Abnormal data monitoring method, device, medium and equipment for central air-conditioning cold supply system

Abstract

The invention relates to an abnormal data monitoring method and device for a central air conditioner cooling system, a medium and equipment, and relates to the technical field of data monitoring, and the method comprises the steps that system operation data of at least one dimension of the central air conditioner cooling system is acquired; and if the system operation data is not in the corresponding normal numerical value range, determining the corresponding system operation data as abnormal operation data, and determining the abnormal operation data based on at least one target fault type and at least one corresponding target data abnormal dimension which are easy to occur in the central air conditioner cold supply system. Determining whether an early warning is given out for the abnormal operation data, wherein the target data exception dimension is a dimension in which the operation data exception is easy to occur when the corresponding target fault type occurs; and if yes, determining a system fault type corresponding to the abnormal operation data, and sending out targeted early warning based on the system fault type. The method has the effect of improving the accuracy of early warning when the operation data is abnormal.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to a method, device, medium and equipment for monitoring abnormal data of a centralized air-conditioning cooling system. Background Art

[0002] Centralized air conditioning and cooling system refers to an efficient and energy-saving system that provides cooling services through a unified pipe network in a specific area. For buildings with high building density and high cooling demand, centralized cooling uses large, advanced and efficient chillers, and its cooling efficiency is much higher than that of ordinary air conditioners. At the same time, considering the peak demand of different buildings, the total installed capacity and use cost are lower than that of each building with a separate air conditioning system. In general, the centralized air conditioning and cooling system has the characteristics of intensive land use, high efficiency and energy saving, peak capacity reduction, and intelligent management and control. It is particularly suitable for promotion and application in areas with high density of commercial buildings. With the complexity and intelligence of modern buildings, centralized air conditioning and cooling systems have become the core part of energy consumption management and comfort control of large buildings. Therefore, it is of great significance to effectively monitor the operating data of the centralized air conditioning and cooling system to ensure the normal operation of the centralized air conditioning and cooling system.

[0003] At present, the method usually adopted for monitoring the operation data of the centralized air-conditioning and cooling system is: using various sensors to collect the operation data of multiple dimensions of the centralized air-conditioning and cooling system in real time, and adopting a warning mechanism with a fixed threshold. Once the operation data exceeds the corresponding fixed threshold, it indicates that the operation data is abnormal and a warning is triggered. However, when the centralized air-conditioning and cooling system has normal load fluctuations, it may also cause abnormal operation data. Under this method, it is easy to mis-trigger the warning, resulting in poor accuracy of the warning when the operation data is abnormal. Summary of the invention

[0004] In order to improve the accuracy of early warning when abnormal operating data occurs, the present application provides a method, device, medium and equipment for monitoring abnormal data of a centralized air-conditioning cooling system.

[0005] In a first aspect of the present application, a method for monitoring abnormal data of a centralized air-conditioning cooling system is provided, which specifically includes: Obtain system operation data of at least one dimension of a central air conditioning and cooling system; If the system operation data is not within the corresponding normal value range, the corresponding system operation data is determined as abnormal operation data, and based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormal dimension, it is determined whether to issue an early warning for the abnormal operation data, wherein the target data abnormal dimension is a dimension in which operation data abnormality is prone to occur when the corresponding target fault type occurs; If so, the system fault type corresponding to the abnormal operation data is determined, and a targeted warning is issued based on the system fault type.

[0006] By adopting the above technical solution, after obtaining the system operation data of at least one dimension, if the system operation data is not within the corresponding normal value range, it means that the centralized air conditioning and cooling system currently has abnormal operation data, which means that the centralized air conditioning and cooling system may have a fault that causes abnormal operation, or it may not have a fault but is only affected by normal load fluctuations. In order to improve the accuracy of the abnormal operation data warning, based on the target fault type that is prone to occur in the centralized air conditioning and cooling system and the corresponding target data abnormal dimension, combined with the current abnormal operation data, the risk of failure of the centralized air conditioning and cooling system is analyzed, and then it is accurately determined whether to issue a targeted warning. If it is determined to issue a warning, then a warning is issued based on the system fault type, thereby avoiding false triggering of the warning and improving the accuracy of the warning when the operation data is abnormal.

[0007] Optionally, the determining whether to issue an early warning for the abnormal operation data based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormality dimension specifically includes: Obtaining historical fault types that have occurred in the centralized air conditioning and cooling system, counting the first occurrence times of each of the historical fault types, and selecting the first number of historical fault types from each of the historical fault types in descending order of the first occurrence times as the target fault type; Obtaining the first historical dimension in which the operation data anomaly occurs when each of the target fault types occurs, counting the second occurrence times of each of the first historical dimensions, and selecting the first historical dimension with the second number from each of the first historical dimensions in descending order of the second occurrence times to determine as the target data anomaly dimension of the corresponding target fault type; Calculate a first weight of each of the target fault types and a second weight of each of the corresponding target data anomaly dimensions, wherein the first weight is a ratio of a first occurrence number of each target fault type to a sum of first occurrence numbers of all target fault types, and the second weight is a ratio of a second occurrence number of a single target data anomaly dimension corresponding to the target fault type to a sum of second occurrence numbers of all corresponding target data anomaly dimensions; It is determined whether to issue an early warning for the abnormal operation data according to at least one abnormal operation data, the first weight and the corresponding second weights.

[0008] By adopting the above technical solution, the greater the number of occurrences of the first, the more likely the central air conditioning cooling system will have the corresponding historical fault type, and then determine the target fault type; the greater the number of occurrences of the second, the more likely the corresponding first historical dimension of operating data will be abnormal when the target fault type occurs, and then determine the target data abnormal dimension corresponding to the target fault type. Finally, combined with the abnormal operating data, the first weight and the corresponding second weights, the overall possibility of the target data abnormal dimension of the target fault type existing fault based on the current abnormal operating data is analyzed, and then accurately determine whether to issue an early warning when abnormal operating data occurs, to avoid false alarms.

