Safety supervision data scoring method and related device

Abstract

The invention provides a safety supervision data scoring method and a related device, and the method comprises the steps: obtaining a plurality of pieces of index information from a storage space when a data analysis instruction is received; performing data processing on the multiple pieces of index information according to the multiple target indexes to obtain multiple pieces of index data; performing data analysis on the multiple pieces of index data to obtain multi-dimensional evaluation data; obtaining a first weight value based on the multi-dimensional evaluation data and the multiple pieces of index data; and obtaining a target safety supervision data score according to the first weight value and the multiple pieces of index data, wherein the target safety supervision score represents the score of the target unit for each target index within the target time. Compared with a strategy of processing expert scoring of the safety condition of thermal power equipment based on an information entropy calculation method in the prior art, the scheme carries out multi-dimensional data evaluation on indexes and index information of more dimensions in an electric power operation scene to obtain a weight value based on multi-dimensional evaluation, so that the safety of the thermal power equipment is improved. And a more comprehensive and intelligent score result combined with the field operation condition is obtained.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a safety monitoring data scoring method and related devices. Background Art

[0002] With the continuous increase in the volume of power grid business, power site operation monitoring is essential for safety. At present, the evaluation dimensions of power site operation monitoring are insufficient. In actual engineering applications, there are many factors related to the power site, but the existing evaluation dimensions are few, which cannot fully control the risk points and improve the quality of on-site operation safety and supervision. In addition, the level of intelligence may not be high. The existing power site operation safety and supervision analysis is more based on rule matching to set thresholds to judge the relationship between safety and supervision data and thresholds and then judge the safety situation. It cannot effectively combine on-site operation safety and supervision data for intelligent and comprehensive evaluation. Summary of the invention

[0003] The embodiments of the present application provide a safety and supervision data scoring method and related devices, which can perform on-site power safety and supervision evaluation in multiple dimensions, making the evaluation of on-site operation safety and supervision data more comprehensive and intelligent.

[0004] In a first aspect, an embodiment of the present application provides a safety monitoring data scoring method, which is applied to a server of a safety monitoring data scoring system, comprising:

[0005] When receiving a data analysis instruction, multiple indicator information is obtained from the storage space, where the data analysis instruction is used to indicate a target time, a target unit, and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to the multiple target indicators;

[0006] According to the multiple target indicators, the multiple indicator information is processed to obtain multiple indicator data, where the multiple indicator data are multiple indicator information represented by numbers;

[0007] Data analysis is performed on multiple indicator data to obtain multidimensional evaluation data, and the multidimensional evaluation data represents the degree of correlation between the multiple indicator data;

[0008] Obtaining a first weight value based on the multi-dimensional evaluation data and the plurality of indicator data, wherein the first weight value includes a plurality of first weight values, and each target indicator corresponds to a first weight value;

[0009] A target safety and supervision data score is obtained according to the first weight value and the plurality of indicator data, wherein the target safety and supervision score represents a score of the target unit for each target indicator within the target time.

[0010] In a possible embodiment, the indicator information is processed according to the target indicator to obtain a plurality of indicator data, including:

[0011] Obtain the indicator standard level according to the target indicator. The indicator standard level is a preset value and is used to grade the indicator information.

[0012] According to the indicator standard level, multiple indicator information corresponding to the target indicator is converted into data indicator information;

[0013] Based on the data indicator information, data completion is performed to obtain a data matrix, and the size of the data matrix is ​​determined by the number of indicator information with the largest number of indicator information of multiple target indicators;

[0014] The data matrix is ​​standardized and normalized to obtain a standard data matrix, and each data in the standard data matrix is ​​a plurality of indicator data.

[0015] In a possible embodiment, completing the data based on the data indicator information to obtain the data matrix includes:

[0016] Construct a regression equation for the data indicator information corresponding to each target indicator;

[0017] Regression fitting is performed based on the regression equation to obtain the predicted missing indicator information, and the predicted missing indicator information is used to fill the number of data indicator information corresponding to each target indicator to the same number;

[0018] The missing indicator information of prediction is filled into the vacant position of the data indicator information to obtain the data matrix.

[0019] In a possible embodiment, the multidimensional evaluation data includes an entropy parameter, a fluctuation parameter, and a correlation coefficient. The multidimensional evaluation data obtained by performing data analysis on multiple indicator data includes:

[0020] Perform entropy calculation on multiple indicator data corresponding to each target indicator to obtain the entropy value parameter of each target indicator;

[0021] Performing fluctuation analysis on multiple indicator data corresponding to each target indicator to obtain fluctuation parameters of each target indicator;

[0022] The correlation coefficient is obtained by calculating the correlation of the data matrix. The size of the correlation coefficient is associated with the number of target indicators. The correlation coefficient is used to characterize the degree of correlation between indicator information.

