A method and device for automatically evaluating the effect of power law enforcement

Abstract

The present invention discloses an automatic evaluation method and device for the effect of power law enforcement, the method comprising: obtaining power fault data uploaded by multiple users in a target area and power law enforcement record data uploaded by a power law enforcement agency; determining the law enforcement necessity parameter of the target area according to the power fault data based on preset keyword matching rules and fault scoring rules; calculating the law enforcement targeted parameters of the target area according to the power fault data and the power law enforcement record data based on a neural network prediction model; determining the necessity of evaluating the power law enforcement effect of the target area according to the law enforcement necessity parameters and the law enforcement targeted parameters. It can be seen that the present invention can more intelligently and accurately determine whether it is necessary to automatically evaluate the power law enforcement effect of a region, improve the timeliness and effectiveness of the power execution effect evaluation, and facilitate the rapid discovery of law enforcement problems.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an automatic evaluation method and device for power law enforcement effects. Background Art

[0002] With the increasing attention paid to the safety of power facilities, local administrative departments are increasingly strictly enforcing policies and regulations on power facilities. However, with the widespread deployment of power facilities and the increase in residential electricity consumption, how to effectively and timely evaluate the law enforcement effect of the executive department has become an important issue. However, the existing technology generally only evaluates the effectiveness of power law enforcement based on the user's evaluation needs. It is not timely, cannot effectively discover law enforcement problems, and has poor evaluation results. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for automatic evaluation of the effectiveness of power law enforcement, which can more intelligently and accurately determine whether it is necessary to automatically evaluate the effectiveness of power law enforcement in a region, improve the timeliness and effectiveness of power enforcement effect evaluation, and facilitate the rapid discovery of law enforcement problems.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an automatic evaluation method for the effect of power law enforcement, the method comprising:

[0005] Obtain power failure data uploaded by multiple users in the target area and power law enforcement record data uploaded by power law enforcement agencies;

[0006] Based on the preset keyword matching rules and fault scoring rules, and according to the power fault data, determine the enforcement necessity parameters of the target area;

[0007] Based on the neural network prediction model, according to the power fault data and the power law enforcement record data, the law enforcement targeted parameters of the target area are calculated;

[0008] The necessity of evaluating the effectiveness of electricity law enforcement in the target area is determined based on the law enforcement necessity parameter and the law enforcement targeting parameter.

[0009] As an optional embodiment, in the first aspect of the present invention, the power failure data includes power risk type labels and text data; the power risk type labels include at least one of the risks of disrupting power production order, damaging power equipment, damaging power facility land, air area of ​​power facilities, foreign body risks of power facilities, natural disaster risks and emergency risks.

[0010] As an optional embodiment, in the first aspect of the present invention, the power law enforcement record data includes multiple law enforcement records of multiple historical time periods; the law enforcement records are power facility inspection records, power facility maintenance records, forest rectification records near power facilities, or power facility destruction prevention records.

[0011] As an optional implementation, in the first aspect of the present invention, the determining the law enforcement necessity parameter of the target area based on the power fault data based on the preset keyword matching rule and fault scoring rule includes:

[0012] For each of the power failure data, based on a preset description degree keyword rule, matching the degree keyword text corresponding to the power failure data;

[0013] Calculate the degree parameter corresponding to the degree keyword text;

[0014] Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data;

[0015] Calculating the product of the fault score and the degree parameter to obtain a fault severity parameter corresponding to the power fault data;

[0016] The average values ​​of the fault severity parameters corresponding to all the power fault data are calculated to obtain the law enforcement necessity parameter of the target area.

[0017] As an optional implementation, in the first aspect of the present invention, the calculating the degree parameter corresponding to the degree keyword text includes:

[0018] The degree keyword text is input into a trained text processing CNN network to obtain the degree parameter corresponding to the degree keyword text; the text processing CNN network is trained by a training data set including multiple training text data and corresponding fault severity annotations.

