Equipment intelligent evaluation method and device based on big data algorithm

Abstract

The present invention discloses an intelligent equipment evaluation method and device based on a big data algorithm. The method can determine the equipment according to the equipment type identification and the supplier identification, and perform an equipment evaluation operation on the equipment to obtain an equipment operation evaluation result, which is used as the supplier evaluation result based on the equipment. A systematic equipment operation evaluation system model is provided, which can combine various data and analyze and process the data to obtain the equipment operation evaluation result, which is conducive to saving unnecessary manpower and material resources and thus improving the evaluation efficiency of the equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the equipment operation evaluation result, thereby improving the evaluation accuracy and evaluation effectiveness of the supplier evaluation result, thereby helping to more accurately and conveniently determine the equipment supplier with better supplier evaluation for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for intelligent equipment evaluation based on big data algorithms. Background Art

[0002] As the scale of power grid equipment continues to expand, old equipment and family defective equipment are gradually increasing, making the external environment faced by the power grid more complex. It is necessary to evaluate the equipment from the perspective of power grid equipment model and equipment supplier, obtain equipment evaluation results, and determine suitable new equipment based on the equipment evaluation results to replace equipment with hidden dangers, or select suitable equipment suppliers to supply equipment based on the equipment evaluation results of equipment supplied by different equipment suppliers to ensure the normal and orderly operation of the power grid.

[0003] Currently, most equipment evaluation methods rely on human operators independently performing evaluations. This human evaluation process is easily affected by multiple factors, including mental state, environmental atmosphere, and subjective consciousness. Furthermore, existing equipment evaluation methods lack information system support, all of which can affect equipment evaluation to a certain extent. Consequently, existing equipment evaluation methods suffer from low accuracy and efficiency. Therefore, it is particularly important to provide an equipment evaluation method that can improve both accuracy and efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent equipment evaluation method and device based on big data algorithm, which can improve the evaluation accuracy and efficiency.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent equipment evaluation method based on a big data algorithm, the method comprising:

[0006] Determining a device type requiring a device evaluation operation, and determining a target supplier requiring a supplier device evaluation operation based on a set of suppliers of devices corresponding to the device type; the set of suppliers of devices corresponding to the device type including at least one supplier that supplies the device corresponding to the device type;

[0007] Determining a device type identifier corresponding to the certain device type and a supplier identifier corresponding to the target supplier, and determining a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier; all devices corresponding to the certain device type include the target type device; the target type device is a device corresponding to the certain device type supplied by the target supplier;

[0008] Determining device evaluation data corresponding to the target type device according to the device identifier corresponding to the target type device;

[0009] Based on the equipment evaluation data and a pre-set equipment operation evaluation index system, the equipment operation evaluation result of the target type equipment is determined as the supplier evaluation result of the target supplier based on the target type equipment; the equipment operation evaluation result is used to evaluate the equipment efficiency, equipment cost and equipment risk of the target type equipment supplied by the target supplier.

[0010] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0011] Obtaining device-related data corresponding to the target type device based on the device type identifier corresponding to the certain device type and the device identifier corresponding to the target type device; the device-related data corresponding to the target type device includes supplier data of all suppliers of devices corresponding to the certain device type, device defect data corresponding to the target type device, device failure data corresponding to the target type device, device inventory data corresponding to the target type device, and device accident event data corresponding to the target type device;

[0012] Determining, based on the device association data, device quality operation data corresponding to the target type device;

[0013] According to a preset first data screening condition and a preset second data screening condition, a data screening operation is performed on the equipment quality operation data to obtain equipment evaluation data corresponding to the target type equipment.

[0014] As an optional embodiment, in the first aspect of the present invention, determining the device operation evaluation result of the target type device based on the device evaluation data and a pre-set device operation evaluation index system includes:

[0015] Determining, based on a pre-set equipment operation evaluation indicator system, an equipment operation evaluation category set corresponding to the equipment operation evaluation indicator system, and determining an equipment operation evaluation dimension set corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; and the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension;

[0016] Determining, according to each of the equipment operation evaluation categories included in the equipment operation evaluation category set, each of the equipment operation evaluation dimensions included in the equipment operation evaluation dimension set, and the equipment evaluation data, sub-equipment evaluation data corresponding to each of the equipment operation evaluation dimensions; the equipment evaluation data including the sub-equipment evaluation data;

[0017] The device operation evaluation result of the target type device is calculated based on the sub-device evaluation data corresponding to each of the device operation evaluation dimensions and the pre-set dimension weight content corresponding to each of the device operation evaluation dimensions.

[0018] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0019] Determining whether the current situation corresponding to the target type device meets a preset device evaluation trigger condition, and when the determination result is yes, triggering execution of the operation of determining a device type requiring a device evaluation operation;

[0020] The step of determining whether the current situation of the target type device satisfies a preset device evaluation trigger condition includes:

[0021] Detecting whether a device evaluation trigger instruction is received, and when the detection result is yes, determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition; or,

[0022] Determining, based on the device association data, evaluation urgency data of the target type device, and judging whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition; if the judgment result is yes, determining that the current situation corresponding to the target type device meets the predetermined device evaluation trigger condition;

[0023] The step of determining whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition includes:

[0024] If the evaluation urgency data of the target type device is the duration of time to be evaluated of the target type device, determining whether the duration of time to be evaluated is greater than or equal to a preset threshold of the duration of time to be evaluated; if the judgment result is yes, determining that the evaluation urgency data of the target type device meets the predetermined device emergency evaluation condition; the duration of time to be evaluated of the target type device is the duration from the time point corresponding to the last time the device operation evaluation result of the target type device was obtained to the current time;

[0025] If the evaluation urgency data of the target type device is the cumulative number of device failures of the target type device, determine whether the cumulative number of device failures is greater than or equal to a preset threshold value of the cumulative number of device failures. When the judgment result is yes, determine that the evaluation urgency data of the target type device meets the device emergency evaluation condition; the cumulative number of device failures includes the cumulative number of device failures of the target type device in the historical time period or the total number of historical cumulative device failures of the target type device.

[0026] As an optional embodiment, in the first aspect of the present invention, after determining the device operation evaluation result of the target type device based on the device evaluation data and the pre-set device operation evaluation index system, the method further includes:

[0027] Determining a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated, and generating an evaluation report corresponding to the target type of device based on the device operation evaluation result of the target type of device and the target evaluation report version, so as to evaluate the target supplier;

[0028] The step of determining a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated includes:

[0029] detecting whether an instruction to generate an evaluation report is received, wherein the instruction to generate an evaluation report includes an evaluation report version corresponding to the evaluation report to be generated;

[0030] When the evaluation report generation instruction is received, determining a target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated according to the evaluation report instruction;

[0031] When it is detected that the instruction to generate the evaluation report is not received, the device operation evaluation result is analyzed to obtain the device operation evaluation level corresponding to the target type device; when the device operation evaluation level is less than or equal to a pre-set device operation evaluation level threshold, a pre-set basic evaluation report version is obtained as the target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated.

[0032] As an optional implementation manner, in the first aspect of the present invention, the device operation evaluation result of the target type device is specifically calculated by the following formula:

[0033] EQE=EOE+COE-ROE=(EI+TS)+(ER+CD+MD+UD)-(SA+SE+BD)

[0034] Among them, EQE is the total score of the equipment operation evaluation of the target type equipment, EOE is the equipment effectiveness index of the target type equipment, COE is the equipment cost index of the target type equipment, ROE is the equipment risk index of the target type equipment; EI is the equipment inventory dimension of the target type equipment; TS is the complaint dimension of the target type equipment; ER is the failure dimension of the target type equipment; CD is the general defect dimension of the target type equipment; MD is the major defect dimension of the target type equipment; UD is the emergency defect dimension of the target type equipment; SA is the accident dimension of the target type equipment; SE is the event dimension of the target type equipment; BD is the batch defect dimension of the target type equipment.

[0035] As an optional implementation, in the first aspect of the present invention, the device ownership dimension EI of the target type of device is calculated by:

[0036]

[0037] Among them, EI Y The number of equipment of the target type supplied by the target supplier that has been in operation for Y years; max The maximum operating life of the target type of equipment supplied by the target supplier, and the operating life of all equipment is rounded up to the nearest integer; EI F A correction value for the total number of online devices of the target type supplied by the target supplier;

[0038]

[0039] Among them, EI N is the normalized value of the total number of corrected values of the target type of equipment supplied by the target supplier; NN is the normalization parameter, max(EI F ) is the maximum value of all the total number of network correction values corresponding to all suppliers of the equipment corresponding to the certain equipment type;

[0040]

[0041]

[0042] Among them, EI a is the equipment holding coefficient;

[0043] And, the complaint dimension TS of the target type device is calculated in the following way:

[0044]

[0045] Among them, ZCi The number of complaints against the target supplier during the evaluation period reported by the grassroots power supply bureau; n i The total number of target type devices supplied by the target supplier reported by the grassroots power supply bureau; d is the number of power supply bureaus that reported the target type devices supplied by the target supplier; n is the total number of target type devices supplied by the target supplier on the network;

[0046] And, the failure dimension ER of the target type equipment is calculated in the following way:

[0047]

[0048]

[0049] Among them, e Y ER is the number of failures of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a N is the total number of failures per year for the target type of equipment supplied by the target supplier during the statistical period; Y The total number of equipment of the target type supplied by the target supplier that has been in operation for Y years; F b is the fault dimension benchmark score;

[0050] And, the device general defect dimension CD of the target type device is calculated by:

[0051]

[0052]

[0053] Among them, cd Y CD is the number of general defects of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a C is the total number of general defects per year of the target type of equipment supplied by the target supplier during the statistical period; b It is the benchmark score of general defect dimension of equipment;

[0054] And, the equipment major defect dimension MD of the target type equipment is calculated in the following way:

[0055]

[0056]

[0057] Among them, md YMD is the number of major defects in the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a M is the total number of major defects per year of the target type of equipment supplied by the target supplier during the statistical period; b Benchmark score for major equipment defects;

[0058] And, the equipment urgent defect dimension UD of the target type equipment is calculated in the following way:

[0059]

[0060]

[0061] Among them, ud Y UD is the number of urgent defects of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a The total number of units / year of urgent defects of the target type of equipment supplied by the target supplier during the statistical period is corrected; U b It is the benchmark score of equipment emergency defect dimension;

[0062] And, the accident dimension SA of the target type equipment is calculated in the following way:

[0063]

[0064] Among them, SG i The number of various accidents caused by quality reasons for the target type of equipment supplied by the target supplier during the evaluation period; i is the deduction coefficient of the accident dimension;

[0065] And, the event dimension SE of the target type device is calculated in the following way:

[0066]

[0067] Among them, SJ i The number of incidents of all levels caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; Sr i is the deduction coefficient of the event dimension;

[0068] And, the batch defect dimension BD of the target type equipment is obtained by:

[0069] Batch defect processing data corresponding to the target type equipment is obtained, and the batch defect dimension corresponding to the target type equipment is determined based on pre-set batch defect evaluation conditions and the batch defect processing data.

