A distribution network project review intelligent supervision system and method

Abstract

The application belongs to the technical field of power grid supervision, and discloses a distribution network project review intelligent supervision system and method. The system comprises an industry expansion project receiving module, a data processing module, a project analysis module and an information output module. According to the received application information of the distribution network project review, the application determines the key project information of the distribution network project, and then obtains the corresponding analogous project based on the key project information. According to the survey level of the field survey data in the analogous project, the key coefficient of the field survey data is calculated, and based on the key coefficient of the field survey data, the key collected data is determined. Then, the key collected data is transmitted to the terminal equipment of the field personnel. The field personnel views the key collected data through the terminal equipment, improves the completion degree of the field survey data collected at one time by the field personnel, and further improves the distribution network project review efficiency and speeds up the project review period.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid supervision, and particularly relates to an intelligent supervision system and method for distribution network project review. BACKGROUND

[0002] In the distribution network project review, not only the materials submitted by the customer need to be evaluated and audited, but also the corresponding human resources need to be allocated to the customer's site for real-time investigation and data collection, and then the distribution network project can be completely evaluated. However, since each customer's project is not completely the same, if the distribution network staff collects the data on the site based on their work experience, the data on the site will be missed due to subjective influence, and the distribution network staff needs to go to the site again to collect the data, which will delay the progress of the distribution network project review and reduce the overall work efficiency. SUMMARY

[0003] The application aims to solve the problems in the prior art and provides an intelligent supervision system and method for distribution network project review.

[0004] In order to achieve the above-mentioned purpose, the application is implemented by the following technical scheme:

[0005] In the first aspect, the application provides an intelligent supervision system for distribution network project review, which comprises an industry expansion project receiving module, a data processing module, a project analysis module and an information output module.

[0006] The industry expansion project receiving module is used for receiving the application information of the distribution network project review and transmitting the application information to the data processing module, and the application information comprises basic information and application materials.

[0007] The data processing module is used for extracting the key data in the application materials according to the basic information to obtain the key project information, and the data processing module transmits the key project information to the project analysis module.

[0008] The project analysis module is used for obtaining the information matching object according to the basic information, and then performing information similarity processing on the key project information and the information matching object to obtain the analogy project. The on-site investigation data in the analogy project is extracted, and the on-site investigation data is set with investigation levels according to the collection times of the power grid staff. Then, the on-site investigation data in all analogy projects is integrated to obtain a level set of the on-site investigation data. The level frequency of each investigation level in the level set is processed, and finally the key coefficient of the on-site investigation data is obtained. According to the key coefficient of the on-site investigation data, the key collection data is determined, and the project analysis module transmits the key collection data to the information output module.

[0009] The information output module is configured to transmit the key collection data to a terminal device of the field staff for viewing.

[0010] Preferably, the data processing module is configured to extract key data in the application materials according to the basic information to obtain the key project information, specifically:

[0011] The basic information is used as an information keyword, and the key data is extracted in the application materials by using a natural language processing technology, and the extracted key data is marked as the key project information.

[0012] Preferably, the project analysis module is configured to obtain an information matching object according to the basic information, and then perform information similarity processing on the key project information and the information matching object to obtain the analogy project, specifically:

[0013] The basic information is extracted, and the basic information is used as an information retrieval condition to perform data retrieval in a cloud platform to determine the information matching object;

[0014] The information matching object and the key project information are converted into data vectors by using a bag-of-words model algorithm, and then the information matching object and the key project information are sequentially calculated by using a cosine similarity algorithm to determine an information similarity value;

[0015] The information similarity value is compared with a similarity threshold X1, and the information matching object corresponding to the information similarity value greater than or equal to the similarity threshold X1 is taken and marked as the analogy project.

[0016] Preferably, the field survey data in the analogy project is extracted, and the field survey data is set with a survey level according to the collection times of the power grid staff, and then the field survey data in all analogy projects is integrated to obtain a level set of the field survey data, and the level frequency of each survey level in the level set is processed to finally obtain a key coefficient of the field survey data, and the key collection data is determined according to the key coefficient of the field survey data, specifically:

[0017] The field survey data in the analogy project is extracted, and the field survey data is divided into survey levels according to the collection times of the power grid staff and is marked as the survey levels;

[0018] The field survey data in all analogy projects is extracted, the same field survey data is merged, and the survey levels corresponding to the same field survey data are obtained, and the survey levels are used as elements to form a level set;

[0019] A level set of field survey data is selected as a target set, and the number Dj of each survey level in the target set is obtained, where j represents the survey level; and the formula obtaining the series frequency Fj, DZ represents the total number of elements in the target set;

[0020] using the formula calculating the key coefficient Gf of the target data, i.e. the field investigation data corresponding to the target set, βj represents the weight factor of the investigation series, and βj-1< βj; m is the total number of investigation series;

[0021] processing the series set of all field investigation data according to the above steps to obtain the key coefficient Gf of all field investigation data;

[0022] comparing the key coefficient Gf of the field investigation data with the data threshold Gy, if Gf > Gy, the corresponding field investigation data is marked as key collection data, and if Gf ≤ Gy, the corresponding field investigation data is marked as regular data.

