Diagnostic method and device for refrigerating and heating equipment of machine room and electronic equipment

Abstract

The invention relates to a diagnosis method and device for refrigerating and heating equipment of a machine room and electronic equipment. The method comprises the steps that a diagnosis scheme uploaded by a target object is obtained, the diagnosis scheme is a scheme edited by using a diagnosis scheme editor, the diagnosis scheme comprises a data cleaning rule, a data time range, diagnosis parameters, operation parameters and a diagnosis strategy, the diagnosis parameters are used for indicating operation data to be diagnosed, and the diagnosis strategy is used for indicating the operation data to be diagnosed; the operation parameter is used for indicating operation executed on the diagnosis parameter; acquiring operation data of the refrigerating and heating equipment from the cloud platform, wherein the operation data of the refrigerating and heating equipment is data uploaded to the cloud platform by the refrigerating and heating equipment; cleaning the operation data in the data time range according to the data cleaning rule, operating data corresponding to the diagnosis parameters in the cleaned operation data according to the operation parameters, and diagnosing an operation result according to the diagnosis strategy to obtain a diagnosis result. The technical problem that the personal monitoring requirement of a user cannot be met is solved.

Description

Technical Field

[0001] The present application relates to the field of diagnosis of cooling and heating equipment, and in particular to a diagnosis method, device and electronic equipment for cooling and heating equipment in a computer room. Background Art

[0002] In the prior art, cooling and heating equipment (mostly with cooling function) is often installed in the computer room to ensure that the temperature in the computer room is suitable for the operation of the machines in the computer room. The status monitoring of the cooling and heating equipment in the computer room can be achieved through the cloud platform. However, in the prior art, the monitoring items of the cloud platform are usually provided by the cloud platform, which cannot meet the personal monitoring needs of users. Summary of the invention

[0003] The present application provides a diagnostic method, device, storage medium and electronic device for cooling and heating equipment in a computer room to solve the technical problem that monitoring items set on a cloud platform cannot meet the personal monitoring needs of users.

[0004] In a first aspect, the present application provides a method for diagnosing cooling and heating equipment in a computer room, comprising: obtaining a diagnostic scheme uploaded by a target object, wherein the diagnostic scheme is a scheme edited using a diagnostic scheme editor, and the diagnostic scheme includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, the diagnostic parameters are used to indicate operating data to be diagnosed, and the operation parameters are used to indicate operations performed on the diagnostic parameters; obtaining operating data of the cooling and heating equipment from a cloud platform, wherein the operating data of the cooling and heating equipment is data uploaded to the cloud platform by the cooling and heating equipment; cleaning the operating data within the data time range according to the data cleaning rules, performing operations on the data corresponding to the diagnostic parameters in the cleaned operating data according to the operation parameters, and diagnosing the operation results according to the diagnostic strategy to obtain diagnostic results.

[0005] In a second aspect, the present application provides a diagnostic device for cooling and heating equipment in a computer room, comprising: a first acquisition module, used to acquire a diagnostic scheme uploaded by a target object, wherein the diagnostic scheme is a scheme edited using a diagnostic scheme editor, and the diagnostic scheme includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters; a second acquisition module, used to acquire the operating data of the cooling and heating equipment from a cloud platform, wherein the operating data of the cooling and heating equipment is the data uploaded to the cloud platform by the cooling and heating equipment; a processing module, used to clean the operating data within the data time range according to the data cleaning rules, perform operations on the data corresponding to the diagnostic parameters in the cleaned operating data according to the operation parameters, and diagnose the operation results according to the diagnostic strategy to obtain the diagnostic results.

[0006] As an optional example, the above-mentioned data cleaning rules include at least one of abnormal data point cleaning, abnormal peak cleaning, and predetermined stage data deletion. The above-mentioned processing module includes: a first processing unit, which is used to determine the data in the above-mentioned operating data that is within a specified range when the above-mentioned data cleaning rules include abnormal data point cleaning, and delete the data within the above-mentioned specified range; when the above-mentioned data cleaning rules include abnormal peak cleaning, delete the data in the above-mentioned operating data whose deviation from the mean within a predetermined time window exceeds a preset deviation; when the above-mentioned data cleaning rules include predetermined stage data deletion, delete the data generated in the above-mentioned operating data within a predetermined time period.

[0007] As an optional example, the above-mentioned operation parameter is a mathematical operator, and the above-mentioned processing module includes: a second processing unit, used to obtain the relative position of the above-mentioned diagnostic parameter and the above-mentioned operation parameter in the above-mentioned diagnostic scheme editor; and use the above-mentioned operation parameter to operate the above-mentioned diagnostic parameter according to the above-mentioned relative position.

