Community service management method based on data mirroring

Abstract

The invention discloses a community service management method based on data mirroring, relates to the technical field of community management, and solves the technical problems that acquired community information is not effectively utilized, and defects existing in community management cannot be well found out and timely processed according to questionnaires and data mirroring technologies. According to the method, the community is reasonably divided, so that the requirements of different residents can be better met. And meanwhile, the resident satisfaction is calculated and secondary analysis is carried out, so that problems existing in a residential area can be found in time, targeted service management is carried out, the resident satisfaction is improved, community resource information is analyzed, the use condition and demand of community resources can be better known, reasonable allocation and utilization are carried out, and the social benefit is improved. By calculating the priority value and the correlation degree of the abnormal problem and the sorting value and the attention degree of the same type of service opinions, a more scientific and effective decision basis can be provided for managers.

Description

Technical Field

[0001] The present invention relates to the technical field of community management, and in particular to a community service management method based on data mirroring. Background Art

[0002] With the continuous development of the community and the improvement of residents' living standards, the importance of community service management has become increasingly prominent. As an advanced data storage and management technology, data mirroring technology can achieve real-time data backup and synchronization, ensuring data security and availability. Applying data mirroring technology to community service management can provide more accurate and timely data support for community service management, and improve the efficiency and quality of community service management.

[0003] According to the publication number CN118761739A, a comprehensive community service management system and method are disclosed, which aims to improve community management efficiency, enhance residents' interaction and participation, optimize resource utilization, ensure user privacy and security, and improve residents' quality of life. The system includes multiple modules that cooperate with each other, such as parking space management, car rental, community interaction, property services, mutual assistance among owners, security precautions, life convenience services, and user privacy and security protection.

[0004] The above patent enables residents to easily access these services through client applications, realizing functions such as parking reservation, vehicle rental, community interaction, online repair, resource sharing, security monitoring, and convenient service reservation. The system server is responsible for data processing and storage to ensure data security and reliability.

[0005] However, some existing community service management methods have the following problems when they are actually implemented: how to accurately obtain community resident information and community resource information, and effectively analyze and process this information to improve resident satisfaction and the efficiency of community resource utilization; how to identify problems in community management based on questionnaires and data mirroring technology, and carry out timely management based on the problems. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a community service management method based on data mirroring, which solves the problem that the acquired community information is not effectively utilized and the defects in community management cannot be found and dealt with in a timely manner based on questionnaires and data mirroring technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a community service management method based on data mirroring, the method specifically comprises the following steps: Step 1: determine the analysis area and obtain community information of the analysis community, wherein the community information includes community resident information and community resource information; Step 2: Analyze and process the residential areas of the community based on the obtained community resident information, calculate the residents' satisfaction in the residential areas, and compare the residents' satisfaction to generate satisfaction information; Step 3: Process the obtained secondary analysis signal, analyze the abnormal questions corresponding to the questionnaire, and perform service management by calculating the priority value of the abnormal questions and combining the corresponding correlation; Step 4: Analyze the public area based on the obtained community resource information, obtain the service opinions of the public area based on historical data, calculate the ranking values ​​of the same type of service opinions, and comprehensively analyze and display them in combination with the importance of the same type of service opinions.

[0008] As a further solution of the present invention: the specific method of analyzing and processing the residential area of ​​the community in step 2 is: After obtaining the analysis area, the analysis community is divided into residential areas and public areas according to the regional functions. After obtaining the residential area, the residents' satisfaction is calculated through a community resident questionnaire in the residential area. The satisfaction is composed of the residential value and the resident score value. The residential value and the resident score value are calculated separately at the same time.

[0009] As a further solution of the present invention: the specific method of calculating the residence value in step 2 is: First, obtain the existing questionnaires and number them as i, where i=1, 2, ..., j, where j represents the number of questionnaires. Obtain the resident residence score from the questionnaire and record it as Fi, and calculate the mean of all Fi and record it as Fp. Substitute Fp into the discrete value formula to calculate the discrete value F1, and calculate the difference between Fi and F1; The Fi with a difference greater than the preset value is recorded as the residence score to be analyzed, and the Fi with a difference less than the preset value is recorded as the normal score mean; Get the number of residential scores to be analyzed and calculate their proportion. If the proportion is greater than the threshold set by the operator, calculate the average of the residential scores to be analyzed and sum it with the average of normal scores to obtain the resident score. If the proportion is less than the threshold, directly use the average of normal scores as the resident score.