[0019] As an optional implementation, in the first aspect of the present invention, the neural network prediction model is based on the power fault data and the power law enforcement record data to calculate the law enforcement targeted parameters of the target area, including:

[0020] Input each identical data pair having the same data record as the power fault data and the corresponding data record in the power law enforcement record into a trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time;

[0021] The average value of the law enforcement correspondence degree parameters of all the same data pairs is calculated to obtain the law enforcement targeted parameters of the target area.

[0022] As an optional implementation, in the first aspect of the present invention, determining the necessity of evaluating the power law enforcement effect in the target area according to the law enforcement necessity parameter and the law enforcement targeted parameter includes:

[0023] Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area;

[0024] Calculating a second parameter difference between the law enforcement targeted parameter and a targeted parameter reference value corresponding to the area type;

[0025] Calculating the similarity between the first parameter difference and the second parameter difference;

[0026] Determine whether the law enforcement necessity parameter is greater than the necessity parameter value and whether the law enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than a preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

[0027] As an optional implementation manner, in the first aspect of the present invention, the calculating the similarity between the first parameter difference and the second parameter difference includes:

[0028] Calculating the product of the first parameter difference and the degree weight to obtain a first parameter value; the degree weight is proportional to the number of degree keyword texts corresponding to all the power fault data;

[0029] Calculating the product of the second parameter difference and the prediction weight to obtain a second parameter value; the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage;

[0030] A difference between the first parameter value and the second parameter value is calculated to obtain a similarity between the first parameter difference and the second parameter difference.

[0031] A second aspect of an embodiment of the present invention discloses an automatic evaluation device for power law enforcement effects, the device comprising:

[0032] An acquisition module is used to acquire power failure data uploaded by multiple users in the target area and power law enforcement record data uploaded by the power law enforcement agency;

[0033] A determination module, configured to determine the law enforcement necessity parameter of the target area according to the power fault data based on a preset keyword matching rule and a fault scoring rule;

[0034] A calculation module, used for calculating the enforcement-targeted parameters of the target area based on the neural network prediction model and the power fault data and the power enforcement record data;

[0035] An evaluation module is used to determine the necessity of evaluating the effectiveness of electricity law enforcement in the target area based on the law enforcement necessity parameter and the law enforcement targeted parameter.

[0036] As an optional embodiment, in the second aspect of the present invention, the power failure data includes power risk type labels and text data; the power risk type labels include at least one of the risks of disrupting power production order, damaging power equipment, damaging power facility land, air area of ​​power facilities, foreign body risks of power facilities, natural disaster risks and emergency risks.

[0037] As an optional embodiment, in the second aspect of the present invention, the power law enforcement record data includes multiple law enforcement records of multiple historical time periods; the law enforcement records are power facility inspection records, power facility maintenance records, forest rectification records near power facilities, or power facility destruction prevention records.

[0038] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the law enforcement necessity parameter of the target area according to the power fault data based on the preset keyword matching rule and the fault scoring rule includes:

[0039] For each of the power failure data, based on a preset description degree keyword rule, matching the degree keyword text corresponding to the power failure data;

[0040] Calculate the degree parameter corresponding to the degree keyword text;

[0041] Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data;

[0042] Calculating the product of the fault score and the degree parameter to obtain a fault severity parameter corresponding to the power fault data;

[0043] The average values ​​of the fault severity parameters corresponding to all the power fault data are calculated to obtain the law enforcement necessity parameter of the target area.

[0044] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module calculates the degree parameter corresponding to the degree keyword text includes:

[0045] The degree keyword text is input into a trained text processing CNN network to obtain the degree parameter corresponding to the degree keyword text; the text processing CNN network is trained by a training data set including multiple training text data and corresponding fault severity annotations.

[0046] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module calculates the enforcement-targeted parameters of the target area based on the neural network prediction model and the power fault data and the power enforcement record data includes:

[0047] Input each identical data pair having the same data record as the power fault data and the corresponding data record in the power law enforcement record into a trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time;

[0048] The average value of the law enforcement correspondence degree parameters of all the same data pairs is calculated to obtain the law enforcement targeted parameters of the target area.

