Evaluation method and system based on comprehensive evaluation system of intelligent heat supply system

Abstract

The invention relates to the technical field of intelligent heat supply, and discloses an evaluation method and system based on an intelligent heat supply system comprehensive evaluation system, and the method comprises the steps: determining a comprehensive evaluation target, building a comprehensive evaluation matrix, building a comprehensive evaluation model, and carrying out the result analysis and suggestion. An evaluation index system is established on the basis of the determined comprehensive evaluation target, and the evaluation index system comprises three evaluation objects which are the data interconnection capability A of a first-level index, the intelligent decision-making capability B of the first-level index and the system self-control capability C of the first-level index; based on the connotation, target and characteristics of the intelligent heat supply system, 14 evaluation indexes are selected from the three aspects of data communication, intelligent decision and system automation, the intelligent heat supply system is divided into 5 grades according to the final evaluation score in combination with an improved structure entropy weight method and a grey relational analysis method, the efficiency of the heat supply system is improved through accurate benchmarking, and the method is suitable for popularization and application. Livelihood services are guaranteed, industry digital transformation is promoted, and energy resource allocation is optimized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent heating technology, and in particular to an evaluation method and system based on a comprehensive evaluation system of an intelligent heating system. Background Art

[0002] As technologies such as the Internet of Things, big data, and cloud computing become more mature, deep integration with existing centralized heating systems is a technical challenge and inevitable trend currently facing my country's heating industry.

[0003] How to establish a comprehensive evaluation system for intelligent heating systems, improve the efficiency of heating systems through precise benchmarking, ensure people's livelihood services, promote the digital transformation of the industry, optimize energy resource allocation, support policy formulation and industry supervision, promote technological innovation, and improve the service quality of heating companies has become a technical challenge that technical personnel in this field have to overcome. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the defects existing in the above-mentioned prior art, thereby providing an evaluation method and system based on a comprehensive evaluation system of an intelligent heating system.

[0005] An evaluation method based on a comprehensive evaluation system of an intelligent heating system comprises the following steps: S1. Establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects, namely, the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; S2. Establish a comprehensive evaluation model, process the indicators in the comprehensive evaluation matrix, and obtain the indicator calculation method; S3. Use the improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; S4. Determine the level standard of intelligent heating system; The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A 1 、Smart heat meter coverage rate A 2 、System database completeness A 3 and network communication level A 4 ; The evaluation index of intelligent decision-making ability B includes 5 secondary indicators: enterprise strategic organizational ability B 1 , heating load prediction accuracy B 2 , System fault location accuracy B 3 , Decision-making center intelligence level B 4 and heat metering charge level B 5 ; The evaluation index of system automation level C includes five secondary indicators: smart heat exchange station coverage rate C 1 , System automation terminal coverage C 2 、System automatic control failure C 3 , Perception control system utilization rate C 4 and business hall self-service level C 5 .

[0006] Preferably, the preparatory work for constructing the evaluation index system is also included: S0 Determine the goal of comprehensive evaluation, specifically including: S01. Collect and organize the data of the intelligent heating system of the evaluated object; S02. Perform pre-processing means of screening, improving and analyzing the indicators in the data until all target evaluation indicators are obtained; wherein all target evaluation indicators are composed of primary indicators and secondary indicators; Construct an evaluation index system based on all target evaluation indicators.

[0007] Preferably, step S1 establishes a comprehensive evaluation matrix, and the steps are as follows: obtaining a factor set for describing the factor ranking survey results of the primary indicators and the secondary indicators through the Delphi method and the structural entropy weight method, and performing standardization processing on the evaluation indicators.

[0008] Preferably, step S2 establishes a comprehensive evaluation model, and the steps are: weighting evaluation indicators, determining factor weight vectors, and determining indicator calculation methods.

