Public agency evaluation method and system based on massive data prediction

Abstract

The application discloses a public institution evaluation method and system based on mass data prediction, and the method comprises the following steps: acquiring project data of multiple energy media of a target public institution; determining the corresponding project correlation between any two energy media according to a preset project correlation rule; determining the energy-saving evaluation data corresponding to each energy medium according to an operation result prediction algorithm and the project data; and calculating the energy-saving effect parameter corresponding to the target public institution according to the project correlation and the energy-saving evaluation data corresponding to each energy medium. It can be seen that the application can realize more efficient and accurate energy-saving effect parameter evaluation of a public institution based on a large amount of project historical data, so that the energy-saving potential of the public institution can be effectively tapped, energy waste can be reduced, and more energy-saving and efficient public institution energy-saving and carbon reduction services can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a public institution evaluation method and system based on massive data prediction. BACKGROUND

[0002] As government agencies, educational institutions, medical institutions and other organizations, public institutions bear important social responsibilities and public service tasks, so the demand for evaluation of their energy-saving potential is also increasing. Effective evaluation of the energy-saving potential of public institutions can help to save energy and reduce carbon emissions, reduce waste of energy, and better serve the public. However, most of the existing energy-saving potential evaluation of public institutions still uses rules set by humans and manual review and evaluation of specific projects, without using data prediction algorithms to evaluate more efficiently based on project-related historical data. Therefore, the evaluation efficiency is low, the labor cost is high, and the evaluation accuracy is also lacking. Therefore, the existing technology has defects and needs to be solved. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a public institution evaluation method and system based on massive data prediction, which can more efficiently and accurately evaluate the energy-saving effect parameters of public institutions based on a large amount of project historical data, effectively tap the energy-saving potential of public institutions, reduce energy waste, and achieve more energy-efficient public institution energy-saving and carbon-reducing services.

[0004] To solve the above technical problems, the first aspect of the present application discloses a public institution evaluation method based on massive data prediction, which comprises:

[0005] Obtaining project data of a plurality of energy media of a target public institution;

[0006] According to the preset project correlation rule, the project correlation relationship between any two energy media is determined;

[0007] According to the operation result prediction algorithm and the project data, the energy-saving evaluation data corresponding to each energy medium is determined;

[0008] According to the project correlation relationship and the energy-saving evaluation data corresponding to each energy medium, the energy-saving effect parameters corresponding to the target public institution are calculated.

[0009] As an optional implementation, in the first aspect of the present application, the project data includes meteorological data, operation data, maintenance data and equipment parameters.

[0010] As an optional implementation, in the first aspect of the present application, the determining of the corresponding project correlation between any two of the energy media according to the preset project correlation rule comprises:

[0011] calculating a correlation degree between the project data of the two energy media;

[0012] calculating an intersection degree between the project development time corresponding to the two energy media;

[0013] calculating the product of the correlation degree and the intersection degree to obtain the project correlation between the two energy media.

[0014] As an optional implementation, in the first aspect of the present application, the calculating of the correlation degree between the project data of the two energy media comprises:

[0015] calculating a first similarity between the meteorological data of the two energy media;

[0016] calculating a second similarity between the operation data of the two energy media;

[0017] calculating a third similarity between the maintenance data of the two energy media;

[0018] calculating a fourth similarity between the equipment self parameters of the two energy media;

[0019] calculating the weighted sum average of the first similarity, the second similarity, the third similarity and the fourth similarity to obtain the correlation degree between the two energy media.

[0020] As an optional implementation, in the first aspect of the present application, the determining of the corresponding energy-saving evaluation data of each energy medium according to the operation result prediction algorithm and the project data comprises:

[0021] based on the project type of the energy medium, determining the meteorological influence result prediction model, the operation influence result prediction model, the maintenance influence result prediction model and the equipment self influence result prediction model corresponding to the project type in the preset model library;

[0022] inputting the meteorological data into the meteorological influence result prediction model to obtain the corresponding meteorological evaluation parameter; the meteorological influence result prediction model is trained by a training data set comprising a plurality of training meteorological data and corresponding energy consumption influence labels;

[0023] inputting the operation data into the operation influence result prediction model to obtain corresponding operation evaluation parameters; the operation influence result prediction model is obtained by training a training data set including a plurality of training operation data and corresponding energy consumption influence labels;

[0024] inputting the maintenance data into the maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is obtained by training a training data set including a plurality of training maintenance data and corresponding energy consumption influence labels;

[0025] inputting the equipment parameter into the equipment influence result prediction model to obtain corresponding equipment evaluation parameters; the equipment influence result prediction model is obtained by training a training data set including a plurality of training equipment parameters and corresponding energy consumption influence labels;

[0026] calculating a weighted sum average of the weather evaluation parameters, the operation evaluation parameters, the maintenance evaluation parameters and the equipment evaluation parameters to obtain the energy-saving evaluation data corresponding to the energy medium.

