Quality evaluation method and device for safety production socialization service

Abstract

The embodiment of the invention discloses a safety production socialization service quality evaluation method and device, and relates to the field of safety production management. The method comprises the following steps: constructing a hierarchical structure index system model; constructing a judgment matrix corresponding to each hierarchy, and determining the weight of each index relative to the index of the previous hierarchy according to the proportion of the feature elements of the feature vectors corresponding to the judgment matrixes; constructing an evaluation set, calculating a fuzzy vector of a last-stage index, and generating a fuzzy evaluation matrix; performing fuzzy synthesis on the fuzzy evaluation matrix and the weight of the last-stage index, and performing fuzzy synthesis upwards step by step until a fuzzy vector of the target layer is obtained; and searching the maximum membership in the fuzzy vector of the target layer, and generating a quality evaluation result according to the comment corresponding to the maximum membership. According to the embodiment of the invention, the service quality of the service institution can be evaluated more comprehensively and accurately, the deviation between the evaluation result and the actual situation is smaller, and a purchaser can select a proper service institution more easily.

Description

Technical Field

[0001] One or more embodiments of this specification relate to production safety management technology, and more particularly, to a quality assessment method and device for socialized production safety services. Background Art

[0002] Socialized production safety services refer to professional services provided by the government, enterprises or third-party organizations to improve the level of production safety. Its purpose is to help enterprises improve their production safety management level, reduce accidents and protect the life and health of employees by introducing professional technical services and management experience.

[0003] At present, in order to select a suitable service agency from a large number of service agencies, the purchaser needs to evaluate the service of the service agency. However, social services involve the purchaser (Party A), the service provider (Party B) and the service recipient (Party C), and the work content is also different. The existing evaluation methods are generally based on a unilateral subjective deduction method, and most of the indicators are selected by using a fixed indicator weight and a fixed number of indicators, which cannot be evaluated more comprehensively and accurately, making it easy for the evaluation results to have large deviations and difficult to select a truly suitable service agency. Summary of the invention

[0004] In order to solve the above problems, one or more embodiments of this specification describe a quality assessment method and device for socialized production safety services.

[0005] According to a first aspect, a quality assessment method for socialized production safety services is provided, the method comprising: Constructing a hierarchical indicator system model for socialized service evaluation of work safety, wherein the hierarchical indicator system model includes, from high level to low level, a target layer, a criterion layer, and a scheme layer, wherein the first indicator of the scheme layer includes a purchaser evaluation, a service provider evaluation, and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the scheme layer; Compare and score each indicator in pairs at each level, build a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; Constructing an evaluation set corresponding to each indicator, calculating the fuzzy vector of the final indicator according to the evaluation set, and generating a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; The fuzzy evaluation matrix and weight of the last-level indicator are fuzzy synthesized to obtain the fuzzy vector of the upper-level indicator, and the fuzzy synthesis is performed upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The maximum membership degree is found in the fuzzy vector of the target layer, and a quality assessment result is generated according to the comment corresponding to the maximum membership degree.

[0006] Preferably, the indicators are compared and scored in pairs at each level, and a judgment matrix corresponding to each level is constructed according to the scoring results, including: The scoring results of each indicator of the same dimension at each level are obtained level by level, and then the first judgment matrix corresponding to the criterion layer and the second judgment matrix corresponding to each second indicator at the solution layer are constructed according to the scoring results.

[0007] Preferably, after constructing the judgment matrix corresponding to each level according to the scoring results, the method further includes: Performing consistency verification on each of the judgment matrices based on the maximum eigenvalue root; When the consistency verification result of any of the judgment matrices is greater than a preset value, the indicators corresponding to the judgment matrices are re-compared and scored pairwise.

[0008] Preferably, the determining the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix includes: Calculate the geometric mean of all matrix elements in each row of the judgment matrix to obtain the eigenvector corresponding to the judgment matrix; After normalizing the characteristic elements of the characteristic vector, the weight of each indicator relative to the indicator of the previous level is determined according to the proportion of the characteristic elements.

[0009] Preferably, the indicator types of the indicators include qualitative indicators and quantitative indicators; After constructing the evaluation set corresponding to each indicator, the following is also included: A membership function is set for the indicator according to the indicator type.