[0070] The second aspect of the present invention discloses an intelligent equipment evaluation device based on a big data algorithm, the device comprising:

[0071] A first determination module is configured to determine a certain device type that requires a device evaluation operation, and determine a target supplier that requires a supplier device evaluation operation based on a set of suppliers of devices corresponding to the certain device type; the set of suppliers of devices corresponding to the certain device type includes at least one supplier that supplies devices corresponding to the certain device type; determine a device type identifier corresponding to the certain device type and a supplier identifier corresponding to the target supplier, and determine a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier; all devices corresponding to the certain device type include the target type device; the target type device is the device corresponding to the certain device type supplied by the target supplier; and determine device evaluation data corresponding to the target type device based on the device identifier corresponding to the target type device;

[0072] The second determination module is used to determine the equipment operation evaluation result of the target type equipment based on the equipment evaluation data and a pre-set equipment operation evaluation index system, which serves as the supplier evaluation result of the target supplier based on the target type equipment; the equipment operation evaluation result is used to evaluate the equipment performance, equipment cost and equipment risk of the target type equipment supplied by the target supplier.

[0073] As an optional embodiment, in the second aspect of the present invention, the device further includes:

[0074] an acquisition module, configured to acquire device-related data corresponding to a target type device based on a device type identifier corresponding to the certain device type and a device identifier corresponding to the target type device; the device-related data corresponding to the target type device includes supplier data of all suppliers of devices corresponding to the certain device type, device defect data corresponding to the target type device, device failure data corresponding to the target type device, device inventory data corresponding to the target type device, and device accident event data corresponding to the target type device;

[0075] The first determining module is further configured to determine the equipment quality operation data corresponding to the target type equipment based on the equipment association data;

[0076] The data processing module is used to perform a data screening operation on the equipment quality operation data according to a preset first data screening condition and a preset second data screening condition to obtain equipment evaluation data corresponding to the target type equipment.

[0077] As an optional embodiment, in the second aspect of the present invention, the second determination module determines the device operation evaluation result of the target type device according to the device evaluation data and a pre-set device operation evaluation index system, specifically including:

[0078] Determining, based on a pre-set equipment operation evaluation indicator system, an equipment operation evaluation category set corresponding to the equipment operation evaluation indicator system, and determining an equipment operation evaluation dimension set corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; and the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension;

[0079] Determining, according to each of the equipment operation evaluation categories included in the equipment operation evaluation category set, each of the equipment operation evaluation dimensions included in the equipment operation evaluation dimension set, and the equipment evaluation data, sub-equipment evaluation data corresponding to each of the equipment operation evaluation dimensions; the equipment evaluation data including the sub-equipment evaluation data;

[0080] The device operation evaluation result of the target type device is calculated based on the sub-device evaluation data corresponding to each of the device operation evaluation dimensions and the pre-set dimension weight content corresponding to each of the device operation evaluation dimensions.

[0081] As an optional embodiment, in the second aspect of the present invention, the device further includes:

[0082] a judgment module, configured to judge whether the current situation corresponding to the target type of device satisfies a preset device evaluation trigger condition, and when the judgment result is yes, trigger the first determination module to execute the operation of determining a device type requiring a device evaluation operation;

[0083] Furthermore, the judgment module judges whether the current situation corresponding to the target type device satisfies the preset device evaluation trigger condition in the following manner:

[0084] Detecting whether a device evaluation trigger instruction is received, and when the detection result is yes, determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition; or,

[0085] Determining, based on the device association data, evaluation urgency data of the target type device, and judging whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition; if the judgment result is yes, determining that the current situation corresponding to the target type device meets the predetermined device evaluation trigger condition;

[0086] Furthermore, the judgment module judges whether the evaluation urgency data of the target type device meets the predetermined device emergency evaluation condition in a manner that specifically includes:

[0087] If the evaluation urgency data of the target type device is the duration of time to be evaluated of the target type device, determining whether the duration of time to be evaluated is greater than or equal to a preset threshold of the duration of time to be evaluated; if the judgment result is yes, determining that the evaluation urgency data of the target type device meets the predetermined device emergency evaluation condition; the duration of time to be evaluated of the target type device is the duration from the time point corresponding to the last time the device operation evaluation result of the target type device was obtained to the current time;

[0088] If the evaluation urgency data of the target type device is the cumulative number of device failures of the target type device, determine whether the cumulative number of device failures is greater than or equal to a preset threshold value of the cumulative number of device failures. When the judgment result is yes, determine that the evaluation urgency data of the target type device meets the device emergency evaluation condition; the cumulative number of device failures includes the cumulative number of device failures of the target type device in the historical time period or the total number of historical cumulative device failures of the target type device.

[0089] As an optional embodiment, in the second aspect of the present invention, the second determining module is further configured to determine a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated after determining the device operation evaluation result of the target type device based on the device evaluation data and a pre-set device operation evaluation index system;

[0090] The device further comprises:

[0091] a report generation module, configured to generate an evaluation report corresponding to the target type of equipment based on the equipment operation evaluation result of the target type of equipment and the target evaluation report version, so as to evaluate the target supplier;

[0092] Furthermore, the second determining module determines the target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated, specifically including:

[0093] detecting whether an instruction to generate an evaluation report is received, wherein the instruction to generate an evaluation report includes an evaluation report version corresponding to the evaluation report to be generated;

[0094] When the evaluation report generation instruction is received, determining a target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated according to the evaluation report instruction;

[0095] When it is detected that the instruction to generate the evaluation report is not received, the device operation evaluation result is analyzed to obtain the device operation evaluation level corresponding to the target type device; when the device operation evaluation level is less than or equal to a pre-set device operation evaluation level threshold, a pre-set basic evaluation report version is obtained as the target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated.

[0096] As an optional implementation manner, in the second aspect of the present invention, the device operation evaluation result of the target type device is specifically calculated by the following formula:

[0097] EQE=EOE+COE-ROE=(EI+TS)+(ER+CD+MD+UD)-(SA+SE+BD)

[0098] Among them, EQE is the total score of the equipment operation evaluation of the target type equipment, EOE is the equipment effectiveness index of the target type equipment, COE is the equipment cost index of the target type equipment, ROE is the equipment risk index of the target type equipment; EI is the equipment inventory dimension of the target type equipment; TS is the complaint dimension of the target type equipment; ER is the failure dimension of the target type equipment; CD is the general defect dimension of the target type equipment; MD is the major defect dimension of the target type equipment; UD is the emergency defect dimension of the target type equipment; SA is the accident dimension of the target type equipment; SE is the event dimension of the target type equipment; BD is the batch defect dimension of the target type equipment.

[0099] As an optional implementation, in the second aspect of the present invention, the device ownership dimension EI of the target type of device is calculated in the following manner:

[0100]

[0101] Among them, EI Y The number of equipment of the target type supplied by the target supplier that has been in operation for Y years; max The maximum operating life of the target type of equipment supplied by the target supplier, and the operating life of all equipment is rounded up to the nearest integer; EI F A correction value for the total number of online devices of the target type supplied by the target supplier;

[0102]

[0103] Among them, EI Nis the normalized value of the total number of corrected values of the target type of equipment supplied by the target supplier; NN is the normalization parameter, max(EI F ) is the maximum value of all the total number of network correction values corresponding to all suppliers of the equipment corresponding to the certain equipment type;

[0104]

[0105]

[0106] Among them, EI a is the equipment holding coefficient;

[0107] And, the complaint dimension TS of the target type device is calculated in the following way:

[0108]

[0109] Among them, ZC i The number of complaints against the target supplier during the evaluation period reported by the grassroots power supply bureau; n i The total number of target type devices supplied by the target supplier reported by the grassroots power supply bureau; d is the number of power supply bureaus that reported the target type devices supplied by the target supplier; n is the total number of target type devices supplied by the target supplier on the network;

[0110] And, the failure dimension ER of the target type equipment is calculated in the following way:

[0111]

[0112]

[0113] Among them, e Y ER is the number of failures of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a N is the total number of failures per year for the target type of equipment supplied by the target supplier during the statistical period; Y The total number of equipment of the target type supplied by the target supplier that has been in operation for Y years; F b is the fault dimension benchmark score;

[0114] And, the device general defect dimension CD of the target type device is calculated by:

[0115]

[0116]

[0117] Among them, cd Y CD is the number of general defects of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a C is the total number of general defects per year of the target type of equipment supplied by the target supplier during the statistical period; b It is the benchmark score of general defect dimension of equipment;

[0118] And, the equipment major defect dimension MD of the target type equipment is calculated in the following way:

[0119]

[0120]

[0121] Among them, md Y MD is the number of major defects in the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a M is the total number of major defects per year of the target type of equipment supplied by the target supplier during the statistical period; b Benchmark score for major equipment defects;

[0122] And, the equipment urgent defect dimension UD of the target type equipment is calculated in the following way:

[0123]

[0124]

[0125] Among them, ud Y UD is the number of urgent defects of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; a The total number of units / year of urgent defects of the target type of equipment supplied by the target supplier during the statistical period is corrected; U b It is the benchmark score of equipment emergency defect dimension;

[0126] And, the accident dimension SA of the target type equipment is calculated in the following way:

[0127]

[0128] Among them, SG i The number of various accidents caused by quality reasons for the target type of equipment supplied by the target supplier during the evaluation period; i is the deduction coefficient of the accident dimension;

[0129] And, the event dimension SE of the target type device is calculated in the following way:

[0130]

[0131] Among them, SJ i The number of incidents of all levels caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; Sr i is the deduction coefficient of the event dimension;

[0132] And, the batch defect dimension BD of the target type equipment is obtained by:

[0133] Batch defect processing data corresponding to the target type equipment is obtained, and the batch defect dimension corresponding to the target type equipment is determined based on pre-set batch defect evaluation conditions and the batch defect processing data.