[0027] As an optional implementation, in the first aspect of the present application, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the weather evaluation parameters is proportional to the proportion of weather abnormal data in the weather data, the weighted calculation weight corresponding to the operation evaluation parameters is proportional to the proportion of equipment fault records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameters is proportional to the average value of the maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment evaluation parameters is proportional to the equipment performance value corresponding to the equipment evaluation parameters.

[0028] As an optional implementation, in the first aspect of the present application, the calculation of the energy-saving effect parameter corresponding to the target public institution according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data comprises:

[0029] for any two energy media, calculating a data average value of the energy-saving evaluation data of the two energy media;

[0030] calculating a relationship weight proportional to the project association relationship corresponding to the two energy media;

[0031] calculating the product of the data average value and the relationship weight to obtain a project association energy-saving parameter corresponding to the two energy media;

[0032] According to all the project-related energy-saving parameters corresponding to the energy media, the energy-saving effect parameter corresponding to the target public institution is calculated.

[0033] As an optional implementation, in the first aspect of the present application, the calculation of the energy-saving effect parameter corresponding to the target public institution according to all the project-related energy-saving parameters corresponding to the energy media comprises:

[0034] The energy media are clustered and grouped based on the project-related energy-saving parameters, to obtain at least one high-efficiency coordination medium set and at least one low-efficiency coordination medium set; the project-related energy-saving parameters between any two energy media in the high-efficiency coordination medium set are greater than a first parameter threshold; the project-related energy-saving parameters between any two energy media in the low-efficiency coordination medium set are lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold;

[0035] The weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the high-efficiency coordination medium sets is calculated, to obtain a first performance parameter;

[0036] The weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the low-efficiency coordination medium sets is calculated, to obtain a second performance parameter;

[0037] The ratio between the first performance parameter and the second performance parameter is calculated, to obtain the energy-saving effect parameter corresponding to the target public institution.

[0038] The second aspect of the embodiment of the present application discloses a public institution evaluation system based on massive data prediction, which comprises:

[0039] An acquisition module is configured to acquire project data of a plurality of energy media of a target public institution.

[0040] A determination module is configured to determine a project-related relationship corresponding to any two energy media according to a preset project-related rule.

[0041] An evaluation module is configured to determine energy-saving evaluation data corresponding to each energy medium according to an operation result prediction algorithm and the project data.

[0042] A calculation module is configured to calculate an energy-saving effect parameter corresponding to the target public institution according to the project-related relationship corresponding to each energy medium and the energy-saving evaluation data.

[0043] As an optional implementation, in the second aspect of the present application, the project data comprises meteorological data, operation data, maintenance data and equipment self parameters.

[0044] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module determines the corresponding project correlation between any two of the energy media according to the preset project correlation rule comprises:

[0045] calculating the correlation degree between the project data of the two energy media;

[0046] calculating the intersection degree between the project implementation times corresponding to the two energy media;

[0047] calculating the product of the correlation degree and the intersection degree to obtain the project correlation between the two energy media.

[0048] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module calculates the correlation degree between the project data of the two energy media comprises:

[0049] calculating the first similarity between the meteorological data of the two energy media;

[0050] calculating the second similarity between the operation data of the two energy media;

[0051] calculating the third similarity between the maintenance data of the two energy media;

[0052] calculating the fourth similarity between the equipment parameters of the two energy media;

[0053] calculating the weighted sum average of the first similarity, the second similarity, the third similarity and the fourth similarity to obtain the correlation degree between the two energy media.

[0054] As an optional implementation, in the second aspect of the present application, the specific manner in which the evaluating module determines the energy-saving evaluation data corresponding to each of the energy media according to the operation result prediction algorithm and the project data comprises:

[0055] based on the project type of the energy media, determining the meteorological influence result prediction model, the operation influence result prediction model, the maintenance influence result prediction model and the equipment influence result prediction model corresponding to the project type in a preset model library;

[0056] inputting the meteorological data into the meteorological influence result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological influence result prediction model is trained by a training data set comprising a plurality of training meteorological data and corresponding energy consumption influence labels;

[0057] inputting the operation data into the operation influence result prediction model to obtain corresponding operation evaluation parameters; the operation influence result prediction model is obtained by training a training data set including a plurality of training operation data and corresponding energy consumption influence labels;

[0058] inputting the maintenance data into the maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is obtained by training a training data set including a plurality of training maintenance data and corresponding energy consumption influence labels;

[0059] inputting the equipment parameter into the equipment influence result prediction model to obtain corresponding equipment evaluation parameters; the equipment influence result prediction model is obtained by training a training data set including a plurality of training equipment parameters and corresponding energy consumption influence labels;

[0060] calculating a weighted sum average of the weather evaluation parameters, the operation evaluation parameters, the maintenance evaluation parameters and the equipment evaluation parameters to obtain the energy-saving evaluation data corresponding to the energy medium.