[0008] As an optional example, the above-mentioned diagnostic strategy includes a diagnostic threshold, and the above-mentioned processing module includes: a third processing unit, used to compare the above-mentioned operation result with the above-mentioned diagnostic threshold to obtain a comparison result of each comparison; and determine whether the above-mentioned diagnostic result is normal or abnormal based on the above-mentioned comparison result.

[0009] As an optional example, the above-mentioned diagnostic strategy also includes diagnostic conditions, and the above-mentioned third processing unit includes: a splicing subunit, used to obtain the above-mentioned diagnostic conditions, wherein each of the above-mentioned diagnostic conditions is a judgment item; according to the logical relationship between the above-mentioned diagnostic conditions, the above-mentioned diagnostic conditions are spliced ​​to obtain the final diagnostic conditions; when the above-mentioned comparison result meets the above-mentioned final diagnostic condition, the above-mentioned diagnostic result is determined to be normal, and when the above-mentioned comparison result does not meet the above-mentioned final diagnostic condition, the above-mentioned diagnostic result is determined to be abnormal.

[0010] As an optional example, the above-mentioned device also includes: a storage module, which is used to store the first diagnostic scheme uploaded by the first object in the above-mentioned cloud platform before obtaining the diagnostic scheme uploaded by the target object, and use the above-mentioned first diagnostic scheme as a sample diagnostic scheme; or, preset a template diagnostic scheme in the above-mentioned cloud platform, and use the above-mentioned template diagnostic scheme as a sample diagnostic scheme.

[0011] As an optional example, the second acquisition module includes: an acquisition unit for acquiring operating data within the data time range from a cloud platform; when the current time point is earlier than the end time point of the data time range, waiting for the refrigeration and heating equipment to upload the operating data within the data time range.

[0012] In a third aspect, the present application provides an electronic device comprising: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus, wherein the memory stores a computer program, and the processor is configured to implement any one of the above-mentioned diagnostic methods for the cooling and heating equipment in the computer room when executing the computer program.

[0013] In a fourth aspect, the present application further provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the diagnostic method for cooling and heating equipment in a computer room according to any one of the above items of the present application.

[0014] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: the solution provided by the embodiment of the present application, by obtaining the diagnostic solution uploaded by the target object, wherein the above diagnostic solution is a solution edited using a diagnostic solution editor, and the above diagnostic solution includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, wherein the above diagnostic parameters are used to indicate the operating data to be diagnosed, and the above operation parameters are used to indicate the operation performed on the above diagnostic parameters; the operating data of the above refrigeration and heating equipment is obtained from the cloud platform, wherein the operating data of the above refrigeration and heating equipment is the data uploaded to the above cloud platform by the above refrigeration and heating equipment; the operating data within the above data time range is cleaned according to the above data cleaning rules, the data corresponding to the above diagnostic parameters in the cleaned operating data are operated according to the above operation parameters, and the operation results are diagnosed according to the above diagnostic strategy to obtain the diagnostic results. Therefore, the user can customize the diagnostic strategy for the operating data of the refrigeration and heating equipment to obtain the corresponding diagnostic results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0018] Figure 1 A flow chart of a method for diagnosing cooling and heating equipment in a computer room provided in an embodiment of the present application;

[0019] Figure 2 A flow chart of another method for diagnosing cooling and heating equipment in a computer room provided in an embodiment of the present application;

[0020] Figure 3 A flowchart of a method for diagnosing cooling and heating equipment in a computer room provided in another embodiment of the present application;

[0021] Figure 4 A flowchart of a method for diagnosing cooling and heating equipment in a computer room provided in another embodiment of the present application;

[0022] Figure 5 A schematic diagram of a diagnostic system for cooling and heating equipment in a computer room provided in an embodiment of the present application;

[0023] Figure 6 A schematic diagram of the structure of a diagnostic device for cooling and heating equipment in a computer room provided in an embodiment of the present application;

[0024] Figure 7 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0026] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0027] In order to solve the technical problem in the prior art that the monitoring projects set on the cloud platform cannot meet the personal monitoring needs of users, the present application provides a diagnosis method for the cooling and heating equipment in a computer room, which can enable users to customize the diagnosis strategy for the operating data of the cooling and heating equipment and obtain the corresponding diagnosis results.

[0028] Figure 1 This is a flow chart of a method for diagnosing cooling and heating equipment in a computer room provided in an embodiment of the present application. Figure 1 As shown, the diagnostic method for the cooling and heating equipment in the above-mentioned computer room includes:

[0029] S102, obtaining a diagnosis plan uploaded by the target object, wherein the diagnosis plan is a plan edited using a diagnosis plan editor, and the diagnosis plan includes data cleaning rules, data time range, diagnosis parameters, operation parameters, and diagnosis strategies, wherein the diagnosis parameters are used to indicate the operation data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnosis parameters;

[0030] S104, obtaining the operating data of the refrigeration and heating equipment from the cloud platform, wherein the operating data of the refrigeration and heating equipment is the data uploaded by the refrigeration and heating equipment to the cloud platform;

[0031] S106, cleaning the operating data within the data time range according to the data cleaning rules, computing the data corresponding to the diagnostic parameters in the cleaned operating data according to the computing parameters, and diagnosing the computing results according to the diagnostic strategy to obtain diagnostic results.