[0134] The third aspect of the present invention discloses another intelligent equipment evaluation device based on big data algorithms, the device comprising:

[0135] a memory storing executable program code;

[0136] a processor coupled to the memory;

[0137] The processor calls the executable program code stored in the memory to execute the intelligent equipment evaluation method based on big data algorithm disclosed in the first aspect of the present invention.

[0138] The fourth aspect of the present invention discloses a computer-storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute the equipment intelligent evaluation method based on big data algorithm disclosed in the first aspect of the present invention.

[0139] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0140] In an embodiment of the present invention, a certain equipment type that requires an equipment evaluation operation is determined, and a target supplier that requires a supplier equipment evaluation operation is determined based on a set of suppliers of equipment corresponding to the certain equipment type; the set of suppliers of equipment corresponding to the certain equipment type includes at least one supplier that supplies equipment corresponding to the certain equipment type; an equipment type identifier corresponding to the certain equipment type and a supplier identifier corresponding to the target supplier are determined, and a target type equipment corresponding to the target supplier is determined based on the equipment type identifier corresponding to the certain equipment type and the supplier identifier corresponding to the target supplier; all equipment corresponding to the certain equipment type includes the target type equipment; the target type equipment is the equipment corresponding to the certain equipment type supplied by the target supplier; based on the equipment identifier corresponding to the target type equipment, equipment evaluation data corresponding to the target type equipment is determined; based on the equipment evaluation data and a pre-set equipment operation evaluation index system, an equipment operation evaluation result of the target type equipment is determined as a supplier evaluation result of the target supplier based on the target type equipment; the equipment operation evaluation result is used to evaluate the equipment efficiency, equipment cost and equipment risk of the target type equipment supplied by the target supplier. It can be seen that the implementation of the present invention can determine the equipment that needs to be evaluated according to the equipment type identification and the supplier identification, and perform the equipment evaluation operation on the equipment according to the equipment evaluation data and the equipment operation evaluation index system of the equipment to obtain the equipment operation evaluation result, which is used as the supplier evaluation result of the supplier based on the equipment, and provides a systematic equipment operation evaluation system model. It can combine various data and analyze and process the data to obtain the equipment operation evaluation result, which is conducive to saving unnecessary manpower and material resources and thus improving the evaluation efficiency of the equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the determined equipment operation evaluation results, thereby improving the evaluation accuracy and evaluation effectiveness of the determined supplier evaluation results, thereby helping to more accurately and conveniently determine equipment suppliers with better supplier evaluations for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0142] Figure 1 This is a flow chart of a method for intelligent equipment evaluation based on a big data algorithm disclosed in an embodiment of the present invention;

[0143] Figure 2This is a flow chart of another method for intelligent equipment evaluation based on big data algorithms disclosed in an embodiment of the present invention;

[0144] Figure 3 This is a schematic diagram of the structure of an intelligent equipment evaluation device based on a big data algorithm disclosed in an embodiment of the present invention;

[0145] Figure 4 This is a schematic structural diagram of another device intelligent evaluation apparatus based on big data algorithms disclosed in an embodiment of the present invention;

[0146] Figure 5 This is a structural diagram of another device intelligent evaluation device based on big data algorithm disclosed in an embodiment of the present invention;

[0147] Figure 6 This is a schematic diagram of the overall structural flow of the equipment operation quality evaluation and analysis disclosed in an embodiment of the present invention;

[0148] Figure 7 It is a schematic diagram of the evaluation index system corresponding to the equipment operation evaluation results disclosed in the embodiment of the present invention. DETAILED DESCRIPTION

[0149] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0150] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or end comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed therein, or may optionally include other steps or elements inherent to such process, method, product, or end.

[0151] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0152] The present invention discloses a method and device for intelligent equipment evaluation based on a big data algorithm. The method and device can determine the equipment that needs to be evaluated according to the equipment type identification and supplier identification, and perform the equipment evaluation operation on the equipment according to the equipment evaluation data and the equipment operation evaluation index system of the equipment to obtain the equipment operation evaluation result. The equipment operation evaluation result is used as the supplier evaluation result of the supplier based on the equipment, providing a systematic equipment operation evaluation system model. The method can combine multiple data and analyze and process the data to obtain the equipment operation evaluation result, which is conducive to saving unnecessary manpower and material resources, thereby improving the evaluation efficiency of the equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the determined equipment operation evaluation result, thereby improving the evaluation accuracy and evaluation effectiveness of the determined supplier evaluation result, thereby helping to more accurately and conveniently determine the equipment supplier with a better supplier evaluation for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network. The following are detailed descriptions.

[0153] Example 1

[0154] See also Figure 1 , Figure 1 This is a flow chart of a method for intelligent equipment evaluation based on big data algorithms disclosed in an embodiment of the present invention. Figure 1 The described method can be applied to an intelligent equipment evaluation device based on a big data algorithm, and the embodiment of the present invention does not limit this. Figure 1 As shown, the intelligent equipment evaluation method based on big data algorithm includes the following operations:

[0155] 101. Determine a device type that requires an equipment evaluation operation, and determine a target supplier that requires a supplier equipment evaluation operation based on a set of suppliers of devices corresponding to the device type.

[0156] In the embodiment of the present invention, optionally, the supplier set of devices corresponding to a certain device type includes at least one supplier that supplies devices corresponding to the certain device type.

[0157] 102. Determine a device type identifier corresponding to a certain device type and a supplier identifier corresponding to a target supplier, and determine a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier.

[0158] In the embodiment of the present invention, optionally, all devices corresponding to a certain device type include target type devices; the target type devices are devices corresponding to a certain device type supplied by a target supplier.

[0159] In an embodiment of the present invention, optionally, the device type identifier of a certain device type can be any device type code identifier that can represent the certain device type. The device type code identifier can represent only the certain device type alone, or it can be a code that matches the certain device type obtained according to a certain classification condition. The embodiment of the present invention does not limit this.

[0160] In an embodiment of the present invention, optionally, the supplier identifier of the target supplier can be any supplier code identifier that can represent the target supplier. The supplier code identifier can represent only the target supplier, or it can be a code that matches the target supplier based on a certain classification condition. The embodiment of the present invention does not limit this.

[0161] In the embodiment of the present invention, further optionally, a device identifier of the target type device is determined to trigger execution of step 103 .

[0162] 103. Determine device evaluation data corresponding to the target type device according to the device identifier corresponding to the target type device.

[0163] In the embodiment of the present invention, the device identifier corresponding to the target type device can optionally be any device code identifier that can represent the target type device. The device code identifier can represent only the target type device, or can be a code identifier that matches the target type device according to a certain classification condition, which is not limited in the embodiment of the present invention. For example, the device identifier can be the device identification number.

[0164] 104. Based on the equipment evaluation data and the pre-set equipment operation evaluation index system, determine the equipment operation evaluation results of the target type equipment as the supplier evaluation results of the target supplier based on the target type equipment.

[0165] In this embodiment of the present invention, the equipment operation evaluation results are optionally used to evaluate the equipment performance, equipment cost, and equipment risk of the target type of equipment supplied by the target supplier. For example, the supplier evaluation results are used to rank different suppliers of the same type of equipment to select suppliers whose equipment performance is more consistent with the supply requirements to supply that type of equipment.

[0166] In the embodiment of the present invention, optionally, the equipment operation evaluation result of the target type equipment can be expressed in the form of numerical value (such as 80 points, 90 points, etc., or 10%, 30%, etc.), or in the form of grades (such as excellent, good, medium, qualified, etc., or first grade, second grade, etc.), or in other forms that can reflect the quality of the equipment operation evaluation, which is not limited in the embodiment of the present invention. Optionally, the way of expressing the supplier evaluation result can refer to but is not limited to the way of expressing the above-mentioned equipment operation evaluation result, which will not be repeated here. Optionally, the supplier evaluation result can be a direct reference to the equipment operation evaluation result, or it can be a new independent evaluation result obtained by analyzing the equipment operation evaluation result, which is not limited in the embodiment of the present invention.

[0167] It can be seen that the intelligent equipment evaluation method based on big data algorithm described in the embodiment of the present invention can determine the equipment that needs to be evaluated according to the equipment type identification and supplier identification, and perform equipment evaluation operations on the equipment according to the equipment evaluation data and equipment operation evaluation index system of the equipment to obtain equipment operation evaluation results. As the supplier evaluation results of the supplier based on the equipment, a systematic equipment operation evaluation system model is provided, which can combine various data and analyze and process the data to obtain equipment operation evaluation results, which is conducive to saving unnecessary manpower and material resources to improve the evaluation efficiency of equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the determined equipment operation evaluation results, thereby improving the evaluation accuracy and evaluation effectiveness of the determined supplier evaluation results, thereby helping to more accurately and conveniently determine equipment suppliers with better supplier evaluations for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network.

[0168] In an optional embodiment, the method may further include the following operations:

[0169] According to the device type identifier corresponding to a certain device type and the device identifier corresponding to the target type device, the device-related data corresponding to the target type device is obtained; the device-related data corresponding to the target type device includes supplier data of all suppliers of devices corresponding to the certain device type, device defect data corresponding to the target type device, device failure data corresponding to the target type device, device ledger data corresponding to the target type device, and device accident event data corresponding to the target type device;

[0170] Determine the equipment quality operation data corresponding to the target type equipment based on the equipment association data;

[0171] According to a preset first data screening condition and a preset second data screening condition, a data screening operation is performed on the equipment quality operation data to obtain equipment evaluation data corresponding to the target type equipment.

[0172] In this optional embodiment, the device-related data may include, but is not limited to, device quality operation data, which is not limited in this embodiment of the present invention. Optionally, the device quality operation data may include, but is not limited to, one or more of device basic information data, device defect information data, device fault information data, and device accident event information data, which is not limited in this embodiment of the present invention.

[0173] For example, the first data screening condition can be a data standardization cleaning screening condition, and the specific operation steps are as follows: sort out the historical names and abbreviations of all suppliers of equipment corresponding to a certain equipment type, uniformly name the suppliers in all equipment quality operation data as the standard equipment supplier names, and group them according to the municipal bureaus and suppliers, and then aggregate the grouped data according to the equipment category to complete the data screening operation of the first data screening condition; the second data screening condition can be a data rationality cleaning screening condition, and the specific operation steps are as follows: filter and remove the equipment and equipment quality operation data corresponding to the suppliers whose supplier names are not in the company's framed recruitment contract among all suppliers of equipment corresponding to a certain equipment type, filter and remove the equipment and equipment quality operation data corresponding to the suppliers with a small number of supplier data (for example, less than 20 data items), filter and remove other unreasonable data (such as equipment quality operation data in non-predetermined rules, equipment quality operation data sets corresponding to bankrupt suppliers, etc.), and complete the data screening operation of the second data screening condition; complete the data screening operation of the first data screening condition and the data screening operation of the second data screening condition to obtain equipment evaluation data corresponding to the target type of equipment.

