Public institution evaluation method and system based on mass data prediction

Abstract

The invention discloses a public institution evaluation method and system based on mass data prediction. The method comprises the steps of obtaining project data of multiple energy media of a target public institution; according to a preset project association rule, determining a corresponding project association relationship between any two energy media; determining energy-saving evaluation data corresponding to each energy medium according to an operation result prediction algorithm and the project data; and according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data, calculating an energy-saving effect parameter corresponding to the target public institution. Therefore, more efficient and accurate energy-saving effect parameter evaluation of the public institution can be realized based on a large amount of project historical data, so that the energy-saving potential of the public institution is effectively excavated, energy waste is reduced, and more energy-saving and efficient public institution energy-saving and carbon-reducing services are realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for evaluating a public institution based on massive data prediction. Background Art

[0002] As government agencies, educational institutions, medical institutions and other organizations, public institutions bear important social responsibilities and public service tasks. Therefore, the demand for the evaluation of their energy-saving potential is increasing. Effective evaluation of the energy-saving potential of public institutions will also help save energy and reduce carbon emissions, reduce energy waste, and better serve the public. However, most of the existing energy-saving potential evaluations for public institutions still use artificially set rules and manual review and evaluation of specific projects. They do not use data prediction algorithms to conduct more efficient evaluations based on historical data related to the project. Therefore, the evaluation efficiency is low, the labor cost is high, and the accuracy of the evaluation is also lacking. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a public institution evaluation method and system based on massive data prediction, which can realize 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.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a public institution evaluation method based on massive data prediction, the method comprising: Obtain project data for multiple energy media for target public agencies; According to the preset project association rules, determine the corresponding project association relationship between any two energy media; Determine energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data; The energy-saving effect parameters corresponding to the target public institution are calculated according to the project association relationship corresponding to each of the energy media and the energy-saving evaluation data.

[0005] As an optional implementation, in the first aspect of the present invention, the project data includes meteorological data, operation data, maintenance data and equipment parameters.

[0006] As an optional implementation, in the first aspect of the present invention, determining the corresponding project association relationship between any two energy media according to a preset project association rule includes: For any two of the energy media, calculating the degree of association between the project data of the two energy media; Calculate the degree of intersection between the project implementation times corresponding to the two energy media; The product of the association degree and the intersection degree is calculated to obtain the project association relationship between the two energy media.

[0007] As an optional implementation, in the first aspect of the present invention, the calculating the degree of association between the project data of the two energy media includes: Calculating a first similarity between the meteorological data of the two energy media; Calculating a second similarity between the operating 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 parameters of the two energy media; A weighted average value of the first similarity, the second similarity, the third similarity and the fourth similarity is calculated to obtain a correlation degree between the two energy media.

[0008] As an optional implementation, in the first aspect of the present invention, determining the energy-saving evaluation data corresponding to each of the energy media according to the operation result prediction algorithm and the project data includes: Based on the project type of the energy medium, a meteorological impact result prediction model, an operation impact result prediction model, a maintenance impact result prediction model and an equipment self impact result prediction model corresponding to the project type are determined in a preset model library; The meteorological data is input into the meteorological impact result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological impact result prediction model is obtained by training a training data set including a plurality of training meteorological data and corresponding energy consumption impact annotations; The operation data is input into the operation impact result prediction model to obtain corresponding operation evaluation parameters; the operation impact result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption impact annotations; Inputting the maintenance data into the maintenance impact result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance impact result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption impact annotations; The device's own parameters are input into the device's own impact result prediction model to obtain corresponding device's own evaluation parameters; the device's own impact result prediction model is obtained by training a training data set including a plurality of training device's own parameters and corresponding energy consumption impact annotations; The weighted average value of the meteorological evaluation parameter, the operation evaluation parameter, the maintenance evaluation parameter and the equipment's own parameter is calculated to obtain energy-saving evaluation data corresponding to the energy medium.

[0009] As an optional embodiment, in the first aspect of the present invention, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameter is proportional to the proportion of meteorological abnormality data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of equipment failure records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment's own evaluation parameter is proportional to the equipment performance value corresponding to the equipment's own parameter.