[0032] This embodiment can be applied in the process of monitoring the operation data of the cooling and heating equipment in the computer room. The cooling and heating equipment in the computer room can be an air conditioner. The operation data of the air conditioner will be uploaded to the cloud platform for storage. The cloud platform can provide storage services for operation data of different computer rooms of different users. Access rights can be set for the operation data of different users.

[0033] The target object mentioned above is a certain user, whose computer room operation data is stored on the cloud platform. The user can set a diagnosis plan, which can be edited through the diagnosis plan editor provided by the cloud platform client, web page or applet. The diagnosis plan provides parameters such as data cleaning rules, data time range, diagnosis parameters, operation parameters and diagnosis strategy.

[0034] The above-mentioned data cleaning rule parameters are used to clean the operating data, that is, to clean out the data that does not need to be monitored or obviously cannot be used as a reference, and then diagnose the remaining data. The above-mentioned data time range can be a provision for diagnosing data within a predetermined time period. The above-mentioned diagnostic parameters can be which operating data to be diagnosed, and the above-mentioned operation parameters can be before diagnosing the operating data, the operating data is calculated according to the operation parameters, and then the operating data after the calculation is diagnosed. The above-mentioned diagnostic strategy can be the basis for diagnosis, that is, under what circumstances can the data be determined to be normal, and under what circumstances can the data be determined to be abnormal.

[0035] After the user provides the above diagnostic plan, the cloud platform obtains the operating data of the user's refrigeration and heating equipment, and then cleans the operating data within the data time range according to the data cleaning rules in the diagnostic plan, calculates the data corresponding to the diagnostic parameters in the cleaned operating data according to the calculation parameters, and uses the diagnostic strategy for diagnosis.

[0036] For example, after the user sets the diagnosis plan, the operation parameters and data time range are set in the diagnosis plan to diagnose the air conditioning outlet temperature data of a certain month in the operation data. Then, the air conditioning outlet temperature data of that month can be cleaned through the data cleaning rules (or all the operation data of the user can be cleaned, and then the air conditioning outlet temperature data of that month can be selected). If the operation parameter is to normalize the air conditioning outlet temperature data of that month, the normalized air conditioning temperature data is diagnosed using the diagnosis strategy. For example, the diagnosis strategy is to check whether the data fluctuation of the normalized air conditioning temperature data exceeds the range. If it exceeds the range, the diagnosis result is abnormal. If it does not exceed the range, the diagnosis result is normal.

[0037] The solution provided in the embodiment of the present application is to obtain the diagnostic solution uploaded by the target object, wherein the diagnostic solution is a solution edited using a diagnostic solution editor, and the diagnostic solution includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, wherein the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters; obtain the operating data of the refrigeration and heating equipment from the cloud platform, wherein the operating data of the refrigeration and heating equipment is the data uploaded to the cloud platform by the refrigeration and heating equipment; clean the operating data within the data time range according to the data cleaning rules, and operate the data corresponding to the diagnostic parameters in the cleaned operating data according to the operation parameters, and diagnose the operation results according to the diagnostic strategy to obtain the diagnostic results. Therefore, the user can customize the diagnostic strategy for the operating data of the refrigeration and heating equipment to obtain the corresponding diagnostic results.

[0038] As an optional example, the above data cleaning rules include at least one rule of abnormal data point cleaning, abnormal peak cleaning, and predetermined stage data deletion, such as Figure 2 As shown, cleaning the operation data within the above data time range according to the above data cleaning rules includes:

[0039] S202, when the data cleaning rule includes cleaning of abnormal data points, determining data in the operating data that is within a specified range, and deleting the data in the specified range;

[0040] S204, when the data cleaning rule includes abnormal peak cleaning, deleting data in the operation data whose deviation from the mean value within a predetermined time window exceeds a preset deviation;

[0041] S206: When the data cleaning rule includes deleting data in a predetermined period, the data generated in the predetermined time period in the operating data is deleted.