[0023] In a possible embodiment, obtaining the first weight value based on the multi-dimensional evaluation data and the plurality of indicator data includes:

[0024] The first weight value is calculated based on the multi-dimensional evaluation data and multiple indicator information according to the following formula:

[0025]

[0026] Among them, W j is the first weight value of the jth target indicator, e j is the entropy parameter, S j is the fluctuation parameter, is the correlation coefficient, n is the number of indicator data of each target indicator, j1 and j2 are positive integers less than or equal to the number of target indicators, and either j1 or j2 is equal to j, and j1 is not equal to j2.

[0027] In a possible embodiment, obtaining the target safety monitoring data score according to the first weight value and the plurality of indicator data includes:

[0028] Perform weighted calculation on each indicator data of the corresponding target indicator by using the first weight value to obtain first score data;

[0029] The first score data are summed up to obtain second score data, where each target indicator corresponds to one second score data;

[0030] A sum calculation is performed based on the second score data to obtain the target safety and supervision data score.

[0031] In a possible embodiment, performing weighted calculation on each indicator data of the corresponding target indicator by using the first weight value includes:

[0032] Obtain a second weight value according to the target indicator, where the second weight value is a preset weight value, and each target indicator corresponds to a second weight value;

[0033] A third weight value is obtained based on the second weight value and the first weight value, and each target indicator corresponds to a third weight value;

[0034] A weighted calculation is performed on each indicator data of the corresponding target indicator using the third weight value.

[0035] In a second aspect, an embodiment of the present application provides a safety monitoring data scoring device, which is applied to a server of a safety monitoring data scoring system, including:

[0036] an acquisition module, used to acquire multiple indicator information from the storage space when receiving a data analysis instruction, the data analysis instruction is used to indicate a target time, a target unit and multiple target indicators to be analyzed, and the multiple indicator information is information corresponding to the multiple target indicators;

[0037] A processing module, used for performing data processing on the multiple indicator information according to the multiple target indicators to obtain multiple indicator data, where the multiple indicator data are multiple indicator information represented by numbers;

[0038] A first analysis module is used to perform data analysis on multiple indicator data to obtain multi-dimensional evaluation data, where the multi-dimensional evaluation data represents the degree of association between the multiple indicator data;

[0039] A second analysis module is used to obtain a first weight value based on the multi-dimensional evaluation data and the plurality of indicator data, wherein the first weight value includes a plurality of first weight values, and each target indicator corresponds to a first weight value;

[0040] The weighting module is used to obtain a target safety and supervision data score according to a first weight value and multiple indicator data, wherein the target safety and supervision score represents the score of the target unit for each target indicator within a target time.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing some or all of the steps described in the first aspect of the embodiment of the present application.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The computer storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the processor executes some or all of the steps described in the first aspect.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0044] By implementing the embodiment of the present application, when a data analysis instruction is received, multiple indicator information is obtained from the storage space, the data analysis instruction is used to indicate the target time, target unit and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to multiple target indicators; the multiple indicator information is processed according to the multiple target indicators to obtain multiple indicator data, and the multiple indicator data is multiple indicator information represented by numbers; the multiple indicator data are analyzed to obtain multidimensional evaluation data, and the multidimensional evaluation data represents the degree of association between the multiple indicator data; based on the multidimensional evaluation data and the multiple indicator data, a first weight value is obtained, and the first weight value includes multiple, each target indicator corresponds to a first weight value; according to the first weight value and the multiple indicator data, the target safety and supervision data score is obtained, and the target safety and supervision score represents the score of the target unit for each target indicator within the target time. Compared with the strategy of processing the expert scoring of the safety situation of thermal power equipment based on the calculation of information entropy method in the prior art, this scheme performs multidimensional data evaluation on indicators and indicator information of more dimensions in the power operation scene, obtains weight values ​​based on multidimensional evaluation, and then obtains a more comprehensive and intelligent scoring result combined with the on-site operation situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0046] Figure 1 This is a schematic diagram of the architecture of a safety monitoring data scoring system provided in an embodiment of the present application;

[0047] Figure 2 It is a flowchart of a safety monitoring data scoring method provided in an embodiment of the present application;

[0048] Figure 3 It is a structural schematic diagram of a safety monitoring data scoring device proposed in an embodiment of the present application;

[0049] Figure 4 It is a structural schematic diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] The safety monitoring data scoring method and related device provided in the embodiments of the present application can be applied to Figure 1 In the safety data scoring system shown, please refer to Figure 1 , Figure 1 It is a schematic diagram of the architecture of a safety and monitoring data scoring system provided in an embodiment of the present application. The safety and monitoring data scoring system 100 includes a user terminal 110, a server 120 and an acquisition terminal 130. The user terminal 110 can communicate with the server 120 through a network or other connection methods, and the acquisition terminal can transmit data with the server 120 through a network or other methods.