[0010] As an optional implementation, in the first aspect of the present invention, the calculating 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 includes: For any two of the energy media, calculating the data average of the energy-saving evaluation data of the two energy media; Calculating a relationship weight proportional to the project association relationship corresponding to the two energy media; Calculate the product of the data average value and the relationship weight to obtain the project-related energy-saving parameters corresponding to the two energy media; The energy-saving effect parameters corresponding to the target public institution are calculated based on the project-associated energy-saving parameters corresponding to all the energy media.

[0011] As an optional implementation, in the first aspect of the present invention, the step of calculating the energy-saving effect parameter corresponding to the target public institution according to the project-associated energy-saving parameters corresponding to all the energy media includes: All the energy media are clustered and grouped based on the project-associated energy-saving parameter to obtain at least one high-efficiency matching medium set and at least one low-efficiency matching medium set; the project-associated energy-saving parameter between any two of the energy media in the high-efficiency matching medium set is greater than a first parameter threshold; the project-associated energy-saving parameter between any two of the energy media in the low-efficiency matching medium set is lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; Calculate the weighted sum of the rating data corresponding to all the energy media in the set of all the high-efficiency matching media to obtain a first performance parameter; Calculating the weighted sum of the rating data corresponding to all the energy media in the set of all the inefficient matching media to obtain a second performance parameter; The ratio between the first performance parameter and the second performance parameter is calculated to obtain an energy-saving effect parameter corresponding to the target public institution.

[0012] A second aspect of an embodiment of the present invention discloses a public institution evaluation system based on massive data prediction, the system comprising: An acquisition module for acquiring project data of multiple energy media of a target public institution; A determination module, used to determine the corresponding project association relationship between any two energy media according to a preset project association rule; An evaluation module, used to determine energy-saving evaluation data corresponding to each of the energy media according to the operation result prediction algorithm and the project data; The calculation module is used to calculate the energy-saving effect parameters corresponding to the target public institution according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data.

[0013] As an optional implementation, in the second aspect of the present invention, the project data includes meteorological data, operation data, maintenance data and equipment parameters.

[0014] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determining module determines the corresponding project association relationship between any two of the energy media according to a preset project association rule includes: For any two of the energy media, calculating the degree of association between the project data of the two energy media; Calculate the degree of intersection between the project implementation times corresponding to the two energy media; The product of the association degree and the intersection degree is calculated to obtain the project association relationship between the two energy media.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module calculates the degree of association between the project data of the two energy media includes: Calculating a first similarity between the meteorological data of the two energy media; Calculating a second similarity between the operating 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 parameters of the two energy media; A weighted average value of the first similarity, the second similarity, the third similarity and the fourth similarity is calculated to obtain a correlation degree between the two energy media.

[0016] As an optional implementation, in the second aspect of the present invention, the evaluation module determines the specific manner of the energy-saving evaluation data corresponding to each of the energy media according to the operation result prediction algorithm and the project data, including: Based on the project type of the energy medium, a meteorological impact result prediction model, an operation impact result prediction model, a maintenance impact result prediction model and an equipment self impact result prediction model corresponding to the project type are determined in a preset model library; The meteorological data is input into the meteorological impact result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological impact result prediction model is obtained by training a training data set including a plurality of training meteorological data and corresponding energy consumption impact annotations; The operation data is input into the operation impact result prediction model to obtain corresponding operation evaluation parameters; the operation impact result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption impact annotations; Inputting the maintenance data into the maintenance impact result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance impact result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption impact annotations; The device's own parameters are input into the device's own impact result prediction model to obtain corresponding device's own evaluation parameters; the device's own impact result prediction model is obtained by training a training data set including a plurality of training device's own parameters and corresponding energy consumption impact annotations; The weighted average value of the meteorological evaluation parameter, the operation evaluation parameter, the maintenance evaluation parameter and the equipment's own parameter is calculated to obtain energy-saving evaluation data corresponding to the energy medium.

[0017] As an optional embodiment, in the second aspect of the present invention, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameter is proportional to the proportion of meteorological abnormality data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of equipment failure records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment's own evaluation parameter is proportional to the equipment performance value corresponding to the equipment's own parameter.

