CN114154780A - Evaluation method and device, electronic equipment and related product - Google Patents
Evaluation method and device, electronic equipment and related product Download PDFInfo
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Abstract
The embodiment of the application discloses an evaluation method, an evaluation device, electronic equipment and related products, wherein the method comprises the following steps: the method comprises the steps of obtaining an original index value, carrying out normalization processing on the original index value to obtain a normalized index value, determining an output weight corresponding to the normalized index value according to a preset model and the normalized index value, and carrying out result integration on the normalized index value according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated. By adopting the embodiment of the application, the accuracy and the objectivity of the operation evaluation system of the central heating system can be improved.
Description
Technical Field
The application relates to the technical field of energy planning design, in particular to an evaluation method, an evaluation device, electronic equipment and related products.
Background
With the development of industrial technology, the central heating system plays an increasingly greater role in improving the living standard of residents. The operation evaluation system of the central heating system is used as an evaluation system of the energy efficiency and the transmission and distribution energy consumption of the central heating system, and the importance of the operation evaluation system is increasingly remarkable. In the related art, it is difficult to objectively and accurately reflect the actual operation state of the central heating system by evaluating the central heating system or each index thereof according to historical experience or a fixed index weight. Therefore, how to improve the accuracy of the operation evaluation system of the central heating system becomes a problem to be solved urgently.
Disclosure of Invention
The embodiment of the application provides an evaluation method, an evaluation device, electronic equipment and related products, and the accuracy and the objectivity of a central heating system operation evaluation system are improved.
In a first aspect, an embodiment of the present application provides an evaluation method, where the method includes:
acquiring an original index value;
carrying out normalization processing on the original index value to obtain a normalized index value;
determining an output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
In a second aspect, an embodiment of the present application provides an evaluation apparatus, including: an acquisition unit, a processing unit, a determination unit and an evaluation unit, wherein,
the acquisition unit is used for acquiring an original index value;
the processing unit is used for carrying out normalization processing on the original index value to obtain a normalized index value;
the determining unit is used for determining the output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and the evaluation unit is used for integrating the result of the normalized index value according to the output weight to obtain a target evaluation value, and the target evaluation value is used for evaluating the object to be evaluated.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that, in the evaluation method, the evaluation device, the electronic device, and the related product described in the embodiments of the present application, an original index value is obtained, normalization processing is performed on the original index value to obtain a normalized index value, an output weight corresponding to the normalized index value is determined according to a preset model and the normalized index value, result integration is performed on the normalized index value according to the output weight to obtain a target evaluation value, and the target evaluation value is used for evaluating an object to be evaluated. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 1B is a schematic flow chart of an evaluation method provided in an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a fuzzy neural network model provided in an embodiment of the present application;
FIG. 3 is a schematic flow chart of an evaluation method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 5A is a block diagram of functional units of an evaluation apparatus according to an embodiment of the present application;
fig. 5B is a block diagram of functional units of an evaluation apparatus according to an embodiment of the present application.
Detailed Description
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The electronic device according to the embodiments of the present application may be a portable electronic device, such as a mobile phone, a tablet computer, a wearable electronic device (e.g., a smart watch) with a wireless communication function, and the like, which includes other functions, such as a personal digital assistant and/or a music player function. Exemplary embodiments of the portable electronic device include, but are not limited to, portable electronic devices that carry an IOS system, an Android system, a Microsoft system, or other operating system. The portable electronic device may also be other portable electronic devices such as a Laptop computer (Laptop) or the like. It should also be understood that in other embodiments, the electronic device may not be a portable electronic device, but may be a desktop computer. The electronic device may also include a server.
As shown in fig. 1A, fig. 1A is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a processor and a memory, etc. Wherein the memory is connected with the processor. The Processor is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions and processes data of the electronic device by running or executing software programs and/or modules stored in the memory and calling the data stored in the memory, thereby performing overall monitoring on the electronic device, and the Processor may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU) or a Network Processing Unit (NPU).
