Intelligent processing system for ticket face violation data of electric power operation site

Abstract

The invention relates to the technical field of electric power information processing, in particular to an electric power work site ticket face violation data intelligent processing system which comprises an information acquisition module, a site data acquisition module, a data storage module, a data analysis module, a terminal identification module and a terminal display module. The method comprises the steps of obtaining a violation set, calculating a set frequency based on the number of elements in the violation set, performing position arrangement on the set frequency, determining key regulations, obtaining historical violation behaviors of an operator, calculating a working coefficient, determining a data signal based on the working coefficient, and determining field warning information of a target analyst according to the data signal. According to the technical scheme of the utility model, the operator entering the field in real time is identified, and the corresponding field warning information is obtained and displayed in the terminal display module, so that the operator entering the electric power operation field is reminded of safety in real time, and the safety awareness of the operator is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power information processing, and in particular to an intelligent processing system for ticket violation data at an electric power operation site. Background Art

[0002] With the continuous advancement of power grid construction and the increasing complexity of power equipment, traditional manual supervision methods can no longer meet the needs of modern power safety management.

[0003] Prior art CN117473132A discloses a method and related device for identifying violations of power operation data, the method includes: by parsing the heterogeneous information to be identified, extracting entity attribute data, and establishing a graph database of information to be identified, so that the content of each type of information is broken up and classified, and connected in the graph database by the attribute connection of the data, breaking away from the limitations of the heterogeneous information itself and enabling fast query. However, at the power operation site, when the safety reminders are given to the operators at the power operation site based on the real-time recorded ticket violation data, there will be a certain hysteresis, thereby reducing the safety of the power operation site. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose an intelligent processing system for ticket violation data at a power operation site.

[0005] The present invention adopts the following technical solution: an intelligent processing system for ticket violation data on power operation site, comprising:

[0006] The data storage module is used to store the operation information, staff information and ticket violation data of the power operation site, and the data storage module and the data analysis module are connected in a one-way communication manner;

[0007] The data analysis module is used to process the overall ticket violation data of the power operation site, determine the collective frequency of the power operation site, and analyze the historical ticket violation data of the operators based on the operators at the power operation site, and determine the data signal of a single operator, wherein the data signal includes a comprehensive data signal and a characteristic data signal;

[0008] Among them, the collection frequency of the power operation site is to identify and obtain the violation data on the ticket according to the project start time of the power project, generate a site violation list, and then perform text recognition processing on the violation content in the site violation list to obtain the violation set, and then calculate the collection frequency based on the number of elements in the violation set, and at the same time, arrange the collection frequency by position to obtain the key regulations;

[0009] Analysis of a single operator: including obtaining the historical violations of the target analyst, combining the target analyst's years of service with the total number of historical violations to obtain the work coefficient, and then determining the data signal based on the work coefficient. At the same time, when the data signal is a characteristic data signal, the individual violation frequency of the target analyst for each regulation is calculated, and then the individual violation frequency is combined with the collective frequency to calculate the regulation violation coefficient, and then the on-site warning information of the target analyst is determined based on the regulation violation coefficient;

[0010] The terminal identification module is used to identify the operators who enter the site in real time, obtain the corresponding on-site warning information according to the operator's data signal, and transmit it to the terminal display module;

[0011] The terminal display module is used to display the on-site warning information according to the identified operating personnel.

[0012] As a further solution of the present invention, a method for calculating the collective frequency of a power operation site includes:

[0013] S01: Taking the start time of the power project as the starting time, identify and obtain the ticket violation data of all work units, and generate a site violation list;

[0014] All work units include substation unit group, transmission unit group and distribution unit group. The information type in the on-site violation list is consistent with the information content in the ticket violation data;

[0015] S02: extracting the violation content in the on-site violation list, performing text recognition processing on the violation content, and then dividing the violation content into multiple violation sets;

[0016] S03: Select any violation set and count the number of elements Yi in the violation set, where the number of elements is the total number of violations in the violation set i;

[0017] Where i represents the number of the selected violation set, and i∈[1,I], I represents the total number of violation sets existing in the power operation site;

[0018] Then use the formula The set frequency Fi is obtained, where Zw represents the sum of the number of elements in all violation sets and a1 is the threshold coefficient.

[0019] As a further solution of the present invention, a method for dividing a violation set includes:

[0020] Use natural language processing technology to identify information in the violation content, including the time of violation, location of violation and violation behavior;

[0021] Obtain the standard regulations of the power operation site, classify the violations according to the violated standard regulations, and integrate the violations belonging to the same related regulations into a violation set;

[0022] Furthermore, the elements in this violation set are the corresponding violations, and the standards and regulations refer to the requirements of the current national, industry and State Grid Corporation's relevant laws, regulations and technical procedures.