[0042] In this embodiment, the data cleaning rules can be multiple, as an example, can include at least one of abnormal data point cleaning, abnormal peak cleaning, and predetermined stage data deletion. When cleaning the operating data within the data time range, if the data cleaning rule includes abnormal data point cleaning, it can be determined that the data within the specified range in the operating data is regarded as abnormal data points, and the data is cleaned. That is, the abnormal data points are removed. When the data is within the specified range, or when the data is equal to a certain set value, the data point is removed. If the data cleaning rule includes abnormal peak cleaning, the spike value can be removed. For example, when the time window and the standard deviation multiple are set, when a certain data in the time window deviates from the mean by more than several times the standard deviation, this data point is regarded as a spike value and is removed. Set the time window and the standard deviation threshold. When the standard deviation of the data in the window is lower than the threshold, the center point of the window is regarded as a stable point. If the data cleaning rule includes predetermined stage data deletion, the predetermined stage data can be deleted. For example, to remove the data during the start-up and shutdown stages: set the elimination time during the start-up and stop stages, and then delete the data with unstable operating conditions during the start-up and shutdown stages (only the parameters that directly represent the operation of the equipment, that is, the data is 0 when stopped and non-0 when running).

[0043] By cleaning the data, irrelevant data and data that obviously cannot be used as a basis for diagnosis are removed, and the remaining data is retained for diagnosis of cooling and heating equipment.

[0044] As an optional example, the above operation parameters are mathematical operators, such as Figure 3 As shown, the data corresponding to the diagnostic parameters in the cleaned operation data are calculated according to the calculation parameters, including:

[0045] S302, obtaining the relative positions of the diagnosis parameters and the operation parameters in the diagnosis scheme editor;

[0046] S304, calculating the diagnosis parameters using the calculation parameters according to the relative positions.

[0047] In this embodiment, the operation parameters can be used to perform operations on the cleaned operation data. When the user edits the scheme through the diagnostic scheme editor, the relative positions of the operation data and the operation parameters are included in the diagnostic scheme editor, and the relative positions are obtained, and the operation function is performed according to the relative positions. For example, the relative positions are operation data 1, operation parameter 1, operation data 2, operation parameter 2, and operation data 3, wherein operation parameter 2 is multiplication and operation parameter 1 is addition, then the operation performed is the result of multiplying operation data 2 by operation data 3 plus operation data 1. The operation can be a mathematical operation, such as addition, subtraction, multiplication, and division, and can also be a function formula, normalization, and the like.

[0048] As an optional example, the above diagnostic strategy includes a diagnostic threshold, such as Figure 4 As shown, the above-mentioned diagnosis strategy is used to diagnose the operation result, and the obtained diagnosis result includes:

[0049] S402, comparing the above calculation result with the above diagnosis threshold to obtain a comparison result of each comparison;

[0050] S404: Determine whether the diagnosis result is normal or abnormal based on the comparison result.

[0051] In this embodiment, the diagnostic strategy may also include a diagnostic frequency. The diagnostic frequency is used to define the time interval between one diagnosis and another. The above diagnostic threshold is used to define the value used for judgment in each diagnosis. For example, if the diagnostic frequency is once a day, then the diagnosis can be performed once every 24 hours according to the diagnostic frequency. In each diagnosis, the diagnostic threshold is used to compare with the above calculation result. If the calculation result does not meet the range of the diagnostic threshold, it can be regarded as an abnormal calculation result.

[0052] As an optional example, the above-mentioned diagnostic strategy also includes diagnostic conditions, and determining whether the above-mentioned diagnostic results are normal or abnormal according to the above-mentioned comparison results includes: obtaining the above-mentioned diagnostic conditions, wherein each of the above-mentioned diagnostic conditions is a judgment item; according to the logical relationship between the above-mentioned diagnostic conditions, splicing the above-mentioned diagnostic conditions to obtain the final diagnostic conditions; when the above-mentioned comparison results meet the above-mentioned final diagnostic conditions, determining that the above-mentioned diagnostic results are normal, and when the above-mentioned comparison results do not meet the above-mentioned final diagnostic conditions, determining that the above-mentioned diagnostic results are abnormal.

[0053] In this embodiment, the diagnostic strategy includes diagnostic conditions. When determining whether the diagnostic result is normal based on the comparison result, the logical relationship between the diagnostic conditions can be obtained, and the diagnostic conditions can be spliced ​​according to the logical relationship. For example, the diagnostic conditions include 3 items. If the 3 diagnostic conditions are in an and logical relationship, then when judging, the judgment result must meet all 3 diagnostic conditions to be considered normal. If the 3 diagnostic conditions are in an or relationship, then as long as one diagnostic condition is met, the diagnostic result can be considered normal.

[0054] As an optional example, before obtaining the diagnostic plan uploaded by the target object, the above method also includes: storing the first diagnostic plan uploaded by the first object in the above cloud platform, and using the above first diagnostic plan as a sample diagnostic plan; or, presetting a template diagnostic plan in the above cloud platform, and using the above template diagnostic plan as a sample diagnostic plan.