Further, the processor may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The memory is used for storing software programs and/or modules, and the processor executes various functional applications of the electronic device by running the software programs and/or modules stored in the memory. The memory mainly comprises a program storage area and a data storage area, wherein the program storage area can store an operating system, a software program required by at least one function and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
Based on the electronic device described in fig. 1A, the following evaluation method can be performed, and the specific steps are as follows:
acquiring an original index value;
carrying out normalization processing on the original index value to obtain a normalized index value;
determining an output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
It can be seen that the electronic device described in this embodiment of the application may acquire an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to a preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
Referring to fig. 1B, fig. 1B is a schematic flowchart of an evaluation method according to an embodiment of the present disclosure, as shown in the figure, the evaluation method is applied to the electronic device shown in fig. 1A, and the evaluation method includes:
101. and acquiring an original index value.
And the original index value is used for reflecting the running state of the object to be evaluated. The object to be evaluated may be any object that needs to be evaluated, for example, a central heating system, and for example, a certain index of the central heating system. The central heating system may include a heat pump station, a thermalization power station, a regional boiler room, and other heating devices, which are not limited herein. The indexes of the central heating system may include a user room temperature index, a heating system reliability index, a fault treatment timeliness index, an exhaust emission index, a noise index, a wastewater index, a heating energy efficiency index, a risk index, a cost index, a power grid frequency index, and the like, which are not limited herein.
102. And carrying out normalization processing on the original index value to obtain a normalized index value.
Specifically, since indexes and index types corresponding to different original index values are different, before evaluating the original index values, normalization processing is performed on the original index values to obtain normalized index values. The normalized index value is beneficial to converting different indexes into the same value biased index, and is beneficial to realizing the unified evaluation of various indexes.
For example, for the cost index, the lower the original index value corresponding to the cost index, that is, the lower the cost, the more desirable the cost index is, the higher the target evaluation value corresponding to the cost index is. For the heat supply energy efficiency index, the higher the original index value corresponding to the heat supply energy efficiency index is, that is, the higher the energy efficiency is, the more desirable the original index value is, the higher the target evaluation value corresponding to the cost index is. Therefore, before the original index value is evaluated, the original index value is normalized, so that the unified evaluation of various original indexes is facilitated, and the adaptability and the accuracy of a central heating system operation evaluation system are improved.
103. And determining the output weight corresponding to the normalization index value according to a preset model and the normalization index value.
The preset model may be set by an administrator or default to the system, and is not limited herein. The output weight may be an output weight determined by the preset model according to the normalization index value, and when the output normalization index value changes, the output weight output by the preset model may also change accordingly.
104. And integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
Specifically, in the related art, for the operation evaluation of the central heating system, a system operation and maintenance worker usually determines a fixed weight corresponding to an index according to historical experience, and then evaluates each index according to the determined weight. Or a new index capable of reflecting the operation state of the central heating system is additionally set, and the central heating system is evaluated according to the new index.
However, the scheme for determining the weight according to the historical experience depends on the subjective judgment of the operation and maintenance personnel, and is low in objectivity and accuracy. The scheme of defining a new index is not favorable for accurately evaluating the central heating system because the relationship between the new index and the original index cannot be clarified.
In the embodiment of the application, the electronic device may acquire an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to a preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, and the target evaluation value is used for evaluating an object to be evaluated. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
In a possible example, in the step 102, the normalizing the original index value to obtain a normalized index value may include the following steps:
21. determining a target index type corresponding to the original index value;
22. determining a target conversion formula corresponding to the target index type according to the corresponding relation between the preset index type and the conversion formula;
23. and carrying out normalization processing on the original index value according to the target conversion formula to obtain the normalized index value.
The electronic device may pre-store a corresponding relationship between a preset index type and the conversion formula. The corresponding relationship between the preset index type and the conversion formula may be set by an administrator or default, and is not limited herein.
Specifically, as described above, since the indexes and the index types corresponding to different original index values are different, before the original index value is evaluated, the original index value is normalized to obtain a normalized index value, which is helpful for converting different indexes into an index with a biased same value, and is helpful for realizing unified evaluation of multiple indexes. Further, a target index type corresponding to the original index value is determined, a target conversion formula corresponding to the target index type is determined according to the corresponding relation between the preset index type and the conversion formula, and then normalization processing is performed on the original index value according to the target conversion formula to obtain a normalized index value.
Taking the cost index as an example, the lower the original index value corresponding to the cost index is, the higher the target evaluation value corresponding to the cost index is. The target index type corresponding to the original index value of the cost index may be determined as the first type. It is understood that the index corresponding to the first type is an index corresponding to a higher target evaluation value the lower the original index value is.