[0009] Optionally, determining whether to issue an early warning for the at least one abnormal operation data according to the at least one abnormal operation data, the first weight, and the corresponding second weights specifically includes: Determine the target abnormal dimension corresponding to each of the abnormal operation data, and determine the target fault type that has the target abnormal dimension in each corresponding target data abnormal dimension as the key fault type; Calculate the first product of the first weight of each of the key fault types and the second weight of each corresponding target abnormal dimension and sum them up to obtain the corresponding sum of the first products; Summing the sums of the first products to obtain a final product sum, and if the final product sum is greater than a preset first product sum threshold, determining to issue an early warning for the abnormal operation data; If the final sum of the products is not greater than the first product sum threshold, it is determined that no warning is issued for the abnormal operation data.

[0010] By adopting the above technical solution, the sum of each first product is re-summed to obtain the final product sum. The larger the final product sum is, the greater the overall possibility of failure of the centralized air conditioning cooling system combined with the currently detected abnormal operation data. If the final product sum is greater than the preset first product sum threshold, it means that the possibility is high, and the abnormal operation data is likely to be caused by a system failure, then it is determined to issue an early warning for these abnormal operation data; conversely, if the final product sum is not greater than the first product sum threshold, it is determined not to issue an early warning for the abnormal operation data, which better avoids misjudgment of the issued warning.

[0011] Optionally, determining the system fault type corresponding to the abnormal operation data specifically includes: Selecting the maximum first product from the first products corresponding to the same target abnormal dimension; Determine the key fault type corresponding to each of the maximum first products as a hidden danger fault type, and determine whether each of the hidden danger fault types is the same; If the hidden danger fault types are all the same, summing up the maximum first products corresponding to the hidden danger fault types to obtain the sum of the second products; If the sum of the second products is greater than a preset threshold of the sum of the first products, the hidden danger fault type is determined as the system fault type corresponding to the abnormal operation data; The target abnormal dimension corresponding to each of the maximum first products is determined as a set of associated dimensions of the hidden danger fault type, and a mapping relationship is established between the hidden danger fault type and the set of associated dimensions.

[0012] By adopting the above technical solution, if the types of hidden danger faults are the same, it means that when the abnormal operation data of each target abnormal dimension appear, the same fault type is more likely to appear, which further indicates that for this hidden danger fault type, the abnormal operation data of the target abnormal dimension corresponding to each maximum first product is the strongly associated abnormal operation data when the fault occurs. If the sum of the second products is greater than the threshold of the sum of the first products, it means that the possibility of a fault occurring when the strongly associated abnormal operation data appears is high, which can not only further verify that the centralized air conditioning cooling system does have a fault, but also accurately determine the type of fault, that is, the hidden danger fault type is used as the system fault type.

[0013] Optionally, the method further includes: Obtaining a second historical dimension in which the central air-conditioning cooling system has abnormal operation data under a historical fault of the system fault type, counting the first occurrence frequency of each of the second historical dimensions, and selecting the second historical dimension with the third number from each of the second historical dimensions in descending order of the first occurrence frequency to determine as a dimension prone to abnormality; Obtaining the associated historical dimension in which abnormal data fluctuation occurs when abnormal operation data of a single dimension prone to abnormality occurs, counting the second occurrence frequency of each of the associated historical dimensions, and selecting the associated historical dimension with the fourth number from each of the associated historical dimensions in descending order of the second occurrence frequency to determine as the easily fluctuating dimension of the corresponding dimension prone to abnormality; Calculating the third weight of each of the dimensions prone to abnormality and the corresponding fourth weight of each of the dimensions prone to fluctuation; Acquire a first actual fluctuation dimension in which abnormal fluctuations occur in the operation data of the centralized air-conditioning cooling system, and perform a rationality check on the system fault type according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights; The issuing of a targeted warning based on the system fault type specifically includes: After the rationality check is passed, a targeted warning is issued based on the system fault type.

[0014] By adopting the above technical solution, the greater the first occurrence frequency, the more likely the corresponding second historical dimension's operating data will be abnormal when this system fault type occurs, and then the abnormal dimension is determined; when abnormal operating data of the abnormal dimension occurs, it is more likely to cause abnormal fluctuations in the corresponding associated historical dimension's operating data, and then the abnormal dimension corresponding to the abnormal dimension is determined. Finally, combined with the dimensions of the actual fluctuation abnormality of the centralized air-conditioning and cooling system, that is, the first actual fluctuation dimension, the third weight and the corresponding fourth weight, the analysis determines the possibility of the system fault type in the current situation of abnormal operating data in the centralized air-conditioning and cooling system, and then the determined system fault type is verified again, so as to more accurately determine the fault type of the centralized air-conditioning and cooling system.

[0015] Optionally, performing rationality check on the system fault type according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights specifically includes: Determine the abnormal dimension in which the first actual fluctuation dimension exists among the corresponding easily fluctuating dimensions as the key abnormal dimension, and determine the target abnormal dimension corresponding to each abnormal operation data; Calculate the second product of the third weight of the key abnormal dimension in each of the target abnormal dimensions and the fourth weight of the corresponding first actual fluctuation dimension; The second products are summed to obtain a sum of third products. If the sum of the third products is greater than a preset second product sum threshold, it is determined that the rationality check of the system fault type has passed.

[0016] By adopting the above technical solution, the larger the second product is, the more likely the central air conditioning and cooling system will have a system fault type when the corresponding operating data fluctuation of the first actual fluctuation dimension and the operating data of the key abnormal dimension are abnormal. Then, the sum of the second products is obtained to obtain the sum of the third products. The larger the sum of the third products is, the greater the possibility of a system fault type in the central air conditioning and cooling system in combination with the current abnormal operating data and the first actual fluctuation dimension where the abnormal fluctuation occurs. Finally, if the sum of the third products is greater than the preset second product sum threshold, then it is determined that the system fault type rationality check has passed, and it is verified again that the current central air conditioning and cooling system has a system fault type.