[0010] Preferably, the quantitative index includes a single-value index of a single-value standard and an interval index of an interval standard, the membership function of the single-value index is a Gaussian membership function, and the membership function of the interval index is a trapezoidal membership function.

[0011] Preferably, the fuzzy evaluation matrix and weight of the final level indicator are fuzzily synthesized to obtain the fuzzy vector of the upper level indicator, including: A weight vector is constructed according to the weights corresponding to the final-level indicators of the same dimension, and the fuzzy evaluation matrix and the weight vector of the final-level indicator are calculated according to the weighted average fuzzy operator to obtain the fuzzy vector of the previous-level indicator.

[0012] According to a second aspect, a quality assessment device for socialized production safety services is provided, the device comprising: A first construction module is used to construct a hierarchical indicator system model for safety production socialized service evaluation, wherein the hierarchical indicator system model includes a target layer, a criterion layer and a scheme layer from a high level to a low level, wherein the first indicator of the scheme layer includes a purchaser evaluation, a service provider evaluation and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the scheme layer; The second construction module is used to compare and score each indicator in pairs at each level, construct a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; The third construction module is used to construct an evaluation set corresponding to each indicator, calculate the fuzzy vector of the final indicator according to the evaluation set, and generate a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; A synthesis module, used for fuzzy synthesis of the fuzzy evaluation matrix and weight of the last-level indicator to obtain the fuzzy vector of the upper-level indicator, and fuzzy synthesis upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The evaluation module is used to find the maximum membership degree in the fuzzy vector of the target layer and generate a quality evaluation result according to the comment corresponding to the maximum membership degree.

[0013] According to a third aspect, there is provided an electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0014] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0015] The solution provided in the embodiments of this specification can collect different evaluation data from three parties by constructing a hierarchical indicator system model, and determine the final evaluation result according to the maximum membership of the target layer by determining the indicator weights and performing fuzzy synthesis calculations on the fuzzy evaluation matrix step by step. This can evaluate the service quality of the service organization more comprehensively and accurately, and the deviation between the evaluation result and the actual situation is smaller, which makes it easier for the purchaser to choose a suitable service organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a quality assessment method for socialized production safety services in one embodiment of this specification.

[0018] Figure 2 It is an example schematic diagram of specific indicators corresponding to each level in the hierarchical indicator system model in one embodiment of this specification.

[0019] Figure 3 It is a schematic diagram of the scale reference of the scoring standard in one embodiment of this specification.

[0020] Figure 4 It is an example schematic diagram of the data corresponding to each first indicator in the evaluation set in one embodiment of this specification.

[0021] Figure 5 It is an example schematic diagram of the membership degree corresponding to the actual value of each first indicator in an embodiment of this specification.

[0022] Figure 6 It is a structural diagram of a quality assessment device for socialized production safety services in one embodiment of this specification.

[0023] Figure 7 It is a structural diagram of an electronic device in one embodiment of this specification. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0025] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0026] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0027] See also Figure 1 , Figure 1 : is a flow chart of a quality assessment method for socialized production safety services provided in an embodiment of the present application. In an embodiment of the present application, the method includes: S101. Construct a hierarchical indicator system model for the evaluation of socialized production safety services.

[0028] Among them, the hierarchical indicator system model includes, from high level to low level, a target layer, a criterion layer and a solution layer, the first indicator of the solution layer includes a purchaser evaluation, a service provider evaluation and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator in the solution layer.

[0029] The execution entity of this application can be a cloud server.