According to the corresponding relation between the preset index type and the conversion formula, determining that the target conversion formula corresponding to the first type is as follows:
wherein r is1Is a normalized index value corresponding to the index of the first type, a1Is the original index value of the index corresponding to the first type,
is the maximum value of the index corresponding to the first type,
is the minimum value of the index corresponding to the first type. The maximum value and the minimum value of the index corresponding to the first type may be obtained by detecting the index corresponding to the first type for multiple times, or may be determined according to an original index value, which is not limited herein.
Taking the energy efficiency index as an example, the higher the original index value corresponding to the energy efficiency index is, the higher the target evaluation value corresponding to the energy efficiency index is. The target index type corresponding to the original index value of the energy efficiency index may be determined as the second type. It is understood that the index corresponding to the second type is an index corresponding to a higher target evaluation value as the original index value is higher.
According to the corresponding relation between the preset index type and the conversion formula, determining that the target conversion formula corresponding to the second type is as follows:
wherein r is2Is a normalized index value corresponding to the index of the second type, a2Original finger of the index corresponding to the second typeThe value of the standard value is marked,
is the maximum value of the index corresponding to the second type,
is the minimum value of the index corresponding to the second type. The maximum value and the minimum value of the index corresponding to the second type may be obtained by detecting the index corresponding to the second type for multiple times, or may be determined according to the original index value, which is not limited herein.
Taking the grid frequency index as an example, the higher the original index value corresponding to the grid frequency index is, the better the original index value is, or the lower the original index value is, but when the original index value is within a certain interval, the higher the target evaluation value corresponding to the grid frequency index is. The target index type corresponding to the original index value of the grid frequency index may be determined as an interval type, an index interval corresponding to the interval type index is a preset interval, and the preset interval may include an interval minimum value and an interval maximum value. It can be understood that the interval type corresponds to the following indexes: when the original index value is located in the preset interval, the corresponding target evaluation value is the highest, and the smaller the difference value between the original index value and the maximum value or the minimum value of the preset interval is, the higher the corresponding target evaluation value is.
According to the corresponding relation between the preset index type and the conversion formula, when the original index value is smaller than the minimum value u of the interval1Then, the first sub-conversion formula determined is:
when the original index value is in the preset interval [ u ]1，u2]Then, the determined second sub-conversion formula is: r is31. When the original index value is larger than the interval minimum value u2Then, the third sub-conversion formula determined is:
wherein r is3Is a normalized index value corresponding to the interval type index, a3Is a sectionOriginal index value of the profile index, u1Is the minimum value of a preset interval, u2Is the maximum value of the preset interval,
is the maximum value of the interval type index,
the max function may be expressed as the minimum value of the interval-type index
And
the largest value is selected as the denominator of the first and third sub-conversion formulas. The maximum value and the minimum value of the interval-type index may be obtained by detecting the interval-type index for a plurality of times, or may be determined by the original index value, which is not limited herein.
It can be seen that, in the embodiment of the present application, a target index type corresponding to an original index value is determined, a target conversion formula corresponding to the target index type is determined according to a preset corresponding relationship between the index type and the conversion formula, and then the original index value is normalized according to the target conversion formula, so as to obtain a normalized index value. Therefore, the target conversion formula corresponding to the target index type is determined, the original index value is normalized, unified evaluation on various original indexes is facilitated, and the adaptability of the operation evaluation system of the central heating system is improved.
In a possible example, the step 103 of determining the output weight corresponding to the normalization index value according to the preset model and the normalization index value may include:
31. inputting the normalization index value through the input layer to obtain first output data;
32. fuzzification processing is carried out on the first output data through the membership function layer to obtain second output data;
33. processing the second output data through the rule layer to obtain third output data;
34. and performing deblurring processing on the third output data through the output layer to obtain an output weight corresponding to the normalization index value.
In one possible example, the membership function layer includes a preset fuzzy set and a membership function, and the step 32 of performing fuzzification processing on the first output data through the membership function layer to obtain second output data may include the following steps:
321. according to the fuzzy set, carrying out fuzzy degree division on the first output data to obtain a plurality of fuzzy values;
322. and according to the membership function, performing the fuzzification processing on the fuzzy values to obtain the second output data.
Wherein the processing function of the input layer may be
And i is less than or equal to n, and i is more than 0.