[0174] Among them, optionally, the equipment operation quality evaluation and analysis plan can specifically include collecting distribution equipment quality operation evaluation related data, integrating multi-source data with the equipment as the center and cleaning abnormal supplier data, and aggregating the cleaned data by unit and voltage level. According to the rule engine of distribution equipment operation evaluation, a distributed computing method is used to calculate the quality score of each equipment supplier, conduct result analysis and effectiveness summary, generate evaluation reports, and realize the goals of automatic acquisition of evaluation data, automatic calculation of evaluation results, and automatic export of evaluation reports. Further optionally, the above equipment operation quality evaluation and analysis plan corresponds to the overall structural process, which can be referred to Figure 6 shown.

[0175] It can be seen that this optional embodiment can filter the relevant data of the equipment and suppliers according to the first data filtering condition and the second data filtering condition to obtain the equipment evaluation data of the equipment for subsequent determination of the equipment operation evaluation results, thereby improving the effectiveness and pertinence of the determined equipment evaluation data, and thus helping to improve the efficiency of subsequent determination of the equipment operation evaluation results based on the equipment evaluation data and improve the accuracy of the determined equipment operation evaluation results, and helping to reduce unnecessary waste of resources due to complex data.

[0176] In another optional embodiment, determining the equipment operation evaluation result of the target type equipment based on the equipment evaluation data and a pre-set equipment operation evaluation index system may include:

[0177] According to a pre-set equipment operation evaluation indicator system, determine an equipment operation evaluation category set corresponding to the equipment operation evaluation indicator system, and determine an equipment operation evaluation dimension set corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; and the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension;

[0178] Determine, based on each equipment operation evaluation category included in the equipment operation evaluation category set, each equipment operation evaluation dimension included in the equipment operation evaluation dimension set, and equipment evaluation data, sub-equipment evaluation data corresponding to each equipment operation evaluation dimension; the equipment evaluation data includes sub-equipment evaluation data;

[0179] The device operation evaluation result of the target type device is calculated based on the sub-device evaluation data corresponding to each device operation evaluation dimension and the pre-set dimension weight content corresponding to each device operation evaluation dimension.

[0180] In this optional embodiment, optionally, for example, regarding the equipment operation evaluation index system, each equipment operation evaluation category included in the equipment operation evaluation category set may be an equipment efficiency index category, an equipment cost index category, and an equipment risk index category, and the equipment operation evaluation dimension set corresponding to the equipment efficiency index category may include an equipment inventory dimension and a complaint dimension, and the equipment operation evaluation dimension set corresponding to the equipment cost index category may include an equipment general defect dimension, an equipment major defect dimension, and an equipment emergency defect dimension, and the equipment operation evaluation dimension set corresponding to the equipment risk index category may include an accident dimension, an event dimension, and a batch defect dimension. It should be noted that the equipment evaluation data corresponding to the target type equipment includes the sub-equipment evaluation data corresponding to each of the above-mentioned equipment operation evaluation dimensions, and for other descriptions of each of the above-mentioned equipment operation evaluation categories and each of the above-mentioned equipment operation evaluation dimensions, please refer to the description below and will not be repeated here.

[0181] In an embodiment of the present invention, for example, the data source of the sub-device evaluation data corresponding to the equipment holdings dimension may be the distribution network equipment ledger, the data source of the sub-device evaluation data corresponding to the fault dimension may be the distribution network fault repair management system, the data source of the sub-device evaluation data corresponding to the equipment general defect dimension, the sub-device evaluation data corresponding to the equipment major defect dimension, and the sub-device evaluation data corresponding to the equipment emergency defect dimension may be the defect management system, the data source of the sub-device evaluation data corresponding to the accident dimension and the sub-device evaluation data corresponding to the event dimension may be the accident event management system, and the data source of the sub-device evaluation data corresponding to the batch defect dimension may be the defect management system.

[0182] It can be seen that this optional embodiment can determine the sub-device evaluation data of different equipment operation evaluation dimensions based on different equipment operation evaluation categories, the different equipment operation evaluation dimensions included in each equipment operation evaluation category, and the equipment evaluation data, and then determine the equipment operation evaluation results based on the sub-device evaluation data of different equipment operation evaluation dimensions, which is conducive to improving the pertinence and matching of the equipment evaluation data and different sub-device evaluation data, and thus is conducive to improving the accuracy and rationality of the determined equipment operation evaluation results, and is conducive to improving the efficiency of determining the equipment operation evaluation results.

[0183] In yet another optional embodiment, the method may further include the following operations:

[0184] It is determined whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition. When the determination result is yes, the above operation of determining a device type that requires device evaluation operation is triggered.

[0185] In this optional embodiment, further optionally, when it is determined that the current situation corresponding to the target type device does not meet the preset device evaluation trigger condition, the above-mentioned operation of determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition is performed.

[0186] It can be seen that this optional embodiment can determine whether the current situation meets the equipment evaluation trigger conditions. When it is judged to be yes, the subsequent steps of determining a certain equipment type that requires equipment evaluation operation are executed, which improves the rationality of executing equipment intelligent evaluation operations, and is conducive to improving the integrity and comprehensiveness of equipment intelligent evaluation operations, and is conducive to reducing unnecessary resource waste caused by arbitrary execution of equipment intelligent evaluation operations.

[0187] In yet another optional embodiment, determining whether the current situation corresponding to the target type device satisfies a preset device evaluation trigger condition may include:

[0188] Detecting whether a device evaluation trigger instruction is received, and when the detection result is yes, determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition; or,

[0189] Based on the device association data, the evaluation urgency data of the target type device is determined, and it is judged whether the evaluation urgency data of the target type device meets the predetermined device emergency evaluation conditions. When the judgment result is yes, it is determined that the current situation corresponding to the target type device meets the predetermined device evaluation trigger conditions.

[0190] In this optional embodiment, optionally, the triggering method corresponding to the device evaluation trigger instruction may include but is not limited to one or more of triggering by mobile phone number, mini program triggering, mobile phone text message triggering, APP triggering, designated web page triggering, device triggering, etc., and is not limited in this embodiment of the present invention.

[0191] In this optional embodiment, further optionally, when it is detected that no device evaluation trigger instruction is received, it is determined that the current situation corresponding to the target type device does not meet the device evaluation trigger condition.

[0192] Further optionally, when it is determined that the evaluation urgency data of the target type device does not meet the device emergency evaluation condition, it is determined that the current situation corresponding to the target type device does not meet the device evaluation trigger condition.

[0193] It can be seen that this optional embodiment provides two methods: detecting whether a device evaluation trigger instruction is received and judging whether the evaluation urgency data meets the device emergency evaluation conditions, to determine whether the current situation meets the device evaluation trigger conditions. When the detection is yes or the judgment is yes, it is determined that the current situation meets the device evaluation trigger conditions, which enriches the diversity of the methods for determining whether the device evaluation trigger conditions are met, and improves the flexibility of the methods for determining whether the device evaluation trigger conditions are met, thereby improving the rationality and effectiveness of the determined judgment results of meeting the device evaluation trigger conditions, thereby improving the accuracy of the determined judgment results of meeting the device evaluation trigger conditions.

[0194] In yet another optional embodiment, determining whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition may include:

[0195] If the evaluation urgency data of the target type device is the time period for which the target type device is to be evaluated, determine whether the time period for which the target type device is to be evaluated is greater than or equal to a preset time period threshold. If the judgment result is yes, determine that the evaluation urgency data of the target type device meets the predetermined equipment emergency evaluation condition. The time period for which the target type device is to be evaluated is the time period from the time point corresponding to the last time the equipment operation evaluation result of the target type device was obtained to the current time.

[0196] If the evaluation urgency data of the target type equipment is the cumulative number of equipment failures of the target type equipment, determine whether the cumulative number of equipment failures is greater than or equal to a preset cumulative number of equipment failures threshold. When the judgment result is yes, determine that the evaluation urgency data of the target type equipment meets the equipment emergency evaluation conditions; the cumulative number of equipment failures includes the cumulative number of equipment failures of the target type equipment in the historical time period or the total number of historical cumulative number of equipment failures of the target type equipment.

[0197] In this optional embodiment, further optionally, when it is determined that the time to be evaluated is less than the time to be evaluated threshold, it is determined that the evaluation urgency data of the target type device does not meet the device emergency evaluation condition.

[0198] Further optionally, when it is determined that the cumulative number of equipment failures is less than the cumulative number of equipment failures threshold, it is determined that the evaluation urgency data of the target type equipment does not meet the equipment emergency evaluation condition.

[0199] In this optional embodiment, the to-be-evaluated time of the target type device may also be the operation duration or operation duration of the target type device, which is not limited in the embodiment of the present invention.

[0200] It can be seen that this optional embodiment can determine whether the evaluation urgency data meets the equipment emergency evaluation conditions from two perspectives: when the evaluation urgency data is the cumulative number of equipment failures or when the evaluation urgency data is the cumulative number of equipment failures. This enriches the diversity of ways to determine whether the evaluation urgency data meets the equipment emergency evaluation conditions, and improves the flexibility of ways to determine whether the evaluation urgency data meets the equipment emergency evaluation conditions, thereby improving the rationality and effectiveness of the determined judgment results of meeting the equipment emergency evaluation conditions, thereby improving the accuracy of the determined judgment results of meeting the equipment emergency evaluation conditions, and is conducive to improving the accuracy of the determined judgment results of meeting the equipment evaluation trigger conditions.

[0201] Example 2

[0202] See also Figure 2 , Figure 2 This is a flow chart of another method for intelligent equipment evaluation based on big data algorithms disclosed in an embodiment of the present invention. Figure 2 The described method can be applied to an intelligent equipment evaluation device based on a big data algorithm, and the embodiment of the present invention does not limit this. Figure 2 As shown, the intelligent equipment evaluation method based on big data algorithm includes the following operations:

[0203] 201. Determine a device type that requires a device evaluation operation, and determine a target supplier that requires a supplier device evaluation operation based on a set of suppliers of devices corresponding to the device type.

[0204] 202. Determine a device type identifier corresponding to a certain device type and a supplier identifier corresponding to a target supplier, and determine a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier.

[0205] 203. Determine device evaluation data corresponding to the target type device according to the device identifier corresponding to the target type device.