[0061] As an optional implementation, in the second aspect of the present application, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the weather evaluation parameters is proportional to the proportion of weather abnormal data in the weather data, the weighted calculation weight corresponding to the operation evaluation parameters is proportional to the proportion of equipment fault records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameters is proportional to the average value of the maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment evaluation parameters is proportional to the equipment performance value corresponding to the equipment evaluation parameters.

[0062] As an optional implementation, in the second aspect of the present application, the specific manner of calculating the energy-saving effect parameter corresponding to the target public institution by the calculation module according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data comprises:

[0063] calculating the data average value of the energy-saving evaluation data of any two energy media;

[0064] calculating the relationship weight proportional to the project association relationship corresponding to the two energy media;

[0065] calculating the product of the data average value and the relationship weight to obtain the project association energy-saving parameter corresponding to the two energy media;

[0066] According to the project-related energy-saving parameters corresponding to all the energy media, the energy-saving effect parameter corresponding to the target public institution is calculated.

[0067] As an optional implementation, in the second aspect of the present application, the specific manner in which the calculation module calculates the energy-saving effect parameter corresponding to the target public institution according to the project-related energy-saving parameters corresponding to all the energy media comprises:

[0068] The energy media are clustered and grouped based on the project-related energy-saving parameters, to obtain at least one high-efficiency coordination medium set and at least one low-efficiency coordination medium set; the project-related energy-saving parameters between any two energy media in the high-efficiency coordination medium set are greater than a first parameter threshold; the project-related energy-saving parameters between any two energy media in the low-efficiency coordination medium set are lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold;

[0069] The weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the high-efficiency coordination medium sets is calculated, to obtain a first performance parameter;

[0070] The weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the low-efficiency coordination medium sets is calculated, to obtain a second performance parameter;

[0071] The ratio between the first performance parameter and the second performance parameter is calculated, to obtain the energy-saving effect parameter corresponding to the target public institution.

[0072] The third aspect of the present application discloses another public institution evaluation system based on massive data prediction, which comprises:

[0073] A memory in which executable program codes are stored;

[0074] A processor coupled with the memory;

[0075] The processor invokes the executable program codes stored in the memory to execute part or all of the steps of the public institution evaluation method based on massive data prediction disclosed in the first aspect of the present application.

[0076] The fourth aspect of the present application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are invoked, part or all of the steps of the public institution evaluation method based on massive data prediction disclosed in the first aspect of the present application are executed.

[0077] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0078] The application can determine the corresponding project association relationship between energy media based on project association rules, determine the energy-saving evaluation data corresponding to each energy medium based on the operation result prediction algorithm and project data, and comprehensively calculate the energy-saving effect parameters corresponding to the target public institution, so as to realize more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tap the energy-saving potential of the public institution, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services. BRIEF DESCRIPTION OF DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0080] Figure 1 is a flow diagram of a public institution evaluation method based on massive data prediction disclosed by an embodiment of the present application.

[0081] Figure 2 is a structural diagram of a public institution evaluation system based on massive data prediction disclosed by an embodiment of the present application.

[0082] Figure 3 is a structural diagram of another public institution evaluation system based on massive data prediction disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to make the person skilled in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0084] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or equipment.

[0085] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that any of the embodiments described herein can be combined with any of the other embodiments unless specifically noted otherwise.

[0086] The application discloses a public institution evaluation method and system based on massive data prediction, which can determine the corresponding project association relationship between energy media based on project association rules, and determine the energy-saving evaluation data corresponding to each energy medium based on the operation result prediction algorithm and project data, so as to comprehensively calculate the energy-saving effect parameters corresponding to the target public institution, so as to realize more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tap the energy-saving potential of the public institution, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services. The following will be described in detail.

[0087] Embodiment one

[0088] Please refer to Figure 1 , Figure 1 is a flowchart of a public institution evaluation method based on massive data prediction disclosed by the embodiment of the application. Wherein, Figure 1 The public institution evaluation method based on massive data prediction described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 1 shown, the public institution evaluation method based on massive data prediction can include the following operations:

[0089] 101, obtaining project data of a plurality of energy media of a target public institution.

[0090] 102, determining the corresponding project association relationship between any two energy media according to the preset project association rules.

[0091] 103, determining the energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data.

[0092] 104, calculating the energy-saving effect parameters corresponding to the target public institution according to the project association relationship and the energy-saving evaluation data corresponding to each energy medium.

[0093] It can be seen that the above embodiments of the application can determine the corresponding project association relationship between the energy media based on the project association rules, and determine the energy-saving evaluation data corresponding to each energy medium based on the operation result prediction algorithm and the project data, so as to comprehensively calculate the energy-saving effect parameters corresponding to the target public institution, thereby realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tapping the energy-saving potential of the public institution, reducing energy waste, and realizing more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0094] As an optional embodiment, in the above step, the project data includes meteorological data, operation data, maintenance data, and equipment parameters.