May represent the input of the ith node in the input layer,
the output of the ith node in the input layer can be represented, and n represents the index number input into the preset model.
Wherein the fuzzy sets in the membership function layer may represent the fuzziness of the first output data. For example, the fuzzy sets may be divided into 5 sets, the 5 sets respectively representing that the first output data is small, the first output data is moderate, the first output data is large, and the first output data is large. The membership function may be
Wherein the content of the first and second substances,
may represent the input of the jth first output data at the kth node in the membership function layer,
can represent the output of the kth node of the jth first output data in the membership function layer, mjkThe mean, σ, of the membership functions of the kth fuzzy set, which may represent the jth first output datajkThe standard deviation of the membership functions of the kth fuzzy set of the jth first output data may be represented. It should be understood that the value range of i is determined according to the number of the normalized index values, for example, if there are n normalized index values input to the above fuzzy neural network model, i is 1 to n. The value range of k is determined according to the number of types of fuzzy sets, for example, m fuzzy sets are provided in the membership function layer, and k is 1 to m.
Wherein the processing function of the rule layer may be
Wherein the content of the first and second substances,
may represent the input of the ith node in the rule layer,
the output of the ith node in the rule layer can be represented, p is the number of the nodes in the rule layer, and the number of the nodes in the rule layer can be preset according to historical training data of the model when the fuzzy neural network model is constructed.
Wherein the processing function of the output layer may be
Wherein the content of the first and second substances,
may represent the input of the output layer corresponding to the ith normalization index,
the output of the output layer corresponding to the ith normalization index, i.e. the weight corresponding to the normalization index value, ωnpThe connection weight between the rule layer and the output layer may be expressed.
Specifically, please refer to fig. 2, wherein fig. 2 is a schematic structural diagram of a fuzzy neural network model according to an embodiment of the present disclosure. As shown in fig. 2, 3 normalized index values are input to the input layer of the fuzzy neural network model to obtain first output data. The input layer passes the first output data to the membership function layer. In the membership function layer, 5 fuzzy sets are set for each first output data, and 15 fuzzy sets, namely 15 nodes, are set in total. And performing fuzzification processing on the normalized index value through a membership function layer to obtain second output data. And processing the second output data through the number of nodes and the processing rule preset in the rule layer to obtain third output data. And performing deblurring processing on the third output data through an output layer to obtain an output weight corresponding to the normalization index value.
It can be seen that, in the embodiment of the present application, the output weight is determined by using the fuzzy neural network model, so that the output weight can be adjusted according to the input normalized index value, and when the input normalized index value changes, the output weight is also adjusted accordingly, which is helpful for improving the accuracy and flexibility of the operation evaluation system of the central heating system.
In one possible example, in the fuzzy neural network model described above, the connection weight ω between the rule layer and the output layernpMean value m of membership functionjkAnd the standard deviation σ of the membership functionsjkThe updating may be performed by:
determining the expected weight d corresponding to the normalization index valuer；
According to the desired weight drAnd the output rightHeavy load
Determining an output error corresponding to the output weight
According to the output error
And the third output data
Determining a connection weight ω between the rule layer and the output layernpCorresponding first error Δ ωnp；
According to the connection weight omeganpThe output error
And the third output data
Determining a second error corresponding to a node in the rule layer
According to the second error
And the second output data
Determining a third error corresponding to a node in the membership function layer
According to the third error
The first output data
And mean m of membership functionsjkDetermining a fourth error Δ m corresponding to the mean value of the membership functionjk；
According to the third error
The first output data
And the standard deviation σ of the membership functionsjkDetermining a fifth error delta sigma corresponding to the standard deviation of the membership functionjk；
According to the first error delta omeganpFor the connection weight ωnpAdjusting to obtain a target connection weight;
according to the fourth error Δ mjkTo the mean value m of the membership functionjkAdjusting to obtain a target mean value;
according to the fifth error delta sigmajkFor the standard deviation σ of the membership functionjkAdjusting to obtain a target standard variance;
and updating the fuzzy neural network model according to the target connection weight, the target mean and the target standard deviation.