[0054] Among them, the user terminal 110 refers to the device used by the user. The user can generate data analysis instructions through the user terminal 110, and the generated data analysis instructions will be sent from the user terminal 110 to the server 120. The specific generation process of the data analysis instructions is not limited here. The device may include a tablet computer, a PDA, a vehicle-mounted electronic device, a server, a laptop computer, a mobile Internet electronic device (MID, Mobile Internet Devices) or a wearable electronic device (such as a smart watch, a Bluetooth headset), a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), etc. The above are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0055] The server 120 refers to a remote computer used to process a large number of computing tasks and store data. In this solution, a program for scoring safety and monitoring data is deployed on the server 120 to score the safety and monitoring data. The server 120 can also be used to collect data during the use of the model to facilitate subsequent optimization of the program for scoring safety and monitoring data. The server 120 can send the scoring result, i.e., the target safety and monitoring data score, to the user terminal 110.

[0056] The acquisition terminal 130 is a device for acquiring power site operation data and safety monitoring data. The device can be a scanning device for bills generated by power site operations, or an instrument for directly monitoring data, such as a sensor for sensing temperature, humidity, etc., a time clock for acquiring operation time, etc. The instrument for monitoring data can include but is not limited to the above. The above scanning device can be a device with optical character recognition technology, or a common scanning device, or any device that can achieve the acquisition of power site safety monitoring data, and is not specifically limited here.

[0057] The server 120 also has a storage space for storing the safety monitoring data transmitted by the acquisition terminal 130. After receiving the data analysis instruction from the user terminal 110, the server 120 obtains the relevant data from the storage space to perform safety monitoring data scoring.

[0058] Based on this, the present application provides a safety monitoring data scoring method and related devices, and the present application is described in detail below in conjunction with the accompanying drawings.

[0059] See also Figure 2 , Figure 2 is a flow chart of a safety supervision data scoring method provided in an embodiment of the present application, the method is applied to a server of a case data scoring system, such as Figure 2 As shown, the method comprises the following steps:

[0060] S210, when a data analysis instruction is received, multiple indicator information is obtained from the storage space, the data analysis instruction is used to indicate the target time, target unit and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to the multiple target indicators.

[0061] Among them, the data analysis instruction is sent from the user terminal. The user can select the target unit to be scored and the target time to be analyzed by inputting, clicking, etc. The above target time can be a certain day, a period of time, or other, which is not limited here. The above target unit can be a group, a department, a unit, etc., which is not limited here. The corresponding one or several completed power field operation tasks can be retrieved through the selected target unit and target time, based on which all the indicators that can be scored included in the ceremony field operation tasks can be obtained. The target indicator can be one or more indicators that can be scored selected by the user, or all indicators that can be scored, which is not limited here.

[0062] Among them, after each power field operation task is completed, or at a unified time, the data generated by the ceremony field operation task will be uploaded. The uploading method can be obtained by the acquisition terminal, which is a device used to obtain power field operation data safety and supervision data. Please refer to Figure 1 The contents of the embodiments will not be repeated here.

[0063] Among them, according to the data analysis instruction transmitted from the user terminal, the indicator information corresponding to the target indicator is retrieved from the storage space, and each target indicator may correspond to one or more indicator information.

[0064] For an example, please refer to Table 1. As shown in Table 1, it is assumed that the target indicators include weather indicators, location indicators, method indicators, time period indicators, duration indicators and plan nature indicators. Correspondingly, each target indicator has corresponding indicator information. It should be noted that the specific content of the above indicator information can be a specific numerical value representing the wind force or text or characters representing whether it is within 4 hours. The specific expression method is not limited here.

[0065]

[0066] Table 1

[0067] S220, performing data processing on the multiple indicator information according to the multiple target indicators to obtain multiple indicator data, where the multiple indicator data are multiple indicator information represented by numbers.

[0068] Among them, the indicator information may be a variety of data types, which may be text, data or other. All indicator information can be converted into digital form through the conversion rules preset for each target indicator. The above preset conversion rules can be set according to the data characteristics of each target indicator. Then, the digital data is completed, standardized, and normalized to obtain a data table with unified standards. The data in the data table that has been processed is the indicator data.

[0069] In a possible embodiment, data processing is performed on indicator information according to the target indicator to obtain multiple indicator data, including: obtaining an indicator standard level according to the target indicator, the indicator standard level is a preset value, and the indicator standard level is used to perform level judgment on the indicator information; performing data conversion on multiple indicator information corresponding to the target indicator according to the indicator standard level to obtain data indicator information; completing data based on the data indicator information to obtain a data matrix, the size of the data matrix is ​​determined by the number of indicator information with the largest number of indicator information of multiple target indicators; performing data standardization and normalization processing on the data matrix to obtain a standard data matrix, each data in the standard data matrix is ​​a plurality of indicator data.