[0055] In this embodiment, a diagnostic plan can be preset on the cloud platform as a sample diagnostic plan, and a diagnostic plan uploaded by any user can be collected as a sample diagnostic plan. When any user edits a plan through a diagnostic plan editor, the sample diagnostic plan can be called to generate a new diagnostic plan by using the sample diagnostic plan or adjusting the parameters in the sample diagnostic plan.

[0056] In this embodiment, the diagnosis solutions can be pushed to each other for use by users who have the same or similar models and sizes of cooling and heating equipment.

[0057] As an optional example, the above-mentioned obtaining the operating data of the above-mentioned refrigeration and heating equipment from the cloud platform includes: obtaining the operating data within the above-mentioned data time range from the cloud platform; when the current time point is earlier than the end time point of the above-mentioned data time range, waiting for the above-mentioned refrigeration and heating equipment to upload the operating data within the above-mentioned data time range.

[0058] In this embodiment, the user can diagnose data that has not yet been generated. For data that has not yet been generated, for example, if the user sets the diagnosis of data from 20 to 26 on the 23rd, the cloud platform can determine whether the diagnostic strategy of the operating data to be diagnosed in the diagnostic scheme is coherent after receiving the diagnostic scheme. If the diagnostic strategy requires that the data does not need to be coherent, the operating data from 20 to 23 can be diagnosed first, and then the data from 23 to 26 can be diagnosed after it is generated. If the diagnostic strategy requires that the data needs to be coherent, it is necessary to wait until the data from 26 is generated before diagnosing the data from 20 to 26.

[0059] In order to solve the problem that different users have different diagnostic requirements for cooling and heating equipment when diagnosing cooling and heating equipment in the computer room, this embodiment proposes a cloud diagnostic platform architecture for central air-conditioning systems, which integrates cloud-based engineering data, customized engineering diagnostic solutions, AI analysis of user diagnostic preferences, and intelligent recommendation of shared diagnostic projects. This architecture provides a custom diagnostic solution creation tool, which automatically identifies the user's diagnostic behavior and preferences by uploading engineering data to the cloud and combining it with AI analysis, generates highly recognized shared diagnostic solutions and recommends them to users, and provides users with intelligent diagnostic decision support, thereby improving operation and maintenance efficiency, reducing energy consumption in computer rooms, and reducing equipment failure rates.

[0060] Figure 5 This is the central air conditioning system cloud diagnosis platform architecture of this embodiment, which mainly includes the following contents:

[0061] (1) The operating data of on-site equipment (refrigeration and heating equipment) (including chillers, refrigeration water pumps, cooling water pumps, cooling towers, sensors, instruments, valves, etc.) is transmitted to cloud storage through controllers and wireless terminal devices (Data Transfer Unit, DTU). The user who owns the project has a series of personal diagnostic projects in the central air-conditioning system intelligent cloud diagnostic platform. Based on these personal diagnostic projects, the project data can be diagnosed in real time and the diagnostic results and operation and maintenance suggestions can be obtained.

[0062] (2) The user's personal diagnostic items can be obtained through the following channels:

[0063] ① Based on the diagnostic scheme template preset by the platform, users can fill in custom logic through the diagnostic scheme editor to obtain new user personal diagnostic items;

[0064] ② The user uses the diagnosis plan editor to edit a new diagnosis plan and obtain a new user personal diagnosis project;

[0065] ③ The user obtains the diagnostic solution from the shared diagnostic solution library and directly adopts it as the user's personal diagnostic project;

[0066] ④ The user obtains the diagnostic scheme from the shared diagnostic scheme library, uses the diagnostic scheme editor to modify the scheme, and obtains a new user personal diagnostic project;

[0067] (3) The personal diagnostic components created by all platform users through various channels will be input into the user preference analysis AI model in the cloud. The model aggregates the diagnostic behaviors and preferences of all users, identifies high-frequency diagnostic solutions, and generates a shared diagnostic solution library, which is recommended to users on each platform.

[0068] When using the Diagnosis Scheme Editor to edit a diagnosis scheme, you can customize data cleaning rules, select data time ranges, select parameters or customize parameters, set description quantities, and build a diagnosis scheme. The details are as follows:

[0069] (1) Custom data cleaning rules

[0070] One or more data cleaning rules can be set. For a data cleaning rule, the user needs to select a parameter from the project data and give the cleaning condition. The cleaning conditions are as follows.

[0071] Eliminate abnormal data points: When the data is within the specified range, or when the data is equal to a certain set value, the data point is eliminated;

[0072] Eliminate spikes: Set the time window and standard deviation multiple. When a data point in the window deviates from the mean by more than several times the standard deviation, this data point is considered as a spike and is eliminated.

[0073] Stable point screening: Set the time window and standard deviation threshold. When the standard deviation of the data in the window is lower than the threshold, the center point of the window is considered a stable point.