[0049] As an optional implementation, in the second aspect of the present invention, the evaluation module determines the specific manner of determining the necessity of evaluating the power law enforcement effect in the target area according to the law enforcement necessity parameter and the law enforcement targeted parameter, including:

[0050] Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area;

[0051] Calculating a second parameter difference between the law enforcement targeted parameter and a targeted parameter reference value corresponding to the area type;

[0052] Calculating the similarity between the first parameter difference and the second parameter difference;

[0053] Determine whether the law enforcement necessity parameter is greater than the necessity parameter value and whether the law enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than a preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

[0054] As an optional implementation, in the second aspect of the present invention, the specific manner in which the evaluation module calculates the similarity between the first parameter difference and the second parameter difference includes:

[0055] Calculating the product of the first parameter difference and the degree weight to obtain a first parameter value; the degree weight is proportional to the number of degree keyword texts corresponding to all the power fault data;

[0056] Calculating the product of the second parameter difference and the prediction weight to obtain a second parameter value; the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage;

[0057] A difference between the first parameter value and the second parameter value is calculated to obtain a similarity between the first parameter difference and the second parameter difference.

[0058] The third aspect of the present invention discloses another automatic evaluation device for power law enforcement effect, the device comprising:

[0059] A memory storing executable program code;

[0060] a processor coupled to the memory;

[0061] The processor calls the executable program code stored in the memory to execute part or all of the steps in the automatic evaluation method for power law enforcement effectiveness disclosed in the first aspect of the present invention.

[0062] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the automatic evaluation method of the power law enforcement effect disclosed in the first aspect of the present invention.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention can determine the necessity of law enforcement in the target area based on the power fault data reported by the user, and calculate the targeted law enforcement parameters of the target area according to the neural network prediction model, and further determine the necessity of evaluating the power law enforcement effect in the target area based on these two parameters. When an evaluation is necessary, the evaluation can be automatically started, so as to more intelligently and accurately determine whether it is necessary to automatically evaluate the power law enforcement effect in the area, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] 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 creative work.

[0066] Figure 1 It is a flow chart of an automatic evaluation method for power law enforcement effectiveness disclosed in an embodiment of the present invention.

[0067] Figure 2 It is a structural schematic diagram of an automatic evaluation device for power law enforcement effects disclosed in an embodiment of the present invention.

[0068] Figure 3 It is a structural schematic diagram of another automatic evaluation device for power law enforcement effects disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] The present invention discloses an automatic evaluation method and device for the effect of power law enforcement, which can determine the necessity of law enforcement in a target area based on power fault data reported by users, and calculate the targeted law enforcement parameters of the target area according to a neural network prediction model, and further determine the necessity of evaluating the effect of power law enforcement in the target area based on these two parameters. When it is necessary to evaluate, the evaluation can be automatically started, so as to more intelligently and accurately determine whether it is necessary to automatically evaluate the effect of power law enforcement in the area, improve the timeliness and effect of power execution effect evaluation, and facilitate the rapid discovery of law enforcement problems. The following are detailed descriptions.

[0073] Embodiment 1

[0074] See also Figure 1 , Figure 1 This is a flow chart of an automatic evaluation method for power law enforcement effect disclosed in an embodiment of the present invention. Figure 1 The described automatic evaluation method for the effect of power law enforcement can be applied to a data processing device / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the automatic evaluation method for the effectiveness of power law enforcement may include the following operations:

[0075] 101. Obtain power failure data uploaded by multiple users in a target area and power law enforcement record data uploaded by a power law enforcement agency.

[0076] 102. Based on the preset keyword matching rules and fault scoring rules, the enforcement necessity parameters of the target area are determined according to the power fault data.

[0077] 103. Based on the neural network prediction model, the enforcement targeting parameters of the target area are calculated according to the power failure data and power law enforcement record data.