[0018] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module calculates the energy-saving effect parameter corresponding to the target public institution according to the project association relationship corresponding to each of the energy media and the energy-saving evaluation data includes: For any two of the energy media, calculating the data average of the energy-saving evaluation data of the two energy media; Calculating a relationship weight proportional to the project association relationship corresponding to the two energy media; Calculate the product of the data average value and the relationship weight to obtain the project-related energy-saving parameters corresponding to the two energy media; The energy-saving effect parameters corresponding to the target public institution are calculated based on the project-associated energy-saving parameters corresponding to all the energy media.

[0019] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module calculates the energy-saving effect parameter corresponding to the target public institution according to the project-associated energy-saving parameters corresponding to all the energy media includes: All the energy media are clustered and grouped based on the project-associated energy-saving parameter to obtain at least one high-efficiency matching medium set and at least one low-efficiency matching medium set; the project-associated energy-saving parameter between any two of the energy media in the high-efficiency matching medium set is greater than a first parameter threshold; the project-associated energy-saving parameter between any two of the energy media in the low-efficiency matching medium set is lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; Calculate the weighted sum of the rating data corresponding to all the energy media in the set of all the high-efficiency matching media to obtain a first performance parameter; Calculating the weighted sum of the rating data corresponding to all the energy media in the set of all the inefficient matching media to obtain a second performance parameter; The ratio between the first performance parameter and the second performance parameter is calculated to obtain an energy-saving effect parameter corresponding to the target public institution.

[0020] The third aspect of the present invention discloses another public institution evaluation system based on massive data prediction, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the public institution assessment method based on massive data prediction disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the public institution evaluation method based on massive data prediction disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine the corresponding project association relationship between energy media based on project association rules, and then 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, thereby being able to achieve 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flowchart of a public institution evaluation method based on massive data prediction disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a public institution evaluation system based on massive data prediction disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another public institution evaluation system based on massive data prediction disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a method and system for evaluating public institutions based on massive data prediction, which can determine the corresponding project association relationship between energy media based on project association rules, and then 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, thereby being able to achieve a 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 achieve more energy-saving and efficient public institution energy-saving and carbon reduction services. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 : is a flow chart of a method for evaluating a public institution based on massive data prediction disclosed in an embodiment of the present invention. Figure 1 The public institution assessment 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). Figure 1 As shown, the public institution evaluation method based on massive data prediction may include the following operations: 101. Obtain project data for multiple energy media for the target public agency.

[0032] 102. According to the preset project association rules, determine the corresponding project association relationship between any two energy media. 103. Based on the operation result prediction algorithm and project data, determine the energy-saving evaluation data corresponding to each energy medium. 104. Based on the project association relationship and energy-saving evaluation data corresponding to each energy medium, calculate the energy-saving effect parameters corresponding to the target public institution.

[0033] It can be seen that the above-mentioned invention embodiments can determine the corresponding project association relationship between energy media based on project association rules, and then 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, thereby being able to achieve 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0034] As an optional embodiment, in the above steps, the project data includes meteorological data, operation data, maintenance data and equipment parameters.

[0035] It can be seen that through the above-mentioned optional embodiments, the content of the project data is clarified, which can more accurately and comprehensively characterize the overall situation of the project, so as to facilitate subsequent energy-saving evaluations, and assist in realizing more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0036] As an optional embodiment, in the above steps, determining the corresponding project association relationship between any two energy media according to a preset project association rule includes: For any two energy media, calculate the degree of correlation between the project data of the two energy media; Calculate the degree of intersection between the project implementation times corresponding to the two energy media; The product of the correlation degree and the intersection degree is calculated to obtain the project correlation relationship between the two energy media.

[0037] It can be seen that through the above-mentioned optional embodiments, the relationship characterization parameters of project collaboration can be calculated based on the project data correlation and development time correlation between any two energy media, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0038] As an optional embodiment, in the above step, calculating the correlation degree between the project data of the two energy media includes: Calculating a first similarity between the meteorological data of the two energy media; Calculating a second similarity between the operating 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 parameters of the two energy media; A weighted average value of the first similarity, the second similarity, the third similarity and the fourth similarity is calculated to obtain a correlation degree between the two energy media.