[0023] Preferably, the investigation series includes a first series, a second series and a third series, the first series refers to field investigation data collected directly after one field investigation by power grid staff, the second series refers to field investigation data collected after two field investigations by power grid staff, and the third series refers to field investigation data collected after three or more field investigations by power grid staff.

[0024] In a second aspect, the present application provides an intelligent monitoring method for distribution network project review, which comprises the following steps:

[0025] Step 1: identifying and receiving the application information of the distribution network project review, extracting the basic information in the application information, taking the basic information as the information keyword, extracting the key data in the application materials, and obtaining the key project information;

[0026] Step 2: taking the basic information as the information retrieval condition, obtaining the information matching object, processing the information similarity between the key project information and the information matching object, and obtaining the analogy project;

[0027] Step 3: extracting the field investigation data in the analogy project, and setting the investigation series of the field investigation data according to the collection times of the power grid staff;

[0028] Step 4: integrating the field investigation data in all analogy projects to obtain the series set of the field investigation data, then processing the series frequency of each investigation series in the series set, and finally obtaining the key coefficient of the field investigation data;

[0029] Step 5: determining the key collection data according to the key coefficient of the field investigation data, then obtaining the terminal device of the field staff, and transmitting the key collection data to the terminal device of the field staff.

[0030] Preferably, in step 1, key data extraction is performed on the application material by using natural language processing technology.

[0031] Preferably, step 2 is specifically as follows: step 2.1, extracting basic information and taking the basic information as an information retrieval condition to perform data retrieval in the cloud platform to determine information matching objects;

[0032] Step 2.2, converting the information matching objects and the key project information into data vectors by using a bag-of-words model algorithm, and then sequentially calculating the information matching objects and the key project information by using a cosine similarity algorithm to determine information similarity values;

[0033] Step 2.3, comparing the information similarity values with a similarity threshold X1, and taking information matching objects corresponding to information similarity values greater than or equal to the similarity threshold X1 and marking them as analogy projects.

[0034] Preferably, step 4 is specifically as follows: step 4.1, obtaining a series frequency Fj by using a formula Dj represents the number of series in each exploration series in the series set, DZ represents the total number of elements in the series set, and j represents the exploration series;

[0035] Step 4.2, calculating a key coefficient Gf of target data by using a formula The target data is the field exploration data corresponding to the exploration series, βj represents a weight factor of the exploration series, and βj-1< βj;

[0036] Step 4.3, processing the series set of all field exploration data according to steps 4.1-4.2 to obtain the key coefficient Gf of all field exploration data.

[0037] Preferably, step 5 is specifically as follows: setting a data threshold Gy, comparing the key coefficient with the data threshold Gy, if the key coefficient is greater than Gy, marking the corresponding field exploration data as key collection data, and if the key coefficient is less than or equal to Gy, marking the corresponding field exploration data as regular data.

[0038] The present application has the following beneficial effects: the present application processes the received application information of the distribution network project review to determine the key project information of the distribution network project, then obtains the corresponding analogy project based on the key project information, calculates the key coefficient of the field exploration data according to the exploration series of the field exploration data in the analogy project, determines the key collection data based on the key coefficient of the field exploration data, then transmits the key collection data to the terminal device of the field personnel, the field personnel views the key collection data through the terminal device, improves the completion degree of the field personnel on the first collection of the field exploration data, and further improves the efficiency of the distribution network project review and speeds up the project review period. Attached Figure Description

[0039] Figure 1 A flowchart of a smart monitoring system for power distribution network project review;

[0040] Figure 2 This is a flowchart of an intelligent monitoring method for power distribution network project review. Detailed Implementation

[0041] The present invention will be further described below with reference to embodiments, but these embodiments are not intended to limit the scope of the invention.

[0042] A smart monitoring system for power distribution network project review includes:

[0043] The business expansion project receiving module is used to receive application information for distribution network project review and transmit it to the data processing module. The application information includes basic information and application materials, such as electricity demand and power supply plan.

[0044] The data processing module is used to extract key data from the application materials based on basic information to obtain key project information. At the same time, the data processing module transmits the key project information to the project analysis module.