[0078] 104. Determine the necessity of evaluating the effectiveness of power law enforcement in the target area based on the parameters of law enforcement necessity and law enforcement targeting.

[0079] It can be seen that the above-mentioned embodiments of the invention can determine the necessity of law enforcement in the target area based on the power fault data reported by the user, and calculate the targeted law enforcement parameters of the target area according to the neural network prediction model, and further determine the necessity of evaluating the power law enforcement effect in the target area based on these two parameters. When an evaluation is necessary, the evaluation can be automatically started, so as to more intelligently and accurately determine whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0080] As an optional embodiment, in the above steps, the power failure data includes power risk type annotation and text data; the power risk type annotation includes at least one of the risk of disrupting power production order, the risk of damage to power equipment, the risk of damage to land of power facilities, the risk of air area of ​​power facilities, the risk of foreign objects outside the power facilities, the risk of natural disasters and the risk of emergencies.

[0081] It can be seen that through the above-mentioned optional embodiments, the content of the power fault data is clarified to achieve a more accurate determination of the necessity of law enforcement, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effectiveness in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0082] As an optional embodiment, in the above steps, the power law enforcement record data includes multiple law enforcement records of multiple historical time periods; the law enforcement records are power facility inspection records, power facility maintenance records, forest rectification records near power facilities, or power facility destruction prevention records.

[0083] It can be seen that through the above-mentioned optional embodiments, the content of the power law enforcement record data is clarified to achieve a more reasonable and comprehensive targeted analysis of power execution, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effects in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0084] As an optional embodiment, in the above steps, based on the preset keyword matching rules and fault scoring rules, determining the enforcement necessity parameters of the target area according to the power fault data includes:

[0085] For each power failure data, based on the preset description degree keyword rule, the degree keyword text corresponding to the power failure data is matched;

[0086] Calculate the degree parameter corresponding to the degree keyword text;

[0087] Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data;

[0088] Calculate the product of the fault score and the degree parameter to obtain the fault severity parameter corresponding to the power fault data;

[0089] The average value of the fault severity parameters corresponding to all power fault data is calculated to obtain the law enforcement necessity parameter of the target area.

[0090] It can be seen that through the above-mentioned optional embodiments, the preset descriptive degree keyword rules can be used to match the degree keyword text corresponding to the power fault data and calculate the degree parameters to accurately assess the fault degree, and then accurately calculate the enforcement necessity parameters of the target area based on the fault scoring rules and degree parameters, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0091] As an optional embodiment, in the above step, calculating the degree parameter corresponding to the degree keyword text includes:

[0092] The degree keyword text is input into the trained text processing CNN network to obtain the degree parameter corresponding to the degree keyword text; the text processing CNN network is trained by a training data set including multiple training text data and corresponding fault severity annotations.

[0093] It can be seen that through the above-mentioned optional embodiments, the degree parameters corresponding to the degree keyword text can be predicted through the trained text processing CNN network, so as to accurately evaluate the degree of fault in the future, thereby assisting in more intelligent and precise determination of whether it is necessary to automatically evaluate the regional power law enforcement effect, improve the timeliness and effectiveness of the power execution effect evaluation, and facilitate the rapid discovery of law enforcement problems.

[0094] As an optional embodiment, in the above steps, based on the neural network prediction model, according to the power fault data and the power law enforcement record data, the law enforcement targeted parameters of the target area are calculated, including:

[0095] Input each identical data pair having the same corresponding data record in the power fault data and the power law enforcement record into the trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; optionally, the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time;

[0096] Calculate the average value of the enforcement correspondence degree parameters of all identical data pairs to obtain the enforcement targeting parameters of the target area.