[0009] Preferably, except for the network communication level A4, the scoring criteria of other secondary indicators are divided into 5 areas from low to high according to the numerical value or degree range of other secondary indicators; The numerical value or degree range of network communication level A4 is divided into 4 areas from low to high; Among them, the quantitative indicator scores of the secondary indicators are continuous values. When the achieved proportion falls within the corresponding score interval, the linear interpolation method is used to obtain the value as the score of the secondary indicator; The scores of qualitative indicators in the secondary indicators are discrete and discontinuous values, and the achievement degree scores are in the form of qualitative descriptions. The requirements for each degree increase with the increase of the degree, and the corresponding scores also increase; each degree requirement includes multiple detailed requirements, and different scores correspond to different items.

[0010] Preferably, S3. adopts the improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating, specifically including: S31. Create an evaluation sequence based on the scores of m companies and select the best sequence: S32. Perform standardization preprocessing on the scores of each indicator and calculate the single-level correlation coefficient; S33. Calculate the correlation of each level based on the single-level correlation; S34. Sort the smart heating development levels of the enterprises based on the size of the final correlation Z values ​​of the m enterprises; m≥1.

[0011] An evaluation system based on a comprehensive evaluation system of an intelligent heating system, comprising: The comprehensive evaluation matrix construction module is used to establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects: the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; The comprehensive evaluation model module is used to process the indicators in the comprehensive evaluation matrix and obtain the indicator calculation method; Comprehensive evaluation module, using improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; Grade confirmation module, which determines the grade standard of the intelligent heating system; The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A 1 、Smart heat meter coverage rate A 2 、System database completeness A 3 and network communication level A 4 ; The evaluation index of intelligent decision-making ability B includes 5 secondary indicators: enterprise strategic organizational ability B 1 , heating load prediction accuracy B 2 , System fault location accuracy B 3 , Decision-making center intelligence level B 4 and heat metering charge level B 5 ; The evaluation index of system automation level C includes five secondary indicators: smart heat exchange station coverage rate C 1 , System automation terminal coverage C 2 、System automatic control failure C 3 , Perception control system utilization rate C 4 and business hall self-service level C 5 .

[0012] The technical solution of the present invention has the following advantages: Different from the traditional evaluation system, the evaluation method of the present invention is based on the connotation, goal and characteristics of the intelligent heating system. 14 evaluation indicators are selected from three aspects: data communication, intelligent decision-making and system automation. Combined with the improved structural entropy weight method and grey correlation analysis method, the intelligent heating system is divided into 5 levels according to the final evaluation score. A comprehensive evaluation system for intelligent heating systems is established to improve the efficiency of heating systems through precise benchmarking, safeguard people's livelihood services, promote the digital transformation of the industry, optimize the allocation of energy resources, support policy formulation and industry supervision, promote technological innovation, and improve the service quality of heating companies. Provide reference and guidance for the comprehensive evaluation process of existing and to-be-built intelligent heating systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a principle diagram of the evaluation index system of the present invention; Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.

[0016] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0017] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0018] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Example 1 This embodiment discloses an evaluation method based on a comprehensive evaluation system of an intelligent heating system, comprising the following steps: S1. Establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects, namely, the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; S2. Establish a comprehensive evaluation model, process the indicators in the comprehensive evaluation matrix, and obtain the indicator calculation method; S3. Use the improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; S4. Determine the level standard of intelligent heating system; like Figure 1 The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A 1 、Smart heat meter coverage rate A 2 、System database completeness A 3 and network communication level A 4 ; The evaluation index of intelligent decision-making ability B includes 5 secondary indicators: enterprise strategic organizational ability B 1 , heating load prediction accuracy B 2 , System fault location accuracy B 3 , Decision-making center intelligence level B 4 and heat metering charge level B 5 ; The evaluation index of system automation level C includes five secondary indicators: smart heat exchange station coverage rate C 1 , System automation terminal coverage C 2 、System automatic control failure C 3 , usage rate of perception control system C 4 and business hall self-service level C 5 .