[0070] Among them, the indicator standard level obtained according to the target indicator is a rule for converting indicator information into data. The indicator information under the target indicator can be divided according to the indicator standard level, and the indicator information falling at different levels can be replaced by corresponding numerical values ​​to obtain digitized indicator information. The above indicator standard level can be preset. After obtaining the digitized indicator information, the digitized indicator information is completed based on the largest number of indicator information corresponding to the target indicator, so that the number of digitized indicator information corresponding to each target indicator is the same. The specific data completion method can be multiple interpolation, mean interpolation, regression interpolation, etc., or other methods, which are not limited here. After data completion, a data matrix is ​​obtained, and then the data matrix is ​​standardized and normalized to obtain a standard data matrix.

[0071] It can be seen that in this embodiment, by digitizing the different types of indicator information obtained under all target indicators and completing the data based on the amount of indicator information, and then standardizing the digitized indicator information, the scoring dimensions can be less restricted, the data inclusiveness can be improved, and the on-site operation safety and supervision data can be analyzed more comprehensively and intelligently.

[0072] In a possible embodiment, completing data based on data indicator information to obtain a data matrix includes: constructing a regression equation for the data indicator information corresponding to each target indicator; performing regression fitting based on the regression equation to obtain predicted missing indicator information, and the predicted missing indicator information is used to fill the number of data indicator information corresponding to each target indicator to the same number; and filling the predicted missing indicator information into the vacant position of the data indicator information to obtain a data matrix.

[0073] Among them, data completion for the digitized indicator information can be carried out according to the regression interpolation method, and a regression equation is constructed for the digitized indicator information corresponding to each target indicator. This regression equation is used for fitting to obtain reasonable fitting data, and the fitting data is used as the predicted missing indicator information. The predicted missing indicator information is filled in the vacant position, so that the number of indicator information corresponding to each target indicator is the same, and a data matrix is ​​obtained.

[0074] It can be seen that in this embodiment, by completing the missing data, a data matrix that is easier to process in a unified manner is obtained, which can reduce the restrictions on the scoring dimensions and improve data inclusiveness, thereby enabling a more comprehensive and intelligent analysis of on-site operation safety and monitoring data.

[0075] For an example, please refer to Table 2. As shown in Table 2, the following table shows multiple indicator data obtained after data processing.

[0076]

[0077] Table 2

[0078] S230, performing data analysis on the multiple indicator data to obtain multi-dimensional evaluation data, where the multi-dimensional evaluation data represents the degree of association between the multiple indicator data.

[0079] Among them, data analysis is performed on the multiple indicator data obtained after digitization and processing to obtain multi-dimensional evaluation data for weight analysis. The data analysis may include at least one of entropy calculation, fluctuation analysis, and correlation calculation.

[0080] In a possible embodiment, the multidimensional evaluation data includes an entropy parameter, a fluctuation parameter and a correlation coefficient. The multidimensional evaluation data is obtained by performing data analysis on multiple indicator data including: performing entropy calculation on multiple indicator data corresponding to each target indicator to obtain the entropy parameter of each target indicator; performing fluctuation analysis on multiple indicator data corresponding to each target indicator to obtain the fluctuation parameter of each target indicator; performing correlation calculation on the data matrix to obtain the correlation coefficient, the size of the correlation coefficient is correlated with the number of target indicators, and the correlation coefficient is used to characterize the degree of correlation between indicator information.

[0081] The process of calculating the entropy value of multiple indicator data corresponding to each target indicator can be as follows: first, calculate the proportion P of the i-th indicator data under the j-th indicator. ij :

[0082]

[0083] where x ij is the i-th indicator data under the j-th indicator. Then based on the following formula and Pij Calculate the entropy parameter e of the j-th target indicator j :

[0084]

[0085] Among them, m is the number of indicator data corresponding to each target indicator.

[0086] Among them, the fluctuation parameter S can be obtained by performing fluctuation analysis on multiple indicator data corresponding to each target indicator according to the following formula: j :

[0087]

[0088] Where n is the number of target indicators.

[0089] Among them, the correlation coefficient can be obtained by calculating the correlation of the data matrix according to the following formula

[0090]

[0091] in, is the i-th indicator data under the j1-th target indicator, is the i-th indicator data under the j2-th target indicator, is the mean of all indicator data under the j1th target indicator, is the mean of all indicator data under the j2th target indicator, is the standard deviation of all indicator data under the j1th target indicator, is the standard deviation of all indicator data under the j2th target indicator.

[0092] It should be noted that and Same, assuming j1 is 2, j2 is 3, r 23 =r 32 .

[0093] It can be seen that in this embodiment, by deeply analyzing the data, multidimensional evaluation data including entropy parameters, fluctuation parameters, and correlation coefficients are obtained, which is conducive to coordinated analysis of multidimensional data, can make the scoring dimensions less restricted, improve data inclusiveness, and thus can analyze on-site operation safety and supervision data more comprehensively and intelligently.