[0010] As a further solution of the present invention: the specific method of calculating the resident scoring value in step 2 is: Get the number of residential scores to be analyzed and calculate their proportion. If the proportion is greater than the threshold set by the operator, calculate the average residential score to be analyzed and sum it with the average normal score to obtain the resident score. If the proportion is less than the threshold, directly use the average normal score as the resident score. The occupancy value is obtained by obtaining the occupancy score value in the questionnaire and analyzing it according to the resident score value; The resident satisfaction value is obtained by summing the resident score and the residential value, and compared with the preset satisfaction value. If the resident satisfaction value is greater than the preset value, the residential area is normal and no processing is required; if it is less than the preset value, the residential area is deficient and a secondary analysis signal is generated.

[0011] As a further solution of the present invention: the specific method of processing the secondary analysis signal in step 3 is: Obtain all questionnaires, find out the questions with scores lower than the average value calculated according to the questionnaires as abnormal questions, classify the abnormal questions into the same type and record them as n, where n=1, 2, ..., m, where m represents the type of abnormal questions of the same type, obtain the number of changes of the abnormal questions of the same type within time T and record them as Cn, and calculate the change frequency Pn according to the number of changes; Substitute the obtained number of changes Cn and change frequency Pn into the formula The priority values ​​Qn of the abnormal problems are calculated, and the calculated priority values ​​Qn are sorted from large to small, and the correlations of the abnormal problems are analyzed in turn.

[0012] As a further solution of the present invention: the specific method of calculating the priority value of the abnormal problem in step 3 and combining the corresponding correlation to perform service management is: The abnormal problem with the largest priority value Qn is obtained as the target object, and then the average time interval of the target object is obtained, and the average time interval is recorded as the relevance value. At the same time, the priority value and the relevance value are summed to obtain the service value; Similarly, the service values ​​of all abnormal problems of the same type are calculated and displayed to the corresponding managers in descending order of service value.

[0013] As a further solution of the present invention: the specific method of analyzing the public area according to the community resource information in step 4 is: Obtain public area service opinions from historical data within time T, classify the service opinions and label them as a, where a=1, 2, …, b, where b represents the type of service opinions of the same type, obtain the number of opinions corresponding to the same type of service opinions a and record it as Ca, and obtain the time interval ta corresponding to the same type of service opinions a, substitute the parameters into the formula Ra=Ca×ta×g to calculate the ranking value Ra of the same type of service opinions, and sort the same type of service opinions a from large to small according to the ranking value Ra.

[0014] As a further solution of the present invention: Step 4 combines the importance of the same type of service opinions for comprehensive analysis and display in a specific manner: Obtain the same type of service opinion with the largest ranking value Ra as the target source, and obtain the number of rectifications Y and the rectification time interval K of the target source at the same time. Then substitute the obtained number of rectifications Y and the rectification time interval K into the formula The importance value H of the target source is calculated, where C is the number of opinions of the target source. is the preset proportional coefficient; Similarly, the same type of service opinions are analyzed and calculated according to the ranking values ​​from large to small to obtain the corresponding importance value H; The calculated ranking value Ra and importance value H are summed to obtain the public area rectification value, and the rectification values ​​are sorted from large to small and displayed to the corresponding management personnel.

[0015] The present invention provides a community service management method based on data mirroring. Compared with the prior art, it has the following beneficial effects: The present invention can better meet the needs of different residents by reasonably dividing the community. At the same time, by calculating the residents' satisfaction and conducting a secondary analysis, it can timely identify the problems existing in the residential area and conduct targeted service management, thereby improving residents' satisfaction. By analyzing the community resource information, it can better understand the use and demand of community resources, so as to reasonably allocate and utilize them. By calculating the priority value and correlation of abnormal problems, as well as the ranking value and importance of the same type of service opinions, it can provide managers with a more scientific and effective decision-making basis. Managers can conduct targeted service management based on these indicators to improve service management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the steps of the present invention; Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0018] For example, see Figure 1 and Figure 2 , the present application provides a community service management method based on data mirroring, the method specifically comprises the following steps: Step 1: Determine the analysis area and obtain the community information of the analysis community, where the community information includes community resident information and community resource information. Specific community resident information includes name, age, gender, home address, and contact information, and this information can be obtained through community household surveys, resident self-registration, etc. Community resource information includes public facilities in the community, the location, quantity, usage status, maintenance records, etc. of the facilities. At the same time, community information is obtained through data mirroring technology.