[0095] It can be seen that through the above optional embodiment, the content of the project data is determined, which can more accurately and comprehensively represent the overall situation of the project, so as to facilitate subsequent energy-saving evaluation, and assist in realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tapping the energy-saving potential of the public institution, reducing energy waste, and realizing more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0096] As an optional embodiment, in the above step, according to the preset project association rules, the corresponding project association relationship between any two energy media is determined, including:

[0097] For any two energy media, calculating the association degree between the project data of the two energy media;

[0098] Calculating the intersection degree between the project development time corresponding to the two energy media;

[0099] Calculating the product of the association degree and the intersection degree to obtain the project association relationship between the two energy media.

[0100] It can be seen that through the above optional embodiment, the relationship representation parameter of project cooperation between any two energy media can be calculated based on the project data correlation degree and the development time correlation degree, so as to facilitate subsequent energy-saving evaluation, and assist in realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tapping the energy-saving potential of the public institution, reducing energy waste, and realizing more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0101] As an optional embodiment, in the above step, the association degree between the project data of the two energy media is calculated, including:

[0102] Calculating the first similarity between the meteorological data of the two energy media;

[0103] calculate a second similarity between the operation data of the two energy media;

[0104] calculate a third similarity between the maintenance data of the two energy media;

[0105] calculate a fourth similarity between the equipment self parameters of the two energy media;

[0106] calculate a weighted sum average of the first similarity, the second similarity, the third similarity, and the fourth similarity to obtain the correlation degree between the two energy media.

[0107] As can be seen, through the above optional embodiments, the correlation degree between the energy media can be accurately calculated based on the similarity between different data types in the project data of the energy media, so as to facilitate subsequent energy saving evaluation, assist in realizing more efficient and accurate energy saving effect parameter evaluation of public institutions based on a large amount of project historical data, effectively tap the energy saving potential of public institutions, reduce energy waste, and realize more energy-saving and efficient public institution energy saving and carbon reduction services.

[0108] As an optional embodiment, in the above steps, the energy saving evaluation data corresponding to each energy medium is determined according to the operation result prediction algorithm and the project data, including:

[0109] Based on the project type of the energy medium, the meteorological influence result prediction model, the operation influence result prediction model, the maintenance influence result prediction model, and the equipment self influence result prediction model corresponding to the project type are determined in the preset model library;

[0110] input the meteorological data into the meteorological influence result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological influence result prediction model is trained by a training data set including a plurality of training meteorological data and corresponding energy consumption influence labels;

[0111] input the operation data into the operation influence result prediction model to obtain corresponding operation evaluation parameters; the operation influence result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption influence labels;

[0112] input the maintenance data into the maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption influence labels;

[0113] input the equipment self parameters into the equipment self influence result prediction model to obtain corresponding equipment self evaluation parameters; the equipment self influence result prediction model is trained by a training data set including a plurality of training equipment self parameters and corresponding energy consumption influence labels;

[0114] The weighted sum average of the meteorological evaluation parameter, the operation evaluation parameter, the maintenance evaluation parameter and the equipment self parameter is calculated to obtain the energy-saving evaluation data corresponding to the energy medium.

[0115] It can be seen that, through the above optional embodiments, the corresponding data evaluation prediction model can be screened based on the project type to perform comprehensive and accurate project performance evaluation prediction, so as to facilitate subsequent energy-saving evaluation, assist in realizing more efficient and accurate energy-saving effect parameter evaluation of public institutions based on a large amount of project historical data, effectively tap the energy-saving potential of public institutions, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0116] As an optional embodiment, in the step of calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameter is proportional to the proportion of meteorological abnormal data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of equipment fault records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average value of the maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment self evaluation parameter is proportional to the equipment performance value corresponding to the equipment self parameter.

[0117] It can be seen that, through the above optional embodiments, the weight rules of each parameter when calculating the energy-saving evaluation data are determined, which can make the energy-saving evaluation data more comprehensive and accurate to reflect the energy-saving evaluation of the energy medium, so as to facilitate subsequent energy-saving evaluation, assist in realizing more efficient and accurate energy-saving effect parameter evaluation of public institutions based on a large amount of project historical data, effectively tap the energy-saving potential of public institutions, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0118] As an optional embodiment, in the step, the energy-saving effect parameter corresponding to the target public institution is calculated according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data, including:

[0119] For any two energy media, the data average value of the energy-saving evaluation data of the two energy media is calculated;

[0120] A relationship weight proportional to the project association relationship corresponding to the two energy media is calculated;

[0121] The product of the data average value and the relationship weight is calculated to obtain the project association energy-saving parameter corresponding to the two energy media;

[0122] The energy-saving effect parameter corresponding to the target public institution is calculated according to the project association energy-saving parameter corresponding to all energy media.

