; then an analysis solution is formed based on the correlated example set and the uncorrelated example set, and the initial event description vectors and the initial relationship description vectors are trained and updated based on the analysis solution, so as to obtain the updated event description vectors of the various reference events and the updated relationship description vectors of the various reference historical air-cooled tower anti-freezing debugging information.
[0112] In summary, when applying the above solution, for the non-single matters to be processed in the matter set that needs to be processed, the intelligent debugging method and system for preventing freezing of the air-cooled tower based on artificial intelligence determine the first historical air-cooled tower anti-freezing debugging information of the non-single matters to be processed according to the reference historical air-cooled tower anti-freezing debugging information of the non-single matters to be processed, where the non-single matters to be processed are included in multiple reference matters; for the single matter to be processed in the matter set that needs to be processed, the second historical air-cooled tower anti-freezing debugging information of the single matter to be processed is analyzed according to multiple reference historical air-cooled tower anti-freezing debugging information, where the single matter to be processed is not included in multiple reference matters; then, based on the first historical air-cooled tower anti-freezing debugging information and the second historical air-cooled tower anti-freezing debugging information, a target intelligent debugging feature chain for preventing freezing of the air-cooled tower is formed. The target intelligent debugging feature chain for preventing freezing of the air-cooled tower is the intelligent debugging feature chain for preventing freezing of the air-cooled tower corresponding to the matter to be processed, and the intelligent debugging feature chain for preventing freezing of the air-cooled tower contains single characters with a connection relationship; finally, the target matter that meets the relationship requirements with the matter to be processed in the matter set that needs to be processed is determined by using the target intelligent debugging feature chain for preventing freezing of the air-cooled tower, and the air-cooled tower anti-freezing debugging result is updated through the target matter, so as to improve the debugging accuracy, accurately perform anti-freezing treatment on the air-cooled tower, and protect the safety of the equipment.
[0113] It should be understood that the present application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.
Claims
1. An intelligent debugging method for preventing freezing of an air cooling tower based on artificial intelligence, characterized in that, The method includes: Obtaining a set of matters to be processed and reference historical air-cooled tower anti-freezing commissioning information of multiple reference matters. The matters to be processed in the set of matters to be processed include single matters to be processed and non-single matters to be processed. The single matters to be processed are not included in the multiple reference matters, and the non-single matters to be processed are included in the multiple reference matters. The reference historical air-cooled tower anti-freezing commissioning information is used to characterize the relationship between at least two reference matters among the multiple reference matters; For non-single matters to be processed, according to the reference historical air-cooled tower anti-freezing commissioning information of the non-single matters to be processed, determine the first historical air-cooled tower anti-freezing commissioning information of the non-single matters to be processed, where the first historical air-cooled tower anti-freezing commissioning information is used to characterize the relationship between the non-single matters to be processed and the remaining one or more reference matters among the multiple reference matters other than the non-single matters to be processed; For single matters to be processed, analyze the second historical air-cooled tower anti-freezing commissioning information of the single matters to be processed according to multiple reference historical air-cooled tower anti-freezing commissioning information, where the second historical air-cooled tower anti-freezing commissioning information is used to characterize the relationship between the single matters to be processed and one or more reference matters among the multiple reference matters; Based on the first historical air-cooled tower anti-freezing commissioning information and the second historical air-cooled tower anti-freezing commissioning information, form a target air-cooled tower anti-freezing intelligent commissioning feature chain; Use the target air-cooled tower anti-freezing intelligent commissioning feature chain to determine target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed, and update the air-cooled tower anti-freezing intelligent commissioning result through the target matters. The target matters belong to the set of matters to be processed or the multiple reference matters; Among them, the using the target air-cooled tower anti-freezing intelligent commissioning feature chain to determine target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed and updating the air-cooled tower anti-freezing intelligent commissioning result through the target matters includes: Obtaining the number of historical air-cooled tower anti-freezing commissioning information included in the target air-cooled tower anti-freezing intelligent commissioning feature chain; When the number of historical air-cooled tower anti-freezing commissioning information exceeds the quantity threshold, determine the matching matters of the set of matters to be processed from the target air-cooled tower anti-freezing intelligent commissioning feature chain, where the matching matters are matters that have a matching relationship with the set of matters to be updated; From the matching matters, determine the target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed.
2. The method according to claim 1, characterized in that, The analyzing the second historical air-cooled tower anti-freezing commissioning information of the single matters to be processed according to the multiple reference historical air-cooled tower anti-freezing commissioning information includes: Determine the common matters of the single matters to be processed from the multiple reference matters; Obtain the third historical air-cooled tower anti-freezing commissioning information containing the common matters from the multiple reference historical air-cooled tower anti-freezing commissioning information; Determine the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed based on the third historical air-cooled tower anti-freezing commissioning information.