[0020] Specifically: like Figure 2 This embodiment also includes preparatory work for constructing an evaluation index system: S0. Determine the goal of comprehensive evaluation, specifically including: S01. Collect and organize the data of the intelligent heating system of the evaluated object; S02. Perform pre-processing means of screening, improving and analyzing the indicators in the data until all target evaluation indicators are obtained; wherein, all target evaluation indicator sets are composed of a primary indicator subset N1 and a secondary indicator subset N2, N=[N1, N2]; an evaluation indicator system is constructed based on all target evaluation indicator sets N. It should be noted that it is assumed that the indicators mentioned in this embodiment are all target evaluation indicators; The construction of the evaluation index system includes the following steps: Step 1. Establish a set of indicators related to the intelligent heating system. The indicator set includes two levels, specifically two subsets N 1 , N 2 .

[0021] Among them, N 1 , N 2 The set matrix expressions are as follows: (1) a represents factor, s and f represent N 1 , N 2 The number of factors of the aggregation matrix.

[0022] Step 2: Determine the set of evaluation factors for the intelligent heating system: First-level indicator subset N 1 It includes three indicators: data interconnection capability A, intelligent decision-making capability B, and system self-control capability C.

[0023] Data communication capability A includes 4 secondary indicators: Room temperature monitoring household rate A 1 : % (2) Where: 1 is the number of users who installed room temperature monitoring equipment, Ψ 1 is the total number of users.

[0024] Smart heat meter coverage rate A 2 : % (3) Where: 2 is the number of users who installed smart heat meters, 1= Ψ 2 ,Ψ2 is the total number of users.

[0025] System database completeness A 3 : % (4) Where: 3 is the data type of the heating enterprise database, Ψ 3 The data types required for the highest level database of the intelligent heating system. The information that a complete intelligent heating system should count includes but is not limited to the following categories: heating operation information, heating complaint information, heating fault information, meteorological information, energy consumption information, etc.; comprehensively consider the completeness of database statistical information and database performance indicators such as database throughput, cache hit rate, and number of connections.

[0026] Network Communication Level A 4 :Score heating enterprises according to their degree of achievement of this item, refer to Table 1.

[0027] Intelligent decision-making capability B includes 5 secondary indicators: Enterprise strategic organizational capabilities B 1 :Score heating enterprises according to their degree of achievement of this item, refer to Table 1.

[0028] Heating load prediction accuracy B 2 : % (5) Where: φ 2 Forecast heat load for heating period, unit kW, Φ 2 It is the actual heat load required to achieve room temperature during the heating period, in kW.

[0029] System fault location accuracy B 3 : % (6) Where: is the correct number of system failure events diagnosed by the decision center, in times, Φ 3 It is the number of actual failure events in the system, in times.

[0030] Decision center intelligence level B 4 :Score heating enterprises according to their degree of achievement of this item, refer to Table 1.

[0031] Heat metering charge level B 5 :Score heating enterprises according to their degree of achievement of this item, refer to Table 1.

[0032] System automation capability C includes 5 secondary indicators: Intelligent heat exchange station coverage rate C 1 : % (7) Where: γ 1 is the number of intelligent heat exchange stations with sensing and control functions, in units, Γ 1 is the total number of heat exchange stations, in units.

[0033] System automation terminal coverage C 2 : % (8) Where: γ 2 is the number of automatic control components in the system, in units, Γ 2 It is the total number of control components in the system, in pieces.

[0034] System automatic control failure C 3 : % (9) Where: γ 3 is the heating load after regulation on heating day, in kW, Γ 3 To predict the heating load required for the heating day, in kW, Λ 3 The heating load required to meet the user's room temperature requirements on the heating day, in kW.

[0035] Perception control system usage rate C 4 : Refers to the comprehensive utilization rate of intelligent heating subsystems such as heat source, heating network, heating station, load monitoring and control system, and secondary network intelligent balance control system.

[0036] % (10) Where: γ 4 Types of monitoring and control systems used by enterprises, types of units, Γ 4 Types and units of monitoring and control systems required for smart heating.