[0030] In the embodiment of this specification, the cloud server will first build a hierarchical index system model for evaluating socialized production safety services according to the model. The hierarchical index system model is a method for organizing and evaluating complex systems or processes. It realizes systematic management of the overall goal by decomposing the goal into multiple levels of sub-goals or evaluation indicators. In this embodiment, the levels of the model are target layer, criterion layer and program layer from high to low. The target layer is the highest level of the entire system, defining the main goals or ultimate goals to be achieved. The criterion layer is located below the goal and is the specific conditions or standards that need to be met to achieve the goal. These criteria are key factors for measuring the achievement of goals from different perspectives. Depending on the specific settings, the criterion layer may be one layer or multiple layers. The program layer is further subdivided into specific quantifiable or qualitatively evaluable indicators under the dimension corresponding to each criterion, which directly reflects the performance of the criterion. For the sake of convenience, the indicator of the criterion layer is called the second indicator, and the indicator of the program layer is called the first indicator. Each second indicator of the criterion layer can be regarded as an evaluation dimension, which should correspond to at least one first indicator in the program layer so that the second indicator can be evaluated by the first indicator. In addition, for the target layer, the second indicators of the criterion layer can be regarded as indicators of the same dimension. In the subsequent specific evaluation process, the first indicators will be processed separately according to the dimension, that is, the first indicators of different dimensions will not be mixed together. As an example, the specific indicators corresponding to each level in the hierarchical indicator system model can be as follows Figure 2 shown.

[0031] S102, comparing and scoring each indicator in pairs at each level, constructing a judgment matrix corresponding to each level according to the scoring results, and determining the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix.

[0032] In the embodiment of this specification, the cloud server will score each indicator level by level, that is, in order from high to low levels, the indicators belonging to the same dimension in each level will be compared and scored in pairs. The specific method of comparison and scoring is to select two indicators and send the two indicators to the terminal used by the corresponding experts, and the experts will score according to the importance of the indicators. The scoring criteria can refer to the following: Figure 3 The "1-9 scale" shown in the figure. After obtaining the scoring results returned by the experts, the judgment matrix of each level will be constructed based on it. The formula of the judgment matrix can be: in, is the importance scale of factor i compared with factor j, that is, the scoring result, and , , where i=1, 2,…, n; j=1, 2,…, n.

[0033] As an example, according to the scoring results, the judgment matrix of the criterion layer can be: After obtaining the judgment matrix, calculate the eigenvector corresponding to the judgment matrix , and after normalizing and transposing the eigenvector, the value ratio of the eigenvector's eigenvalues ​​to the whole can be determined. This ratio can be regarded as the weight of the indicator corresponding to the eigenvalue relative to the indicator of the previous level. Then, the weight ratio of each indicator of the same dimension relative to the corresponding indicator of the previous level can be determined. The eigenvalues ​​of the eigenvector And weight The calculation formula can be: Using the judgment matrix in the previous example for calculation, each row corresponds to a characteristic element, that is, an indicator, and the calculated weights are 0.561, 0.054, 0.126, and 0.259 respectively.

[0034] In one possible implementation, the indicators are compared and scored in pairs at each level, and a judgment matrix corresponding to each level is constructed according to the scoring results, including: The scoring results of each indicator of the same dimension at each level are obtained level by level, and then the first judgment matrix corresponding to the criterion layer and the second judgment matrix corresponding to each second indicator at the solution layer are constructed according to the scoring results.

[0035] In the embodiments of this specification, considering that the indicators in the solution layer are divided into indicators of different dimensions according to the different specific indicators corresponding to the criterion layer, when scoring, only the indicators of the same dimension in each level will be selected for pairwise comparison and scoring. Finally, a judgment matrix will be constructed according to the scoring results corresponding to each dimension of each level to construct a first judgment matrix corresponding to the criterion layer and a second judgment matrix corresponding to each dimension of the solution layer.

[0036] In one possible implementation, after constructing the judgment matrix corresponding to each level according to the scoring results, the method further includes: Performing consistency verification on each of the judgment matrices based on the maximum eigenvalue root; When the consistency verification result of any of the judgment matrices is greater than a preset value, the indicators corresponding to the judgment matrices are re-compared and scored pairwise.

[0037] In the embodiments of this specification, after each judgment matrix is ​​constructed, it is necessary to calculate the maximum characteristic root of the judgment matrix, so as to calculate the consistency of the judgment matrix according to the maximum characteristic root and perform consistency verification on it. The calculation formula is: Still using the data from the previous example for calculation, =4.122, CI=0.0407. RI is the average random consistency index, which can be obtained by looking up the table. Here it is 0.90, and the final CR=0.0452.