[0094] S240, obtaining a first weight value based on the multi-dimensional evaluation data and the plurality of indicator data, wherein the first weight value includes a plurality of first weight values, and each target indicator corresponds to a first weight value.

[0095] Among them, further calculation is performed based on the multi-dimensional evaluation data, and a first weight value is obtained based on the indicator data. Each target indicator corresponds to a first weight value, and the first weight value is used to weight each indicator data under the corresponding target indicator.

[0096] In a possible embodiment, obtaining the first weight value based on the multi-dimensional evaluation data and the plurality of indicator data includes:

[0097] The first weight value is calculated based on the multi-dimensional evaluation data and multiple indicator information according to the following formula:

[0098]

[0099] Among them, W j is the first weight value of the jth target indicator, e j is the entropy parameter, S j is the fluctuation parameter, is the correlation coefficient, n is the number of indicator data of each target indicator, j1 and j2 are positive integers less than or equal to the number of target indicators, and either j1 or j2 is equal to j, and j1 is not equal to j2.

[0100] It should be noted that if W2 is to be found, that is, j = 2, then In this formula, The j2 of increases with j, that is,

[0101] It should be noted that the calculation process can be implemented by code or program, or in other forms, which are not limited here.

[0102] It can be seen that in this embodiment, the correlation coefficient, entropy parameter and fluctuation parameter are combined to obtain the first weight value of each target indicator, so that the first weight value is multidimensional and inclusive, which is conducive to the coordinated analysis of multidimensional data, can make the scoring dimension less restricted, and improve data inclusiveness, so as to analyze the on-site operation safety and supervision data more comprehensively and intelligently.

[0103] In a possible embodiment, the method further includes, after obtaining W j Afterwards, to W j The values ​​of are normalized, and the data are re-completed to obtain another data matrix, and then the entire process after the above data completion is repeated to obtain another W j ', and W j 'Perform normalization and convert the normalized W j and the normalized W j'Calculate the difference to obtain the weight coefficient change value. If the weight coefficient change value is less than the weight coefficient change threshold or reaches the maximum number of iterations, stop the iteration; otherwise, repeat the above process until the weight coefficient change value is less than the weight coefficient change threshold or reaches the maximum number of iterations. The above weight coefficient change threshold is a preset value, and the maximum number of iterations is also a preset value.

[0104] It can be seen that in this embodiment, the data with data completion is repeatedly iterated according to the relationship between the weight coefficient change value and the threshold value to obtain a relatively stable weight value, so that the weight assignment is more accurate.

[0105] S250: Obtain a target safety and supervision data score according to the first weight value and the plurality of indicator data, wherein the target safety and supervision score represents a score of the target unit for each target indicator within a target time.

[0106] Among them, the target safety and supervision data score can be obtained by performing assignment calculation based on the first weight value and the indicator data under each indicator.

[0107] In a possible embodiment, obtaining a target safety and monitoring data score based on a first weight value and multiple indicator data includes: performing a weighted calculation on each indicator data of the corresponding target indicator using the first weight value to obtain first score data; summing the first score data to obtain second score data, wherein each target indicator corresponds to a second score data; and summing the second score data to obtain a target safety and monitoring data score.

[0108] Among them, the assignment calculation is performed based on the first weight value and the indicator data under each indicator to obtain the indicator data after assignment under each indicator, and then the assigned indicator data are added to obtain the score of each target indicator, and then the score of each target indicator is added to obtain the target safety supervision data score.

[0109] For example, the target safety and supervision data score can be: score = score1 + score2 + score3 + score4 + score5 + score6, where score1 is the second score of the first target indicator, score2 is the second score of the second target indicator, score3 is the second score of the third target indicator, score4 is the second score of the fourth target indicator, score5 is the second score of the fifth target indicator, and score6 is the second score of the sixth target indicator.

[0110] It can be seen that in this embodiment, by assigning the first weight value obtained by multi-dimensional calculation to the indicator data under each target indicator, a more accurate safety and supervision data score with a higher degree of inclusiveness can be obtained, and then the on-site operation safety and supervision data can be analyzed more comprehensively and intelligently.

[0111] In a possible embodiment, weighted calculation is performed on each indicator data of the corresponding target indicator through a first weight value, including: obtaining a second weight value according to the target indicator, the second weight value is a preset weight value, and each target indicator corresponds to a second weight value; obtaining a third weight value based on the second weight value and the first weight value, and each target indicator corresponds to a third weight value; weighted calculation is performed on each indicator data of the corresponding target indicator through the third weight value.

[0112] Wherein, on the basis of using the first weight value, a second weight value is added, and the second weight value can be obtained through expert scoring, empirical data, or other methods other than data analysis, which are not limited here. The third weight value is obtained by superimposing the first weight value and the second weight value, and the superposition method can be to achieve the superposition effect by multiplication, or other methods, which are not limited here.