[0074] Remove the start-up and shutdown stages: Set the start-up and stop-stage exclusion durations to delete data with unstable operating conditions during the start-up and shutdown stages (only parameters that directly represent the operation of the equipment, that is, the data is 0 when stopped and non-0 when running)

[0075] (2) Select parameters or customize parameters

[0076] Select parameters: select one or more parameters from the project data;

[0077] Custom parameters: Select one or more parameters from the project data, and use arithmetic operators (such as "+", "-", "×", "÷", "^", etc.) to calculate these parameters to obtain new custom parameters.

[0078] (3) Data time range selection

[0079] You can choose a recent time range, such as the last 24 hours, the last 7 natural days, etc.

[0080] Optional. Specifies the time range from the specified time to the present, for example, from January 1, 2024 to the present.

[0081] (4) Description quantity setting

[0082] Based on the parameters obtained in the previous step, select a custom time range, calculate the descriptive quantity for each parameter (such as average, standard deviation, maximum, minimum, start value, end value, range, percentile, rate of change, etc.), and name the descriptive quantity;

[0083] Based on the above description quantities, arithmetic operators can be used to perform operations to obtain new description quantities;

[0084] Based on the above description quantity, the description quantity calculation can be performed again;

[0085] Based on the above description quantities, new description quantities (correlation coefficients) can be calculated.

[0086] (5) Establishment of diagnostic scheme

[0087] Set the name of the diagnostic item: Customize the input diagnostic item name;

[0088] Set the diagnosis frequency: You can customize the input diagnosis interval, such as once every 10 minutes;

[0089] Set threshold: You can select the description quantity and enter the threshold corresponding to the description quantity. The number of thresholds is the same as the number of description quantities set in the previous step;

[0090] Set the output text of the diagnosis result: You can customize the text output by the platform when the diagnosis is a fault, and embed the calculated descriptive quantity results and thresholds into the preset text;

[0091] Splicing diagnostic logic: Use comparison operators to splice existing description quantities and thresholds to obtain several conditions; use logical operators to splice the above conditions to obtain the final diagnostic condition; when the condition is true, it is diagnosed as a fault.

[0092] For the above preset diagnosis scheme templates, the preset diagnosis scheme templates are part of the diagnosis scheme content set according to specific rules based on a certain type of diagnosis project, but do not specify the exact diagnosis data, description quantity, threshold, etc. The preset diagnosis scheme templates include trend attenuation diagnosis, multi-dimensional correlation diagnosis, data consistency diagnosis, etc.

[0093] The steps for using preset diagnosis scheme templates are as follows: the user selects a preset diagnosis scheme template, enters the diagnosis scheme editor, edits the preset diagnosis scheme according to the prompts, and generates a new user personal diagnosis project after completing all necessary logic editing.

[0094] Taking the trend decay diagnosis template as an example, some of the contents that have been set in the preset diagnosis scheme template include:

[0095] (1) Description quantity setting

[0096] Contains the prompt text "Create descriptive quantities of system / equipment performance parameter indicators at different time stages, calculate the correlation coefficient of the performance parameter indicator and the number of cycles, and calculate the rate of change of the performance parameter indicator";

[0097] By default, 14 descriptive quantities have been created, including the performance parameter index of the last 12 months, the correlation coefficient between the performance parameter index and the number of months, and the change rate of the performance parameter index.

[0098] (2) Establishment of diagnostic scheme

[0099] Contains the prompt text "When the attenuation coefficient is less than a certain value, the diagnostic device will have a performance attenuation problem";

[0100] Default diagnosis frequency: monthly;

[0101] The default thresholds are: correlation coefficient threshold is 0.7; change rate threshold is -0.2;

[0102] Default diagnostic logic: When the correlation coefficient threshold is ≥ 0.7 and the change rate is ≤ -0.2, it is diagnosed as a fault.

[0103] When the user uses the preset diagnosis scheme template, if the user needs to diagnose whether the Coefficient Of Performance (COP) of chiller No. 1 has performance degradation, the steps are as follows:

[0104] (1) Custom data cleaning rules

[0105] No data cleaning is required, so skip this step.

[0106] (2) Select parameters or customize parameters

[0107] Select parameters: cumulative cooling capacity of unit 1, cumulative power consumption of unit 1.

[0108] (3) Data time range selection

[0109] Select the last 12 calendar months.

[0110] (4) Description quantity setting

[0111] Create the starting and ending values ​​of the cooling capacity and power consumption in each month;

[0112] By calculating the above described quantities through arithmetic operators, the COP of chiller No. 1 in each month is obtained as follows:

[0113]

[0114] The template already contains the correlation coefficient and rate of change, so no setting is required.

[0115] (5) Establishment of diagnostic scheme

[0116] Enter the name of the diagnostic project "Unit 1 COP decay diagnosis";

[0117] Input the diagnostic result output text "The COP of Unit 1 is attenuated, and maintenance is recommended";

[0118] Other contents have been set in the template and do not need to be set.