[0039] It can be seen that through the above-mentioned optional embodiments, the degree of correlation between energy media can be accurately calculated based on the similarity between data of different data types in the project data of the energy media, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0040] As an optional embodiment, in the above steps, determining the energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data includes: Based on the project type of energy medium, determine the meteorological impact result prediction model, operation impact result prediction model, maintenance impact result prediction model and equipment self impact result prediction model corresponding to the project type in the preset model library; The meteorological data is input into the meteorological impact result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological impact result prediction model is trained by a training data set including a plurality of training meteorological data and corresponding energy consumption impact annotations; Inputting the operation data into the operation impact result prediction model to obtain corresponding operation evaluation parameters; the operation impact result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption impact annotations; Inputting the maintenance data into the maintenance impact result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance impact result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption impact annotations; Inputting the device's own parameters into the device's own impact result prediction model to obtain the corresponding device's own evaluation parameters; the device's own impact result prediction model is trained by a training data set including a plurality of training device's own parameters and corresponding energy consumption impact annotations; Calculate the weighted average of meteorological evaluation parameters, operation evaluation parameters, maintenance evaluation parameters and equipment parameters to obtain energy-saving evaluation data corresponding to the energy medium.

[0041] It can be seen that through the above-mentioned optional embodiments, the corresponding data evaluation and prediction models can be screened out based on the project type to conduct comprehensive and accurate project performance evaluation and prediction, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0042] As an optional embodiment, in the above steps, when calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameter is proportional to the proportion of meteorological abnormality data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of equipment failure records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment's own evaluation parameter is proportional to the equipment performance value corresponding to the equipment's own parameter.

[0043] It can be seen that through the above-mentioned optional embodiments, the weight rules of various parameters when calculating the energy-saving evaluation data are clarified, which can make the energy-saving evaluation data more comprehensive and accurate reflect the energy-saving evaluation of the energy medium, so as to facilitate subsequent energy-saving evaluations, and assist in realizing more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0044] As an optional embodiment, in the above steps, calculating the energy-saving effect parameters corresponding to the target public institution according to the project association relationship and energy-saving evaluation data corresponding to each energy medium includes: For any two energy media, calculate the data average of the energy-saving evaluation data of the two energy media; Calculate a relationship weight that is proportional to the project association relationship corresponding to the two energy media; Calculate the product of the data average value and the relationship weight to obtain the project-related energy-saving parameters corresponding to the two energy media; According to the project-related energy-saving parameters corresponding to all energy media, the energy-saving effect parameters corresponding to the target public institutions are calculated.

[0045] It can be seen that through the above-mentioned optional embodiments, the project-associated energy-saving parameters corresponding to the medium can be determined by calculating the relationship between the project association degree and the energy-saving evaluation data between the two energy media, and then the energy-saving effect parameters corresponding to the target public institution can be accurately calculated based on the project-associated energy-saving parameters corresponding to all energy media, so as to achieve a more efficient and accurate energy-saving evaluation of public institutions based on a large amount of project collaborative work data, so as to effectively improve the supervision efficiency of public institutions and thereby improve the operating results of public institutions.

[0046] As an optional embodiment, in the above steps, calculating the energy-saving effect parameters corresponding to the target public institution according to the project-associated energy-saving parameters corresponding to all energy media includes: All energy media are clustered and grouped based on the project-associated energy-saving parameters to obtain at least one high-efficiency matching medium set and at least one low-efficiency matching medium set; optionally, the project-associated energy-saving parameter between any two energy media in the high-efficiency matching medium set is greater than a first parameter threshold; the project-associated energy-saving parameter between any two energy media in the low-efficiency matching medium set is lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; Calculate the weighted sum of the rating data corresponding to all energy media in the set of all efficient matching media to obtain the first performance parameter; Calculate the weighted sum of the rating data corresponding to all energy media in the set of all inefficient matching media to obtain the second performance parameter; The ratio between the first performance parameter and the second performance parameter is calculated to obtain an energy-saving effect parameter corresponding to the target public institution.

[0047] It can be seen that through the above-mentioned optional embodiments, the media can be grouped into efficient and inefficient ones based on the project-associated energy-saving parameters, and the energy-saving effect parameters corresponding to the target public institutions 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 achieve a more efficient and accurate energy-saving evaluation of public institutions based on a large amount of project collaborative work data, so as to effectively improve the supervision efficiency of public institutions, and then improve the operating results of public institutions.