[0113] For example, the target safety and supervision data score can be: score = A1score1 + A2score2 + A3score3 + A4score4 + A5score5 + A6score6, where A1 is the second weight value of the first target indicator, A2 is the second weight value of the second target indicator, A3 is the second weight value of the third target indicator, A4 is the second weight value of the fourth target indicator, A5 is the second weight value of the fifth target indicator, and A6 is the second weight value of the sixth target indicator.

[0114] It can be seen that in this embodiment, a second weight value is added on the basis of the first weight value, and the two weight values ​​work together to obtain a third weight value, so that the third weight value comprehensively considers multi-dimensional analysis and other scoring situations, so that the on-site operation safety and supervision data can be analyzed more comprehensively and intelligently.

[0115] It can be seen that in this embodiment, when a data analysis instruction is received, multiple indicator information is obtained from the storage space, and the data analysis instruction is used to indicate the target time, target unit and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to multiple target indicators; multiple indicator information is processed according to multiple target indicators to obtain multiple indicator data, and the multiple indicator data are multiple indicator information represented by numbers; multiple indicator data are analyzed to obtain multidimensional evaluation data, and the multidimensional evaluation data represents the degree of association between multiple indicator data; based on the multidimensional evaluation data and multiple indicator data, a first weight value is obtained, and the first weight value includes multiple, each target indicator corresponds to a first weight value; based on the first weight value and multiple indicator data, a target safety and supervision data score is obtained, and the target safety and supervision score represents the score of the target unit for each target indicator within the target time. Compared with the strategy of processing the expert scoring of the safety situation of thermal power equipment based on the calculation of information entropy method in the prior art, this scheme performs multidimensional data evaluation on indicators and indicator information of more dimensions in the power operation scene, obtains weight values ​​based on multidimensional evaluation, and then obtains a more comprehensive and intelligent scoring result combined with the on-site operation situation.

[0116] See also Figure 3 , Figure 3 The structure diagram of a safety monitoring data scoring device proposed in the embodiment of the present application is shown in FIG. The case data scoring device is applied to the server of the safety monitoring data scoring system. The safety monitoring data scoring device 300 includes: an acquisition module 310, a processing module 320, a first analysis module 330, a second analysis module 340, and a weighting module 350, wherein:

[0117] The acquisition module 310 is used to acquire multiple indicator information from the storage space when receiving a data analysis instruction, the data analysis instruction is used to indicate a target time, a target unit and multiple target indicators to be analyzed, and the multiple indicator information is information corresponding to the multiple target indicators;

[0118] The processing module 320 is used to process the multiple indicator information according to the multiple target indicators to obtain multiple indicator data, where the multiple indicator data are multiple indicator information represented by numbers;

[0119] A first analysis module 330 is used to perform data analysis on multiple indicator data to obtain multi-dimensional evaluation data, where the multi-dimensional evaluation data represents the degree of association between the multiple indicator data;

[0120] A second analysis module 340 is used to obtain a first weight value based on the multi-dimensional evaluation data and the multiple indicator data, where the first weight value includes multiple first weight values, and each target indicator corresponds to a first weight value;

[0121] The weighting module 350 is used to obtain a target safety and supervision data score according to the first weight value and multiple indicator data. The target safety and supervision score represents the score of the target unit for each target indicator within the target time.

[0122] In a possible implementation, the processing module 320 performs data processing on the indicator information according to the target indicator to obtain multiple indicator data, specifically for:

[0123] Obtain the indicator standard level according to the target indicator. The indicator standard level is a preset value and is used to grade the indicator information.

[0124] According to the indicator standard level, multiple indicator information corresponding to the target indicator is converted into data indicator information;

[0125] Based on the data indicator information, data completion is performed to obtain a data matrix, and the size of the data matrix is ​​determined by the number of indicator information with the largest number of indicator information of multiple target indicators;

[0126] The data matrix is ​​standardized and normalized to obtain a standard data matrix, and each data in the standard data matrix is ​​a plurality of indicator data.

[0127] In a possible implementation, the first analysis module 330 is specifically used to complete the data based on the data indicator information to obtain the data matrix:

[0128] Construct a regression equation for the data indicator information corresponding to each target indicator;

[0129] Regression fitting is performed based on the regression equation to obtain the predicted missing indicator information, and the predicted missing indicator information is used to fill the number of data indicator information corresponding to each target indicator to the same number;

[0130] The missing indicator information of prediction is filled into the vacant position of the data indicator information to obtain the data matrix.

[0131] In a possible implementation, the first analysis module 330 performs data analysis on multiple indicator data to obtain multi-dimensional evaluation data, specifically for:

[0132] Perform entropy calculation on multiple indicator data corresponding to each target indicator to obtain the entropy value parameter of each target indicator;

[0133] Performing fluctuation analysis on multiple indicator data corresponding to each target indicator to obtain fluctuation parameters of each target indicator;

[0134] The correlation coefficient is obtained by calculating the correlation of the data matrix. The size of the correlation coefficient is associated with the number of target indicators. The correlation coefficient is used to characterize the degree of correlation between indicator information.