[0119] Figure 6 This is a schematic diagram of the structure of a diagnostic device for cooling and heating equipment in a computer room provided in an embodiment of the present application. Figure 6 As shown, the diagnostic device for the cooling and heating equipment in the above-mentioned machine room includes:

[0120] The first acquisition module 602 is used to acquire the diagnostic scheme uploaded by the target object, wherein the diagnostic scheme is a scheme edited using a diagnostic scheme editor, and the diagnostic scheme includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, wherein the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters;

[0121] The second acquisition module 604 is used to acquire the operation data of the refrigeration and heating equipment from the cloud platform, wherein the operation data of the refrigeration and heating equipment is the data uploaded by the refrigeration and heating equipment to the cloud platform;

[0122] The processing module 606 is used to clean the operating data within the data time range according to the data cleaning rules, calculate the data corresponding to the diagnostic parameters in the cleaned operating data according to the calculation parameters, and diagnose the calculation results according to the diagnosis strategy to obtain the diagnosis results.

[0123] This embodiment can be applied in the process of monitoring the operation data of the cooling and heating equipment in the computer room. The cooling and heating equipment in the computer room can be an air conditioner. The operation data of the air conditioner will be uploaded to the cloud platform for storage. The cloud platform can provide storage services for operation data of different computer rooms of different users. Access rights can be set for the operation data of different users.

[0124] The target object mentioned above is a certain user, whose computer room operation data is stored on the cloud platform. The user can set a diagnosis plan, which can be edited through the diagnosis plan editor provided by the cloud platform client, web page or applet. The diagnosis plan provides parameters such as data cleaning rules, data time range, diagnosis parameters, operation parameters and diagnosis strategy.

[0125] The above-mentioned data cleaning rule parameters are used to clean the operating data, that is, to clean out the data that does not need to be monitored or obviously cannot be used as a reference, and then diagnose the remaining data. The above-mentioned data time range can be a provision for diagnosing data within a predetermined time period. The above-mentioned diagnostic parameters can be which operating data to be diagnosed, and the above-mentioned operation parameters can be before diagnosing the operating data, the operating data is calculated according to the operation parameters, and then the operating data after the calculation is diagnosed. The above-mentioned diagnostic strategy can be the basis for diagnosis, that is, under what circumstances can the data be determined to be normal, and under what circumstances can the data be determined to be abnormal.

[0126] After the user provides the above diagnostic plan, the cloud platform obtains the operating data of the user's refrigeration and heating equipment, and then cleans the operating data within the data time range according to the data cleaning rules in the diagnostic plan, calculates the data corresponding to the diagnostic parameters in the cleaned operating data according to the calculation parameters, and uses the diagnostic strategy for diagnosis.

[0127] For example, after the user sets the diagnosis plan, the operation parameters and data time range are set in the diagnosis plan to diagnose the air conditioning outlet temperature data of a certain month in the operation data. Then, the air conditioning outlet temperature data of that month can be cleaned through the data cleaning rules (or all the operation data of the user can be cleaned, and then the air conditioning outlet temperature data of that month can be selected). If the operation parameter is to normalize the air conditioning outlet temperature data of that month, the normalized air conditioning temperature data is diagnosed using the diagnosis strategy. For example, the diagnosis strategy is to check whether the data fluctuation of the normalized air conditioning temperature data exceeds the range. If it exceeds the range, the diagnosis result is abnormal. If it does not exceed the range, the diagnosis result is normal.

[0128] The solution provided in the embodiment of the present application is to obtain the diagnostic solution uploaded by the target object, wherein the diagnostic solution is a solution edited using a diagnostic solution editor, and the diagnostic solution includes data cleaning rules, data time range, diagnostic parameters, operation parameters and diagnostic strategies, wherein the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters; obtain the operating data of the refrigeration and heating equipment from the cloud platform, wherein the operating data of the refrigeration and heating equipment is the data uploaded to the cloud platform by the refrigeration and heating equipment; clean the operating data within the data time range according to the data cleaning rules, and operate the data corresponding to the diagnostic parameters in the cleaned operating data according to the operation parameters, and diagnose the operation results according to the diagnostic strategy to obtain the diagnostic results. Therefore, the user can customize the diagnostic strategy for the operating data of the refrigeration and heating equipment to obtain the corresponding diagnostic results.

[0129] For other examples of this embodiment, please refer to the above examples and will not be described in detail here.

[0130] like Figure 7 As shown, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0131] Memory 113, used for storing computer programs;

[0132] In one embodiment of the present application, the processor 111 is used to implement the diagnosis method of the cooling and heating equipment of the computer room provided by any one of the aforementioned method embodiments when executing the program stored in the memory 113.