[0123] It can be seen that, through the above optional embodiments, the project association energy-saving parameters corresponding to the media can be determined by calculating the relationship between the project association degree and the energy-saving evaluation data between two energy media, and the energy-saving effect parameters corresponding to the target public institution can be accurately calculated based on the project association energy-saving parameters corresponding to all energy media, so as to realize more efficient and accurate energy-saving evaluation of the public institution based on a large amount of project collaborative work data, effectively improve the supervision efficiency of the public institution, and further improve the operation result of the public institution.

[0124] As an optional embodiment, in the above step, the energy-saving effect parameters corresponding to the target public institution are calculated based on the project association energy-saving parameters corresponding to all energy media, including:

[0125] The energy media are clustered and grouped based on the project association energy-saving parameters, and at least one efficient cooperation medium set and at least one inefficient cooperation medium set are obtained; optionally, the project association energy-saving parameters between any two energy media in the efficient cooperation medium set are greater than a first parameter threshold; the project association energy-saving parameters between any two energy media in the inefficient cooperation medium set are less than a second parameter threshold; the second parameter threshold is less than the first parameter threshold;

[0126] The weighted sum value of the energy-saving evaluation data corresponding to all energy media in all efficient cooperation medium sets is calculated to obtain a first performance parameter;

[0127] The weighted sum value of the energy-saving evaluation data corresponding to all energy media in all inefficient cooperation medium sets is calculated to obtain a second performance parameter;

[0128] The ratio between the first performance parameter and the second performance parameter is calculated to obtain the energy-saving effect parameters corresponding to the target public institution.

[0129] It can be seen that, through the above optional embodiments, the media can be efficiently and inefficiently grouped based on the project association energy-saving parameters, and the energy-saving effect parameters corresponding to the target public institution can be accurately calculated based on the calculation and ratio of the energy-saving evaluation data of the efficient set and the inefficient set, so as to realize more efficient and accurate energy-saving evaluation of the public institution based on a large amount of project collaborative work data, effectively improve the supervision efficiency of the public institution, and further improve the operation result of the public institution.

[0130] Embodiment two

[0131] Please refer to Figure 2 , Figure 2 is a structure schematic diagram of a public institution evaluation system based on massive data prediction disclosed by the embodiments of the present application. Among them, Figure 2The public institution evaluation system based on mass data prediction can be applied in a data processing system / data processing device / data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in Figure 2 The public institution evaluation system based on mass data prediction can comprise:

[0132] An acquisition module 201 is configured to acquire project data of a plurality of energy media of a target public institution.

[0133] A determination module 202 is configured to determine a corresponding project correlation between any two energy media according to a preset project correlation rule.

[0134] An evaluation module 203 is configured to determine energy-saving evaluation data corresponding to each energy medium according to an operation result prediction algorithm and the project data.

[0135] A calculation module 204 is configured to calculate an energy-saving effect parameter corresponding to the target public institution according to the corresponding project correlation and the energy-saving evaluation data of each energy medium.

[0136] It can be seen that the above embodiment can determine the corresponding project correlation between the energy media based on the project correlation rule, and determine the energy-saving evaluation data corresponding to each energy medium based on the operation result prediction algorithm and the project data, so as to comprehensively calculate the energy-saving effect parameter corresponding to the target public institution, thereby realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tapping the energy-saving potential of the public institution, reducing energy waste, and realizing more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0137] As an optional embodiment, the project data comprises meteorological data, operation data, maintenance data, and equipment parameters.

[0138] It can be seen that the above optional embodiment clearly defines the content of the project data, which can more accurately and comprehensively represent the overall situation of the project, so as to facilitate subsequent energy-saving evaluation, and assist in realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tapping the energy-saving potential of the public institution, reducing energy waste, and realizing more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0139] As an optional embodiment, the specific manner in which the determination module determines the corresponding project correlation between any two energy media according to the preset project correlation rule comprises:

[0140] For any two energy media, calculating the correlation degree between the project data of the two energy media;

[0141] Calculate the intersection degree between the project development time corresponding to the two energy media;

[0142] Calculate the product of the correlation degree and the intersection degree to obtain the project correlation relationship between the two energy media.

[0143] As can be seen, through the above optional embodiments, the relationship representation parameters of project synergy between any two energy media can be calculated based on the project data correlation and development time correlation, so as to facilitate subsequent energy saving evaluation, and assist in realizing more efficient and accurate energy saving effect parameter evaluation of public institutions based on a large amount of project historical data, so as to effectively tap the energy saving potential of public institutions, reduce energy waste, and realize more energy-efficient public institution energy saving and carbon reduction services.