[0038] A preset value, such as 0.1, is pre-set in the cloud server. When CR is not greater than 0.1, it is generally considered that the consistency of the judgment matrix is ​​acceptable, that is, a smaller CR value indicates that the consistency of the judgment matrix A is good. If CR is greater than 0.1, the consistency of the judgment matrix is ​​considered unacceptable, and experts need to re-score the indicators in pairs and reconstruct the judgment matrix.

[0039] In one possible implementation, determining the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix includes: Calculate the geometric mean of all matrix elements in each row of the judgment matrix to obtain the eigenvector corresponding to the judgment matrix; After normalizing the characteristic elements of the characteristic vector, the weight of each indicator relative to the indicator of the previous level is determined according to the proportion of the characteristic elements.

[0040] In the embodiment of this specification, the cloud server will obtain the eigenvector of the judgment matrix by calculating the geometric mean of all elements in each row of the judgment matrix. Then, after normalizing the eigenvector, the weight of the indicator corresponding to each characteristic element relative to the indicator of the previous level can be determined according to the proportion of each characteristic element.

[0041] S103, constructing an evaluation set corresponding to each indicator, calculating the fuzzy vector of the final-level indicator according to the evaluation set, and generating a fuzzy evaluation matrix corresponding to each final-level indicator of the same dimension.

[0042] In the embodiments of this specification, an evaluation set corresponding to each indicator will be constructed. , and the staff inputs the actual value and evaluation criteria of each indicator according to the actual situation and needs, and obtains the evaluation set with the corresponding data input. As an example, the evaluation criteria corresponding to the first indicator in the evaluation set can be as follows Figure 4As shown, the evaluation criteria can be a judgment condition or a membership function, which can be set differently according to different indicators and actual needs. Then, according to the actual value of each indicator and the specific evaluation criteria, the membership of each indicator relative to each evaluation can be calculated, and then the fuzzy vector corresponding to the indicator can be constructed. By integrating the fuzzy vectors of each indicator of the same dimension, the corresponding fuzzy evaluation matrix can be obtained. Among them, the final indicator refers to the indicator with the lowest level, which can generally be regarded as the indicator of the solution layer. The reason for defining it according to the final indicator is that there may be multiple layers of the criterion layer according to different settings, so simply using the first indicator of the solution layer and the second indicator of the criterion layer cannot accurately describe the process of processing upwards layer by layer.

[0043] As an example, the membership of each first indicator determined according to the evaluation set can be as follows: Figure 5 As shown in the figure, the fuzzy evaluation matrices finally constructed are as follows: In one possible implementation, the indicator type of the indicator includes a qualitative indicator and a quantitative indicator; After constructing the evaluation set corresponding to each indicator, the following is also included: A membership function is set for the indicator according to the indicator type.

[0044] In the embodiments of this specification, the indicator types of each indicator are divided into qualitative indicators and quantitative indicators, and different membership functions will be set for each indicator according to the indicator type. The membership function of qualitative indicators is generally used to calculate the probability of each comment, so as to determine the membership of the indicator to each evaluation. The membership function of quantitative indicators is generally used to perform Gaussian calculation, gradient calculation, etc. on the actual value of the indicator according to the actual content of the indicator, so as to determine the membership of the actual value to each evaluation. As an example, a qualitative indicator can be satisfaction, and the result is obtained after 10 people evaluate according to the evaluation set. Assuming that the result is 2 good, 5 average, 3 bad, and 0 very bad, the fuzzy vector corresponding to its membership is (good, average, bad, very bad) = (0.2, 0.5, 0.3, 0). A quantitative indicator may be the number of safety accidents. Assuming that there are actually 0 accidents, the evaluation criteria are that 1 accident is good, 3 accidents are fair, 5 accidents are poor, and 7 accidents are very poor. Then, the fuzzy vector corresponding to the indicator value of 0 can be determined by performing standard deviation calculation, Gaussian calculation, gradient calculation, etc. according to the corresponding membership function.

[0045] In one possible implementation, the quantitative index includes a single-value index of a single-value standard and an interval index of an interval standard, the membership function of the single-value index is a Gaussian membership function, and the membership function of the interval index is a trapezoidal membership function.