[0045] Specifically, using basic information as keywords, key data is extracted from application materials using natural language processing technology, and the extracted key data is marked as key project information.

[0046] Specifically, the process involves: first, using a pre-trained named entity recognition model, such as spaCy's en_core_web_sm model, to perform named entity recognition on the text; then, using regular expressions to perform keyword matching on the named entities in the application materials to obtain the key data for each named entity; and finally, integrating and outputting the information, where the named entities are the basic information.

[0047] The project analysis module is used to obtain information matching objects based on basic information, then perform information similarity processing on key project information and information matching objects to obtain analogy projects, extract field survey data from analogy projects, and set survey levels for field survey data according to the number of times power grid staff collect data. Then, the field survey data from all analogy projects are integrated to obtain a set of levels for field survey data. The level frequency of each survey level in the level set is processed to obtain the key coefficient of the field survey data. Based on the key coefficient of the field survey data, the key data to be collected is determined, and the project analysis module transmits the key data to the information output module.

[0048] Specifically, 1, extract basic information, and take the basic information as an information retrieval condition to perform data retrieval in a cloud platform to determine information matching objects, which are shared by the cloud platform and the distribution network project review intelligent supervision system;

[0049] 2, convert the information matching objects and the key project information into data vectors using a bag-of-words model algorithm, and then calculate the information matching objects and the key project information in turn using a cosine similarity algorithm to determine information similarity values;

[0050] 3, compare the information similarity values with a similarity threshold X1, and take the information matching objects corresponding to the information similarity values greater than or equal to the similarity threshold X1 and mark them as analogy projects;

[0051] Extract the field survey data in the analogy projects, divide the field survey data according to the collection times of the power grid staff, and mark them as survey levels, including the first level, the second level, and the third level;

[0052] The first level means that the field survey data is directly collected after one field survey by the power grid staff, the second level means that the field survey data is collected after two field surveys by the power grid staff, and the third level means that the field survey data is collected after three or more field surveys by the power grid staff;

[0053] 4, data integration of field survey data: extract the field survey data in all analogy projects, merge the same field survey data, and obtain the survey levels corresponding to the same field survey data, take the survey levels as elements, and form a level set;

[0054] 5, select a level set of field survey data as a target set, obtain the number Dj of each survey level in the target set, where j takes values of 1, 2, and 3, representing the first level, the second level, and the third level, respectively; use the formula to obtain the level frequency Fj, and DZ represents the total number of elements in the target set;

[0055] Use the formula to calculate the key coefficient Gf of the target data, which is the field survey data corresponding to the target set, and βj represents the weight factor of the survey level, and β2<β3;

[0056] 6, process all level sets of field survey data according to the above steps to obtain the key coefficients Gf of all field survey data;

[0057] The key coefficient Gf of the field investigation data is compared with the data threshold Gy, if Gf>Gy, the corresponding field investigation data is marked as key collection data, if Gf≤Gy, the corresponding field investigation data is marked as normal data.

[0058] The information output module is used for transmitting the key collection data to the terminal device of the field personnel for viewing.

[0059] A kind of distribution network project review intelligent supervision method, the method comprises the following steps:

[0060] Step one: identify and receive the application information of distribution network project review, extract basic information in application information simultaneously, with basic information as information keyword, carry out key data extraction in application material, obtain key project information;

[0061] Step two: again, with basic information as information retrieval condition, obtain information matching object, carry out information similarity processing to key project information and information matching object, obtain analogy project;

[0062] Step three: extract field investigation data in analogy project, and set investigation level according to the collection times of power grid staff to field investigation data;

[0063] Step four: integrate field investigation data in all analogy projects, obtain the level set of field investigation data, then process the level frequency of each investigation level in level set, finally obtain the key coefficient of field investigation data;

[0064] Step five: according to the key coefficient of field investigation data, determine key collection data, then obtain the terminal device of field personnel, and transmit key collection data to the terminal device of field personnel.

[0065] The basic principle, main features and advantages of the present application are shown and described above. However, the above-mentioned is only a specific embodiment of the present application, and the technical features of the present application are not limited to this. Any other implementation mode obtained by those skilled in the art without departing from the technical solution of the present application should be covered in the patent range of the present application.