[0097] It can be seen that through the above-mentioned optional embodiments, the enforcement correspondence degree parameters of the same data pairs with the same data records can be calculated in combination with the enforcement correspondence degree prediction neural network, so as to accurately predict the targeted enforcement parameters of the target area, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0098] As an optional embodiment, in the above steps, determining the necessity of evaluating the power law enforcement effect in the target area according to the law enforcement necessity parameter and the law enforcement targeting parameter includes:

[0099] Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area;

[0100] Calculate the second parameter difference between the enforcement targeted parameter and the targeted parameter reference value corresponding to the area type;

[0101] Calculate the similarity between the first parameter difference and the second parameter difference;

[0102] Determine whether the enforcement necessity parameter is greater than the necessity parameter value and whether the enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than the preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

[0103] It can be seen that through the above-mentioned optional embodiments, the similarity between the deviation of the necessity of regional law enforcement and the targeted law enforcement can be calculated based on the parameter reference corresponding to the regional type of the target area, so as to evaluate whether the target area has simultaneously experienced a high-risk power failure and carried out highly corresponding power law enforcement and whether the two situations are extremely similar, so that it is necessary to timely evaluate the law enforcement effect of the target area, thereby achieving more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect of the region, improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0104] As an optional embodiment, in the above step, calculating the similarity between the first parameter difference and the second parameter difference includes:

[0105] Calculate the product of the first parameter difference and the degree weight to obtain the first parameter value; optionally, the degree weight is proportional to the number of degree keyword texts corresponding to all power fault data;

[0106] Calculate the product of the second parameter difference and the prediction weight to obtain the second parameter value; optionally, the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage;

[0107] The difference between the first parameter value and the second parameter value is calculated to obtain the similarity between the first parameter difference and the second parameter difference.

[0108] It can be seen that through the above-mentioned optional embodiments, the degree weight proportional to the text quantity of the degree keyword text and the prediction weight proportional to the prediction accuracy of the law enforcement corresponding degree prediction neural network in the verification stage can be introduced to fine-tune the two parameter differences, and the similarity between the two parameter differences is determined based on the difference calculation, which is then used to accurately judge the evaluation timing, thereby achieving more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, improving the timeliness and effectiveness of the power execution effect evaluation, and facilitating the rapid discovery of law enforcement problems.

[0109] Embodiment 2

[0110] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of an automatic evaluation device for power law enforcement effect disclosed in an embodiment of the present invention. Figure 2 The described automatic evaluation device for the effect of power law enforcement can be applied to a data processing device / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the automatic evaluation device for the effect of power law enforcement may include:

[0111] The acquisition module 201 is used to acquire the power fault data uploaded by multiple users in the target area and the power law enforcement record data uploaded by the power law enforcement agency.

[0112] The determination module 202 is used to determine the law enforcement necessity parameters of the target area based on the preset keyword matching rules and fault scoring rules according to the power fault data.

[0113] The calculation module 203 is used to calculate the enforcement-specific parameters of the target area based on the neural network prediction model, according to the power fault data and the power enforcement record data.

[0114] The evaluation module 204 is used to determine the necessity of evaluating the effectiveness of power law enforcement in the target area based on the law enforcement necessity parameter and the law enforcement targeting parameter.

[0115] It can be seen that the above-mentioned embodiments of the invention can determine the necessity of law enforcement in the target area based on the power fault data reported by the user, and calculate the targeted law enforcement parameters of the target area according to the neural network prediction model, and further determine the necessity of evaluating the power law enforcement effect in the target area based on these two parameters. When an evaluation is necessary, the evaluation can be automatically started, so as to more intelligently and accurately determine whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0116] As an optional embodiment, the power failure data includes power risk type labels and text data; the power risk type labels include at least one of the risks of disrupting power production order, damaging power equipment, damaging power facility land, air area of ​​power facilities, foreign body risks of power facilities, natural disaster risks and emergency risks.

[0117] It can be seen that through the above-mentioned optional embodiments, the content of the power fault data is clarified to achieve a more accurate determination of the necessity of law enforcement, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effectiveness in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0118] As an optional embodiment, the power law enforcement record data includes multiple law enforcement records of multiple historical time periods; the law enforcement records are power facility inspection records, power facility maintenance records, forest rectification records near power facilities, or power facility destruction prevention records.