[0023] As a further solution of the present invention, the threshold coefficient a1 represents the influence of the number of samples on the set frequency Fi;

[0024] Furthermore, a1 = -λ Zw +1, λ is the impact factor, and λ is 0.924;

[0025] When the Zw value is larger, the threshold coefficient a1 is closer to 1, which means that for The more accurate the ontology is calculated, the higher the confidence in the statistical inferences drawn from the sample data will generally be.

[0026] As a further solution of the present invention, a method for obtaining key regulations includes:

[0027] Calculate the collective frequency Fi of all violation sets i, and arrange the collective frequency Fi in descending order to obtain a collective sequence;

[0028] Take the first N violation sets in the set sequence, and mark the regulations corresponding to the first N violation sets as key regulations, where N is 5.

[0029] As a further solution of the present invention, a method for acquiring a data signal of a single operator includes:

[0030] S11: randomly select an operator who enters the power operation site as a target analyst, obtain the working years in the staff information of the target analyst, and extract the historical violation behaviors of the target analyst;

[0031] Among them, historical violations refer to violations that occurred during the power operation sites of previous power projects by the target analyst;

[0032] S12: Using formula Calculate the work coefficient GZj of the target analyst, where j represents the number of different operators in the power operation site, Gwj represents the total number of historical violations of target analyst j, and Nxj represents the working years of target analyst j;

[0033] S13: Compare the work coefficient GZj of the target analyst j with the work threshold X1. If GZj≤X1, a comprehensive data signal is generated. Otherwise, if GZj>X1, a characteristic data signal is generated.

[0034] When the working years Nxj is 0, that is, the target analyst j is a new employee or an intern, the comprehensive data signal is directly generated;

[0035] S14: When generating a characteristic data signal, using The individual violation frequency Dm of the target analyst is obtained, where m represents the regulations and Lm represents the number of times the target analyst j violates the regulations m.

[0036] As a further solution of the present invention, a method for determining on-site warning information includes:

[0037] Identify the operators who enter the power operation site in real time and obtain data signals based on the identity information of the operators;

[0038] If the data signal of the operator is a comprehensive data signal, the key rules are obtained and marked as on-site warning information for the operator;

[0039] If the data signal of the operator is a characteristic data signal, the single violation frequency Dm of the operator is obtained, and the violation coefficient ZFm is obtained by using the formula ZFm=Dm×β1+Fi×β2, where β1 and β2 are both proportional factors, and β1+β2=1;

[0040] When calculating the regulation violation coefficient ZFm, the regulation corresponding to the single violation frequency Dm is the same as the regulation corresponding to the violation set i in the set frequency Fi;

[0041] Arrange the regulation violation coefficients ZFm in descending order to obtain a regulation violation sequence, take the first N regulation violation coefficients ZFm in the regulation violation sequence, and mark the regulatory regulations corresponding to the first N regulation violation coefficients ZFm as on-site warning information for operators.

[0042] As a further solution of the present invention, the data analysis module includes a field analysis unit and a personal analysis unit;

[0043] The on-site analysis unit is used to process the overall ticket violation data of the power operation site and determine the collection frequency of the power operation site;

[0044] The personal analysis unit is used to analyze the historical ticket violation data of the operators at the power operation site and determine the data signal of a single operator.

[0045] As a further solution of the present invention, the operation information and the staff information of the power operation site are collected by the information collection module and transmitted to the data storage module for storage;

[0046] The ticket violation data at the power operation site is collected by the on-site data acquisition module and transmitted to the data storage module for storage.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] The present invention generates an on-site violation list by identifying and acquiring the violation data on the ticket, then processes the violation content in the on-site violation list to obtain a violation set, calculates the set frequency based on the number of elements in the violation set, and simultaneously arranges the set frequency by position to obtain key regulations, then obtains the historical violation behaviors of the target analyst, calculates the work coefficient, then determines the data signal based on the work coefficient, and determines the on-site warning information of the target analyst based on the data signal, identifies the operating personnel who enter the site in real time, and obtains the corresponding on-site warning information based on the data signal of the operating personnel, and displays the on-site warning information in the terminal display module, so as to provide real-time safety reminders to the operating personnel entering the power operation site, thereby enhancing the safety awareness of the operating personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the system flow structure of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely 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, rather than all the embodiments.