[0019] Step 2: Analyze and process the residential areas of the community based on the obtained community resident information, calculate the resident satisfaction of the residential areas, and compare the resident satisfaction to generate satisfaction information.

[0020] After obtaining the analysis area, the analysis community is divided into different functional areas according to the regional functions. Specifically, the analysis community can be divided into residential areas and public areas. At the same time, the residential areas obtained by the division are obtained, and the community residents' questionnaires of the residential areas are obtained. Then, the residents' satisfaction is calculated according to the questionnaires, where the residents' satisfaction includes the residential value and the residents' scoring value; The calculation method of the resident score is as follows: obtain the existing questionnaires and label them as i, where i=1, 2, …, j, where j represents the number of questionnaires. A specific single user corresponds to only one questionnaire, and the questionnaires are randomly distributed. At the same time, the residential scores of the residents in the questionnaire are obtained and recorded as Fi. Then, the overall score mean is calculated based on the residential score Fi and recorded as Fp. The obtained score mean Fp is substituted into the discrete value formula to calculate the residential score discrete value F1, and the discrete value formula is: , compare the residence score Fi with the discrete value F1, and calculate the difference between the residence score Fi and the discrete value F1, and at the same time screen the residence score Fi with a difference greater than a preset value as the residence score to be analyzed, and the value of the preset value here is set by the operator, and the residence score Fi with a difference less than the preset value is recorded as a normal score, and at the same time calculate the mean of all normal scores and record it as the mean of normal scores; Then, the number of residential scores to be analyzed is obtained, and the number ratio of the residential scores to be analyzed is calculated, and the number ratio is judged. If the number ratio is greater than the threshold, and the value of the threshold here is set by the operator, the average value of the residential scores to be analyzed is calculated, and the sum is obtained by adding the normal score average to obtain the resident score value. If the number ratio is less than the threshold, the normal score average is recorded as the resident score value; The calculation method of the residence value is: obtain the questionnaire, and at the same time obtain the occupancy score of the residents in the questionnaire, and the occupancy score here is expressed as the occupancy feeling score of the residents, and the residence value is obtained by analyzing the resident score in the same way; Then, the resident score and the living value are summed to obtain the resident satisfaction value, and the resident satisfaction value is compared with the preset satisfaction value. The specific value of the preset satisfaction value here is set by the operator. It is a standard value to measure whether the current user is satisfied with the residence. If the resident satisfaction value is greater than the preset satisfaction value, it means that the residential area is normal and no processing is required. On the contrary, if the resident satisfaction value is less than the preset satisfaction value, it means that there are deficiencies in the residential area and a secondary analysis signal is generated.

[0021] Step three, the obtained secondary analysis signal is processed by analyzing the abnormal questions corresponding to the questionnaire and performing service management by calculating the priority value of the abnormal questions and combining the corresponding correlation.

[0022] All questionnaires are obtained, and the corresponding abnormal questions in the questionnaires are obtained at the same time. Abnormal questions refer to questions with scores lower than the average value. The value of the average value is calculated according to the questionnaire. For example, if the average value corresponding to the question is 8 points, the questions with scores lower than 8 points in the screened questionnaires are marked as abnormal questions, and the obtained abnormal questions are classified into the same type to obtain abnormal questions of the same type. For example, the same type of classification includes noise problems, water and power outages, and parking space shortages, etc., and the same type of abnormal questions are recorded as n, and n=1, 2, ..., m, where m represents the type of abnormal question of the same type. Then, the number of changes of the same type of abnormal questions within time T is obtained and recorded as Cn, and the number of changes here includes the number of increases or decreases. At the same time, the corresponding change frequency is obtained according to the number of changes Cn and recorded as Pn. The specific change frequency calculation formula is: change frequency = number of changes ÷ time.