[0119] It can be seen that through the above-mentioned optional embodiments, the content of the power law enforcement record data is clarified to achieve a more reasonable and comprehensive targeted analysis of power execution, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effects in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0120] As an optional embodiment, the determination module determines the specific manner of the enforcement necessity parameter of the target area based on the preset keyword matching rule and fault scoring rule according to the power fault data, including:

[0121] For each power failure data, based on the preset description degree keyword rule, the degree keyword text corresponding to the power failure data is matched;

[0122] Calculate the degree parameter corresponding to the degree keyword text;

[0123] Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data;

[0124] Calculate the product of the fault score and the degree parameter to obtain the fault severity parameter corresponding to the power fault data;

[0125] The average value of the fault severity parameters corresponding to all power fault data is calculated to obtain the law enforcement necessity parameter of the target area.

[0126] It can be seen that through the above-mentioned optional embodiments, the preset descriptive degree keyword rules can be used to match the degree keyword text corresponding to the power fault data and calculate the degree parameters to accurately assess the fault degree, and then accurately calculate the enforcement necessity parameters of the target area based on the fault scoring rules and degree parameters, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0127] As an optional embodiment, the specific manner in which the determination module calculates the degree parameter corresponding to the degree keyword text includes:

[0128] The degree keyword text is input into the trained text processing CNN network to obtain the degree parameter corresponding to the degree keyword text; the text processing CNN network is trained by a training data set including multiple training text data and corresponding fault severity annotations.

[0129] It can be seen that through the above-mentioned optional embodiments, the degree parameters corresponding to the degree keyword text can be predicted through the trained text processing CNN network, so as to accurately evaluate the degree of fault in the future, thereby assisting in more intelligent and precise determination of whether it is necessary to automatically evaluate the regional power law enforcement effect, improve the timeliness and effectiveness of the power execution effect evaluation, and facilitate the rapid discovery of law enforcement problems.

[0130] As an optional embodiment, the calculation module calculates the specific method of the enforcement targeted parameters of the target area based on the neural network prediction model according to the power fault data and the power enforcement record data, including:

[0131] Input each identical data pair having the same corresponding data record in the power fault data and the power law enforcement record into the trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; optionally, the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time;

[0132] Calculate the average value of the enforcement correspondence degree parameters of all identical data pairs to obtain the enforcement targeting parameters of the target area.

[0133] It can be seen that through the above-mentioned optional embodiments, the enforcement correspondence degree parameters of the same data pairs with the same data records can be calculated in combination with the enforcement correspondence degree prediction neural network, so as to accurately predict the targeted enforcement parameters of the target area, thereby assisting in more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, thereby improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0134] As an optional embodiment, the evaluation module determines the specific manner of determining the necessity of evaluating the power law enforcement effect in the target area according to the law enforcement necessity parameter and the law enforcement targeting parameter, including:

[0135] Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area;

[0136] Calculate the second parameter difference between the enforcement targeted parameter and the targeted parameter reference value corresponding to the area type;

[0137] Calculate the similarity between the first parameter difference and the second parameter difference;

[0138] Determine whether the enforcement necessity parameter is greater than the necessity parameter value and whether the enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than the preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

[0139] It can be seen that through the above-mentioned optional embodiments, the similarity between the deviation of the necessity of regional law enforcement and the targeted law enforcement can be calculated based on the parameter reference corresponding to the regional type of the target area, so as to evaluate whether the target area has simultaneously experienced a high-risk power failure and carried out highly corresponding power law enforcement and whether the two situations are extremely similar, so that it is necessary to timely evaluate the law enforcement effect of the target area, thereby achieving more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect of the region, improving the timeliness and effectiveness of the power execution effect evaluation, so as to quickly discover law enforcement problems.