[0206] 204. Determine the equipment operation evaluation result of the target type equipment based on the equipment evaluation data and the pre-set equipment operation evaluation index system, and use it as the supplier evaluation result of the target supplier based on the target type equipment.

[0207] 205. Determine a target evaluation report version of an evaluation report corresponding to the equipment operation evaluation result to be generated.

[0208] 206. Generate an evaluation report corresponding to the target type of equipment based on the equipment operation evaluation result of the target type of equipment and the target evaluation report version to evaluate the target supplier.

[0209] In an embodiment of the present invention, optionally, different evaluation report versions may have different evaluation report format layouts, different content emphases corresponding to the evaluation reports, different usage permissions corresponding to the evaluation reports, or other obvious differences in the evaluation reports, which are not limited in the embodiment of the present invention.

[0210] In the embodiment of the present invention, for other descriptions of steps 201 to 204, please refer to the detailed description of steps 101 to 104 in the first embodiment, which will not be repeated in the embodiment of the present invention.

[0211] It can be seen that the embodiment of the present invention can determine the equipment that needs to be evaluated based on the equipment type identifier and the supplier identifier, and perform the equipment evaluation operation on the equipment according to the equipment evaluation data and the equipment operation evaluation index system of the equipment to obtain the equipment operation evaluation result, which is used as the supplier evaluation result based on the equipment, and provides a systematic equipment operation evaluation system model. It can combine multiple aspects of data and analyze and process the data to obtain the equipment operation evaluation result, which is conducive to saving unnecessary manpower and material resources and thus improving the evaluation efficiency of the equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the determined equipment operation evaluation result, thereby improving the evaluation accuracy and evaluation effectiveness of the determined supplier evaluation result, thereby helping to more accurately and conveniently determine the equipment supplier with a better supplier evaluation for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network; and it can also provide the function of generating an evaluation report of the equipment operation evaluation result, which is conducive to expanding the intelligent function of the equipment intelligent evaluation, improving the integrity and comprehensiveness of the equipment intelligent evaluation, not only improving the user experience, but also improving the user stickiness of the equipment intelligent evaluation device.

[0212] In an optional embodiment, in step 205, determining a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated may include:

[0213] Detecting whether an instruction to generate an evaluation report is received, where the instruction to generate an evaluation report includes an evaluation report version corresponding to the evaluation report to be generated;

[0214] When the detector receives the instruction to generate an evaluation report, it determines the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated according to the evaluation report instruction;

[0215] When the detection does not receive the instruction to generate an evaluation report, the equipment operation evaluation results are analyzed to obtain the equipment operation evaluation level corresponding to the target type of equipment. When the equipment operation evaluation level is less than or equal to the pre-set equipment operation evaluation level threshold, the pre-set basic evaluation report version is obtained as the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated.

[0216] In this optional embodiment, it should be noted that, optionally, it can also be set so that when the equipment operation evaluation level is greater than or equal to a pre-set equipment operation evaluation level threshold, a subsequent operation of obtaining a pre-set basic evaluation report version is performed as the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated, and this embodiment of the present invention does not limit this.

[0217] In this optional embodiment, further optionally, the method may further include the following operations:

[0218] Determine whether the equipment operation evaluation level is less than or equal to a preset equipment operation evaluation level threshold. When the judgment result is no, determine that the equipment operation evaluation result corresponding to the target type equipment does not need to generate an evaluation report.

[0219] It can be seen that this optional embodiment can detect whether an instruction to generate an evaluation report is received, and provide a matching method for determining the target evaluation report version based on different detection results, enriching the diversity of the method for determining the target evaluation report version, and improving the flexibility of the method for determining the target evaluation report version, which is conducive to dealing with situations where the user does not actively select the evaluation report version, and improves the comprehensiveness and systematicness of the evaluation report generation function.

[0220] In another optional embodiment, the device operation evaluation result of the target type device can be specifically calculated using the following formula:

[0221] EQE=EOE+COE-ROE=(EI+TS)+(ER+CD+MD+UD)-(SA+SE+BD)

[0222] Among them, EQE is the total score of the equipment operation evaluation of the target type equipment, EOE is the equipment efficiency index of the target type equipment, COE is the equipment cost index of the target type equipment, ROE is the equipment risk index of the target type equipment; EI is the equipment inventory dimension of the target type equipment; TS is the complaint dimension of the target type equipment; ER is the failure dimension of the target type equipment; CD is the general defect dimension of the target type equipment; MD is the major defect dimension of the target type equipment; UD is the emergency defect dimension of the target type equipment; SA is the accident dimension of the target type equipment; SE is the event dimension of the target type equipment; BD is the batch defect dimension of the target type equipment.

[0223] Optionally, the equipment operation evaluation results of the above target type equipment correspond to the evaluation index system, which can be referred to Figure 7 shown.

[0224] In this optional embodiment, for example, the weights of the scores of the various device operation evaluation dimensions corresponding to the target type devices may be shown in the following table:

[0225]

[0226] It can be seen that this optional embodiment provides a calculation formula for the equipment operation evaluation results, which improves the scientificity and rationality of the method for determining the equipment operation evaluation results, and thus improves the rationality and effectiveness of the determined equipment operation evaluation results, which is conducive to improving the creativity and scientificity of the equipment intelligent evaluation.

[0227] In another optional embodiment, the device ownership dimension EI of the target type of device can be calculated in the following way:

[0228]

[0229] Among them, EI Y The number of target type equipment supplied by the target supplier that has been in operation for Y years; max The maximum operating life of the target type equipment supplied by the target supplier and the operating life of all equipment is rounded up to the nearest integer; EI F The correction value for the total number of online devices of the target type supplied by the target supplier.

[0230]

[0231] Among them, EI N is the normalized value of the total number of network correction values corresponding to the target type of equipment supplied by the target supplier; NN is the normalization parameter, max(EI F ) is the maximum value of all the total number of correction values of all suppliers of devices corresponding to a certain device type.

[0232]

[0233]

[0234] Among them, EI a It is the equipment holding coefficient.

[0235] In this optional embodiment, for example, NN=100 is taken.

[0236] It can be seen that this optional embodiment provides a calculation formula for the equipment holding dimension score results, which improves the scientificity and rationality of the method for determining the equipment holding dimension score results, and thus improves the rationality and effectiveness of the determined equipment holding dimension score results, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is conducive to improving the creativity and scientificity of equipment intelligent evaluation.

[0237] In another optional embodiment, the complaint dimension TS of the target type device can be calculated as follows:

[0238]

[0239] Among them, ZC i is the number of complaints received by the target supplier reported by the grassroots power supply bureau during the evaluation period; ni is the total number of target type equipment supplied by the target supplier reported by the grassroots power supply bureau; d is the number of power supply bureaus reporting the target type equipment supplied by the target supplier; n is the total number of target type equipment supplied by the target supplier in the network.

[0240] In this optional embodiment, for example, take ZC i The maximum value is 5.

[0241] It can be seen that this optional embodiment provides a calculation formula for the complaint dimension score results, which improves the scientificity and rationality of the method for determining the complaint dimension score results, and thus improves the rationality and effectiveness of the determined complaint dimension score results, which is beneficial to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is beneficial to improving the creativity and scientificity of the equipment intelligent evaluation.

[0242] In yet another optional embodiment, the failure dimension ER of the target type device may be calculated in the following manner:

[0243]

[0244]

[0245] Among them, e Y ER is the number of failures of target type equipment of the target supplier with a Y-year operation period during the statistical period; a N is the correction value of the total number of failures per year of target type equipment supplied by the target supplier during the statistical period; Y The total number of target type equipment supplied by the target supplier that has been in operation for Y years; F b It is the benchmark score of the fault dimension.

[0246] In this optional embodiment, for example, take F b =40.

[0247] It can be seen that this optional embodiment provides a calculation formula for the fault dimension score results, which improves the scientificity and rationality of the method for determining the fault dimension score results, and thus improves the rationality and effectiveness of the determined fault dimension score results, which is beneficial to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is beneficial to improving the creativity and scientificity of the equipment intelligent evaluation.

[0248] In yet another optional embodiment, the general defect dimension CD of the target type of equipment may be calculated as follows:

[0249]

[0250]

[0251] Among them, cd Y CD is the number of general defects of the target type equipment that has been in operation for Y years by the target supplier during the statistical period; a C is the total number of general defects per year of target type equipment supplied by the target supplier during the statistical period; b It is the benchmark score for general defect dimension of equipment.

[0252] In this optional embodiment, for example, take C b =10.

[0253] It can be seen that this optional embodiment provides a calculation formula for the score results of the general defect dimension of the equipment, which improves the scientificity and rationality of the method for determining the score results of the general defect dimension of the equipment, and thus improves the rationality and effectiveness of the determined score results of the general defect dimension of the equipment, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is conducive to improving the creativity and scientificity of the intelligent evaluation of the equipment.

[0254] In another optional embodiment, the equipment major defect dimension MD of the target type equipment can be calculated as follows:

[0255]

[0256]

[0257] Among them, md Y MD is the number of major defects of the target type equipment of the target supplier with a period of Y years in operation during the statistical period; a M is the total number of major defects per year of target type equipment supplied by the target supplier during the statistical period; b It is the benchmark score for the dimension of major equipment defects.

[0258] In this optional embodiment, for example, take M b =15.

[0259] It can be seen that this optional embodiment provides a calculation formula for the score results of the major defect dimension of the equipment, which improves the scientificity and rationality of the method of determining the score results of the major defect dimension of the equipment, and thus improves the rationality and effectiveness of the determined score results of the major defect dimension of the equipment, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is conducive to improving the creativity and scientificity of the intelligent evaluation of the equipment.

[0260] In another optional embodiment, the equipment urgent defect dimension UD of the target type equipment can be calculated in the following way:

[0261]

[0262]

[0263] Among them, ud Y UD is the number of urgent defects of the target type equipment of the target supplier with a Y-year operation period during the statistical period; a The correction value of the total number of urgent defects per year for the target type of equipment supplied by the target supplier during the statistical period; U b It is the benchmark score for the equipment emergency defect dimension.

[0264] In this optional embodiment, for example, take U b =20.

[0265] It can be seen that this optional embodiment provides a calculation formula for the equipment emergency defect dimension score results, which improves the scientificity and rationality of the method for determining the equipment emergency defect dimension score results, and thus improves the rationality and effectiveness of the determined equipment emergency defect dimension score results, which is beneficial to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is beneficial to improving the creativity and scientificity of the equipment intelligent evaluation.