[0135] In a possible implementation, the second analysis module 340 is specifically used to obtain the first weight value based on the multi-dimensional evaluation data and the multiple indicator data:

[0136] The first weight value is calculated based on the multi-dimensional evaluation data and multiple indicator information according to the following formula:

[0137]

[0138] Among them, W j is the first weight value of the jth target indicator, e j is the entropy parameter, S j is the fluctuation parameter, is the correlation coefficient, n is the number of indicator data of each target indicator, j1 and j2 are positive integers less than or equal to the number of target indicators, and either j1 or j2 is equal to j, and j1 is not equal to j2.

[0139] In a possible implementation, the weighting module 350 is specifically used to obtain the target safety monitoring data score according to the first weight value and the multiple indicator data:

[0140] Perform weighted calculation on each indicator data of the corresponding target indicator by using the first weight value to obtain first score data;

[0141] The first score data are summed up to obtain second score data, where each target indicator corresponds to one second score data;

[0142] A sum calculation is performed based on the second score data to obtain the target safety and supervision data score.

[0143] In a possible implementation, the weighting module 350 performs weighted calculation on each indicator data of the corresponding target indicator by using the first weight value, specifically for:

[0144] Obtain a second weight value according to the target indicator, where the second weight value is a preset weight value, and each target indicator corresponds to a second weight value;

[0145] A third weight value is obtained based on the second weight value and the first weight value, and each target indicator corresponds to a third weight value;

[0146] A weighted calculation is performed on each indicator data of the corresponding target indicator using the third weight value.

[0147] It is worth noting that the specific functional implementation of the safety monitoring data scoring device 300 is shown in the above Figure 2The description of the safety monitoring data scoring method shown in the figure, for example, the acquisition module 310 is used to implement the relevant content of executing S210. The various units or modules in the safety monitoring data scoring device 300 can be separately or completely combined into one or several other units or modules to form, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0148] It can be seen that the safety and supervision data scoring device described in the embodiment of the present application obtains multiple indicator information from the storage space when receiving the data analysis instruction, the data analysis instruction is used to indicate the target time, target unit and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to multiple target indicators; the multiple indicator information is processed according to the multiple target indicators to obtain multiple indicator data, and the multiple indicator data is multiple indicator information represented by numbers; the multiple indicator data are analyzed to obtain multidimensional evaluation data, and the multidimensional evaluation data represents the degree of association between the multiple indicator data; based on the multidimensional evaluation data and the multiple indicator data, a first weight value is obtained, and the first weight value includes multiple, each target indicator corresponds to a first weight value; based on the first weight value and the multiple indicator data, the target safety and supervision data score is obtained, and the target safety and supervision score represents the score of the target unit for each target indicator within the target time. Compared with the strategy of processing the expert scoring of the safety situation of thermal power equipment based on the calculation of information entropy method in the prior art, this scheme performs multidimensional data evaluation on indicators and indicator information of more dimensions in the power operation scene, obtains weight values ​​based on multidimensional evaluation, and then obtains a more comprehensive and intelligent scoring result combined with the on-site operation situation.

[0149] See also Figure 4 , Figure 4 It is a structural diagram of an electronic device proposed in an embodiment of the present application. As shown in the figure, the electronic device 400 includes a processor 410, a memory 420, a communication interface 430 and one or more programs 421. The one or more programs 421 are stored in the memory 420 and are configured to be executed by the processor 410.

[0150] The processor 410, the memory 420, and the communication interface 430 are connected to each other and perform communication work with each other;

[0151] The memory 420 may be a volatile memory such as a dynamic random access memory DRAM, or a non-volatile memory such as a mechanical hard disk. The memory 420 is used to store a set of executable program codes, and the processor 410 is used to call one or more programs 421 stored in the memory 420, and can execute the above-mentioned Figure 2 Part or all of the steps of any safety monitoring data scoring method recorded in the embodiments.

[0152] Among them, the electronic device 400 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0153] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0154] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0155] 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.

[0156] 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.

[0157] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned 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 interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0158] The units described above 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.

[0159] 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.

[0160] If the above-mentioned 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 is essentially 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, and the computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0161] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0162] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A safety supervision data scoring method, characterized in that: The server used in the safety supervision data scoring system includes: When receiving a data analysis instruction, multiple indicator information is obtained from the storage space, wherein the data analysis instruction is used to indicate a target time, a target unit, and multiple target indicators that need to be analyzed, and the multiple indicator information is information corresponding to the multiple target indicators; Processing the plurality of indicator information according to the plurality of target indicators to obtain a plurality of indicator data, wherein the plurality of indicator data is the plurality of indicator information represented by numbers; Performing data analysis on the multiple indicator data to obtain multi-dimensional evaluation data, wherein the multi-dimensional evaluation data represents the degree of association between the multiple indicator data; Obtaining a first weight value based on the multi-dimensional evaluation data and the plurality of indicator data, wherein the first weight value includes a plurality of first weight values, and each target indicator corresponds to one first weight value; A target safety and supervision data score is obtained according to the first weight value and the plurality of indicator data, wherein the target safety and supervision score represents a score of the target unit for each of the target indicators within the target time.