[0140] As an optional embodiment, the specific manner in which the evaluation module calculates the similarity between the first parameter difference and the second parameter difference includes:

[0141] Calculate the product of the first parameter difference and the degree weight to obtain the first parameter value; optionally, the degree weight is proportional to the number of degree keyword texts corresponding to all power fault data;

[0142] Calculate the product of the second parameter difference and the prediction weight to obtain the second parameter value; optionally, the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage;

[0143] The difference between the first parameter value and the second parameter value is calculated to obtain the similarity between the first parameter difference and the second parameter difference.

[0144] It can be seen that through the above-mentioned optional embodiments, the degree weight proportional to the text quantity of the degree keyword text and the prediction weight proportional to the prediction accuracy of the law enforcement corresponding degree prediction neural network in the verification stage can be introduced to fine-tune the two parameter differences, and the similarity between the two parameter differences is determined based on the difference calculation, which is then used to accurately judge the evaluation timing, thereby achieving more intelligent and accurate determination of whether it is necessary to automatically evaluate the power law enforcement effect in the region, improving the timeliness and effectiveness of the power execution effect evaluation, and facilitating the rapid discovery of law enforcement problems.

[0145] Embodiment 3

[0146] See also Figure 3 , Figure 3 It is another automatic evaluation device for power law enforcement effects disclosed in an embodiment of the present invention. Figure 3 The described automatic evaluation device for the effect of power law enforcement is applied to a data processing device / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the automatic evaluation device for the effect of power law enforcement may include:

[0147] A memory 301 storing executable program codes;

[0148] a processor 302 coupled to the memory 301;

[0149] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the automatic evaluation method for the effectiveness of power law enforcement described in the first embodiment.

[0150] Embodiment 4

[0151] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for automatically evaluating the effectiveness of power law enforcement described in the first embodiment.

[0152] Embodiment 5

[0153] 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 automatic evaluation method for the effectiveness of power law enforcement described in Example 1.

[0154] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0155] The devices, means, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0156] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0157] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, devices, or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0158] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0162] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0163] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0164] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0165] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0166] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0167] Finally, it should be noted that the automatic evaluation method and device for the effectiveness of power law enforcement disclosed in the embodiments of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments may still be modified, or some of the technical features therein may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. An automatic evaluation method for power law enforcement effect, characterized in that: The method comprises: Obtain power failure data uploaded by multiple users in the target area and power law enforcement record data uploaded by power law enforcement agencies; Based on the preset keyword matching rules and fault scoring rules, and according to the power fault data, the enforcement necessity parameters of the target area are determined, including: For each of the power failure data, based on a preset description degree keyword rule, matching the degree keyword text corresponding to the power failure data; Calculate the degree parameter corresponding to the degree keyword text; Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data; Calculating the product of the fault score and the degree parameter to obtain a fault severity parameter corresponding to the power fault data; Calculating the average value of the fault severity parameters corresponding to all the power fault data to obtain the law enforcement necessity parameter of the target area; Based on the neural network prediction model, according to the power fault data and the power law enforcement record data, the law enforcement targeted parameters of the target area are calculated, including: Input each identical data pair having the same data record as the power fault data and the corresponding data record in the power law enforcement record into a trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time; Calculating the average value of the law enforcement correspondence degree parameters of all the same data pairs to obtain the law enforcement targeted parameters of the target area; Determining the necessity of evaluating the effect of power law enforcement in the target area according to the law enforcement necessity parameter and the law enforcement targeting parameter, including: Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area; Calculating a second parameter difference between the law enforcement targeted parameter and a targeted parameter reference value corresponding to the area type; Calculating the product of the first parameter difference and the degree weight to obtain a first parameter value; the degree weight is proportional to the number of degree keyword texts corresponding to all the power fault data; Calculating the product of the second parameter difference and the prediction weight to obtain a second parameter value; the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage; Calculating a difference between the first parameter value and the second parameter value to obtain a similarity between the first parameter difference and the second parameter difference; Determine whether the law enforcement necessity parameter is greater than the necessity parameter value and whether the law enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than a preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

2. The automatic evaluation method for power law enforcement effect according to claim 1 is characterized in that: The power failure data includes power risk type labels and text data; the power risk type labels include at least one of the risks of disrupting power production order, damaging power equipment, damaging power facility land, air area of ​​power facilities, foreign body risks of power facilities, natural disaster risks and emergency risks.