[0144] As an optional embodiment, the specific manner in which the determination module calculates the correlation degree between the project data of the two energy media includes:

[0145] Calculate the first similarity between the meteorological data of the two energy media;

[0146] Calculate the second similarity between the operation data of the two energy media;

[0147] Calculate the third similarity between the maintenance data of the two energy media;

[0148] Calculate the fourth similarity between the equipment parameters of the two energy media;

[0149] Calculate the weighted sum average of the first similarity, the second similarity, the third similarity, and the fourth similarity to obtain the correlation degree between the two energy media.

[0150] As can be seen, through the above optional embodiments, the correlation degree between energy media can be accurately calculated based on the similarity between different data types in the project data of the energy media, so as to facilitate subsequent energy saving evaluation, and assist in realizing more efficient and accurate energy saving effect parameter evaluation of public institutions based on a large amount of project historical data, so as to effectively tap the energy saving potential of public institutions, reduce energy waste, and realize more energy-efficient public institution energy saving and carbon reduction services.

[0151] As an optional embodiment, the specific manner in which the evaluation module determines the energy saving evaluation data corresponding to each energy medium based on the operation result prediction algorithm and the project data includes:

[0152] Based on the project type of the energy medium, determine the meteorological influence result prediction model, the operation influence result prediction model, the maintenance influence result prediction model, and the equipment influence result prediction model corresponding to the project type in the preset model library;

[0153] inputting the meteorological data into a meteorological influence result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological influence result prediction model is trained by a training data set including a plurality of training meteorological data and corresponding energy consumption influence labels;

[0154] inputting the operation data into an operation influence result prediction model to obtain corresponding operation evaluation parameters; the operation influence result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption influence labels;

[0155] inputting the maintenance data into a maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption influence labels;

[0156] inputting the device itself parameters into a device itself influence result prediction model to obtain corresponding device itself evaluation parameters; the device itself influence result prediction model is trained by a training data set including a plurality of training device itself parameters and corresponding energy consumption influence labels;

[0157] calculating a weighted sum average of the meteorological evaluation parameters, the operation evaluation parameters, the maintenance evaluation parameters and the device itself parameters to obtain corresponding energy-saving evaluation data of the energy medium.

[0158] As can be seen, through the above optional embodiments, corresponding data evaluation prediction models can be selected based on the project type to perform comprehensive and accurate project performance evaluation prediction, so as to facilitate subsequent energy-saving evaluation, and to assist in realizing more efficient and accurate energy-saving effect parameter evaluation of public institutions based on a large amount of project historical data, so as to effectively tap the energy-saving potential of public institutions, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0159] As an optional embodiment, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameters is proportional to the proportion of meteorological abnormal data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameters is proportional to the proportion of device fault records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameters is proportional to the average value of the maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the device itself evaluation parameters is proportional to the device performance value corresponding to the device itself parameters.

[0160] It can be seen that through the above optional embodiments, the weight rules of each parameter in calculating the energy-saving evaluation data are clear, which can make the energy-saving evaluation data more comprehensive and accurate to reflect the energy-saving evaluation of the energy medium, so as to facilitate subsequent energy-saving evaluation, assist in realizing more efficient and accurate energy-saving effect parameter evaluation of the public institution based on a large amount of project historical data, effectively tap the energy-saving potential of the public institution, reduce energy waste, and realize more energy-saving and efficient public institution energy-saving and carbon reduction services.

[0161] As an optional embodiment, the specific manner of the calculation module for calculating the energy-saving effect parameter corresponding to the target public institution according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data comprises:

[0162] For any two energy media, calculating the data average value of the energy-saving evaluation data of the two energy media;

[0163] Calculating a relationship weight proportional to the project association relationship corresponding to the two energy media;

[0164] Calculating the product of the data average value and the relationship weight to obtain the project association energy-saving parameter corresponding to the two energy media;

[0165] According to the project association energy-saving parameters corresponding to all energy media, calculating the energy-saving effect parameter corresponding to the target public institution.

[0166] It can be seen that through the above optional embodiments, the project association degree between two energy media and the relationship between the energy-saving evaluation data can be calculated and determined to obtain the project association energy-saving parameter corresponding to the medium, and then the energy-saving effect parameter corresponding to the target public institution can be accurately calculated based on the project association energy-saving parameters corresponding to all energy media, so as to realize more efficient and accurate energy-saving evaluation of the public institution based on a large amount of project collaborative work data, effectively improve the supervision efficiency of the public institution, and further improve the operation result of the public institution.

[0167] As an optional embodiment, the specific manner of the calculation module for calculating the energy-saving effect parameter corresponding to the target public institution according to the project association energy-saving parameters corresponding to all energy media comprises:

[0168] Based on the project association energy-saving parameters, all energy media are clustered and grouped to obtain at least one efficient cooperation medium set and at least one inefficient cooperation medium set; optionally, the project association energy-saving parameters between any two energy media in the efficient cooperation medium set are greater than a first parameter threshold; the project association energy-saving parameters between any two energy media in the inefficient cooperation medium set are lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold;

[0169] Calculate the weighted sum value of the energy-saving evaluation data corresponding to all energy media in all high-efficiency matching medium sets, and obtain a first performance parameter;

[0170] Calculate the weighted sum value of the energy-saving evaluation data corresponding to all energy media in all low-efficiency matching medium sets, and obtain a second performance parameter;

[0171] Calculate the ratio between the first performance parameter and the second performance parameter, and obtain the energy-saving effect parameter corresponding to the target public institution.