In particular, according to the desired weight drAnd output weight
Determining an output error corresponding to the output weight as
According to output error
And a third output numberAccording to
Determining a connection weight ω between a rule layer and an output layernpCorresponding first error is
According to the connection weight omeganpOutput error of the output signal
And third output data
Determining a second error corresponding to a node in the rule layer as
According to the second error
And second output data
Determining a third error corresponding to a node in the membership function layer as
Wherein the content of the first and second substances,
may represent the output of the jth node in the membership function layer connected to the ith node of the rule layer
The output of the kth node in the membership function layer connected to the l-th node of the rule layer can be represented, and j ≠ k.
According to the third error
First output data
And mean m of membership functionsjkDetermining a fourth error corresponding to the mean of the membership function as
According to the third error
First output data
And the standard deviation σ of the membership functionsjkDetermining a fifth error corresponding to the standard deviation of the membership function as
According to the first error delta omeganpTo the connection weight ωnpAnd adjusting to obtain the target connection weight. According to the fourth error Δ mjkTo the mean value m of the membership functionjkAnd adjusting to obtain a target mean value. According to the fifth error Δ σjkStandard deviation σ of membership functionjkAnd adjusting to obtain the target standard deviation. The adjustment method may be, but is not limited to, addition of the first error and the connection weight, or subtraction, multiplication, or the like. And updating the fuzzy neural network model according to the target connection weight, the target mean and the target standard deviation.
It can be seen that, in the embodiment of the present application, the fuzzy neural network model is updated by adjusting the target connection weight, the target mean and the target standard deviation, and the output weight is determined by using the fuzzy neural network model, so that the output weight can be adjusted according to the input normalization index value, and when the input normalization index value changes, the output weight is correspondingly adjusted, which is beneficial to improving the accuracy and flexibility of the operation evaluation system of the central heating system.
In one possible example, the normalizing index value includes a first sub-normalizing index value and a second sub-normalizing index value, and the output weight includes a first sub-output weight and a second sub-output weight, wherein the first sub-normalizing index value corresponds to the first sub-output weight, and the second sub-normalizing index value corresponds to the second sub-output weight, in the step 104, the integrating the normalizing index value according to the output weight to obtain the target evaluation value may include:
1041. according to the first sub-output weight, performing result integration on the first sub-normalization index value to obtain a first sub-normalization index value;
1042. according to the second sub-output weight, performing result integration on the second sub-normalization index value to obtain a second sub-normalization index value;
1043. and determining the target evaluation value according to the first sub-target evaluation value and the second sub-target evaluation value.
Specifically, in the case where the above-described original index value includes a plurality of sub-original index values, accordingly, the normalized index value also includes a plurality of sub-normalized index values, and the output weight includes a plurality of sub-output weights. In this case, the target evaluation value may be determined based on the first sub-target evaluation value and the second sub-target evaluation value.
For example, if the normalized index value is { r }iAnd (i ═ 1, 2.. times, n), the output weight determined according to the preset model and the normalized index value is { ω ═ wiWhere 1, 2, n, the first sub-target index value is y1＝ω1r1The second sub-index value is y2＝ω2r2And so on. According to the plurality of sub-target evaluation values, determining a target evaluation value as
It can be seen that, in the embodiment of the present application, the first sub-normalization index value is result-integrated according to the first sub-output weight to obtain the first sub-goal index value, the second sub-normalization index value is result-integrated according to the second sub-output weight to obtain the second sub-goal index value, the target evaluation value is determined according to the first sub-goal evaluation value and the second sub-goal evaluation value, and when a plurality of sub-original index values exist, the plurality of sub-goal index values are processed to facilitate uniform evaluation of the plurality of sub-goal index values, which is helpful for improving the adaptability of the operation evaluation system of the central heating system.
It should be noted that the formulas listed in the embodiments of the present application are intended to illustrate possible conversion formulas or corresponding relationships, and should not be understood as unique conversion formulas or corresponding relationships. Other formulas capable of achieving the same or similar effects may also be used in the embodiments of the present application, and are not limited herein.
In one possible example, the method may further comprise the steps of:
105. and optimizing an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold value.
The target evaluation threshold may be determined according to a historical target evaluation value, for example, an average of a plurality of historical target evaluation values, and the like, and is not limited herein.
Specifically, when the target evaluation value is less than or equal to the preset target evaluation threshold value, an index corresponding to the target evaluation value is determined, and operation and maintenance personnel are prompted correspondingly, so that the operation and maintenance personnel can know the index to be optimized and optimize the index, and objective data support is provided for optimization of the object to be evaluated, which is beneficial to improving the overall operation state of the object to be evaluated, such as a central heating system. The specific optimization mode can be determined comprehensively according to factors such as the application occasion of the central heating system, the heating demand and the like, and is not limited herein.