[0266] In another optional embodiment, the accident dimension SA of the target type equipment can be calculated in the following way:

[0267]

[0268] Among them, SG i The number of various accidents caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; i is the deduction coefficient for the accident dimension.

[0269] In this optional embodiment, for example, i=1, 2, 3, 4, corresponding to the statistical data of particularly serious accidents, serious accidents, major accidents and general accidents respectively; when it is a particularly serious accident, So1=30; when it is a major accident, So2=20; when it is a major accident, So3=15; when it is a general accident, So4=10.

[0270] It can be seen that this optional embodiment provides a calculation formula for the accident dimension score results, which improves the scientificity and rationality of the method for determining the accident dimension score results, and thus improves the rationality and effectiveness of the determined accident dimension score results, which is beneficial to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is beneficial to improving the creativity and scientificity of the equipment intelligent evaluation.

[0271] In yet another optional embodiment, the event dimension SE of the target type device may be calculated in the following manner:

[0272]

[0273] Among them, SJ i The number of incidents of all levels caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; Sr i It is the deduction coefficient of the event dimension.

[0274] In this optional embodiment, for example, i=1, 2, 3, 4, which correspond to statistical data of level one to level eight events respectively. When the event level is level one, Sr1=10; when the event level is level two, Sr2=9; and so on, when the event level is level eight, Sr8=3.

[0275] It can be seen that this optional embodiment provides a calculation formula for the event dimension score results, which improves the scientificity and rationality of the method for determining the event dimension score results, and thus improves the rationality and effectiveness of the determined event dimension score results, which is beneficial to improving the rationality and effectiveness of the determined equipment operation evaluation results, and thus is beneficial to improving the creativity and scientificity of the equipment intelligent evaluation.

[0276] In yet another optional embodiment, the batch defect dimension BD of the target type equipment may be obtained in the following manner:

[0277] The batch defect processing data corresponding to the target type of equipment is obtained, and the batch defect dimensions corresponding to the target type of equipment are determined based on the pre-set batch defect evaluation conditions and batch defect processing data.

[0278] In this optional embodiment, for example, points are calculated based on the discovery and handling of batch defects of suppliers, reflecting the quality problems existing in the supplier's equipment when it leaves the factory. For the same type of equipment from the same supplier (regardless of voltage level), the number of batch defects uniformly defined by the company is counted from the time the equipment is connected to the network, and points are deducted based on the supplier's rectification and cooperation. If the supplier can take the initiative to recall and arrange for replacement and repair, no points will be deducted; if the supplier takes the initiative to explain the situation and actively cooperates with the recall and rectification when the operating unit discovers batch defects and latent fault defects in the product, 2 points will be deducted per case, which will be valid for 3 years from the time of deduction; if the supplier conceals batch defects, does not take the initiative to recall, and does not cooperate with rectification, 8 points will be deducted per case, and the deduction of 8 points per case will be valid until 3 years have passed since the supplier cooperated with the rectification.

[0279] It can be seen that this optional embodiment provides a method for determining the batch defect dimension score results, improves the rationality and adaptability of the method for determining the fault dimension score results, and thus improves the rationality and effectiveness of the determined fault dimension score results, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results.

[0280] Example 3

[0281] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent equipment evaluation device based on big data algorithm disclosed in an embodiment of the present invention. Figure 3 As shown, the intelligent equipment evaluation device based on big data algorithm may include:

[0282] The first determination module 301 is used to determine a certain device type that needs to be evaluated, and determine a target supplier that needs to be evaluated based on a set of suppliers of devices corresponding to the certain device type; the set of suppliers of devices corresponding to the certain device type includes at least one supplier that supplies devices corresponding to the certain device type; determine a device type identifier corresponding to the certain device type and a supplier identifier corresponding to the target supplier, and determine a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier; all devices corresponding to the certain device type include the target type device; the target type device is the device corresponding to the certain device type supplied by the target supplier; and determine device evaluation data corresponding to the target type device based on the device identifier corresponding to the target type device.

[0283] The second determination module 302 is used to determine the equipment operation evaluation results of the target type equipment based on the equipment evaluation data and a pre-set equipment operation evaluation index system, which is used as the supplier evaluation results of the target supplier based on the target type equipment; the equipment operation evaluation results are used to evaluate the equipment efficiency, equipment cost and equipment risk of the target type equipment supplied by the target supplier.

[0284] It can be seen that implementation Figure 3 The described equipment intelligent evaluation device based on big data algorithm can determine the equipment that needs to be evaluated according to the equipment type identification and supplier identification, and perform equipment evaluation operations on the equipment according to the equipment evaluation data and equipment operation evaluation index system of the equipment to obtain equipment operation evaluation results, which are used as supplier evaluation results based on the equipment of the supplier. Combined with the systematic equipment operation evaluation system, it can combine multiple data and analyze and process the data to obtain equipment operation evaluation results, which is conducive to saving unnecessary manpower and material resources and thus improving the evaluation efficiency of equipment operation evaluation, and is conducive to improving the evaluation accuracy and evaluation rationality of the determined equipment operation evaluation results, thereby improving the evaluation accuracy and evaluation effectiveness of the determined supplier evaluation results, thereby helping to more accurately and conveniently determine equipment suppliers with better supplier evaluations for equipment supply, which can improve the equipment quality of the supplied equipment and further ensure the safe and stable operation of the distribution network.

[0285] In an optional embodiment, if Figure 4 As shown, the device may also include:

[0286] The acquisition module 303 is used to obtain the device-related data corresponding to the target type device based on the device type identifier corresponding to a certain device type and the device identifier corresponding to the target type device; the device-related data corresponding to the target type device includes the supplier data of all suppliers of devices corresponding to a certain device type, the device defect data corresponding to the target type device, the device failure data corresponding to the target type device, the device ledger data corresponding to the target type device, and the device accident event data corresponding to the target type device.

[0287] The first determining module 301 is further configured to determine the equipment quality operation data corresponding to the target type equipment according to the equipment association data.

[0288] The data processing module 304 is configured to perform a data screening operation on the equipment quality operation data according to a preset first data screening condition and a preset second data screening condition, and obtain equipment evaluation data corresponding to the target type of equipment.

[0289] It can be seen that implementation Figure 4The described device can filter the relevant data of the equipment and the supplier according to the first data filtering condition and the second data filtering condition to obtain the equipment evaluation data of the equipment for subsequent operation of determining the equipment operation evaluation result, thereby improving the effectiveness and pertinence of the determined equipment evaluation data, and thus helping to improve the efficiency of subsequent determination of the equipment operation evaluation result based on the equipment evaluation data and improve the accuracy of the determined equipment operation evaluation result, and helping to reduce unnecessary waste of resources due to complex data.

[0290] In another optional embodiment, the second determining module 302 determines the device operation evaluation result of the target type device according to the device evaluation data and a pre-set device operation evaluation index system in the following manner:

[0291] According to a pre-set equipment operation evaluation indicator system, determine an equipment operation evaluation category set corresponding to the equipment operation evaluation indicator system, and determine an equipment operation evaluation dimension set corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; and the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension;

[0292] Determine, based on each equipment operation evaluation category included in the equipment operation evaluation category set, each equipment operation evaluation dimension included in the equipment operation evaluation dimension set, and equipment evaluation data, sub-equipment evaluation data corresponding to each equipment operation evaluation dimension; the equipment evaluation data includes sub-equipment evaluation data;

[0293] The device operation evaluation result of the target type device is calculated based on the sub-device evaluation data corresponding to each device operation evaluation dimension and the pre-set dimension weight content corresponding to each device operation evaluation dimension.

[0294] It can be seen that implementation Figure 4 The described device can also determine the sub-device evaluation data of different equipment operation evaluation dimensions based on different equipment operation evaluation categories, the different equipment operation evaluation dimensions included in each equipment operation evaluation category, and the equipment evaluation data, and then determine the equipment operation evaluation results based on the sub-device evaluation data of different equipment operation evaluation dimensions, which is conducive to improving the pertinence and matching of the equipment evaluation data and different sub-device evaluation data, and thus is conducive to improving the accuracy and rationality of the determined equipment operation evaluation results, and is conducive to improving the efficiency of determining the equipment operation evaluation results.

[0295] In another optional embodiment, Figure 4 As shown, the device may also include:

[0296] The judgment module 305 is used to judge whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition. When the judgment result is yes, the first determination module 301 is triggered to perform the above-mentioned operation of determining a device type that requires device evaluation operation.

[0297] It can be seen that implementation Figure 4 The described device can also determine whether the current situation meets the equipment evaluation trigger conditions. When the judgment is yes, it executes the subsequent steps of determining a certain equipment type that requires equipment evaluation operation, thereby improving the rationality of executing equipment intelligent evaluation operations, and thus helping to improve the integrity and comprehensiveness of equipment intelligent evaluation operations, and helping to reduce unnecessary resource waste caused by arbitrary execution of equipment intelligent evaluation operations.

[0298] In another optional embodiment, the determination module 305 determines whether the current situation corresponding to the target type device satisfies the preset device evaluation trigger condition in the following manner:

[0299] Detecting whether a device evaluation trigger instruction is received, and when the detection result is yes, determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition; or,

[0300] Based on the device association data, the evaluation urgency data of the target type device is determined, and it is judged whether the evaluation urgency data of the target type device meets the predetermined device emergency evaluation conditions. When the judgment result is yes, it is determined that the current situation corresponding to the target type device meets the predetermined device evaluation trigger conditions.

[0301] It can be seen that implementation Figure 4 The described device can also determine whether the current situation meets the equipment evaluation trigger conditions by detecting whether an equipment evaluation trigger instruction is received or judging whether the evaluation urgency data meets the equipment emergency evaluation conditions. When the detection is yes or the judgment is yes, it is determined that the current situation meets the equipment evaluation trigger conditions, which enriches the diversity of the methods for determining whether the equipment evaluation trigger conditions are met and improves the flexibility of the methods for determining whether the equipment evaluation trigger conditions are met, thereby improving the rationality and effectiveness of the determined judgment results of whether the equipment evaluation trigger conditions are met, thereby improving the accuracy of the determined judgment results of whether the equipment evaluation trigger conditions are met.

[0302] In another optional embodiment, the determination module 305 determines whether the evaluation urgency data of the target type device meets the predetermined device emergency evaluation condition in the following manner:

[0303] If the evaluation urgency data of the target type device is the time period for which the target type device is to be evaluated, determine whether the time period for which the target type device is to be evaluated is greater than or equal to a preset time period threshold. If the judgment result is yes, determine that the evaluation urgency data of the target type device meets the predetermined equipment emergency evaluation condition. The time period for which the target type device is to be evaluated is the time period from the time point corresponding to the last time the equipment operation evaluation result of the target type device was obtained to the current time.