2. The method according to claim 1, characterized in that The step of processing the indicator information according to the target indicator to obtain a plurality of indicator data includes: Acquire an indicator standard level according to the target indicator, wherein the indicator standard level is a preset value and is used to grade the indicator information; Performing data conversion on the plurality of indicator information corresponding to the target indicator according to the indicator standard level to obtain data indicator information; Performing data completion based on the data indicator information to obtain a data matrix, wherein the size of the data matrix is ​​determined by the number of indicator information with the largest number of indicator information of the multiple target indicators; A standard data matrix is ​​obtained by performing data standardization and normalization processing on the data matrix, and each data in the standard data matrix is ​​the multiple indicator data.

3. The method according to claim 2, characterized in that The data matrix obtained by performing data completion based on the data indicator information includes: Constructing a regression equation for the data indicator information corresponding to each of the target indicators; Perform regression fitting based on the regression equation to obtain predicted missing indicator information, where the predicted missing indicator information is used to fill the number of data indicator information corresponding to each of the target indicators to the same number; The predicted missing indicator information is filled into the vacant position of the data indicator information to obtain the data matrix.

4. The method according to claim 3, characterized in that The multidimensional evaluation data includes an entropy parameter, a fluctuation parameter, and a correlation coefficient. The multidimensional evaluation data obtained by performing data analysis on the multiple indicator data includes: Performing entropy calculation on the multiple indicator data corresponding to each of the target indicators to obtain an entropy value parameter of each of the target indicators; Performing fluctuation analysis on the plurality of indicator data corresponding to each of the target indicators to obtain a fluctuation parameter of each of the target indicators; The correlation coefficient is obtained by performing correlation calculation on the data matrix. The size of the correlation coefficient is associated with the number of the target indicators. The correlation coefficient is used to characterize the degree of correlation between the indicator information.

5. The method according to claim 4, characterized in that The obtaining of the first weight value based on the multi-dimensional evaluation data and the plurality of indicator data comprises: The first weight value is calculated based on the multi-dimensional evaluation data and the multiple indicator information according to the following formula: Among them, W j is the first weight value of the jth target indicator, e j is the entropy parameter, S j is the fluctuation parameter, is the correlation coefficient, n is the number of the indicator data of each target indicator, j1 and j2 are positive integers less than or equal to the number of target indicators, and either j1 or j2 is equal to j, and j1 is not equal to j2.

6. The method according to claim 1, characterized in that The step of obtaining a target safety monitoring data score according to the first weight value and the plurality of indicator data comprises: Perform weighted calculation on each indicator data of the corresponding target indicator by using the first weight value to obtain first score data; The first score data are summed to obtain second score data, wherein each target indicator corresponds to one second score data; A sum calculation is performed based on the second score data to obtain the target safety monitoring data score.

7. The method according to claim 6, characterized in that The weighting calculation of each indicator data of the corresponding target indicator by using the first weight value includes: Acquire a second weight value according to the target indicator, where the second weight value is a preset weight value, and each target indicator corresponds to one second weight value; Obtaining a third weight value based on the second weight value and the first weight value, each target indicator corresponding to one third weight value; The third weight value is used to perform weighted calculation on each indicator data of the corresponding target indicator.

8. A safety monitoring data scoring device, characterized in that: The server used in the safety supervision data scoring system includes: an acquisition module, configured to acquire a plurality of indicator information from a storage space when receiving a data analysis instruction, wherein the data analysis instruction is used to indicate a target time, a target unit, and a plurality of target indicators to be analyzed, and the plurality of indicator information is information corresponding to the plurality of target indicators; A processing module, configured to perform data processing on the plurality of indicator information according to the plurality of target indicators to obtain a plurality of indicator data, wherein the plurality of indicator data are the plurality of indicator information represented by digits; A first analysis module, configured to perform data analysis on the plurality of indicator data to obtain multi-dimensional evaluation data, wherein the multi-dimensional evaluation data represents the degree of association between the plurality of indicator data; A second analysis module is used to obtain a first weight value based on the multi-dimensional evaluation data and the plurality of indicator data, wherein the first weight value includes a plurality of first weight values, and each target indicator corresponds to one first weight value; A weighting module is used to obtain a target safety and supervision data score according to the first weight value and the multiple indicator data, wherein the target safety and supervision score represents the score of the target unit for each of the target indicators within the target time.

9. A computer-readable storage medium, characterized in that: A safety and monitoring data scoring program is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

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