[0051] Reference Figure 1 , an intelligent processing system for ticket violation data on power operation sites, including an information collection module, a field data collection module, a data storage module, a data analysis module, a terminal identification module and a terminal display module;

[0052] The information collection module is used to collect the operation information and staff information of the power operation site, wherein the operation information refers to the on-site work items of the power operation site, and the staff information refers to the information of the personnel performing the power operation site, including but not limited to name, work number, department, years of service, work content and work area. Then, the information collection module transmits the operation information and staff information of the power operation site to the data storage module for storage;

[0053] The on-site data collection module is used to collect the ticket violation data at the power operation site, where the ticket violation data involves the work unit, work leader, team members, related equipment and violation content;

[0054] It should be further explained that, in this embodiment, the working units include a substation unit group, a transmission unit group, and a distribution unit group;

[0055] Furthermore, the on-site data collection module includes an equipment collection unit and a manual collection unit. The equipment collection unit refers to the use of machine recognition to automatically identify illegal actions on the surveillance camera screen through computer vision, image processing, machine learning and other technologies, and automatically generate ticket violation data based on the illegal actions. The manual collection unit refers to the ticket violation data recorded by manual on-site inspections or monitoring personnel observing or analyzing the surveillance screen in real time;

[0056] Afterwards, the on-site data collection module transmits the ticket violation data to the data storage module for storage, and at the same time, the data storage module and the data analysis module are connected in a one-way communication;

[0057] The data analysis module is used to process the ticket violation data at the power operation site, wherein the data analysis module includes a site analysis unit and a personal analysis unit;

[0058] The on-site analysis unit is used to process the overall ticket violation data of the power operation site and determine the collection frequency of the power operation site;

[0059] The personal analysis unit is used to analyze the historical ticket violation data of the operators at the power operation site as a unit, and determine the data signal of the individual operator, wherein the data signal includes a comprehensive data signal and a characteristic data signal;

[0060] The method for the on-site analysis unit to determine the high-incidence violation information on the power operation site is as follows:

[0061] S01: Taking the start time of the power project as the starting time, identify and obtain the ticket violation data of all work units, and generate a site violation list;

[0062] Among them, all work units include substation unit group, transmission unit group and distribution unit group, and the information type in the on-site violation list is consistent with the information content in the ticket violation data;

[0063] S02: extracting the violation content from the on-site violation list, performing text recognition processing on the violation content, and then dividing the violation content into multiple violation sets. The specific division method includes:

[0064] Using natural language processing technology to identify information in the violation content, where the identified information includes the time of the violation, the location of the violation, and the violation behavior;

[0065] It should be further explained that natural language processing technology is an existing technology, and its specific processing process will not be described in detail here;

[0066] Obtain the standard regulations of the power operation site, classify the violations according to the violated standard regulations, integrate the violations belonging to the same related regulations into a violation set, and further, the elements in this violation set are the violations to which they belong;

[0067] Among them, standards and regulations refer to the requirements of current national, industry and State Grid Corporation laws, regulations, specifications and technical procedures;

[0068] S03: Select any violation set and count the number of elements Yi in the violation set, where the number of elements is the total number of violations in the violation set i;

[0069] Where i represents the number of the selected violation set, and i∈[1,I], I represents the total number of violation sets existing in the power operation site;

[0070] Then use the formula Get the set frequency Fi, where Zw represents the sum of the number of elements in all violation sets, that is, the total number of violation contents in the violation list, and a1 is the threshold coefficient;

[0071] When the set frequency Fi is larger, it means that the probability of the regulations corresponding to the violation set i being ignored is greater. On the contrary, when the set frequency Fi is smaller, it means that the regulations corresponding to the violation set i can be taken seriously.

[0072] In another embodiment of the present invention, the threshold coefficient a1 represents the influence of the number of samples on the set frequency Fi. Further, a1=-λ Zw +1, where λ is an impact factor. The specific value of λ is obtained by those skilled in the art after big data calculation. In this embodiment, λ is 0.924;

[0073] When the Zw value is larger, the threshold coefficient a1 is closer to 1, which means that for The more accurate the ontology calculations, the higher the confidence in the statistical inferences obtained from the sample data;

[0074] Then, according to the above steps S01 to S03, the set frequencies Fi of all violation sets i are calculated, and the set frequencies Fi are arranged in descending order to obtain a set sequence, and the first N violation sets in the set sequence are taken, and the regulations corresponding to the first N violation sets are marked as key regulations, wherein the specific value of N is determined by those skilled in the art according to big data analysis, and in this embodiment, the value of N is 5;