[0046] In the embodiments of this specification, the quantitative index can be further divided into a single value index of a single value standard and an interval index of an interval standard. The membership function of the single value index can select a Gaussian membership function, and its formula is: in, represents the output value of the membership function, x is the actual value of the indicator, c is the standard value of the indicator, is the standard deviation of the normal distribution.

[0047] The membership function of the interval indicator can choose the trapezoidal membership function, and its formula is: in, represents the output value of the membership function, x is the actual value of the indicator, a, b, c, d represent the left endpoint, the end point of the left ascending edge, the starting point of the right descending edge, and the right endpoint of the function respectively, if is the judgment condition, that is, when x is less than or equal to a, the function value is 0, when x is greater than a and less than or equal to b, the function value is wait.

[0048] S104, fuzzy synthesis is performed on the fuzzy evaluation matrix and weight of the last-level indicator to obtain the fuzzy vector of the upper-level indicator, and fuzzy synthesis is performed upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained.

[0049] In the embodiment of the present specification, the fuzzy evaluation matrix and weight corresponding to the last-level indicator of the same dimension are fuzzy synthesized by a fuzzy synthesis operator to obtain the fuzzy vector corresponding to the indicator of the previous level. Then the above process is repeated to construct the fuzzy evaluation matrix of the indicator of the previous level according to the fuzzy vectors of the indicator of the previous level, and then the fuzzy evaluation matrix is ​​fuzzy synthesized according to the weight of the indicator of the previous level to obtain the fuzzy vector of the indicator of the next level. By repeating the above process step by step, the fuzzy vector of the target layer can be finally obtained. Among them, the fuzzy synthesis operator can be a weighted average operator, an OWA (Ordered Weighted Averaging) operator, etc.

[0050] In one possible implementation, the fuzzy evaluation matrix and weight of the final level indicator are fuzzily synthesized to obtain the fuzzy vector of the upper level indicator, including: A weight vector is constructed according to the weights corresponding to the final-level indicators of the same dimension, and the fuzzy evaluation matrix and the weight vector of the final-level indicator are calculated according to the weighted average fuzzy operator to obtain the fuzzy vector of the previous-level indicator.

[0051] In the embodiments of the present specification, a corresponding weight vector is constructed according to the weights of each final-level indicator of the same dimension, and the weight vector and the fuzzy evaluation matrix are weighted calculated according to a weighted average fuzzy operator, i.e., the M (·, +) operator, to obtain the fuzzy vector of the indicator at the previous level.

[0052] As an example, after calculating the above-mentioned data, the fuzzy vector of the target layer can be obtained as .

[0053] S105 , searching for a maximum membership degree in the fuzzy vector of the target layer, and generating a quality assessment result according to a comment corresponding to the maximum membership degree.

[0054] In the embodiment of this specification, according to the maximum membership principle, the maximum membership is searched in the fuzzy vector of the target layer, and the comment corresponding to the maximum membership in the evaluation criteria is used as the final quality evaluation result, thereby determining the comprehensive evaluation result of a service agency.

[0055] The following will be combined Figure 6 , the quality assessment device for socialized production safety services provided by the embodiment of the present application is introduced in detail. It should be noted that, Figure 6 The quality assessment device for socialized production safety services shown is used to implement this application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0056] See also Figure 6 , Figure 6 Schematic diagram of a quality assessment device for socialized production safety services provided by an embodiment of the present application. Figure 6 As shown, the device comprises: The first construction module 601 is used to construct a hierarchical indicator system model for the evaluation of socialized production safety services, wherein the hierarchical indicator system model includes a target layer, a criterion layer, and a solution layer from a high level to a low level, wherein the first indicator of the solution layer includes a purchaser evaluation, a service provider evaluation, and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the solution layer; The second construction module 602 is used to compare and score each indicator in pairs at each level, construct a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; The third construction module 603 is used to construct an evaluation set corresponding to each indicator, calculate the fuzzy vector of the final indicator according to the evaluation set, and generate a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; The synthesis module 604 is used to perform fuzzy synthesis on the fuzzy evaluation matrix and weight of the last-level indicator to obtain the fuzzy vector of the upper-level indicator, and perform fuzzy synthesis upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The evaluation module 605 is used to find the maximum membership degree in the fuzzy vector of the target layer, and generate a quality evaluation result according to the comment corresponding to the maximum membership degree.