[0048] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a public institution evaluation system based on massive data prediction disclosed in an embodiment of the present invention. Figure 2 The public institution evaluation system 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). Figure 2 As shown, the public institution evaluation system based on massive data prediction may include: The acquisition module 201 is used to acquire project data of multiple energy media of a target public institution.

[0049] The determination module 202 is used to determine the corresponding project association relationship between any two energy media according to a preset project association rule. The evaluation module 203 is used to determine the energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data. The calculation module 204 is used to calculate the energy-saving effect parameters corresponding to the target public institution according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data.

[0050] It can be seen that the above-mentioned invention embodiments can determine the corresponding project association relationship between energy media based on project association rules, and then 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, thereby being able to achieve 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0051] As an optional embodiment, the project data includes meteorological data, operation data, maintenance data and equipment parameters.

[0052] It can be seen that through the above-mentioned optional embodiments, the content of the project data is clarified, which can more accurately and comprehensively characterize the overall situation of the project, so as to facilitate subsequent energy-saving evaluations, and assist in realizing more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0053] As an optional embodiment, the specific manner in which the determination module determines the corresponding project association relationship between any two energy media according to a preset project association rule includes: For any two energy media, calculate the degree of correlation between the project data of the two energy media; Calculate the degree of intersection between the project implementation times corresponding to the two energy media; The product of the correlation degree and the intersection degree is calculated to obtain the project correlation relationship between the two energy media.

[0054] It can be seen that through the above-mentioned optional embodiments, the relationship characterization parameters of project collaboration can be calculated based on the project data correlation and development time correlation between any two energy media, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0055] As an optional embodiment, the specific manner in which the determination module calculates the degree of association between the project data of the two energy media includes: Calculating a first similarity between the meteorological data of the two energy media; Calculating a second similarity between the operating 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 parameters of the two energy media; A weighted average value of the first similarity, the second similarity, the third similarity and the fourth similarity is calculated to obtain a correlation degree between the two energy media.

[0056] It can be seen that through the above-mentioned optional embodiments, the degree of correlation between energy media can be accurately calculated based on the similarity between data of different data types in the project data of the energy media, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0057] As an optional embodiment, the evaluation module determines the specific method of energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data, including: Based on the project type of energy medium, determine the meteorological impact result prediction model, operation impact result prediction model, maintenance impact result prediction model and equipment self impact result prediction model corresponding to the project type in the preset model library; The meteorological data is input into the meteorological impact result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological impact result prediction model is trained by a training data set including a plurality of training meteorological data and corresponding energy consumption impact annotations; Inputting the operation data into the operation impact result prediction model to obtain corresponding operation evaluation parameters; the operation impact result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption impact annotations; Inputting the maintenance data into the maintenance impact result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance impact result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption impact annotations; Inputting the device's own parameters into the device's own impact result prediction model to obtain the corresponding device's own evaluation parameters; the device's own impact result prediction model is trained by a training data set including a plurality of training device's own parameters and corresponding energy consumption impact annotations; Calculate the weighted average of meteorological evaluation parameters, operation evaluation parameters, maintenance evaluation parameters and equipment parameters to obtain energy-saving evaluation data corresponding to the energy medium.

[0058] It can be seen that through the above-mentioned optional embodiments, the corresponding data evaluation and prediction models can be screened out based on the project type to conduct comprehensive and accurate project performance evaluation and prediction, so as to facilitate subsequent energy-saving evaluations, and assist in achieving more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0059] As an optional embodiment, when 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 failure records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment's own evaluation parameter is proportional to the equipment performance value corresponding to the equipment's own parameter.

[0060] It can be seen that through the above-mentioned optional embodiments, the weight rules of various parameters when calculating the energy-saving evaluation data are clarified, which can make the energy-saving evaluation data more comprehensive and accurate reflect the energy-saving evaluation of the energy medium, so as to facilitate subsequent energy-saving evaluations, and assist in realizing more efficient and accurate energy-saving effect parameter evaluations for 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 achieve more energy-saving and efficient energy-saving and carbon reduction services for public institutions.