[0017] Optionally, the method further includes: If there is no abnormal operation data in the central air-conditioning and cooling system, obtaining a second actual fluctuation dimension in which the operation data in the central air-conditioning and cooling system fluctuates abnormally; Determine the easily abnormal dimension in which the second actual fluctuation dimension exists in the corresponding easily abnormal dimensions as an important abnormal dimension, and calculate and sum the third products of the third weight of each important abnormal dimension and the fourth weight of each corresponding second actual fluctuation dimension to obtain the corresponding sum of the fourth products; Selecting a maximum sum of the fourth products from the sums of the fourth products, and issuing a fault risk reminder of the system fault type if the maximum sum of the fourth products is greater than a preset third product sum threshold; The normal value range of the important abnormal dimension corresponding to the sum of the maximum fourth products is adjusted and optimized.

[0018] By adopting the above technical solution, the larger the sum of the fourth products, the greater the possibility of abnormal operation data in the corresponding important abnormal dimension, and the greater the risk of overall system failure type. If the maximum sum of the fourth products is greater than the preset third product sum threshold, it means that the possibility of abnormal data in the important abnormal dimension corresponding to the maximum sum of the fourth products and the risk of overall system failure type are both greater, then a system failure type failure risk reminder is issued to the terminal to remind personnel to go for timely maintenance. At the same time, it also means that the abnormal operation data of the important abnormal dimension corresponding to the maximum sum of the fourth products has not been identified, indicating that the normal value range of the important abnormal dimension corresponding to the maximum sum of the fourth products is not reasonable enough, and it needs to be adjusted and optimized to make the normal value range corresponding to this important abnormal dimension more reasonable and accurate.

[0019] In a second aspect of the present application, a device for monitoring abnormal data of a centralized air-conditioning and cooling system is provided, which specifically includes: A data acquisition module, used to acquire system operation data of at least one dimension of a central air-conditioning cooling system; an abnormality verification module, for determining the corresponding system operation data as abnormal operation data if the system operation data is not within the corresponding normal value range, and determining whether to issue an early warning for the abnormal operation data based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormality dimension, wherein the target data abnormality dimension is a dimension in which operation data abnormality is prone to occur when the corresponding target fault type occurs; The abnormal warning module is used to determine the system fault type corresponding to the abnormal operation data and issue a targeted warning based on the system fault type.

[0020] By adopting the above technical solution, the data acquisition module acquires system operation data of at least one dimension. When the system operation data is determined to be abnormal operation data, the abnormal verification module determines whether to issue a targeted warning based on the target fault type and at least one corresponding target data abnormal dimension. Finally, when the abnormal warning module determines to issue a warning, it determines the system fault type corresponding to the abnormal operation data and issues a targeted warning based on the system fault type.

[0021] In a third aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is loaded and executed by a processor, the method steps described in any one of the first aspects are performed.

[0022] In a fourth aspect of the present application, an electronic device is provided, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor is used to load and execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: If the system operation data is not within the corresponding normal value range, it means that the centralized air conditioning and cooling system currently has abnormal operation data, which means that the centralized air conditioning and cooling system may have a fault that causes abnormal operation, or it may not have a fault but is only affected by normal load fluctuations. In order to improve the accuracy of abnormal data warnings, based on the target fault types that are prone to occur in the centralized air conditioning and cooling system and the corresponding target data abnormal dimensions, combined with the current abnormal operation data, the risk of failure of the centralized air conditioning and cooling system is analyzed, and then it is accurately determined whether to issue a targeted warning. If it is determined to issue a warning, then a warning is issued based on the system fault type, thereby avoiding false triggering of the warning and improving the accuracy of abnormal data warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for monitoring abnormal data of a centralized air-conditioning cooling system provided in an embodiment of the present application; Figure 2 It is a flow chart of another method for monitoring abnormal data of a centralized air-conditioning cooling system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an abnormal data monitoring device for a centralized air-conditioning cooling system provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of another abnormal data monitoring device for a centralized air-conditioning cooling system provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 11. Data acquisition module; 12. Abnormal verification module; 13. Abnormal warning module; 14. Fault verification module; 15. Range adjustment module. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0028] In the description of the embodiments of the present application, the term "and / or" is only a kind of association relationship describing the associated objects, indicating that there may be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] See also Figure 1 The present application embodiment discloses a flow chart of a method for monitoring abnormal data of a centralized air conditioning and cooling system, which can be implemented by a computer program or run on a centralized air conditioning and cooling system abnormal data monitoring device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S101: Obtain system operation data of at least one dimension of a central air-conditioning cooling system.

[0030] Specifically, in an embodiment of the present application, the application scenario of the centralized air conditioning cooling system can be a commercial building or a data center. In other embodiments, the application scenario can also be a hospital or a large venue. The system operation data is various key parameters and performance indicators collected during the operation of the system, which are used to monitor, analyze and maintain the normal operation of the system. The dimensions of the system operation data include but are not limited to temperature dimension, flow dimension, pressure dimension and equipment status dimension. Among them, the temperature includes the chilled water supply temperature, return water temperature, cooling water inlet temperature and outlet temperature, etc., reflecting the refrigeration effect and heat exchange efficiency of the system. The flow rate includes the flow rate of chilled water and cooling water, which directly affects the refrigeration capacity and energy consumption level of the system. The pressure includes the water supply pressure and return water pressure of the system. By monitoring the pressure, it can be determined whether the system is blocked or leaking. The equipment parameters include the operating status of the main equipment such as the chiller, water pump, cooling tower, etc. in the system.