[0037] Business hall self-service level C 5 :Score according to the degree of achievement of this item by the heating enterprise, refer to Table 1 The scoring criteria of this embodiment divides the numerical value or degree range of each indicator into 5 areas from low to high according to the score, and A4 is 4 areas, among which qualitative indicators are divided by the degree of enterprise realization, and quantitative indicators are divided by specific proportion values. The score of indicators that are not within the proportion value range is 0. The quantitative indicator scores of the secondary indicators in Table 1 are continuous values. When the achieved proportion falls within the corresponding score interval, the linear interpolation method is used to obtain the value, which is the score of the secondary indicator: (11) Among them, k represents the proportion of the corresponding secondary index, σ 1 , σ 2 Indicates the lower and upper limits of the score interval where the proportion k is located, k 1 , k 2 Indicates the lower and upper limits of the numerical interval of the proportion k.

[0038] The scores of qualitative indicators in the secondary indicators are discrete discontinuous values, and the scores of the degree of achievement are in the form of qualitative descriptions. As the degree of achievement increases, the requirements of each degree increase, and the corresponding scores also increase. Each degree requirement includes multiple detailed requirements, and different scores correspond to different requirements.

[0039] Table 1 Scoring criteria for each indicator

[0040] Step S1 establishes a comprehensive evaluation matrix, and the steps are as follows: using the Delphi method and the structural entropy weight method to obtain a factor set for describing the factor ranking survey results of the primary and secondary indicators, and performing standardization of the evaluation indicators.

[0041] Step S2 establishes a comprehensive evaluation model, and the steps are: assigning weights to evaluation indicators, determining factor weight vectors, and determining indicator calculation methods.

[0042] The specific execution contents of step S1 and step S2 are: The Delphi method is used to weight the primary and secondary indicators. Experts’ opinions on questionnaire questions are surveyed, collected and fed back anonymously for multiple times. The opinions of multiple rounds of experts are summarized, modified and consulted, and the relatively consistent opinions of experts are analyzed and summarized as the final prediction results.

[0043] The following questionnaire was designed based on the Delphi method and the structural entropy weight method: Table 2 Questionnaire table for ranking comprehensive evaluation index factors of intelligent heating system

[0044] (2) Membership calculation Let x be the importance ranking number of a certain evaluator for a certain indicator, then the corresponding membership function value σ(x): (12) σ(x)∈[0,1], 0 means completely unacceptable, 1 means completely acceptable, h=n+2, h represents the total number of indicators, n represents the number of indicators in the same level, if a certain level of indicators contains 4 indicators, then n=4, h=6. The typical ranking results are quantitatively converted into the membership value m that reflects uncertainty ij =σ(t ij ), m ij represents the membership value of the i-th evaluator to the j-th indicator; t ij It is the importance ranking number of the i-th evaluator for the j-th indicator. i represents the number of evaluators, j represents the number of indicators. At the same level, if there are n indicators, the value range of j is 1 to n.