It can be seen that, in the embodiment of the present application, when the target evaluation value is less than or equal to the preset target evaluation threshold, the index corresponding to the target evaluation value is optimized. Therefore, the accuracy and the objectivity of the operation evaluation system of the central heating system are improved, and the overall operation state of the object to be evaluated is promoted.
Referring to fig. 3, in accordance with the embodiment shown in fig. 1B, fig. 3 is a schematic flowchart of an evaluation method provided in an embodiment of the present application, applied to the electronic device shown in fig. 1A, where the evaluation method includes:
201. and acquiring an original index value.
202. And carrying out normalization processing on the original index value to obtain a normalized index value.
203. And determining the output weight corresponding to the normalization index value according to a preset model and the normalization index value.
204. And integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
205. And optimizing an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold value.
For the detailed description of the steps 201 to 205, reference may be made to the corresponding steps of the evaluation method described in the above fig. 1B, and details are not repeated here.
It can be seen that, in the evaluation method described in this embodiment of the application, the electronic device may obtain an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to the preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated, and optimize an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
Referring to fig. 4 in keeping with the above embodiments, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in the drawing, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:
acquiring an original index value;
carrying out normalization processing on the original index value to obtain a normalized index value;
determining an output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
It can be seen that the electronic device described in this embodiment of the application may acquire an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to a preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
In one possible example, in normalizing the original metric value to obtain a normalized metric value, the program further includes instructions for performing the following steps:
determining a target index type corresponding to the original index value;
determining a target conversion formula corresponding to the target index type according to the corresponding relation between the preset index type and the conversion formula;
and carrying out normalization processing on the original index value according to the target conversion formula to obtain the normalized index value.
In one possible example, the target indicator type includes an interval-type indicator, an indicator interval corresponding to the interval-type indicator is a preset interval, the preset interval includes an interval minimum value and an interval maximum value, the target conversion formula includes a first sub-conversion formula, a second sub-conversion formula and a third sub-conversion formula, and in terms of the normalization processing of the original indicator value according to the target conversion formula, the program includes instructions for performing the following steps:
when the original index value is smaller than the interval minimum value, carrying out normalization processing on the original index value according to the first sub-conversion formula;
when the original index value is located in the preset interval, carrying out normalization processing on the original index value according to the second sub-conversion formula;
and when the original index value is larger than the maximum interval value, carrying out normalization processing on the original index value according to the third sub-conversion formula.
In one possible example, the preset model comprises a fuzzy neural network model, the fuzzy neural network model comprises an input layer, a membership function layer, a rule layer and an output layer, and the program comprises instructions for executing the following steps in terms of determining an output weight corresponding to the normalization index value according to the preset model and the normalization index value:
inputting the normalization index value through the input layer to obtain first output data;
fuzzification processing is carried out on the first output data through the membership function layer to obtain second output data;
processing the second output data through the rule layer to obtain third output data;
and performing deblurring processing on the third output data through the output layer to obtain an output weight corresponding to the normalization index value.
In one possible example, the membership function layer includes a preset fuzzy set and a membership function, and in the step of fuzzifying the first output data by the membership function layer to obtain a second output data, the program includes instructions for performing the following steps:
according to the fuzzy set, carrying out fuzzy degree division on the first output data to obtain a plurality of fuzzy values;
and according to the membership function, performing the fuzzification processing on the fuzzy values to obtain the second output data.
In one possible example, the normalization index value includes a first sub-normalization index value and a second sub-normalization index value, and the output weight includes a first sub-output weight and a second sub-output weight, wherein the first sub-normalization index value corresponds to the first sub-output weight, and the second sub-normalization index value corresponds to the second sub-output weight, and in the aspect of integrating the normalization index values according to the output weights to obtain the target evaluation value, the program further includes instructions for:
according to the first sub-output weight, performing result integration on the first sub-normalization index value to obtain a first sub-normalization index value;
according to the second sub-output weight, performing result integration on the second sub-normalization index value to obtain a second sub-normalization index value;
and determining the target evaluation value according to the first sub-target evaluation value and the second sub-target evaluation value.