[0172] As can be seen, through the above optional embodiments, the media can be grouped into high-efficiency and low-efficiency groups based on the project-related energy-saving parameters, and the energy-saving effect parameter corresponding to the target public institution can be accurately calculated based on the calculation of the energy-saving evaluation data of the high-efficiency set and the low-efficiency set and the ratio, so as to realize more efficient and accurate energy-saving evaluation of the public institution based on a large amount of project collaborative work data, thereby effectively improving the supervision efficiency of the public institution and further improving the operation result of the public institution.

[0173] Embodiment three

[0174] Please refer to Figure 3 , Figure 3 The application discloses a public institution evaluation system based on massive data prediction. Figure 3 The public institution evaluation system based on massive data prediction is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As shown in Figure 3 The public institution evaluation system based on massive data prediction can include:

[0175] a memory 301 storing executable program codes;

[0176] a processor 302 coupled with the memory 301;

[0177] The processor 302 calls the executable program codes stored in the memory 301, and is used for executing the steps of the public institution evaluation method based on massive data prediction described in embodiment one.

[0178] Embodiment four

[0179] The application discloses a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes the computer to execute the steps of the public institution evaluation method based on massive data prediction described in embodiment one.

[0180] Embodiment five

[0181] The embodiment of the present application discloses a computer program product, which comprises a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the public institution evaluation method based on massive data prediction described in the embodiment one.

[0182] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in which they are recited in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily have to be performed in the specific order described or in sequential order, but can be performed in other orders or concurrently. In some embodiments, multitasking and parallel processing can be advantageous.

[0183] The system, device, module or unit illustrated by the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer may, for example, be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0184] For the convenience of description, the above device is described as various units divided by functions during description. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware when implementing the present specification.

[0185] Those skilled in the art will understand that the embodiments of the present specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] The present specification is described with reference to flowcharts and / or block diagrams of methods, apparatus (system) and computer program products according to embodiments of the present specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a machine that implements the functions described in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or blocks Figure 1 means for performing the function specified by the block or blocks.

[0187] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 one or more processes and / or blocks Figure 1 means for performing the function specified by the block or blocks.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 one or more processes and / or blocks Figure 1 means for performing the function specified by the block or blocks.

[0189] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0190] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer readable media.

[0191] Computer readable media includes permanent and non-permanent, moveable and non- moveable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that is accessible to a computing device. According to the definition provided herein, a computer readable medium does not include transitory media, such as modulated data signals and carrier waves.

[0192] It should also be noted that the terms "comprising," "including," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0193] The specification can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of steps and methods described in this specification is not necessarily required to be executed in any specific order, unless otherwise constrained by a particular implementation.

[0194] Various embodiments of the present specification are described in progressive manner, and the same or similar parts between various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0195] Finally, it should be noted that the public institution evaluation method and system based on massive data prediction disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. The modification or replacement does not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A public institution evaluation method based on mass data prediction, characterized by, The method comprises: acquiring project data of a plurality of energy media in a target public institution; the energy media are energy-consuming unit devices in the target public institution; the project data comprises meteorological data, operation data, maintenance data and device parameters; determining a corresponding project correlation between any two of the energy media according to a preset project correlation rule, comprising: for any two of the energy media, calculating a correlation degree between the project data of the two energy media; calculating an intersection degree between the project development times corresponding to the two energy media; calculating a product of the correlation degree and the intersection degree to obtain the project correlation between the two energy media; determining energy-saving evaluation data corresponding to each of the energy media according to an operation result prediction algorithm and the project data, comprising: based on the project type of the energy media, determining a meteorological influence result prediction model, an operation influence result prediction model, a maintenance influence result prediction model and a device influence result prediction model corresponding to the project type in a preset model library; inputting the meteorological data into the meteorological influence result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological influence result prediction model is trained by a training data set comprising a plurality of training meteorological data and corresponding energy consumption influence labels; inputting the operation data into the operation influence result prediction model to obtain corresponding operation evaluation parameters; the operation influence result prediction model is trained by a training data set comprising a plurality of training operation data and corresponding energy consumption influence labels; inputting the maintenance data into the maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is trained by a training data set comprising a plurality of training maintenance data and corresponding energy consumption influence labels; inputting the device parameters into the device influence result prediction model to obtain corresponding device evaluation parameters; the device influence result prediction model is trained by a training data set comprising a plurality of training device parameters and corresponding energy consumption influence labels; calculating a weighted sum average of the meteorological evaluation parameters, the operation evaluation parameters, the maintenance evaluation parameters and the device evaluation parameters to obtain the energy-saving evaluation data corresponding to the energy media; calculating energy-saving effect parameters corresponding to the target public institution according to the project correlation and the energy-saving evaluation data corresponding to each of the energy media, comprising: for any two of the energy media, calculating a data average of the energy-saving evaluation data of the two energy media; calculating a relationship weight proportional to the project correlation corresponding to the two energy media; calculating a product of the data average and the relationship weight to obtain a project correlation energy-saving parameter corresponding to the two energy media; calculating the energy-saving effect parameters corresponding to the target public institution according to the project correlation energy-saving parameters corresponding to all the energy media.