3. The automatic evaluation method for power law enforcement effect according to claim 1 is characterized in that: The power law enforcement record data includes multiple law enforcement records of multiple historical time periods; the law enforcement records are power facility inspection records, power facility maintenance records, forest rectification records near power facilities, or power facility destruction prevention records.

4. The automatic evaluation method for power law enforcement effect according to claim 1 is characterized in that: The step of calculating the degree parameter corresponding to the degree keyword text includes: The degree keyword text is input into a trained text processing CNN network to obtain the degree parameter corresponding to the degree keyword text; the text processing CNN network is trained by a training data set including multiple training text data and corresponding fault severity annotations.

5. An automatic evaluation device for power law enforcement effect, characterized in that: The device comprises: An acquisition module is used to acquire power failure data uploaded by multiple users in the target area and power law enforcement record data uploaded by the power law enforcement agency; A determination module is used to determine the law enforcement necessity parameters of the target area according to the power fault data based on preset keyword matching rules and fault scoring rules, including: For each of the power failure data, based on a preset description degree keyword rule, matching the degree keyword text corresponding to the power failure data; Calculate the degree parameter corresponding to the degree keyword text; Based on the fault scoring rule, calculate the fault score corresponding to the fault type of the power fault data; Calculating the product of the fault score and the degree parameter to obtain a fault severity parameter corresponding to the power fault data; Calculating the average value of the fault severity parameters corresponding to all the power fault data to obtain the law enforcement necessity parameter of the target area; A calculation module is used to calculate the enforcement-targeted parameters of the target area based on the neural network prediction model according to the power fault data and the power enforcement record data, including: Input each identical data pair having the same data record as the power fault data and the corresponding data record in the power law enforcement record into a trained law enforcement correspondence degree prediction neural network to obtain the law enforcement correspondence degree parameter of each identical data pair; the law enforcement correspondence degree prediction neural network is trained by a training data set including a plurality of training data pairs and corresponding law enforcement correspondence degree annotations; the training data pairs include training power fault data and training power law enforcement records corresponding to time; Calculating the average value of the law enforcement correspondence degree parameters of all the same data pairs to obtain the law enforcement targeted parameters of the target area; An evaluation module, used to determine the necessity of evaluating the power law enforcement effect in the target area according to the law enforcement necessity parameter and the law enforcement targeted parameter, including: Calculating a first parameter difference between the law enforcement necessity parameter and a necessity parameter reference value corresponding to the area type of the target area; Calculating a second parameter difference between the law enforcement targeted parameter and a targeted parameter reference value corresponding to the area type; Calculating the product of the first parameter difference and the degree weight to obtain a first parameter value; the degree weight is proportional to the number of degree keyword texts corresponding to all the power fault data; Calculating the product of the second parameter difference and the prediction weight to obtain a second parameter value; the prediction weight is proportional to the prediction accuracy of the law enforcement correspondence degree prediction neural network in the verification stage; Calculating a difference between the first parameter value and the second parameter value to obtain a similarity between the first parameter difference and the second parameter difference; Determine whether the law enforcement necessity parameter is greater than the necessity parameter value and whether the law enforcement targeted parameter is greater than the targeted parameter reference value and whether the similarity is greater than a preset similarity threshold. If so, it is necessary to evaluate the power law enforcement effect in the target area and send an evaluation instruction to the corresponding evaluation device.

6. An automatic evaluation device for power law enforcement effect, 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 automatic evaluation method for the effectiveness of power law enforcement as described in any one of claims 1-4.

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