3. The method according to claim 2, characterized in that, The determining the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed based on the third historical air-cooled tower anti-freezing commissioning information includes: Obtain the commissioning parameters corresponding to the third historical air-cooled tower anti-freezing commissioning information; Use the third historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the first commissioning threshold as the second historical air-cooled tower anti-freezing commissioning information of the single matter to be processed.
4. The method according to claim 2, characterized in that Determine the common matters of the single matter to be processed from the multiple reference matters, including: Obtain the important description factor data of the single matter to be processed and the important description factor data of each reference matter, where the important description factor data includes at least one of operation information, scenario category information, and equipment information; Determine the common matters from each reference matter that meet the common parameter requirements with the important description factor data of the single matter to be processed.
5. The method according to claim 1, wherein Each reference historical air-cooled tower anti-freezing commissioning information corresponds to commissioning parameters. The determining the first historical air-cooled tower anti-freezing commissioning information of the matter that needs to be processed according to the reference historical air-cooled tower anti-freezing commissioning information of the matter that does not need to be processed singly includes: Obtain the commissioning parameters corresponding to the reference historical air-cooled tower anti-freezing commissioning information of each matter that does not need to be processed singly; Use the reference historical air-cooled tower anti-freezing commissioning information of the matter that does not need to be processed singly with commissioning parameters higher than the second commissioning threshold as the first historical air-cooled tower anti-freezing commissioning information of the matter that does not need to be processed singly.
6. The method according to claim 1, wherein The target air-cooled tower anti-freezing intelligent commissioning feature chain includes the commissioning parameters of the historical air-cooled tower anti-freezing commissioning information. Determining the matching matters of the set of matters to be processed from the target air-cooled tower anti-freezing intelligent commissioning feature chain includes: Determine the candidate matters from the target air-cooled tower anti-freezing intelligent commissioning feature chain that meet the matching requirements with the set of matters to be processed; Determine the candidate historical air-cooled tower anti-freezing commissioning information containing the candidate matters; Obtain the commissioning parameters of each candidate historical air-cooled tower anti-freezing commissioning information from the target air-cooled tower anti-freezing intelligent commissioning feature chain; Use the candidate historical air-cooled tower anti-freezing commissioning information with commissioning parameters higher than the third commissioning threshold as the selected historical air-cooled tower anti-freezing commissioning information; Use the matters included in the selected historical air-cooled tower anti-freezing commissioning information as the matching matters of the set of matters to be processed.
7. The method according to claim 1, wherein The target air-cooled tower anti-freezing intelligent commissioning feature chain includes the matter description vectors of each matter to be processed and the relationship description vectors of relationship requirements. Using the target air-cooled tower anti-freezing intelligent commissioning feature chain to determine the target matters that meet the relationship requirements with the matters to be processed in the set of matters to be processed, and updating the air-cooled tower anti-freezing intelligent commissioning result through the target matters further includes: When the number of historical air-cooled tower anti-freezing commissioning information is not greater than the quantity threshold, obtain a first matter description vector of the matters to be processed, a second matter description vector of the remaining matters to be processed, and a relationship description vector of the relationship requirements, where the remaining matters to be processed are the matters to be processed in the set of matters to be processed except for the matters to be processed; Perform weight processing on the first matter description vector and the relationship description vector of the relationship requirements to obtain a weight vector; Determine the vector differences between the weight vector and each second matter description vector; Use the matters corresponding to the vector differences smaller than the difference threshold as the target matters that meet the relationship requirements with the matters to be processed, and update the intelligent commissioning result of the air-cooled tower anti-freezing through the target matters.
8. The method according to claim 1, characterized in that, The method further includes: Obtain the initial matter description vectors of each reference matter and the initial relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information; Perform training and update on the initial matter description vectors and the initial relationship description vectors to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information; Among them, performing training and update on the initial matter description vectors and the initial relationship description vectors to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information includes: Obtain an associated example set; Determine a non-associated example set based on the associated example set; Form an analysis solution based on the first differences of each associated example in the associated example set and the second differences of each non-associated example in the non-associated example set; Perform training and update on each initial matter description vector and each initial relationship description vector based on the analysis solution to obtain the updated matter description vectors of each reference matter and the updated relationship description vectors of each reference historical air-cooled tower anti-freezing commissioning information; Among them, each of the associated examples includes a start matter, a relationship, and an end matter, and determining the non-associated example set based on the associated example set includes: Obtain the c-th associated example, where c = 1, 2,..., M, and M is the total number of examples of the associated examples; Replace the c-th start matter in the c-th associated example with a first random matter to obtain the c-th non-associated example, where the first random matter is different from the c-th start matter; or replace the c-th end matter in the c-th associated example with a second random matter to obtain the c-th non-associated example, where the second random matter is different from the c-th end matter.
9. An intelligent debugging system for preventing freezing of an air cooling tower based on artificial intelligence, characterized in that, It includes a processor and a memory that communicate with each other, and the processor is used to read and execute a computer program from the memory to implement the method according to any one of claims 1-8.