Claims

1. An intelligent supervision system for distribution network project review, characterized in that, The application comprises an industry expansion project receiving module, a data processing module, a project analysis module and an information output module. The industry expansion project receiving module is used for receiving application information of distribution network project review and transmitting the application information to the data processing module. The data processing module is used for extracting key data in the application materials according to the basic information to obtain key project information, and transmitting the key project information to the project analysis module. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. Optionally, a series of field investigation data is set as a target set, and the number Dj of each investigation series in the target set is obtained, where j represents the investigation series; the formula is used to obtain the series frequency Fj, and DZ represents the total number of elements in the target set. The key coefficient Gf of the target data is calculated by the formula wherein βj represents a weight factor of the exploration level, and the higher the exploration level, the greater the weight; m is the total number of the exploration levels; and the target data is the field investigation data corresponding to the target set. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects.

2. The intelligent monitoring system for network project review according to claim 1, wherein, The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. The information output module is used for transmitting the key collection data to the terminal device of the field personnel for viewing.

3. The intelligent monitoring system for network configuration project review according to claim 1, wherein, The data processing module is used for extracting key data in the application materials according to the basic information to obtain key project information. The basic information is used as an information keyword, and the natural language processing technology is used to extract key data in the application materials, and the extracted key data is marked as key project information. The project analysis module is used for obtaining information matching objects according to the basic information, and then performing information similarity processing on the key project information and the information matching objects to obtain analogy projects. The basic information is extracted and used as an information retrieval condition to perform data retrieval in the cloud platform to determine the information matching objects. The information matching objects and the key project information are converted into data vectors by using the bag-of-words model algorithm, and then the information matching objects and the key project information are calculated in sequence by using the cosine similarity algorithm to determine the information similarity value. The information similarity value is compared with a similarity threshold X1, and information matching objects corresponding to information similarity values greater than or equal to the similarity threshold X1 are taken as analogy items and marked.

4. The intelligent monitoring system for network configuration project review according to claim 1, wherein, The survey levels include a first level, a second level and a third level, the first level refers to that the on-site survey data is directly collected after one on-site survey by the power grid staff, the second level refers to that the on-site survey data is collected after two on-site surveys by the power grid staff, and the third level refers to that the on-site survey data is collected after three or more on-site surveys by the power grid staff.

5. The intelligent supervision method for project review of network distribution is realized by using the system of any one of claims 1-4, characterized in that, The method comprises the following steps: Step 1: identifying and receiving application information of the power distribution network project review, extracting basic information in the application information, taking the basic information as information keywords, extracting key data in the application materials, and obtaining key project information; Step 2: taking the basic information as an information retrieval condition, obtaining information matching objects, performing information similarity processing on the key project information and the information matching objects, and obtaining analogy items; Step 3: extracting on-site survey data in the analogy items, and setting survey levels for the on-site survey data according to the number of times of collection by the power grid staff; Step 4: integrating the on-site survey data in all analogy items to obtain a level set of the on-site survey data, then processing the level frequency of each survey level in the level set, and finally obtaining a key coefficient of the on-site survey data; Step 5: determining key collection data according to the key coefficient of the on-site survey data, then obtaining a terminal device of the on-site staff, and transmitting the key collection data to the terminal device of the on-site staff.

6. The intelligent supervision method for network configuration project review according to claim 5, characterized in that, In step 1, the natural language processing technology is used to extract key data in the application materials.

7. The intelligent supervision method for network configuration project review according to claim 5, characterized in that, Step 2 specifically comprises: Step 2.1: extracting basic information and taking the basic information as an information retrieval condition to perform data retrieval in a cloud platform and determine information matching objects; Step 2.2: converting the information matching objects and the key project information into data vectors by using a bag-of-words model algorithm, then sequentially calculating the information matching objects and the key project information by using a cosine similarity algorithm to determine information similarity values; Step 2.3: comparing the information similarity values with a similarity threshold X1, taking information matching objects corresponding to information similarity values greater than or equal to the similarity threshold X1 as analogy items and marking them.

8. The intelligent supervision method for network configuration project review according to claim 5, characterized in that, Step 4 specifically comprises: Step 4.1, using the formula where Fj, Dj represents the number of series in the series set for each survey series, Dz represents the total number of elements in the series set; j represents the survey series. Step 4.2, using the formula Calculate the focus coefficient Gf of the target data, i.e. the field survey data corresponding to the survey level, and βj represents the weight factor of the survey level, and the higher the survey level, the greater the weight. Step 4.3: processing the level set of all on-site survey data according to steps 4.1-4.2 to obtain key coefficients Gf of all on-site survey data.

9. The intelligent supervision method for network configuration project review according to claim 5, characterized in that, Step 5 specifically comprises: A data threshold Gy is set, the key coefficient is compared with the data threshold Gy, if the key coefficient is greater than Gy, the corresponding on-site survey data is marked as key collection data, and if the key coefficient is less than or equal to Gy, the corresponding on-site survey data is marked as regular data.

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