[0023] Substitute the obtained number of changes Cn and change frequency Pn into the formula The priority value Qn of the abnormal problem is calculated, where It is a preset proportional coefficient, the specific value is set by the operator, and the calculated priority values ​​Qn are sorted from large to small, and the correlation of abnormal problems is analyzed in turn; The abnormal problem corresponding to the maximum priority value Qn is obtained as the target object, and then the average time interval of the target object is obtained, and the average time interval here is represented by the average time interval of the number of changes of the target object. The average time interval is recorded as the correlation value, and the priority value and the correlation value are summed to obtain the service value; Similarly, the service values ​​of all abnormal problems of the same type are calculated and displayed to the corresponding managers in descending order of service value.

[0024] Step 4: Analyze the public area based on the obtained community resource information, obtain the service opinions of the public area based on historical data, calculate the ranking values ​​of the same type of service opinions, and comprehensively analyze and display them in combination with the importance of the same type of service opinions.

[0025] Obtain service opinions in a common area of ​​historical data, and the historical data obtained here is historical data within time T. Taking the current time point as the end point, obtain historical data within the previous time T, and classify the service opinions to obtain service opinions of the same type, and mark them as a, and a=1, 2, ..., b, where b represents the type of service opinions of the same type. Then obtain the number of opinions corresponding to the service opinions of the same type a and record them as Ca, and obtain the time interval ta corresponding to the service opinions of the same type a, and the time interval here is represented as the mean time interval. Substitute the obtained parameters into the formula Ra=Ca×ta×g to calculate the ranking value Ra of the service opinions of the same type, where g is a preset proportional coefficient, and the specific value is set by the operator. At the same time, sort the service opinions of the same type a from large to small according to the ranking value Ra; Then, obtain the same type of service opinion with the largest ranking value Ra as the target source, and obtain the number of rectifications Y corresponding to the target source. The number of rectifications here is expressed as the number of rectifications of the corresponding service opinion, and obtain the rectification time interval K corresponding to the target source. Then substitute the obtained number of rectifications Y and rectification time interval K into the formula The importance value H of the target source is calculated, where C is the number of opinions of the target source. It is a preset proportional coefficient, and the specific value is set by the operator; Similarly, the same type of service opinions are analyzed and calculated according to the ranking values ​​from large to small to obtain the corresponding importance value H; The calculated ranking value Ra and importance value H are summed to obtain the public area rectification value, and the rectification values ​​are sorted from large to small and displayed to the corresponding management personnel.

[0026] Embodiment 2, as Embodiment 2 of the present invention, is implemented on the basis of Embodiment 1, and differs from Embodiment 1 in that: The division of the analysis area in step 2 can also be divided according to different resident groups, such as age, elderly resident areas, young resident areas, and children and family areas. It can also be divided according to residential types, such as multi-story residential areas, high-rise residential areas, and villa areas.

[0027] Specific multi-storey residential areas are usually residential buildings with less than 6 floors. These areas generally have a moderate population density. High-rise residential areas are generally buildings with 7 floors or more, with a relatively high population density. Villa areas: Villa communities focus on privacy and independence and can be divided into villa groups. For example, several adjacent villas can be divided into a group based on roads.

[0028] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0029] Some of the data in the above formulas are numerically calculated by removing their dimensions. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0030] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A community service management method based on data mirroring, characterized in that: The method specifically comprises the following steps: Step 1: determine the analysis area and obtain community information of the analysis community, wherein the community information includes community resident information and community resource information; Step 2: Analyze and process the residential areas of the community based on the obtained community resident information, calculate the residents' satisfaction in the residential areas, and compare the residents' satisfaction to generate satisfaction information; Step 3: Process the obtained secondary analysis signal, analyze the abnormal questions corresponding to the questionnaire, and perform service management by calculating the priority value of the abnormal questions and combining the corresponding correlation; Step 4: Analyze the public area based on the obtained community resource information, obtain the service opinions of the public area based on historical data, calculate the ranking values ​​of the same type of service opinions, and comprehensively analyze and display them in combination with the importance of the same type of service opinions.

2. The community service management method based on data mirroring according to claim 1 is characterized in that: The specific method of analyzing and processing the residential areas of the community in step 2 is as follows: After obtaining the analysis area, the analysis community is divided into residential areas and public areas according to the regional functions. After obtaining the residential area, the residents' satisfaction is calculated through a community resident questionnaire in the residential area. The satisfaction is composed of the residential value and the resident score value. The residential value and the resident score value are calculated separately at the same time.