[0061] As an optional embodiment, the specific method of calculating the energy-saving effect parameters 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 includes: For any two energy media, calculate the data average of the energy-saving evaluation data of the two energy media; Calculate a relationship weight that is proportional to the project association relationship corresponding to the two energy media; Calculate the product of the data average value and the relationship weight to obtain the project-related energy-saving parameters corresponding to the two energy media; According to the project-related energy-saving parameters corresponding to all energy media, the energy-saving effect parameters corresponding to the target public institutions are calculated.

[0062] It can be seen that through the above-mentioned optional embodiments, the project-associated energy-saving parameters corresponding to the medium can be determined by calculating the relationship between the project association degree and the energy-saving evaluation data between the two energy media, and then the energy-saving effect parameters corresponding to the target public institution can be accurately calculated based on the project-associated energy-saving parameters corresponding to all energy media, so as to achieve a more efficient and accurate energy-saving evaluation of public institutions based on a large amount of project collaborative work data, so as to effectively improve the supervision efficiency of public institutions and thereby improve the operating results of public institutions.

[0063] As an optional embodiment, the specific manner in which the calculation module calculates the energy-saving effect parameters corresponding to the target public institution according to the project-associated energy-saving parameters corresponding to all energy media includes: All energy media are clustered and grouped based on the project-associated energy-saving parameters to obtain at least one high-efficiency matching medium set and at least one low-efficiency matching medium set; optionally, the project-associated energy-saving parameter between any two energy media in the high-efficiency matching medium set is greater than a first parameter threshold; the project-associated energy-saving parameter between any two energy media in the low-efficiency matching medium set is lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; Calculate the weighted sum of the rating data corresponding to all energy media in the set of all efficient matching media to obtain the first performance parameter; Calculate the weighted sum of the rating data corresponding to all energy media in the set of all inefficient matching media to obtain the second performance parameter; The ratio between the first performance parameter and the second performance parameter is calculated to obtain an energy-saving effect parameter corresponding to the target public institution.

[0064] It can be seen that through the above-mentioned optional embodiments, the media can be grouped into efficient and inefficient ones based on the project-associated energy-saving parameters, and the energy-saving effect parameters corresponding to the target public institutions 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 achieve a more efficient and accurate energy-saving evaluation of public institutions based on a large amount of project collaborative work data, so as to effectively improve the supervision efficiency of public institutions, and then improve the operating results of public institutions.

[0065] Embodiment 3 See also Figure 3 , Figure 3 This is another public institution evaluation system based on massive data prediction disclosed in an embodiment of the present invention. Figure 3The 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). Figure 3 As shown, the public institution evaluation system based on massive data prediction may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the public institution evaluation method based on massive data prediction described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the public institution assessment method based on massive data prediction described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the public institution assessment method based on massive data prediction described in Example 1.

[0068] 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 recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, 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.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the public institution assessment method and system based on massive data prediction disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating public institutions based on massive data prediction, characterized in that: The method comprises: Obtain project data for multiple energy media for target public agencies; According to the preset project association rules, determine the corresponding project association relationship between any two energy media; Determine energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data; The energy-saving effect parameters corresponding to the target public institution are calculated according to the project association relationship corresponding to each of the energy media and the energy-saving evaluation data.

2. The method for evaluating public institutions based on massive data prediction according to claim 1, characterized in that: The project data includes meteorological data, operation data, maintenance data and equipment parameters.

3. The method for evaluating public institutions based on massive data prediction according to claim 2, characterized in that: Determining the corresponding project association relationship between any two energy media according to the preset project association rule includes: For any two of the energy media, calculating the degree of association between the project data of the two energy media; Calculate the degree of intersection between the project implementation times corresponding to the two energy media; The product of the association degree and the intersection degree is calculated to obtain the project association relationship between the two energy media.

4. The method for evaluating public institutions based on massive data prediction according to claim 3 is characterized in that: The calculating the correlation degree between the project data of the two energy media includes: Calculating a first similarity between the meteorological data of the two energy media; Calculating a second similarity between the operating 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 parameters of the two energy media; A weighted average value of the first similarity, the second similarity, the third similarity and the fourth similarity is calculated to obtain a correlation degree between the two energy media.