[0133] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for diagnosing cooling and heating equipment in a computer room provided in any one of the aforementioned method embodiments is implemented.

[0134] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0136] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0137] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A diagnostic method for cooling and heating equipment in a machine room, characterized in that: include: Obtaining a diagnostic scheme uploaded by the target object, wherein the diagnostic scheme is a scheme edited using a diagnostic scheme editor, and the diagnostic scheme includes data cleaning rules, data time range, diagnostic parameters, operation parameters, and diagnostic strategies, wherein the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters; Acquire the operating data of the refrigeration and heating equipment from the cloud platform, wherein the operating data of the refrigeration and heating equipment is the data uploaded by the refrigeration and heating equipment to the cloud platform; The operating data within the data time range is cleaned according to the data cleaning rule, the data corresponding to the diagnostic parameters in the cleaned operating data are calculated according to the calculation parameters, and the calculation results are diagnosed according to the diagnostic strategy to obtain a diagnostic result.

2. The method according to claim 1, characterized in that The data cleaning rule includes at least one of abnormal data point cleaning, abnormal peak cleaning, and predetermined stage data deletion. Cleaning the operating data within the data time range according to the data cleaning rule includes: When the data cleaning rule includes cleaning of abnormal data points, determining data in the operating data that is within a specified range, and deleting the data within the specified range; When the data cleaning rule includes abnormal peak cleaning, data in the operation data whose deviation from the mean value within a predetermined time window exceeds a preset deviation is deleted; When the data cleaning rule includes deleting data in a predetermined period, the data generated in the predetermined time period in the operating data is deleted.

3. The method according to claim 1, characterized in that The operation parameter is a mathematical operator, and the operation of the data corresponding to the diagnostic parameter in the cleaned operation data according to the operation parameter includes: Obtaining the relative position of the diagnostic parameter and the operation parameter in the diagnostic scheme editor; The diagnostic parameter is operated using the operation parameter according to the relative position.

4. The method according to claim 1, characterized in that The diagnostic strategy includes a diagnostic threshold, and the diagnostic result obtained by diagnosing the operation result according to the diagnostic strategy includes: Comparing the operation result with the diagnosis threshold to obtain a comparison result of each comparison; The diagnosis result is determined to be normal or abnormal according to the comparison result.

5. The method according to claim 4, characterized in that The diagnostic strategy also includes diagnostic conditions, and determining whether the diagnostic result is normal or abnormal based on the comparison result includes: Acquire the diagnostic conditions, wherein each of the diagnostic conditions is a judgment item; According to the logical relationship between the diagnostic conditions, the diagnostic conditions are spliced ​​to obtain the final diagnostic condition; When the comparison result meets the final diagnosis condition, the diagnosis result is determined to be normal, and when the comparison result does not meet the final diagnosis condition, the diagnosis result is determined to be abnormal.

6. The method according to claim 1, characterized in that Before obtaining the diagnostic solution uploaded by the target object, the method further includes: The cloud platform stores a first diagnostic solution uploaded by a first subject, and uses the first diagnostic solution as a sample diagnostic solution; or, A template diagnosis scheme is preset in the cloud platform, and the template diagnosis scheme is used as a sample diagnosis scheme.

7. The method according to claim 1, characterized in that The obtaining of the operation data of the refrigeration and heating equipment from the cloud platform includes: Obtaining the operating data within the data time range from the cloud platform; When the current time point is earlier than the end time point of the data time range, the cooling and heating device is waited to upload the operation data within the data time range.

8. A diagnostic device for cooling and heating equipment in a machine room, characterized in that: include: A first acquisition module is used to acquire a diagnostic scheme uploaded by a target object, wherein the diagnostic scheme is a scheme edited using a diagnostic scheme editor, and the diagnostic scheme includes data cleaning rules, data time range, diagnostic parameters, operation parameters, and diagnostic strategies, wherein the diagnostic parameters are used to indicate the operating data to be diagnosed, and the operation parameters are used to indicate the operation performed on the diagnostic parameters; A second acquisition module is used to acquire the operation data of the refrigeration and heating equipment from the cloud platform, wherein the operation data of the refrigeration and heating equipment is the data uploaded by the refrigeration and heating equipment to the cloud platform; A processing module is used to clean the operating data within the data time range according to the data cleaning rules, calculate the data corresponding to the diagnostic parameters in the cleaned operating data according to the calculation parameters, and diagnose the calculation results according to the diagnostic strategy to obtain a diagnostic result.

9. An electronic device, characterized in that: include: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor coupled to the at least one bus; At least one memory connected to the at least one bus, wherein the memory stores a computer program, and when the processor executes the computer program, the diagnostic method for the cooling and heating equipment of the computer room described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-executable instructions for executing the method for diagnosing cooling and heating equipment in a computer room according to any one of claims 1 to 7 of the present application.

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