In one possible example, the program includes instructions for performing the steps of:
and optimizing an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold value.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that in order to implement the above functions, it includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the functional units may be divided according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Referring to fig. 5A, fig. 5A is a block diagram of functional units of an evaluation apparatus 500 according to an embodiment of the present application, where the apparatus 500 includes: an acquisition unit 501, a processing unit 502, a determination unit 503 and an evaluation unit 504, wherein,
the obtaining unit 501 is configured to obtain an original index value;
the processing unit 502 is configured to perform normalization processing on the original index value to obtain a normalized index value;
the determining unit 503 is configured to determine an output weight corresponding to the normalization index value according to a preset model and the normalization index value;
the evaluation unit 504 is configured to perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated.
It can be seen that the evaluation device described in this embodiment of the application may acquire an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to the preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
In a possible example, in terms of performing normalization processing on the original index value to obtain a normalized index value, the processing unit 502 is specifically configured to:
determining a target index type corresponding to the original index value;
determining a target conversion formula corresponding to the target index type according to the corresponding relation between the preset index type and the conversion formula;
and carrying out normalization processing on the original index value according to the target conversion formula to obtain the normalized index value.
In a possible example, the target indicator type includes an interval-type indicator, an indicator interval corresponding to the interval-type indicator is a preset interval, the preset interval includes an interval minimum value and an interval maximum value, the target conversion formula includes a first sub-conversion formula, a second sub-conversion formula and a third sub-conversion formula, and in terms of normalizing the original indicator value according to the target conversion formula, the processing unit 502 is specifically configured to:
when the original index value is smaller than the interval minimum value, carrying out normalization processing on the original index value according to the first sub-conversion formula;
when the original index value is located in the preset interval, carrying out normalization processing on the original index value according to the second sub-conversion formula;
and when the original index value is larger than the maximum interval value, carrying out normalization processing on the original index value according to the third sub-conversion formula.
In a possible example, the preset model includes a fuzzy neural network model, the fuzzy neural network model includes an input layer, a membership function layer, a rule layer, and an output layer, and in the aspect of determining the output weight corresponding to the normalization index value according to the preset model and the normalization index value, the determining unit 503 is further specifically configured to:
inputting the normalization index value through the input layer to obtain first output data;
fuzzification processing is carried out on the first output data through the membership function layer to obtain second output data;
processing the second output data through the rule layer to obtain third output data;
and performing deblurring processing on the third output data through the output layer to obtain an output weight corresponding to the normalization index value.
In a possible example, the membership function layer includes a preset fuzzy set and a membership function, and in the aspect that the fuzzification processing is performed on the first output data through the membership function layer to obtain a second output data, the determining unit 503 is specifically configured to:
according to the fuzzy set, carrying out fuzzy degree division on the first output data to obtain a plurality of fuzzy values;
and according to the membership function, performing the fuzzification processing on the fuzzy values to obtain the second output data.
In a possible example, the normalization index value includes a first sub-normalization index value and a second sub-normalization index value, and the output weight includes a first sub-output weight and a second sub-output weight, where the first sub-normalization index value corresponds to the first sub-output weight, and the second sub-normalization index value corresponds to the second sub-output weight, and in terms of the integrating the normalization index values according to the output weights to obtain the target evaluation value, the evaluation unit 504 is specifically configured to:
according to the first sub-output weight, performing result integration on the first sub-normalization index value to obtain a first sub-normalization index value;
according to the second sub-output weight, performing result integration on the second sub-normalization index value to obtain a second sub-normalization index value;
and determining the target evaluation value according to the first sub-target evaluation value and the second sub-target evaluation value.
In one possible example, as shown in fig. 5B, the apparatus 500 may further include, as compared to fig. 5A described above: an optimization unit 505 is provided, wherein,
the optimizing unit 505 is configured to optimize an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold.
It can be seen that the evaluation device provided in this embodiment of the application may acquire an original index value, perform normalization processing on the original index value to obtain a normalized index value, determine an output weight corresponding to the normalized index value according to a preset model and the normalized index value, perform result integration on the normalized index value according to the output weight to obtain a target evaluation value, where the target evaluation value is used to evaluate an object to be evaluated, and optimize an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold. On one hand, the method is beneficial to carrying out unified evaluation on various original indexes by carrying out normalization processing on the original index values, and improves the adaptability of a central heating system operation evaluation system; on the other hand, the output weight is determined by using the preset model, so that the output weight can be adjusted according to the input normalized index value, and the accuracy and the flexibility of the operation evaluation system of the central heating system are improved.