[0057] In one possible implementation, the second building module 602 is specifically used to: The scoring results of each indicator of the same dimension at each level are obtained level by level, and then the first judgment matrix corresponding to the criterion layer and the second judgment matrix corresponding to each second indicator at the solution layer are constructed according to the scoring results.

[0058] In one possible implementation, the second building module 602 is further configured to: Performing consistency verification on each of the judgment matrices based on the maximum eigenvalue root; When the consistency verification result of any of the judgment matrices is greater than a preset value, the indicators corresponding to the judgment matrices are re-compared and scored pairwise.

[0059] In one possible implementation, the second building module 602 is further configured to: Calculate the geometric mean of all matrix elements in each row of the judgment matrix to obtain the eigenvector corresponding to the judgment matrix; After normalizing the characteristic elements of the characteristic vector, the weight of each indicator relative to the indicator of the previous level is determined according to the proportion of the characteristic elements.

[0060] In one possible implementation, the indicator type of the indicator includes a qualitative indicator and a quantitative indicator; The third building block 603 is specifically used for: A membership function is set for the indicator according to the indicator type.

[0061] In one possible implementation, the quantitative index includes a single-value index of a single-value standard and an interval index of an interval standard, the membership function of the single-value index is a Gaussian membership function, and the membership function of the interval index is a trapezoidal membership function.

[0062] In one possible implementation, the synthesis module 604 is specifically used for: A weight vector is constructed according to the weights corresponding to the final-level indicators of the same dimension, and the fuzzy evaluation matrix and the weight vector of the final-level indicator are calculated according to the weighted average fuzzy operator to obtain the fuzzy vector of the previous-level indicator.

[0063] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0064] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0065] See also Figure 7 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 7 As shown, the electronic device 700 may include: at least one processor 701 , at least one network interface 704 , a user interface 703 , a memory 705 , and at least one communication bus 702 .

[0066] The communication bus 702 is used to realize the connection and communication between these components.

[0067] The user interface 703 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 703 may also include a standard wired interface and a wireless interface.

[0068] The network interface 704 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0069] Among them, the processor 701 may include one or more processing cores. The processor 701 uses various interfaces and lines to connect various parts of the entire electronic device 700, and executes various functions and processes data of the electronic device 700 by running or executing instructions, programs, code sets or instruction sets stored in the memory 705, and calling data stored in the memory 705. Optionally, the processor 701 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 701 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 701, and it can be implemented separately through a chip.

[0070] Among them, the memory 705 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 705 includes a non-transitory computer-readable storage medium. The memory 705 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 705 may optionally be at least one storage device located away from the aforementioned processor 701. As Figure 7 As shown, the memory 705 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0071] exist Figure 7In the electronic device 700 shown, the user interface 703 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 701 can be used to call the quality assessment application of the socialized safety production service stored in the memory 705, and specifically perform the following operations: Constructing a hierarchical indicator system model for socialized service evaluation of work safety, wherein the hierarchical indicator system model includes, from high level to low level, a target layer, a criterion layer, and a scheme layer, wherein the first indicator of the scheme layer includes a purchaser evaluation, a service provider evaluation, and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the scheme layer; Compare and score each indicator in pairs at each level, build a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; Constructing an evaluation set corresponding to each indicator, calculating the fuzzy vector of the final indicator according to the evaluation set, and generating a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; The fuzzy evaluation matrix and weight of the last-level indicator are fuzzy synthesized to obtain the fuzzy vector of the upper-level indicator, and the fuzzy synthesis is performed upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The maximum membership degree is found in the fuzzy vector of the target layer, and a quality assessment result is generated according to the comment corresponding to the maximum membership degree.