[0031] Exemplarily, the pressure value of the pressure dimension can be obtained through a preset pressure sensor, and the operating data of the flow dimension can be obtained through a preset flow sensor, etc., and the operating data of some dimensions can be determined as the system operating data. In other embodiments, more other related sensors can also be used to obtain the operating data of all dimensions as the system operating data. In addition, the executor of abnormal data monitoring of a centralized air-conditioning and cooling system of the present application is a server, and the server is wirelessly connected to various sensors such as pressure sensors, flow sensors, and terminals. A client or applet related to data monitoring is installed in the terminal. The server is the background server of the client or applet, which can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. Furthermore, when personnel need to monitor the operating data of the centralized air-conditioning and cooling system and warn of abnormalities, a monitoring start instruction is sent through the client or applet in the terminal. After receiving the instruction, the server obtains system operating data of at least one dimension through the corresponding sensor.

[0032] S102: If the system operation data is not within the corresponding normal value range, the corresponding system operation data is determined as abnormal operation data, and based on at least one target fault type that is prone to occur in the central air-conditioning and cooling system and at least one corresponding target data abnormal dimension, it is determined whether to issue a warning for the abnormal operation data.

[0033] Specifically, after the system operation data is acquired, the system operation data of each dimension is compared with the normal value range of the corresponding dimension, wherein the normal value range is the value range of the operation data of the corresponding dimension when the centralized air conditioning and cooling system is operating normally. If the system operation data is not within the corresponding normal value range, it means that the system operation data of the corresponding dimension is abnormal, and then it is determined to be abnormal operation data. At this time, the centralized air conditioning and cooling system may have a fault that causes abnormal operation, or it may not have a fault but is only affected by normal load fluctuations. In order to improve the accuracy of the abnormal data warning, it is necessary to further verify whether there is a fault risk, so as to accurately determine whether to issue a warning. In an embodiment of the present application, a feasible implementation method is: based on the historical fault inspection and maintenance records of the centralized air conditioning and cooling system, the historical fault inspection and maintenance records include but are not limited to the historical fault types that need to be repaired and the corresponding historical operation data with abnormalities, determine the historical fault types that have appeared, and then count the first occurrence times of each historical fault type. The larger the first occurrence times, the more likely the centralized air conditioning and cooling system is to have the corresponding historical fault type. Then, according to the order of the first occurrence times from large to small, the historical fault type with the preset first number is selected from each historical fault type to be determined as the target fault type, that is, the fault type that is easy to occur.

[0034] Furthermore, based on the above-mentioned historical fault inspection and repair records, the first historical dimension of the historical operating data in which abnormalities exist when a single target fault type occurs in the central air-conditioning and cooling system is determined, and then the second occurrence number of each first historical dimension is counted, wherein the larger the second occurrence number, the more likely the operating data of the corresponding first historical dimension is to be abnormal when the target fault type occurs, and in descending order of the second occurrence number, the first historical dimension with the second number is selected from each first historical dimension to be determined as the target data abnormal dimension of the corresponding target fault type, that is, the dimension in which the operating data is more likely to be abnormal when the target fault type occurs.

[0035] Furthermore, the first weight of each target fault type and the second weight of each corresponding target data anomaly dimension are calculated, the first weight being the ratio of the first occurrence number of each target fault type to the sum of the first occurrence numbers of all target fault types, and the second weight being the ratio of the second occurrence number of a single target data anomaly dimension corresponding to the target fault type to the sum of the second occurrence numbers of all corresponding target data anomaly dimensions.

[0036] Finally, the abnormal operation data of each dimension, each first weight and the corresponding second weight are used to determine whether it is enough to issue an early warning for the abnormal operation data. A feasible determination method is: determine the corresponding target abnormal dimension through the attributes of the sensor collected by the abnormal operation data of each dimension. For example, if the abnormal operation data is collected by the temperature sensor, then the corresponding target abnormal dimension is the temperature dimension. Then, the target fault type with the target abnormal dimension in the corresponding target data abnormal dimension is determined as the key fault type, and the first product of the first weight of each key fault type and the second weight of the corresponding target abnormal dimension is calculated. The larger the first product, the greater the possibility of the corresponding key fault type appearing when the abnormal operation data of the target abnormal dimension appears. Then, sum up each first product to obtain the corresponding sum of the first products, and sum up each first product again to obtain the final sum of the products. The larger the final sum of the products, the greater the overall possibility of failure of the centralized air conditioning cooling system combined with the currently detected abnormal operation data. If the sum of the final products is greater than the preset threshold of the sum of the first products, it means that there is a high possibility that the abnormal operating data is caused by a system failure, and it is determined that an early warning will be issued for these abnormal operating data; conversely, if the sum of the final products is not greater than the threshold of the sum of the first products, it is determined that no early warning will be issued for the abnormal operating data, so as to better avoid misjudgment of the issued data.

[0037] S103: If yes, determine the system fault type corresponding to the abnormal operation data, and issue a targeted warning based on the system fault type.

[0038] Specifically, if it is determined to issue an early warning, then determine the system fault type that causes the abnormal operation data. A feasible determination method is: select the first products corresponding to the same target abnormal dimension from the first products corresponding to each key fault type, and select the maximum first product corresponding to this target abnormal dimension. The key fault type corresponding to the maximum first product is the fault type that is most likely to exist when the abnormal operation data of this single target abnormal dimension appears, that is, the hidden danger fault type. By analogy, the hidden danger fault types corresponding to multiple target abnormal dimensions can be determined. Furthermore, if the hidden danger fault types are the same, it means that when the abnormal operation data of each target abnormal dimension appears, the same fault type is more likely to appear, which further indicates that for this hidden danger fault type, the abnormal operation data of the target abnormal dimension corresponding to each maximum first product is the strongly correlated abnormal operation data when the fault occurs. Furthermore, the sum of each maximum first product is then calculated to obtain the sum of the second products. If the sum of the second products is greater than the threshold of the sum of the first products, it indicates that the possibility of a fault occurring when strongly associated abnormal operating data occurs is high, thereby not only further verifying that the centralized air-conditioning cooling system does have a fault, and the result of issuing the early warning determined in step S102 is correct, but also accurately determining the type of fault that has occurred, that is, taking the hidden danger fault type as the system fault type. Further, the target abnormal dimension corresponding to each maximum first product is determined as the associated dimension set of this hidden danger fault type, and a mapping relationship is established between this hidden danger fault type and this associated dimension set. This is convenient for subsequent personnel, once the operating data of each dimension in this associated dimension set are abnormal, it can be used to quickly determine that the system has a fault risk and accurately lock the corresponding fault type. Finally, the early warning information related to the system fault type is sent to the terminal of the maintenance personnel, thereby assisting the maintenance personnel to promptly and accurately solve the hidden faults that cause abnormal operating data.