3. The community service management method based on data mirroring according to claim 2 is characterized in that: The specific method for calculating the residence value in step 2 is: First, obtain the existing questionnaires and number them as i, where i=1, 2, ..., j, where j represents the number of questionnaires. Obtain the resident residence score from the questionnaire and record it as Fi, and calculate the mean of all Fi and record it as Fp. Substitute Fp into the discrete value formula to calculate the discrete value F1, and calculate the difference between Fi and F1; The Fi with a difference greater than the preset value is recorded as the residence score to be analyzed, and the Fi with a difference less than the preset value is recorded as the normal score mean; Get the number of residential scores to be analyzed and calculate their proportion. If the proportion is greater than the threshold set by the operator, calculate the average of the residential scores to be analyzed and sum it with the average of normal scores to obtain the resident score. If the proportion is less than the threshold, directly use the average of normal scores as the resident score.

4. The community service management method based on data mirroring according to claim 2 is characterized in that: The specific method for calculating the resident scoring value in step 2 is: Obtain the number of residential scores to be analyzed, calculate their proportion, and if the proportion is greater than the threshold set by the operator, calculate the average of the residential scores to be analyzed and sum it with the average of the normal scores to obtain the resident score; If the proportion is less than the threshold, the average normal score will be used to score the residents; The occupancy value is obtained by obtaining the occupancy score value in the questionnaire and analyzing it according to the resident score value; The resident satisfaction value is obtained by summing the resident score and the residential value, and compared with the preset satisfaction value. If the resident satisfaction value is greater than the preset value, the residential area is normal and no treatment is required; If it is less than the preset value, the residential area is deficient and a secondary analysis signal is generated.

5. The community service management method based on data mirroring according to claim 1 is characterized in that: The specific method of processing the secondary analysis signal in step 3 is: Obtain all questionnaires, find out the questions with scores lower than the average value calculated according to the questionnaires as abnormal questions, classify the abnormal questions into the same type and record them as n, where n=1, 2, ..., m, where m represents the type of abnormal questions of the same type, obtain the number of changes of the abnormal questions of the same type within time T and record them as Cn, and calculate the change frequency Pn according to the number of changes; Substitute the obtained number of changes Cn and change frequency Pn into the formula The priority values ​​Qn of the abnormal problems are calculated, and the calculated priority values ​​Qn are sorted from large to small, and the correlations of the abnormal problems are analyzed in turn.

6. The community service management method based on data mirroring according to claim 1 is characterized in that: The specific method of calculating the priority value of the abnormal problem in step 3 and combining it with the corresponding correlation to perform service management is as follows: The abnormal problem with the largest priority value Qn is obtained as the target object, and then the average time interval of the target object is obtained, and the average time interval is recorded as the relevance value. At the same time, the priority value and the relevance value are summed to obtain the service value; Similarly, the service values ​​of all abnormal problems of the same type are calculated and displayed to the corresponding managers in descending order of service value.

7. The community service management method based on data mirroring according to claim 1 is characterized in that: The specific method of analyzing the public area based on community resource information in step 4 is as follows: Obtain public area service opinions from historical data within time T, classify the service opinions and label them as a, where a=1, 2, …, b, where b represents the type of service opinions of the same type, obtain the number of opinions corresponding to the same type of service opinions a and record it as Ca, and obtain the time interval ta corresponding to the same type of service opinions a, substitute the parameters into the formula Ra=Ca×ta×g to calculate the ranking value Ra of the same type of service opinions, and sort the same type of service opinions a from large to small according to the ranking value Ra.

8. The community service management method based on data mirroring according to claim 1 is characterized in that: Step 4: Combined analysis and presentation of the importance of similar service opinions: Obtain the same type of service opinion with the largest ranking value Ra as the target source, and obtain the number of rectifications Y and the rectification time interval K of the target source at the same time. Then substitute the obtained number of rectifications Y and the rectification time interval K into the formula The importance value H of the target source is calculated, where C is the number of opinions of the target source. is the preset proportional coefficient; Similarly, the same type of service opinions are analyzed and calculated according to the ranking values ​​from large to small to obtain the corresponding importance value H; The calculated ranking value Ra and importance value H are summed to obtain the public area rectification value, and the rectification values ​​are sorted from large to small and displayed to the corresponding management personnel.

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