[0075] Then the on-site analysis unit transmits the set frequency Fi and key regulations of the violation set i to the terminal identification module;

[0076] The method for the personal analysis unit to analyze the operators at the power operation site includes:

[0077] S11: randomly select an operator who enters the power operation site as a target analyst, obtain the working years in the staff information of the target analyst, and extract the historical violations of the target analyst;

[0078] Among them, historical violations refer to violations that occurred during the power operation sites of previous power projects by the target analyst;

[0079] S12: Using formula Calculate the work coefficient GZj of the target analyst, where j represents the number of different operators in the power operation site, Gwj represents the total number of historical violations of target analyst j, and Nxj represents the working years of target analyst j;

[0080] Furthermore, the work coefficient GZj indicates the error rate of the target analyst j when performing power operations at the power operation site. The larger the work coefficient GZj is, the higher the frequency of irregular behavior of the target analyst j when performing power operations. On the contrary, the smaller the work coefficient GZj is, the more likely the target analyst j is to perform his work in strict accordance with the regulations when performing power operations.

[0081] S13: Compare the work coefficient GZj of the target analyst j with the work threshold X1. If GZj≤X1, a comprehensive data signal is generated. Otherwise, if GZj>X1, a characteristic data signal is generated. The specific value of the work threshold X1 is obtained by a technician in this field after big data calculation.

[0082] It should be further explained that when the working years Nxj takes the value of 0, that is, the target analyst j is a new employee or an intern, the comprehensive data signal is directly generated;

[0083] S14: When the characteristic data signal is generated, the single violation frequency Dm of the target analyst is calculated. Specifically, Where m represents the regulation, Lm represents the number of times the target analyst j violates regulation m;

[0084] The personal analysis unit then transmits the generated data signal and the individual violation frequency Dm to the terminal identification module;

[0085] The terminal identification module is used to identify the operators who enter the power operation site in real time and obtain data signals based on the identity information of the operators;

[0086] If the data signal of the operator is a comprehensive data signal, the key rules are obtained and marked as on-site warning information for the operator;

[0087] If the data signal of the operator is a characteristic data signal, the individual violation frequency Dm of the operator is obtained, and the individual violation frequency Dm is combined with the collective frequency Fi, and the formula ZFm=Dm×β1+Fi×β2 is used to obtain the regulation violation coefficient ZFm, wherein β1 and β2 are both proportional factors, and β1+β2=1, and the specific values ​​of β1 and β2 are obtained by technicians in this field after big data calculation;

[0088] It should be further explained that when calculating the regulation violation coefficient ZFm, the regulation corresponding to the single violation frequency Dm is the same as the regulation corresponding to the violation set i in the set frequency Fi;

[0089] Then, the regulation violation coefficients ZFm are arranged in descending order to obtain a regulation violation sequence, the first N regulation violation coefficients ZFm in the regulation violation sequence are taken, and the regulations corresponding to the first N regulation violation coefficients ZFm are marked as on-site warning information for the operating personnel;

[0090] Afterwards, the terminal identification module will transmit the real-time information of the workers entering the site to the terminal display module. The terminal display module will display the corresponding on-site warning information of the workers according to the identified workers, thereby providing real-time safety reminders to the workers entering the power operation site, thereby enhancing the safety awareness of the workers.

[0091] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent processing system for ticket violation data at power operation sites, characterized in that: include: The data storage module is used to store the operation information, staff information and ticket violation data of the power operation site, and the data storage module and the data analysis module are connected in a one-way communication manner; The data analysis module is used to process the overall ticket violation data of the power operation site, determine the collective frequency of the power operation site, and analyze the historical ticket violation data of the operators based on the operators at the power operation site, and determine the data signal of a single operator, wherein the data signal includes a comprehensive data signal and a characteristic data signal; Among them, the collection frequency of the power operation site is obtained by identifying and obtaining the violation data on the ticket according to the engineering start time of the power project, generating a site violation list, and then performing text recognition processing on the violation content in the site violation list to obtain the violation set, and then calculating it based on the number of elements in the violation set; at the same time, the collection frequency is arranged by position to obtain the key regulations; The terminal identification module is used to identify the operators who enter the site in real time, obtain the corresponding on-site warning information according to the operator's data signal, and transmit it to the terminal display module; The terminal display module is used to display the on-site warning information according to the identified operating personnel.