[0039] See also Figure 2 , the embodiment of the present application discloses a flow chart of another method for monitoring abnormal data of a centralized air conditioning and cooling system, which can be implemented by a computer program and can also be run on a centralized air conditioning and cooling system abnormal data monitoring device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S201: Obtain system operation data of at least one dimension of a central air-conditioning cooling system.

[0040] S202: If the system operation data is not within the corresponding normal value range, the corresponding system operation data is determined as abnormal operation data, and based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormal dimension, it is determined whether to issue a warning for the abnormal operation data.

[0041] S203: If yes, determine the system fault type corresponding to the abnormal operation data.

[0042] For details, please refer to steps S101-103, which will not be described in detail here.

[0043] S204: Obtain the second historical dimension of abnormal operation data of the central air-conditioning cooling system under historical faults of the system fault type, count the first occurrence frequency of each second historical dimension, and select the third second historical dimension from each second historical dimension in descending order of the first occurrence frequency to determine it as a dimension prone to abnormality.

[0044] S205: Obtain the associated historical dimensions in which abnormal data fluctuations occur when an abnormal operation data of a single abnormality-prone dimension occurs, count the second occurrence frequency of each associated historical dimension, and select the associated historical dimension with the fourth number from each associated historical dimension in descending order of the second occurrence frequency to determine it as the easily fluctuating dimension of the corresponding abnormality-prone dimension.

[0045] Specifically, after determining the system fault type with a high probability of existing in the centralized air-conditioning and cooling system, based on the above-mentioned historical fault inspection and repair records, screen the second historical dimension in which the central air-conditioning and cooling system has abnormal operating data when the historical fault type is a system fault type, that is, when a historical fault of the system fault type occurs, and count the first occurrence frequency of the second historical dimension. The greater the first occurrence frequency, the more likely it is that the operating number of the corresponding second historical dimension will be abnormal when this system fault type occurs. Then, in order of the first occurrence frequency from large to small, select the second historical dimension with the third number from each second historical dimension, and determine it as a dimension prone to abnormality, that is, a dimension in which the operating data is more likely to be abnormal when a system fault type occurs.

[0046] Furthermore, under the premise of the existence of a system fault type, when the operating data of a single dimension prone to abnormality is abnormal, the abnormal historical time node is determined through the corresponding sensor, and then the operating data of the remaining dimensions in the centralized air-conditioning and cooling system at this historical time node and the operating data before this historical time node are obtained from the historical data collection records of other sensors, and then the corresponding historical operating data change curve is drawn through the MATLAB tool, and the historical operating data change curve is fitted with the corresponding normal operating data change curve. If the fitting rate is lower than the preset fitting rate threshold, it means that the operating data fluctuation of this remaining dimension is abnormal, and then the corresponding remaining dimension is determined as the associated historical dimension of abnormal data fluctuation. Further, the second occurrence frequency of each associated historical dimension is counted. The larger the second occurrence frequency, the more likely it is to cause abnormal fluctuation of the operating data of the corresponding associated historical dimension when abnormal operating data of the dimension prone to abnormality occurs. Finally, in the order of the second occurrence frequency from large to small, the associated historical dimension of the fourth number is selected from each associated historical dimension, and determined as the easily fluctuating dimension of the corresponding dimension prone to abnormality.

[0047] S206: Calculate the third weight of each dimension prone to abnormality and the corresponding fourth weight of each dimension prone to fluctuation.

[0048] S207: Obtain a first actual fluctuation dimension in which abnormal fluctuations occur in the operating data of the centralized air-conditioning cooling system, and perform a rationality check on the system fault type according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights.

[0049] Specifically, in the embodiment of the present application, the third weight is the ratio of the first occurrence frequency of each dimension prone to abnormality to the sum of the first occurrence frequencies of all dimensions prone to abnormality, and the fourth weight is the ratio of the second occurrence frequency of a single dimension prone to fluctuation corresponding to the dimension prone to abnormality to the sum of the second occurrence frequencies of all corresponding dimensions prone to fluctuation. Further, through the corresponding sensor, the operation data of the dimensions other than the abnormal operation data at the current time and before the current time are obtained, and then the operation data of each dimension are fitted through MATLAB to obtain the actual fluctuation curve, and fit it with the corresponding normal fluctuation curve. If the fitting rate does not exceed the preset fitting rate threshold, it means that the operation data fluctuation of the corresponding dimension is abnormal, and then the corresponding dimension is determined as the first actual fluctuation dimension.

[0050] Further, the easily abnormal dimension with the first actual fluctuation dimension in the corresponding easily fluctuating dimensions is determined as the key abnormal dimension, and the target abnormal dimension corresponding to each abnormal operation data is determined, and then the second product of the third weight of the key abnormal dimension in each target abnormal temperature and the fourth weight of the corresponding first actual fluctuation dimension is calculated. The larger the second product is, the more likely the centralized air conditioning and cooling system is to have a system fault type when the operation data fluctuation of the corresponding first actual fluctuation dimension and the operation data of the key abnormal dimension are abnormal. Then, the sum of each second product is obtained to obtain the sum of the third product. The larger the sum of the third product is, the greater the possibility of the system fault type in the centralized air conditioning and cooling system in combination with the current abnormal operation data and the first actual fluctuation dimension with abnormal fluctuation. Finally, if the sum of the third product is greater than the preset second product sum threshold, then it is determined that the system fault type rationality check has passed, and the current centralized air conditioning and cooling system is verified again to have a system fault type.