[0045] Array {m 1j ,m 2j ,…,m kj} is the discourse power of k evaluators on the indicator, then the average recognition is: (13) According to the definition of entropy in information theory, the uncertainty of k evaluators’ cognition of the indicator is recorded as the cognitive blindness Q j Its definition is: (14) The overall recognition of the indicator by k evaluators is: (15) Then x j The normalized structural entropy weight is: (16) The weight of each indicator is: (17) Since the number of secondary indicators contained in the primary indicators is different, in order to reduce the inaccuracy of the traditional structural entropy weight method, the weight of the primary indicators needs to be corrected. The formula for correcting the weight of the primary indicators is: (18) Among them, n 1 Refers to the number of secondary indicators contained in the primary indicator, n 2 Refers to the maximum number of secondary indicators contained in all primary indicators. After normalizing the revised primary indicator value again, the revised primary indicator weight can be obtained as: (19) S3. The improved structural entropy weight method-grey correlation method is used to conduct a comprehensive evaluation of enterprise intelligent heating, including: S31. Create an evaluation sequence based on the scores of m companies and select the best sequence: S32. Perform standardization preprocessing on the scores of each indicator and calculate the single-level correlation coefficient; S33. Calculate the correlation of each level based on the single-level correlation; S34. Sort the smart heating development levels of the enterprises according to the final correlation Z values ​​of the m enterprises; m≥1; Specifically: Establish an evaluation sequence based on the score and select the optimal sequence. Assuming that there are g companies participating in the evaluation, with a total of p evaluation indicators, the companies are scored according to the established scoring criteria, and a g×p evaluation sequence X can be obtained: (20) Where i=1,2,…,g; j=1,2,…,p, and X u * is the highest score of the pth indicator among all participating enterprises, u=1,2,…,p, then the reference sequence and the optimal sequence are obtained: (twenty one) The index scores are preprocessed by standardization, and the single-level correlation coefficient is calculated. Since the evaluation criteria and dimensional properties of each index are different, each index is preprocessed by linearization, and the processed values ​​are: (twenty two) Where i=1,2,…,p. According to the grey correlation theory, the calculation formula of the correlation coefficient between the pth index of the i-th enterprise and the optimal value of the pth index among the participating enterprises is ζ iu for: (twenty three) Among them, ρ is the discrimination coefficient, ρ∈[0,1], and generally ρ=0.5. The correlation coefficient matrix E of each indicator is obtained as follows: (twenty four) Calculate the correlation degree of each level. The calculation formula of single-level correlation degree is: = (25) Where i=1,2,…,g; k=1,2,…,p. The obtained single-level correlation matrix D i Multiplying the weight of the first-level indicator W by the algorithm can obtain the final correlation Z of the multi-level system. i , and its calculation formula is: (26) By comparing the final correlation Z values ​​of g companies, the companies’ smart heating development levels are ranked.

[0046] Step S4: Determine the intelligent heating system grade standard.

[0047] The development of enterprise smart heating is divided into the following five levels: Level 1 is the planning level, Level 2 is the standard level, Level 3 is the integration level, Level 4 is the excellent level, and Level 5 is the leading level. The score, definition and specific enterprise requirements of each level are shown in the following table: Table 3 Enterprise smart heating level standards;

[0048] Example 2 An evaluation system based on a comprehensive evaluation system of an intelligent heating system, comprising: The comprehensive evaluation matrix construction module is used to establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects: the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; The comprehensive evaluation model module is used to process the indicators in the comprehensive evaluation matrix and obtain the indicator calculation method; Comprehensive evaluation module, using improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; Grade confirmation module, which determines the grade standard of the intelligent heating system; The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A 1 、Smart heat meter coverage rate A 2 、System database completeness A 3 and network communication level A 4 ; The evaluation index of intelligent decision-making ability B includes 5 secondary indicators: enterprise strategic organizational ability B 1 , heating load prediction accuracy B 2 , System fault location accuracy B 3 , Decision-making center intelligence level B 4 and heat metering charge level B 5 ; The evaluation index of system automation level C includes five secondary indicators: smart heat exchange station coverage rate C 1 , System automation terminal coverage C 2 、System automatic control failure C 3 , usage rate of perception control system C 4and business hall self-service level C 5 .

[0049] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. An evaluation method based on a comprehensive evaluation system of an intelligent heating system, characterized in that: The following steps are involved: S1. Establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects, namely, the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; S2. Establish a comprehensive evaluation model, process the indicators in the comprehensive evaluation matrix, and obtain the indicator calculation method; S3. Use the improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; S4. Determine the level standard of intelligent heating system; The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A1, smart heat meter coverage rate A2, system database completeness A3 and network communication level A4; The evaluation index of intelligent decision-making capability B includes five secondary indicators: enterprise strategic organizational capability B1, heating load forecast accuracy B2, system fault location accuracy B3, decision-making center intelligence level B4 and heating metering and charging level B5; The evaluation indicators of system automation level C include 5 secondary indicators: smart heat exchange station coverage rate C1, system automation terminal coverage rate C2, system automatic control failure C3, perception and control system utilization rate C4 and business hall self-service level C5.