[0304] If the evaluation urgency data of the target type equipment is the cumulative number of equipment failures of the target type equipment, determine whether the cumulative number of equipment failures is greater than or equal to a preset cumulative number of equipment failures threshold. When the judgment result is yes, determine that the evaluation urgency data of the target type equipment meets the equipment emergency evaluation conditions; the cumulative number of equipment failures includes the cumulative number of equipment failures of the target type equipment in the historical time period or the total number of historical cumulative number of equipment failures of the target type equipment.

[0305] It can be seen that implementation Figure 4 The described device can also determine whether the evaluation urgency data meets the equipment emergency evaluation conditions from two perspectives: when the evaluation urgency data is the cumulative number of equipment failures or when the evaluation urgency data is the cumulative number of equipment failures. This enriches the diversity of ways to determine whether the evaluation urgency data meets the equipment emergency evaluation conditions, and improves the flexibility of ways to determine whether the evaluation urgency data meets the equipment emergency evaluation conditions, thereby improving the rationality and effectiveness of the determined judgment results of meeting the equipment emergency evaluation conditions, thereby improving the accuracy of the determined judgment results of meeting the equipment emergency evaluation conditions, and is conducive to improving the accuracy of the determined judgment results of meeting the equipment evaluation trigger conditions.

[0306] In another optional embodiment, the second determination module 302 is further used to determine the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated after determining the equipment operation evaluation result of the target type equipment based on the equipment evaluation data and a pre-set equipment operation evaluation index system.

[0307] like Figure 4 As shown, the device may also include:

[0308] The report generation module 306 is configured to generate an evaluation report corresponding to the target type of equipment according to the equipment operation evaluation result of the target type of equipment and the target evaluation report version, so as to evaluate the target supplier.

[0309] It can be seen that implementation Figure 4The described device can also provide the function of generating an evaluation report of the equipment operation evaluation results, which is conducive to expanding the intelligent functions of the device and improving the integrity and comprehensiveness of the equipment intelligent evaluation. It can not only improve the user experience, but also increase the user stickiness of using the device.

[0310] In another optional embodiment, the second determining module 302 determines the target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated, specifically including:

[0311] Detecting whether an instruction to generate an evaluation report is received, where the instruction to generate an evaluation report includes an evaluation report version corresponding to the evaluation report to be generated;

[0312] When the detector receives the instruction to generate an evaluation report, it determines the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated according to the evaluation report instruction;

[0313] When the detection does not receive the instruction to generate an evaluation report, the equipment operation evaluation results are analyzed to obtain the equipment operation evaluation level corresponding to the target type of equipment. When the equipment operation evaluation level is less than or equal to the pre-set equipment operation evaluation level threshold, the pre-set basic evaluation report version is obtained as the target evaluation report version of the evaluation report corresponding to the equipment operation evaluation result to be generated.

[0314] It can be seen that implementation Figure 4 The described device can also detect whether an instruction to generate an evaluation report is received, and provide a matching method for determining the target evaluation report version based on different detection results, thereby enriching the diversity of the methods for determining the target evaluation report version and improving the flexibility of the methods for determining the target evaluation report version. This is conducive to dealing with situations where the user does not actively select the evaluation report version, and improves the comprehensiveness and systematicness of the evaluation report generation function.

[0315] In another optional embodiment, the device operation evaluation result of the target type device can be calculated specifically by the following formula:

[0316] EQE=EOE+COE-ROE=(EI+TS)+(ER+CD+MD+UD)-(SA+SE+BD)

[0317] Among them, EQE is the total score of the equipment operation evaluation of the target type equipment, EOE is the equipment efficiency index of the target type equipment, COE is the equipment cost index of the target type equipment, ROE is the equipment risk index of the target type equipment; EI is the equipment inventory dimension of the target type equipment; TS is the complaint dimension of the target type equipment; ER is the failure dimension of the target type equipment; CD is the general defect dimension of the target type equipment; MD is the major defect dimension of the target type equipment; UD is the emergency defect dimension of the target type equipment; SA is the accident dimension of the target type equipment; SE is the event dimension of the target type equipment; BD is the batch defect dimension of the target type equipment.

[0318] It can be seen that implementation Figure 4 The described device can also determine the equipment operation evaluation results through calculation formulas, thereby improving the scientificity and rationality of the method for determining the equipment operation evaluation results, thereby improving the rationality and effectiveness of the determined equipment operation evaluation results, and is conducive to improving the creativity and scientificity of equipment intelligent evaluation.

[0319] In another optional embodiment, the device ownership dimension EI of the target type of device can be calculated in the following way:

[0320]

[0321] Among them, EI Y The number of target type equipment supplied by the target supplier that has been in operation for Y years; max The maximum operating life of the target type equipment supplied by the target supplier and the operating life of all equipment is rounded up to the nearest integer; EI F The correction value for the total number of online devices of the target type supplied by the target supplier.

[0322]

[0323] Among them, EI N is the normalized value of the total number of network correction values corresponding to the target type of equipment supplied by the target supplier; NN is the normalization parameter, max(EI F ) is the maximum value of all the total number of correction values of all suppliers of devices corresponding to a certain device type.

[0324]

[0325]

[0326] Among them, EI a It is the equipment holding coefficient.

[0327] In this optional embodiment, the complaint dimension TS of the target type device may be calculated as follows:

[0328]

[0329] Among them, ZC i The number of complaints against the target supplier reported by the grassroots power supply bureau during the evaluation period; n i The total number of target type equipment supplied by the target supplier reported by the grassroots power supply bureau; d is the number of power supply bureaus that reported the target type equipment supplied by the target supplier; n is the total number of target type equipment supplied by the target supplier on the network.

[0330] In this optional embodiment, the complaint dimension ER of the target type device can be calculated as follows:

[0331]

[0332]

[0333] Among them, e Y ER is the number of failures of target type equipment of the target supplier with a Y-year operation period during the statistical period; a N is the correction value of the total number of failures per year of target type equipment supplied by the target supplier during the statistical period; Y The total number of target type equipment supplied by the target supplier that has been in operation for Y years; F b It is the benchmark score of the fault dimension.

[0334] In this optional embodiment, the general defect dimension CD of the target type of equipment may be calculated as follows:

[0335]

[0336]

[0337] Among them, cd Y CD is the number of general defects of the target type equipment that has been in operation for Y years by the target supplier during the statistical period; a C is the total number of general defects per year of target type equipment supplied by the target supplier during the statistical period; b It is the benchmark score for general defect dimension of equipment.

[0338] In this optional embodiment, the equipment major defect dimension MD of the target type equipment may be calculated as follows:

[0339]

[0340]

[0341] Among them, md Y MD is the number of major defects of the target type equipment of the target supplier with a period of Y years in operation during the statistical period; a M is the total number of major defects per year of target type equipment supplied by the target supplier during the statistical period; b It is the benchmark score for the dimension of major equipment defects.

[0342] In this optional embodiment, the equipment urgent defect dimension UD of the target type equipment may be calculated as follows:

[0343]

[0344]

[0345] Among them, ud Y UD is the number of urgent defects of the target type equipment of the target supplier with a Y-year operation period during the statistical period; a The correction value of the total number of urgent defects per year for the target type of equipment supplied by the target supplier during the statistical period; U b It is the benchmark score for the equipment emergency defect dimension.

[0346] In this optional embodiment, the accident dimension SA of the target type equipment may be calculated as follows:

[0347]

[0348] Among them, SG i The number of various accidents caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; i is the deduction coefficient for the accident dimension.

[0349] In this optional embodiment, the event dimension SE of the target type device may be calculated as follows:

[0350]

[0351] Among them, SJ i The number of incidents of all levels caused by quality reasons for the target type equipment supplied by the target supplier during the evaluation period; Sr i It is the deduction coefficient of the event dimension.

[0352] In this optional embodiment, the batch defect dimension BD of the target type equipment may be obtained in the following manner:

[0353] The batch defect processing data corresponding to the target type of equipment is obtained, and the batch defect dimensions corresponding to the target type of equipment are determined based on the pre-set batch defect evaluation conditions and batch defect processing data.

[0354] It can be seen that implementation Figure 4 The described device can also calculate the score results of different equipment operation evaluation dimensions through matching formulas, thereby improving the scientificity and rationality of the method of determining the score results of different equipment operation evaluation dimensions, and thus improving the rationality and effectiveness of the determined score results of different equipment operation evaluation dimensions, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results, thereby facilitating the improvement of the creativity and scientificity of equipment intelligent evaluation; and, it can also improve the rationality and adaptability of the method of determining the fault dimension score results, thereby improving the rationality and effectiveness of the determined fault dimension score results, which is conducive to improving the rationality and effectiveness of the determined equipment operation evaluation results.

[0355] Example 4

[0356] See also Figure 5 , Figure 5 This is a structural diagram of another device intelligent evaluation device based on big data algorithm disclosed in an embodiment of the present invention. Figure 5 As shown, the device may include:

[0357] A memory 401 storing executable program code;

[0358] a processor 402 coupled to the memory 401;

[0359] Furthermore, it may also include an input interface 403 and an output interface 404 coupled to the processor 402;

[0360] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the device intelligent evaluation method based on big data algorithm described in the first or second embodiment.

[0361] Example 5

[0362] An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the device intelligent evaluation method based on big data algorithm described in Example 1 or Example 2.

[0363] Example 6

[0364] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the device intelligent evaluation method based on big data algorithm described in Example 1 or Example 2.

[0365] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Those skilled in the art can understand and implement the present invention without inventive effort.