It can be understood that the functions of each program module of the evaluation apparatus in this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the related description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enables a computer to execute part or all of the steps of any one of the methods as described in the above method embodiments, and the computer includes a control platform.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, the computer comprising the control platform.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An evaluation method, characterized in that the method comprises:
acquiring an original index value;
carrying out normalization processing on the original index value to obtain a normalized index value;
determining an output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and integrating results of the normalized index values according to the output weight to obtain a target evaluation value, wherein the target evaluation value is used for evaluating an object to be evaluated.
2. The method of claim 1, wherein the normalizing the original metric value to obtain a normalized metric value comprises:
determining a target index type corresponding to the original index value;
determining a target conversion formula corresponding to the target index type according to the corresponding relation between the preset index type and the conversion formula;
and carrying out normalization processing on the original index value according to the target conversion formula to obtain the normalized index value.
3. The method according to claim 2, wherein the target indicator type comprises an interval indicator, the indicator interval corresponding to the interval indicator is a preset interval, the preset interval comprises an interval minimum value and an interval maximum value, and the target conversion formula comprises a first sub-conversion formula, a second sub-conversion formula and a third sub-conversion formula;
the normalizing the original index value according to the target conversion formula comprises:
when the original index value is smaller than the interval minimum value, carrying out normalization processing on the original index value according to the first sub-conversion formula;
when the original index value is located in the preset interval, carrying out normalization processing on the original index value according to the second sub-conversion formula;
and when the original index value is larger than the maximum interval value, carrying out normalization processing on the original index value according to the third sub-conversion formula.
4. The method of any one of claims 1-3, wherein the pre-set model comprises a fuzzy neural network model comprising an input layer, a membership function layer, a rule layer, and an output layer;
the determining the output weight corresponding to the normalization index value according to the preset model and the normalization index value comprises the following steps:
inputting the normalization index value through the input layer to obtain first output data;
fuzzification processing is carried out on the first output data through the membership function layer to obtain second output data;
processing the second output data through the rule layer to obtain third output data;
and performing deblurring processing on the third output data through the output layer to obtain an output weight corresponding to the normalization index value.
5. The method of claim 4, wherein the membership function layer comprises a preset fuzzy set and a membership function, and the fuzzifying the first output data by the membership function layer to obtain a second output data comprises:
according to the fuzzy set, carrying out fuzzy degree division on the first output data to obtain a plurality of fuzzy values;
and according to the membership function, performing the fuzzification processing on the fuzzy values to obtain the second output data.
6. The method of claim 1, wherein the normalization index value comprises a first sub-normalization index value and a second sub-normalization index value, and the output weight comprises a first sub-output weight and a second sub-output weight, wherein the first sub-normalization index value corresponds to the first sub-output weight and the second sub-normalization index value corresponds to the second sub-output weight;
the performing result integration on the normalized index value according to the output weight to obtain a target evaluation value includes:
according to the first sub-output weight, performing result integration on the first sub-normalization index value to obtain a first sub-normalization index value;
according to the second sub-output weight, performing result integration on the second sub-normalization index value to obtain a second sub-normalization index value;
and determining the target evaluation value according to the first sub-target evaluation value and the second sub-target evaluation value.
7. The method of claim 1, further comprising:
and optimizing an index corresponding to the target evaluation value when the target evaluation value is less than or equal to a preset target evaluation threshold value.
8. An evaluation device, characterized in that the device comprises: an acquisition unit, a processing unit, a determination unit and an evaluation unit, wherein,
the acquisition unit is used for acquiring an original index value;
the processing unit is used for carrying out normalization processing on the original index value to obtain a normalized index value;
the determining unit is used for determining the output weight corresponding to the normalization index value according to a preset model and the normalization index value;
and the evaluation unit is used for integrating the result of the normalized index value according to the output weight to obtain a target evaluation value, and the target evaluation value is used for evaluating the object to be evaluated.
9. An electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps of the method of any of claims 1-7.
10. A computer-readable storage medium, characterized by storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute instructions of the steps in the method according to any one of claims 1-7.
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