2. The evaluation method based on the comprehensive evaluation system of intelligent heating system according to claim 1 is characterized in that: It also includes the preparatory work for building the evaluation index system: S0. Determine the goals of the comprehensive evaluation, including: S01. Collect and organize the data of the intelligent heating system of the evaluated object; S02. Perform pre-processing means of screening, improving and analyzing the indicators in the data until all target evaluation indicators are obtained; wherein all target evaluation indicators are composed of primary indicators and secondary indicators; Construct an evaluation index system based on all target evaluation indicators.

3. The evaluation method based on the comprehensive evaluation system of intelligent heating system according to claim 1 is characterized in that: Step S1 establishes a comprehensive evaluation matrix, and the steps are as follows: using the Delphi method and the structural entropy weight method to obtain a factor set for describing the factor ranking survey results of the primary and secondary indicators, and performing standardization of the evaluation indicators.

4. The evaluation method based on the comprehensive evaluation system of intelligent heating system according to claim 1 is characterized in that: Step S2 establishes a comprehensive evaluation model, and the steps are: assigning weights to evaluation indicators, determining factor weight vectors, and determining indicator calculation methods.

5. The evaluation method based on the comprehensive evaluation system of intelligent heating system according to claim 1 is characterized in that: Except for the network communication level A4, the scoring criteria for other secondary indicators are divided into 5 areas from low to high according to the numerical value or degree range of other secondary indicators; The numerical value or degree range of network communication level A4 is divided into 4 areas from low to high; Among them, the quantitative indicator scores of the secondary indicators are continuous values. When the achieved proportion falls within the corresponding score interval, the linear interpolation method is used to obtain the value as the score of the secondary indicator; The scores of qualitative indicators in the secondary indicators are discrete and discontinuous values, and the achievement degree scores are in the form of qualitative descriptions. The requirements for each degree increase with the increase of the degree, and the corresponding scores also increase; each degree requirement includes multiple detailed requirements, and different scores correspond to different items.

6. The evaluation method based on the comprehensive evaluation system of intelligent heating system according to claim 1 is characterized in that: S3. The improved structural entropy weight method-grey correlation method is used to conduct a comprehensive evaluation of enterprise intelligent heating, including: S31. Create an evaluation sequence based on the scores of m companies and select the best sequence: S32. Perform standardization preprocessing on the scores of each indicator and calculate the single-level correlation coefficient; S33. Calculate the correlation of each level based on the single-level correlation; S34. Sort the smart heating development levels of the enterprises based on the size of the final correlation Z values ​​of the m enterprises; m≥1.

7. An evaluation system based on a comprehensive evaluation system of an intelligent heating system, characterized in that: include: The comprehensive evaluation matrix construction module is used to establish a comprehensive evaluation matrix based on the pre-built evaluation index system. The evaluation index system includes three evaluation objects: the data interconnection capability A of the first-level indicator, the intelligent decision-making capability B of the first-level indicator, and the system self-control capability C of the first-level indicator; The comprehensive evaluation model module is used to process the indicators in the comprehensive evaluation matrix and obtain the indicator calculation method; Comprehensive evaluation module, using improved structural entropy weight method-grey correlation method to conduct comprehensive evaluation of enterprise intelligent heating; Grade confirmation module, which determines the grade standard of the intelligent heating system; The evaluation index of data interconnection capability A includes four secondary indicators: room temperature monitoring penetration rate A1, smart heat meter coverage rate A2, system database completeness A3 and network communication level A4; The evaluation index of intelligent decision-making capability B includes five secondary indicators: enterprise strategic organizational capability B1, heating load forecast accuracy B2, system fault location accuracy B3, decision-making center intelligence level B4 and heating metering and charging level B5; The evaluation indicators of system automation level C include 5 secondary indicators: smart heat exchange station coverage rate C1, system automation terminal coverage rate C2, system automatic control failure C3, perception and control system utilization rate C4 and business hall self-service level C5.

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