[0366] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0367] Finally, it should be noted that the intelligent equipment evaluation method and device based on big data algorithm disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for intelligent equipment evaluation based on big data algorithm, characterized in that: The method comprises: Determining a device type requiring a device evaluation operation, and determining a target supplier requiring a supplier device evaluation operation based on a set of suppliers of devices corresponding to the device type; the set of suppliers of devices corresponding to the device type including at least one supplier that supplies the device corresponding to the device type; Determining a device type identifier corresponding to the certain device type and a supplier identifier corresponding to the target supplier, and determining a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier; all devices corresponding to the certain device type include the target type device; the target type device is a device corresponding to the certain device type supplied by the target supplier; Determining device evaluation data corresponding to the target type device according to the device identifier corresponding to the target type device; Determining, based on a pre-set equipment operation evaluation indicator system, an equipment operation evaluation category set corresponding to the equipment operation evaluation indicator system, and determining an equipment operation evaluation dimension set corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; and the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension; Determining, according to each of the equipment operation evaluation categories included in the equipment operation evaluation category set, each of the equipment operation evaluation dimensions included in the equipment operation evaluation dimension set, and the equipment evaluation data, sub-equipment evaluation data corresponding to each of the equipment operation evaluation dimensions; the equipment evaluation data including the sub-equipment evaluation data; Calculate the equipment operation evaluation result of the target type equipment based on the sub-equipment evaluation data corresponding to each of the equipment operation evaluation dimensions and the pre-set dimension weight content corresponding to each of the equipment operation evaluation dimensions; the equipment operation evaluation result is used to evaluate the equipment performance, equipment cost, and equipment risk of the target type equipment supplied by the target supplier; Obtaining device-related data corresponding to the target type device based on the device type identifier corresponding to the certain device type and the device identifier corresponding to the target type device; the device-related data corresponding to the target type device includes supplier data of all suppliers of devices corresponding to the certain device type, device defect data corresponding to the target type device, device failure data corresponding to the target type device, device inventory data corresponding to the target type device, and device accident event data corresponding to the target type device; Determining, based on the device association data, device quality operation data corresponding to the target type device; According to a preset first data screening condition and a preset second data screening condition, a data screening operation is performed on the equipment quality operation data to obtain equipment evaluation data corresponding to the target type equipment.

2. The intelligent equipment evaluation method based on big data algorithm according to claim 1 is characterized in that: The method further comprises: Determining whether the current situation corresponding to the target type device meets a preset device evaluation trigger condition, and when the determination result is yes, triggering execution of the operation of determining a device type requiring a device evaluation operation; The step of determining whether the current situation of the target type device satisfies a preset device evaluation trigger condition includes: Detecting whether a device evaluation trigger instruction is received, and when the detection result is yes, determining whether the current situation corresponding to the target type device meets the preset device evaluation trigger condition; or, Determining, based on the device association data, evaluation urgency data of the target type device, and judging whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition; if the judgment result is yes, determining that the current situation corresponding to the target type device meets the predetermined device evaluation trigger condition; The step of determining whether the evaluation urgency data of the target type device meets a predetermined device emergency evaluation condition includes: If the evaluation urgency data of the target type device is the duration of time to be evaluated of the target type device, determining whether the duration of time to be evaluated is greater than or equal to a preset threshold of the duration of time to be evaluated; if the judgment result is yes, determining that the evaluation urgency data of the target type device meets the predetermined device emergency evaluation condition; the duration of time to be evaluated of the target type device is the duration from the time point corresponding to the last time the device operation evaluation result of the target type device was obtained to the current time; If the evaluation urgency data of the target type device is the cumulative number of device failures of the target type device, determine whether the cumulative number of device failures is greater than or equal to a preset threshold value of the cumulative number of device failures. When the judgment result is yes, determine that the evaluation urgency data of the target type device meets the device emergency evaluation condition; the cumulative number of device failures includes the cumulative number of device failures of the target type device in the historical time period or the total number of historical cumulative device failures of the target type device.

3. The intelligent equipment evaluation method based on big data algorithm according to claim 2 is characterized in that: After determining the equipment operation evaluation result of the target type equipment based on the equipment evaluation data and a pre-set equipment operation evaluation index system, the method further includes: Determining a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated, and generating an evaluation report corresponding to the target type of device based on the device operation evaluation result of the target type of device and the target evaluation report version, so as to evaluate the target supplier; The step of determining a target evaluation report version of an evaluation report corresponding to the device operation evaluation result to be generated includes: detecting whether an instruction to generate an evaluation report is received, wherein the instruction to generate an evaluation report includes an evaluation report version corresponding to the evaluation report to be generated; When the evaluation report generation instruction is received, determining a target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated according to the evaluation report instruction; When it is detected that the instruction to generate the evaluation report is not received, the device operation evaluation result is analyzed to obtain the device operation evaluation level corresponding to the target type device; when the device operation evaluation level is less than or equal to a pre-set device operation evaluation level threshold, a pre-set basic evaluation report version is obtained as the target evaluation report version of the evaluation report corresponding to the device operation evaluation result to be generated.

4. The intelligent equipment evaluation method based on big data algorithm according to any one of claims 1 to 3, characterized in that: The equipment operation evaluation results of the target type equipment are specifically calculated using the following formula: in, The total score of the device run evaluation for the target type of device, is the device performance index of the target type device, is the equipment cost indicator of the target type equipment, Equipment risk indicators for the target type of equipment; The device ownership dimension for the target type of device; The complaint dimension for the target type of device; The fault dimension of the target type device; The general defect dimension of the equipment for the target type equipment; The equipment major defect dimension for the target type equipment; Equipment urgent defect dimension for equipment of the target type; The accident dimension for the target type equipment; The event dimension for the target type device; is the batch defect dimension of the target type of equipment.

5. The intelligent equipment evaluation method based on big data algorithm according to claim 4 is characterized in that: The dimension of the number of devices of the target type It is calculated in the following way: in, The number of in-operation equipment of the target type supplied by the target supplier that has been in operation for Y years; The maximum operating life of the target type of equipment supplied by the target supplier, with the operating life of all equipment rounded up to the nearest integer; A correction value for the total number of online devices of the target type supplied by the target supplier; in, A normalized value of the total number of online devices corresponding to the target type supplied by the target supplier; is the normalization parameter, The maximum value of all corrected values of the total number of devices on the network corresponding to all suppliers of the device type; in, is the equipment holding coefficient; And, the complaint dimensions of the target type of equipment It is calculated in the following way: in, The number of complaints received by the target supplier during the evaluation period as reported by the grassroots power supply bureau; The total number of target type equipment supplied by the target supplier reported by the grassroots power supply bureau; The number of power supply bureaus reporting the target type of equipment supplied by the target supplier; The total number of online devices of the target type supplied by the target supplier; And, the failure dimension of the target type device It is calculated in the following way: in, The number of failures of the target type of equipment of the target supplier with a Y-year operation period during the statistical period; The total number of failures per year for the target type of equipment supplied by the target supplier during the statistical period is the revised value; The total number of equipment of the target type supplied by the target supplier that has been in operation for Y years; is the fault dimension benchmark score; And, the general defect dimension of the target type device It is calculated in the following way: in, The number of general defects of the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; The total number of general defects per year for the target type of equipment supplied by the target supplier during the statistical period is the revised value; It is the benchmark score of general defect dimension of equipment; and, the equipment major defect dimension of the target type equipment It is calculated in the following way: in, The number of major defects in the target type of equipment that has been in operation for Y years by the target supplier during the statistical period; The revised value of the total number of major defects per year for the target type of equipment supplied by the target supplier during the statistical period; Benchmark score for major equipment defects; and, equipment emergency defect dimension of the target type equipment It is calculated in the following way: in, The number of urgent defects of the target type of equipment of the target supplier with a period of Y years in operation during the statistical period; The revised value of the total number of units / year with urgent defects for the target type of equipment supplied by the target supplier during the statistical period; It is the benchmark score of equipment emergency defect dimension; And, the accident dimension of the target type equipment It is calculated in the following way: in, The number of various accidents caused by quality reasons for the target type of equipment supplied by the target supplier during the evaluation period; is the deduction coefficient of the accident dimension; And, the event dimension of the target type device It is calculated in the following way: in, The number of incidents of all levels caused by quality reasons for the target type of equipment supplied by the target supplier during the evaluation period; is the deduction coefficient of the event dimension; And, the batch defect dimension of the target type equipment It is obtained in the following way: Batch defect processing data corresponding to the target type equipment is obtained, and the batch defect dimension corresponding to the target type equipment is determined based on pre-set batch defect evaluation conditions and the batch defect processing data.

6. An intelligent equipment evaluation device based on big data algorithm, characterized in that: The device comprises: A first determination module is configured to determine a certain device type that requires a device evaluation operation, and determine a target supplier that requires a supplier device evaluation operation based on a set of suppliers of devices corresponding to the certain device type; the set of suppliers of devices corresponding to the certain device type includes at least one supplier that supplies devices corresponding to the certain device type; determine a device type identifier corresponding to the certain device type and a supplier identifier corresponding to the target supplier, and determine a target type device corresponding to the target supplier based on the device type identifier corresponding to the certain device type and the supplier identifier corresponding to the target supplier; all devices corresponding to the certain device type include the target type device; the target type device is the device corresponding to the certain device type supplied by the target supplier; and determine device evaluation data corresponding to the target type device based on the device identifier corresponding to the target type device; A second determination module is configured to determine, based on a preset equipment operation evaluation indicator system, a set of equipment operation evaluation categories corresponding to the equipment operation evaluation indicator system, and determine a set of equipment operation evaluation dimensions corresponding to each equipment operation evaluation type included in the equipment operation evaluation category set; the equipment operation evaluation category set includes at least one equipment operation evaluation category; the equipment operation evaluation dimension set includes at least one equipment operation evaluation dimension; based on each equipment operation evaluation category included in the equipment operation evaluation category set, each equipment operation evaluation dimension included in the equipment operation evaluation dimension set, and the equipment evaluation data, determine sub-equipment evaluation data corresponding to each equipment operation evaluation dimension; the equipment evaluation data includes sub-equipment evaluation data; based on the sub-equipment evaluation data corresponding to each equipment operation evaluation dimension and the preset dimension weight content corresponding to each equipment operation evaluation dimension, calculate an equipment operation evaluation result for the target type equipment; the equipment operation evaluation result is used to evaluate the equipment performance, equipment cost, and equipment risk of the target type equipment supplied by the target supplier; an acquisition module, configured to acquire device-related data corresponding to a target type device based on a device type identifier corresponding to the certain device type and a device identifier corresponding to the target type device; the device-related data corresponding to the target type device includes supplier data of all suppliers of devices corresponding to the certain device type, device defect data corresponding to the target type device, device failure data corresponding to the target type device, device inventory data corresponding to the target type device, and device accident event data corresponding to the target type device; The first determining module is further configured to determine the equipment quality operation data corresponding to the target type equipment based on the equipment association data; The data processing module is used to perform a data screening operation on the equipment quality operation data according to a preset first data screening condition and a preset second data screening condition to obtain equipment evaluation data corresponding to the target type equipment.

7. An intelligent equipment evaluation device based on big data algorithm, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent equipment evaluation method based on big data algorithm as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which, when called, are used to execute the equipment intelligent evaluation method based on big data algorithm as described in any one of claims 1 to 5.

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