2. The method of claim 1, wherein, The calculating the correlation degree between the project data of the two energy media comprises: calculating a first similarity between the weather data of the two energy media; calculating a second similarity between the operation data of the two energy media; calculating a third similarity between the maintenance data of the two energy media; calculating a fourth similarity between the device itself parameters of the two energy media; calculating a weighted sum average of the first similarity, the second similarity, the third similarity and the fourth similarity to obtain the correlation degree between the two energy media.

3. The method of claim 1, wherein, In the calculation of the energy-saving evaluation data, the weighted calculation weight corresponding to the weather evaluation parameter is proportional to the proportion of weather abnormal data in the weather data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of device fault records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average value of the maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the device itself evaluation parameter is proportional to the device performance value corresponding to the device itself parameter.

4. The method of claim 1, wherein, The calculating the energy-saving effect parameter corresponding to the target public institution according to the project correlation energy-saving parameters corresponding to all the energy media comprises: grouping all the energy media based on the project correlation energy-saving parameters to obtain at least one high-efficiency cooperation medium set and at least one low-efficiency cooperation medium set; the project correlation energy-saving parameters between any two energy media in the high-efficiency cooperation medium set are greater than a first parameter threshold; the project correlation energy-saving parameters between any two energy media in the low-efficiency cooperation medium set are lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; calculating a weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the high-efficiency cooperation medium sets to obtain a first performance parameter; calculating a weighted sum value of the energy-saving evaluation data corresponding to all the energy media in all the low-efficiency cooperation medium sets to obtain a second performance parameter; calculating a ratio between the first performance parameter and the second performance parameter to obtain the energy-saving effect parameter corresponding to the target public institution.

5. A public institution evaluation system based on mass data prediction, characterized by, The system comprises: an acquisition module configured to acquire project data of a plurality of energy media of a target public institution; the energy media are energy consumption unit devices in the target public institution; the project data comprises weather data, operation data, maintenance data and device itself parameters; a determination module configured to determine a project correlation relationship between any two energy media according to a preset project correlation rule, comprising: calculating a correlation degree between the project data of the two energy media; calculating an intersection degree between the project development times corresponding to the two energy media; calculating a product of the correlation degree and the intersection degree to obtain the project correlation relationship between the two energy media; An evaluation module is configured to predict an algorithm according to a running result and project data, and determine energy-saving evaluation data corresponding to each energy medium, including: Based on the project type of the energy medium, a meteorological influence result prediction model, a running influence result prediction model, a maintenance influence result prediction model, and a device itself influence result prediction model corresponding to the project type are determined in a preset model library; The meteorological data is input into the meteorological influence result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological influence result prediction model is trained by a training data set including a plurality of training meteorological data and corresponding energy consumption influence labels; The running data is input into the running influence result prediction model to obtain corresponding running evaluation parameters; the running influence result prediction model is trained by a training data set including a plurality of training running data and corresponding energy consumption influence labels; The maintenance data is input into the maintenance influence result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance influence result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption influence labels; The device itself parameter is input into the device itself influence result prediction model to obtain corresponding device itself evaluation parameters; the device itself influence result prediction model is trained by a training data set including a plurality of training device itself parameters and corresponding energy consumption influence labels; The weighted sum average of the meteorological evaluation parameters, the running evaluation parameters, the maintenance evaluation parameters, and the device itself parameters is calculated to obtain the energy-saving evaluation data corresponding to the energy medium, and an energy-saving effect parameter corresponding to the target public institution is calculated; A calculation module is configured to calculate an energy-saving effect parameter corresponding to the target public institution according to the project correlation of each energy medium and the energy-saving evaluation data, including: For any two energy media, a data average of the energy-saving evaluation data of the two energy media is calculated; A relationship weight proportional to the project correlation corresponding to the two energy media is calculated; The product of the data average and the relationship weight is calculated to obtain a project correlation energy-saving parameter corresponding to the two energy media; An energy-saving effect parameter corresponding to the target public institution is calculated according to the project correlation energy-saving parameters corresponding to all energy media.

6. A public institution evaluation system based on mass data prediction, characterized by, The system includes: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the public institution evaluation method based on massive data prediction according to any one of claims 1-4.

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