[0072] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0073] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0074] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0078] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0079] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0080] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A quality assessment method for socialized production safety services, characterized in that: The method comprises: Constructing a hierarchical indicator system model for socialized service evaluation of work safety, wherein the hierarchical indicator system model includes, from high level to low level, a target layer, a criterion layer, and a scheme layer, wherein the first indicator of the scheme layer includes a purchaser evaluation, a service provider evaluation, and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the scheme layer; Compare and score each indicator in pairs at each level, build a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; Constructing an evaluation set corresponding to each indicator, calculating the fuzzy vector of the final indicator according to the evaluation set, and generating a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; The fuzzy evaluation matrix and weight of the last-level indicator are fuzzy synthesized to obtain the fuzzy vector of the upper-level indicator, and the fuzzy synthesis is performed upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The maximum membership degree is found in the fuzzy vector of the target layer, and a quality assessment result is generated according to the comment corresponding to the maximum membership degree.

2. The method according to claim 1, characterized in that The indicators are compared and scored in pairs at each level, and a judgment matrix corresponding to each level is constructed according to the scoring results, including: The scoring results of each indicator of the same dimension at each level are obtained level by level, and then the first judgment matrix corresponding to the criterion layer and the second judgment matrix corresponding to each second indicator at the solution layer are constructed according to the scoring results.

3. The method according to claim 1, characterized in that: After constructing the judgment matrix corresponding to each level according to the scoring results, it also includes: Performing consistency verification on each of the judgment matrices based on the maximum eigenvalue root; When the consistency verification result of any of the judgment matrices is greater than a preset value, the indicators corresponding to the judgment matrices are re-compared and scored pairwise.

4. The method according to claim 1, characterized in that: The determining the weight of each indicator relative to the indicator of the previous level according to the proportion of the characteristic elements of the characteristic vector corresponding to the judgment matrix includes: Calculate the geometric mean of all matrix elements in each row of the judgment matrix to obtain the eigenvector corresponding to the judgment matrix; After normalizing the characteristic elements of the characteristic vector, the weight of each indicator relative to the indicator of the previous level is determined according to the proportion of the characteristic elements.

5. The method according to claim 1, characterized in that The indicator types of the indicators include qualitative indicators and quantitative indicators; After constructing the evaluation set corresponding to each indicator, the following is also included: A membership function is set for the indicator according to the indicator type.

6. The method according to claim 5, characterized in that The quantitative index includes a single-value index of a single-value standard and an interval index of an interval standard. The membership function of the single-value index is a Gaussian membership function, and the membership function of the interval index is a trapezoidal membership function.

7. The method according to claim 1, characterized in that The fuzzy evaluation matrix and weight of the final level indicator are fuzzily synthesized to obtain the fuzzy vector of the upper level indicator, including: A weight vector is constructed according to the weights corresponding to the final-level indicators of the same dimension, and the fuzzy evaluation matrix and the weight vector of the final-level indicator are calculated according to the weighted average fuzzy operator to obtain the fuzzy vector of the previous-level indicator.

8. A quality assessment device for socialized production safety services, characterized in that: The device comprises: A first construction module is used to construct a hierarchical indicator system model for safety production socialized service evaluation, wherein the hierarchical indicator system model includes a target layer, a criterion layer and a scheme layer from a high level to a low level, wherein the first indicator of the scheme layer includes a purchaser evaluation, a service provider evaluation and a service object evaluation, and each second indicator of the criterion layer corresponds to at least one first indicator of the scheme layer; The second construction module is used to compare and score each indicator in pairs at each level, construct a judgment matrix corresponding to each level according to the scoring results, and determine the weight of each indicator relative to the indicator of the previous level according to the proportion of characteristic elements of the characteristic vector corresponding to the judgment matrix; The third construction module is used to construct an evaluation set corresponding to each indicator, calculate the fuzzy vector of the final indicator according to the evaluation set, and generate a fuzzy evaluation matrix corresponding to each final indicator of the same dimension; A synthesis module, used for fuzzy synthesis of the fuzzy evaluation matrix and weight of the last-level indicator to obtain the fuzzy vector of the upper-level indicator, and fuzzy synthesis upward step by step according to the fuzzy vector of the upper-level indicator until the fuzzy vector of the target layer is obtained; The evaluation module is used to find the maximum membership degree in the fuzzy vector of the target layer and generate a quality evaluation result according to the comment corresponding to the maximum membership degree.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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