[0051] S208: After the rationality check is passed, a targeted warning is issued based on the system fault type.

[0052] Specifically, please refer to step S103, which will not be repeated here. In other embodiments, if there is no abnormal operation data in the system operation data of all dimensions obtained, then the second actual fluctuation dimension in which the operation data in the current centralized air-conditioning cooling system fluctuates abnormally is obtained. Then, the easily abnormal dimension with the second actual fluctuation dimension in the corresponding easily fluctuating dimensions is determined as an important abnormal dimension, and the third product of the third weight of each important abnormal dimension and the fourth weight of each corresponding second actual fluctuation dimension is calculated and summed to obtain the corresponding sum of the fourth products. The larger the sum of the fourth products, the greater the possibility of abnormal operation data in the corresponding important abnormal dimension, and the greater the risk of overall system failure type. Further, the maximum sum of the fourth products is selected from the sums of the fourth products. If the maximum sum of the fourth products is greater than the preset third product sum threshold, it means that the possibility of abnormal data in the important abnormal dimension corresponding to the maximum sum of the fourth products and the risk of overall system failure type are greater, then a fault risk reminder of the system failure type is issued to the terminal to remind personnel to go for timely maintenance. At the same time, it also shows that the operating data of the important abnormal dimension corresponding to the sum of the largest fourth products is abnormal but not identified, indicating that the normal value range of the important abnormal dimension corresponding to the sum of the largest fourth products is not reasonable enough, and it needs to be adjusted and optimized to make the normal value range corresponding to this important abnormal dimension more reasonable and accurate, so as to avoid the problem of missed judgment of subsequent abnormalities. In an embodiment of the present application, a feasible adjustment and optimization method is: receiving the target normal value range sent by the terminal, so that the current operating data of the important abnormal dimension corresponding to the sum of the largest fourth products is not within this target normal value range.

[0053] The implementation principle of a method for monitoring abnormal data of a centralized air conditioning and cooling system in an embodiment of the present application is as follows: if the system operation data is not within the corresponding normal value range, it means that the centralized air conditioning and cooling system currently has abnormal operation data, indicating that the centralized air conditioning and cooling system may have a fault that causes abnormal operation, or it may not have a fault but is only affected by normal load fluctuations. In order to improve the accuracy of abnormal data warning, based on the target fault types that are prone to occur in the centralized air conditioning and cooling system and the corresponding target data abnormal dimensions, combined with the currently occurring abnormal operation data, the risk of failure of the centralized air conditioning and cooling system is analyzed, and then it is accurately determined whether to issue a targeted warning. If it is determined to issue a warning, then a warning is issued based on the system fault type, thereby avoiding false triggering of the warning and improving the accuracy of abnormal data warnings.

[0054] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0055] See also Figure 3 , is a schematic diagram of the structure of the abnormal data monitoring device for the centralized air conditioning and cooling system provided in the embodiment of the present application. The abnormal data monitoring device for the centralized air conditioning and cooling system can be implemented as all or part of the device through software, hardware or a combination of both. The device includes a data acquisition module 11, an abnormal verification module 12 and an abnormal warning module 13.

[0056] A data acquisition module 11 is used to acquire system operation data of at least one dimension of a central air conditioning and cooling system; The abnormality verification module 12 is used to determine the corresponding system operation data as abnormal operation data if the system operation data is not within the corresponding normal value range, and determine whether to issue an early warning for the abnormal operation data based on at least one target fault type that is prone to occur in the centralized air-conditioning cooling system and at least one corresponding target data abnormal dimension, where the target data abnormal dimension is a dimension in which the operation data abnormality is prone to occur when the corresponding target fault type occurs; The abnormal warning module 13 is used to determine the system fault type corresponding to the abnormal operation data and issue a targeted warning based on the system fault type.

[0057] Optionally, the abnormality verification module 12 is specifically used for: Obtain the historical fault types that have occurred in the centralized air conditioning cooling system, count the first occurrence times of each historical fault type, and select the first historical fault type from each historical fault type in descending order of the first occurrence times to determine it as the target fault type; Obtain the first historical dimension where the running data anomaly occurs when each target fault type occurs, count the second occurrence times of each first historical dimension, and select the first historical dimension with the second number from each first historical dimension in descending order of the second occurrence times to determine it as the target data anomaly dimension of the corresponding target fault type; Calculate the first weight of each target fault type and the second weight of each corresponding target data anomaly dimension, the first weight being the ratio of the first number of occurrences of each target fault type to the sum of the first number of occurrences of all target fault types, and the second weight being the ratio of the second number of occurrences of a single target data anomaly dimension corresponding to the target fault type to the sum of the second number of occurrences of all corresponding target data anomaly dimensions; Whether to issue an early warning for the abnormal operating data is determined according to at least one abnormal operating data, the first weight and the corresponding second weights.

[0058] Optionally, the abnormality verification module 12 is specifically used for: Determine the target abnormal dimension corresponding to each abnormal operation data, and determine the target fault type that has the target abnormal dimension in each corresponding target data abnormal dimension as the key fault type; Calculate the first product of the first weight of each key fault type and the second weight of each corresponding target abnormal dimension and sum them up to obtain the corresponding sum of the first products; The sum of the first products is summed to obtain a final product sum. If the final product sum is greater than a preset first product sum threshold, it is determined that an early warning is issued for the abnormal operation data. If the final product sum is not greater than the first product sum threshold, it is determined that no warning is issued for the abnormal operation data.