2. According to claim 1, a system for intelligently processing ticket violation data on power operation sites, characterized in that: The calculation method of the collective frequency at the power operation site includes: S01: Taking the start time of the power project as the starting time, identify and obtain the ticket violation data of all work units, and generate a site violation list; All work units include substation unit group, transmission unit group and distribution unit group. The information type in the on-site violation list is consistent with the information content in the ticket violation data; S02: extracting the violation content in the on-site violation list, performing text recognition processing on the violation content, and then dividing the violation content into multiple violation sets; S03: Select any violation set and count the number of elements Yi in the violation set, where the number of elements is the total number of violations in the violation set i; Where i represents the number of the selected violation set, and i∈[1,I], I represents the total number of violation sets existing in the power operation site; Then use the formula The set frequency Fi is obtained, where Zw represents the sum of the number of elements in all violation sets and a1 is the threshold coefficient.

3. The intelligent processing system for ticket violation data on power operation site according to claim 2 is characterized in that: The methods for dividing the violation set include: Use natural language processing technology to identify information in the violation content, including the time of violation, location of violation and violation behavior; Obtain the standard regulations and specifications of the power operation site, classify the violations according to the standard regulations and specifications they violate, and integrate the violations belonging to the same related regulations into a violation set.

4. The intelligent processing system for ticket violation data on power operation site according to claim 2 is characterized in that: The threshold coefficient a1 represents the effect of the number of samples on the set frequency Fi; Where a1 = -λ Zw +1, λ is the impact factor.

5. The intelligent processing system for ticket violation data on power operation site according to claim 2 is characterized in that: Methods for obtaining key regulations include: Calculate the collective frequency Fi of all violation sets i, and arrange the collective frequency Fi in descending order to obtain a collective sequence; Take the first N violation sets in the set sequence, and mark the regulations corresponding to the first N violation sets as key regulations, where N is 5.

6. The intelligent processing system for ticket violation data on power operation site according to claim 2 is characterized in that: The method for acquiring the data signal of a single operator includes: S11: randomly select an operator who enters the power operation site as a target analyst, obtain the working years in the staff information of the target analyst, and extract the historical violations of the target analyst; Among them, historical violations refer to violations that occurred during the power operation sites of previous power projects by the target analyst; S12: Using formula Calculate the work coefficient GZj of the target analyst, where j represents the number of different operators in the power operation site, Gwj represents the total number of historical violations of target analyst j, and Nxj represents the working years of target analyst j; S13: Compare the work coefficient GZj of the target analyst j with the work threshold value X1. If GZj≤X1, a comprehensive data signal is generated. Otherwise, if GZj>X1, a characteristic data signal is generated. When the working years Nxj is 0, that is, the target analyst j is a new employee or an intern, the comprehensive data signal is directly generated; S14: When generating a characteristic data signal, using The individual violation frequency Dm of the target analyst is obtained, where m represents the regulations and Lm represents the number of times the target analyst j violates the regulations m.

7. The intelligent processing system for ticket violation data on power operation site according to claim 6 is characterized in that: Methods for determining on-site warning information include: Identify the operators who enter the power operation site in real time and obtain data signals based on the identity information of the operators; If the data signal of the operator is a comprehensive data signal, the key rules are obtained and marked as on-site warning information for the operator; If the data signal of the operator is a characteristic data signal, the single violation frequency Dm of the operator is obtained, and the violation coefficient ZFm is obtained by using the formula ZFm=Dm×β1+Fi×β2, where β1 and β2 are both proportional factors, and β1+β2=1; When calculating the regulation violation coefficient ZFm, the regulation corresponding to the single violation frequency Dm is the same as the regulation corresponding to the violation set i in the set frequency Fi; Arrange the regulation violation coefficients ZFm in descending order to obtain a regulation violation sequence, take the first N regulation violation coefficients ZFm in the regulation violation sequence, and mark the regulatory regulations corresponding to the first N regulation violation coefficients ZFm as on-site warning information for operators.

8. The intelligent processing system for ticket violation data on power operation site according to claim 1 is characterized in that: The data analysis module includes field analysis unit and personal analysis unit; The on-site analysis unit is used to process the overall ticket violation data of the power operation site and determine the collection frequency of the power operation site; The personal analysis unit is used to analyze the historical ticket violation data of the operators at the power operation site as a unit to determine the data signal of a single operator.

9. The intelligent processing system for ticket violation data on power operation site according to claim 1 is characterized in that: The operation information and staff information of the power operation site are collected by the information collection module and transmitted to the data storage module for storage; The ticket violation data at the power operation site is collected by the on-site data acquisition module and transmitted to the data storage module for storage.

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