5. The method for evaluating public institutions based on massive data prediction according to claim 2, characterized in that: Determining the energy-saving evaluation data corresponding to each energy medium according to the operation result prediction algorithm and the project data includes: Based on the project type of the energy medium, a meteorological impact result prediction model, an operation impact result prediction model, a maintenance impact result prediction model and an equipment self impact result prediction model corresponding to the project type are determined in a preset model library; The meteorological data is input into the meteorological impact result prediction model to obtain corresponding meteorological evaluation parameters; the meteorological impact result prediction model is obtained by training a training data set including a plurality of training meteorological data and corresponding energy consumption impact annotations; The operation data is input into the operation impact result prediction model to obtain corresponding operation evaluation parameters; the operation impact result prediction model is trained by a training data set including a plurality of training operation data and corresponding energy consumption impact annotations; Inputting the maintenance data into the maintenance impact result prediction model to obtain corresponding maintenance evaluation parameters; the maintenance impact result prediction model is trained by a training data set including a plurality of training maintenance data and corresponding energy consumption impact annotations; The device's own parameters are input into the device's own impact result prediction model to obtain corresponding device's own evaluation parameters; the device's own impact result prediction model is obtained by training a training data set including a plurality of training device's own parameters and corresponding energy consumption impact annotations; The weighted average value of the meteorological evaluation parameter, the operation evaluation parameter, the maintenance evaluation parameter and the equipment's own parameter is calculated to obtain energy-saving evaluation data corresponding to the energy medium.

6. The method for evaluating public institutions based on massive data prediction according to claim 5, characterized in that: When calculating the energy-saving evaluation data, the weighted calculation weight corresponding to the meteorological evaluation parameter is proportional to the proportion of meteorological abnormality data in the meteorological data, the weighted calculation weight corresponding to the operation evaluation parameter is proportional to the proportion of equipment failure records corresponding to the operation data, the weighted calculation weight corresponding to the maintenance evaluation parameter is proportional to the average maintenance time of all maintenance records of the maintenance data, and the weighted calculation weight corresponding to the equipment's own evaluation parameter is proportional to the equipment performance value corresponding to the equipment's own parameter.

7. The method for evaluating public institutions based on massive data prediction according to claim 3, characterized in that: The 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 includes: For any two of the energy media, calculating the data average of the energy-saving evaluation data of the two energy media; Calculating a relationship weight proportional to the project association relationship corresponding to the two energy media; Calculate the product of the data average value and the relationship weight to obtain the project-related energy-saving parameters corresponding to the two energy media; The energy-saving effect parameters corresponding to the target public institution are calculated based on the project-associated energy-saving parameters corresponding to all the energy media.

8. The method for evaluating public institutions based on massive data prediction according to claim 7, characterized in that: The step of calculating the energy-saving effect parameter corresponding to the target public institution according to the project-associated energy-saving parameters corresponding to all the energy media includes: All the energy media are clustered and grouped based on the project-associated energy-saving parameter to obtain at least one high-efficiency matching medium set and at least one low-efficiency matching medium set; the project-associated energy-saving parameter between any two of the energy media in the high-efficiency matching medium set is greater than a first parameter threshold; the project-associated energy-saving parameter between any two of the energy media in the low-efficiency matching medium set is lower than a second parameter threshold; the second parameter threshold is lower than the first parameter threshold; Calculate the weighted sum of the rating data corresponding to all the energy media in the set of all the high-efficiency matching media to obtain a first performance parameter; Calculating the weighted sum of the rating data corresponding to all the energy media in the set of all the inefficient matching media to obtain a second performance parameter; The ratio between the first performance parameter and the second performance parameter is calculated to obtain an energy-saving effect parameter corresponding to the target public institution.

9. A public institution evaluation system based on massive data prediction, characterized in that: The system comprises: An acquisition module for acquiring project data of multiple energy media of a target public institution; A determination module, used to determine the corresponding project association relationship between any two energy media according to a preset project association rule; An evaluation module, used to determine energy-saving evaluation data corresponding to each of the energy media according to the operation result prediction algorithm and the project data; The calculation module is used to calculate the energy-saving effect parameters corresponding to the target public institution according to the project association relationship corresponding to each energy medium and the energy-saving evaluation data.

10. A public institution evaluation system based on massive data prediction, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the public institution assessment method based on massive data prediction as described in any one of claims 1-8.

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