[0059] Optionally, the abnormal warning module 13 is specifically used for: Select the maximum first product from the first products corresponding to the same target abnormal dimension; Determine the key fault type corresponding to each largest first product as a hidden danger fault type, and determine whether each hidden danger fault type is the same; If the hidden danger fault types are the same, then the maximum first products corresponding to the hidden danger fault types are summed to obtain the sum of the second products; If the sum of the second products is greater than a preset threshold of the sum of the first products, the hidden danger fault type is determined to be a system fault type corresponding to the abnormal operation data; The target abnormal dimension corresponding to each maximum first product is determined as a set of associated dimensions of the hidden danger fault type, and a mapping relationship is established between the hidden danger fault type and the associated dimension set.

[0060] Optional, such as Figure 4As shown, the device also includes a fault checking module 14, which is specifically used to: Obtain the second historical dimension of abnormal operation data of the central air-conditioning cooling system under the historical fault of the system fault type, count the first occurrence frequency of each second historical dimension, and select the second historical dimension with the third number from each second historical dimension in descending order of the first occurrence frequency to determine it as the dimension prone to abnormality; Obtain the associated historical dimensions where abnormal data fluctuation occurs when the running data of a single dimension prone to abnormality occurs, count the second occurrence frequency of each associated historical dimension, and select the associated historical dimension with the fourth number from each associated historical dimension in descending order of the second occurrence frequency to determine it as the easily fluctuating dimension of the corresponding dimension prone to abnormality; Calculate the third weight of each dimension prone to abnormality and the corresponding fourth weight of each dimension prone to fluctuation; The first actual fluctuation dimension in which abnormal fluctuations occur in the operating data of the centralized air-conditioning cooling system is obtained, and the rationality of the system fault type is checked according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights.

[0061] Optionally, the abnormal warning module 13 is further used for: After the rationality check is passed, targeted warnings are issued based on the system failure type.

[0062] Optionally, the fault checking module 14 is specifically used for: Determine the abnormal dimension with the first actual fluctuation dimension among the corresponding easily fluctuating dimensions as the key abnormal dimension, and determine the target abnormal dimension corresponding to each abnormal operation data; Calculate the second product of the third weight of the key abnormal dimension in each target abnormal dimension and the fourth weight of the corresponding first actual fluctuation dimension; The second products are summed to obtain the sum of the third products. If the sum of the third products is greater than a preset threshold of the sum of the second products, it is determined that the rationality check of the system fault type has passed.

[0063] Optional, such as Figure 4 As shown, the device also includes a range adjustment module 15, which is specifically used to: If there is no abnormal operation data in the central air-conditioning cooling system, obtaining a second actual fluctuation dimension in which the operation data in the central air-conditioning cooling system fluctuates abnormally; Determine the easily abnormal dimension with the second actual fluctuation dimension in the corresponding easily fluctuating dimensions as the important abnormal dimension, and calculate and sum the third product of the third weight of each important abnormal dimension and the fourth weight of the corresponding second actual fluctuation dimensions to obtain the corresponding sum of the fourth products; A maximum sum of the fourth products is selected from the sums of the fourth products, and if the maximum sum of the fourth products is greater than a preset third product sum threshold, a fault risk reminder of the system fault type is issued; The normal value range of the important abnormal dimension corresponding to the sum of the maximum fourth products is adjusted and optimized.

[0064] It should be noted that the above-mentioned embodiment provides a centralized air-conditioning and cooling system abnormal data monitoring device. When executing the centralized air-conditioning and cooling system abnormal data monitoring method, the division of the above-mentioned functional modules is only used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment is divided into different functional modules to complete all or part of the functions described above. In addition, the above-mentioned embodiment provides a centralized air-conditioning and cooling system abnormal data monitoring device and a centralized air-conditioning and cooling system abnormal data monitoring method embodiment belong to the same concept. The embodiment and implementation process thereof are detailed in the method embodiment, which will not be repeated here.

[0065] An embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, a method for monitoring abnormal data of a centralized air-conditioning cooling system of the above embodiment is adopted.

[0066] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0067] Among them, through this computer-readable storage medium, a centralized air-conditioning cooling system abnormal data monitoring method of the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0068] An embodiment of the present application also discloses an electronic device, in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, the above-mentioned method for monitoring abnormal data of a centralized air-conditioning cooling system is adopted.

[0069] The electronic device may be a desktop computer, a laptop computer, a cloud server or other electronic device, and the electronic device includes but is not limited to a processor and a memory. For example, the electronic device may also include input and output devices, a network access device, and a bus.

[0070] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0071] Among them, the memory can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device, or it can be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the electronic device. Moreover, the memory can also be a combination of an internal storage unit and an external storage device of the electronic device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0072] Among them, through this electronic device, a centralized air-conditioning cooling system abnormal data monitoring method of the above embodiment is stored in the memory of the electronic device, and is loaded and executed on the processor of the electronic device for easy use.

[0073] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for monitoring abnormal data of a centralized air conditioning cooling system, characterized in that: The method comprises: Obtain system operation data of at least one dimension of a central air conditioning and cooling system; If the system operation data is not within the corresponding normal value range, the corresponding system operation data is determined as abnormal operation data, and based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormal dimension, it is determined whether to issue an early warning for the abnormal operation data, wherein the target data abnormal dimension is a dimension in which operation data abnormality is prone to occur when the corresponding target fault type occurs; If so, the system fault type corresponding to the abnormal operation data is determined, and a targeted warning is issued based on the system fault type.

2. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 1, characterized in that: The determining whether to issue an early warning for the abnormal operation data based on at least one target fault type that is prone to occur in the centralized air conditioning and cooling system and at least one corresponding target data abnormal dimension specifically includes: Obtaining historical fault types that have occurred in the centralized air conditioning and cooling system, counting the first occurrence times of each of the historical fault types, and selecting the first number of historical fault types from each of the historical fault types in descending order of the first occurrence times as the target fault type; Obtaining a first historical dimension in which operation data anomalies occur when each of the target fault types occurs, counting the second occurrence times of each of the first historical dimensions, and selecting a first historical dimension with a second number from each of the first historical dimensions in descending order of the second occurrence times to determine as a target data anomaly dimension for the corresponding target fault type; Calculate a first weight of each of the target fault types and a second weight of each of the corresponding target data anomaly dimensions, wherein the first weight is a ratio of a first occurrence number of each target fault type to a sum of first occurrence numbers of all target fault types, and the second weight is a ratio of a second occurrence number of a single target data anomaly dimension corresponding to the target fault type to a sum of second occurrence numbers of all corresponding target data anomaly dimensions; It is determined whether to issue an early warning for the abnormal operation data according to at least one abnormal operation data, the first weight and the corresponding second weights.

3. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 1, characterized in that: The determining, according to at least one abnormal operation data, the first weight and the corresponding second weights, whether to issue an early warning for the abnormal operation data specifically includes: Determine the target abnormal dimension corresponding to each of the abnormal operation data, and determine the target fault type that has the target abnormal dimension in each corresponding target data abnormal dimension as the key fault type; Calculate the first product of the first weight of each of the key fault types and the second weight of each corresponding target abnormal dimension and sum them up to obtain the corresponding sum of the first products; Summing the sums of the first products to obtain a final product sum, and if the final product sum is greater than a preset first product sum threshold, determining to issue an early warning for the abnormal operation data; If the final sum of the products is not greater than the first product sum threshold, it is determined that no warning is issued for the abnormal operation data.

4. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 3, characterized in that: The determining the system fault type corresponding to the abnormal operation data specifically includes: Selecting the maximum first product from the first products corresponding to the same target abnormal dimension; Determine the key fault type corresponding to each of the maximum first products as a hidden danger fault type, and determine whether each of the hidden danger fault types is the same; If the hidden danger fault types are all the same, summing up the maximum first products corresponding to the hidden danger fault types to obtain the sum of the second products; If the sum of the second products is greater than a preset threshold of the sum of the first products, the hidden danger fault type is determined as the system fault type corresponding to the abnormal operation data; The target abnormal dimension corresponding to each of the maximum first products is determined as a set of associated dimensions of the hidden danger fault type, and a mapping relationship is established between the hidden danger fault type and the set of associated dimensions.

5. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 1, characterized in that: The method further comprises: Obtaining a second historical dimension in which the central air-conditioning cooling system has abnormal operation data under a historical fault of the system fault type, counting the first occurrence frequency of each of the second historical dimensions, and selecting the second historical dimension with the third number from each of the second historical dimensions in descending order of the first occurrence frequency to determine as a dimension prone to abnormality; Obtaining the associated historical dimension in which abnormal data fluctuation occurs when abnormal operation data of a single dimension prone to abnormality occurs, counting the second occurrence frequency of each of the associated historical dimensions, and selecting the associated historical dimension with the fourth number from each of the associated historical dimensions in descending order of the second occurrence frequency to determine as the easily fluctuating dimension of the corresponding dimension prone to abnormality; Calculating the third weight of each of the dimensions prone to abnormality and the corresponding fourth weight of each of the dimensions prone to fluctuation; Acquire a first actual fluctuation dimension in which abnormal fluctuations occur in the operation data of the centralized air-conditioning cooling system, and perform a rationality check on the system fault type according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights; The issuing of a targeted warning based on the system fault type specifically includes: After the rationality check is passed, a targeted warning is issued based on the system fault type.

6. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 5, characterized in that: The performing rationality check on the system fault type according to the first actual fluctuation dimension, the third weight and the corresponding fourth weights specifically includes: Determine the abnormal dimension in which the first actual fluctuation dimension exists among the corresponding easily fluctuating dimensions as the key abnormal dimension, and determine the target abnormal dimension corresponding to each abnormal operation data; Calculate the second product of the third weight of the key abnormal dimension in each of the target abnormal dimensions and the fourth weight of the corresponding first actual fluctuation dimension; The second products are summed to obtain a sum of third products. If the sum of the third products is greater than a preset second product sum threshold, it is determined that the rationality check of the system fault type has passed.

7. The method for monitoring abnormal data of a centralized air conditioning cooling system according to claim 5, characterized in that: The method further comprises: If there is no abnormal operation data in the centralized air-conditioning and cooling system, obtaining a second actual fluctuation dimension in which the operation data in the centralized air-conditioning and cooling system fluctuates abnormally; Determine the easily abnormal dimension in which the second actual fluctuation dimension exists in the corresponding easily abnormal dimensions as an important abnormal dimension, and calculate and sum the third products of the third weight of each important abnormal dimension and the fourth weight of each corresponding second actual fluctuation dimension to obtain the corresponding sum of the fourth products; Selecting a maximum sum of the fourth products from the sums of the fourth products, and issuing a fault risk reminder of the system fault type if the maximum sum of the fourth products is greater than a preset third product sum threshold; The normal value range of the important abnormal dimension corresponding to the sum of the maximum fourth products is adjusted and optimized.

8. A device for monitoring abnormal data of a centralized air conditioning cooling system, characterized in that: include: A data acquisition module (11) is used to acquire system operation data of at least one dimension of a centralized air conditioning and cooling system; an abnormality verification module (12), configured to determine the corresponding system operation data as abnormal operation data if the system operation data is not within the corresponding normal value range, and determine whether to issue an early warning for the abnormal operation data based on at least one target fault type that is prone to occur in the centralized air-conditioning and cooling system and at least one corresponding target data abnormality dimension, wherein the target data abnormality dimension is a dimension in which the operation data abnormality is prone to occur when the corresponding target fault type occurs; The abnormal warning module (13) is used to determine the system fault type corresponding to the abnormal operation data and issue a targeted warning based on the system fault type.

9. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is adopted.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is adopted.

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