Mobile intelligent switch station and diagnostic control system thereof

Abstract

The application discloses a kind of mobile intelligent switch station and its diagnostic control system, it is related to switch station control technical field, the system of the present application includes net point model construction module, energy consumption balance analysis module, district energy consumption analysis module and risk instruction control module;Net point model construction module is used to construct net point model to obtain the building net point distribution map in target area;Energy consumption balance analysis module calls the historical energy consumption data of each building net point and analyzes the periodic load energy consumption rate and energy consumption balance index analysis of building net point;District energy consumption analysis module divides building net point window area, and carries out energy consumption balance index classification index analysis to building net point in window area;Risk instruction control module compares the energy consumption classification index of each building net point window area, generates maintenance route, and carries out maintenance risk index analysis, outputs maintenance instruction;The present application realizes the self-employment of mobile switch station, improves the intelligent operation of mobile switch station.

Description

Technical Field

[0001] This invention relates to the field of switch station control technology, specifically a mobile intelligent switch station and its diagnostic and control system. Background Technology

[0002] The development of mobile intelligent switching stations stems from the actual needs of the power grid and the driving force of technological iteration. It is a power switching facility that integrates intelligent monitoring, protection and measurement functions and has the characteristics of mobility. With the current expansion of power grids and the acceleration of urban construction, issues such as surges in localized temporary loads, emergency response to power grid faults, and load transfer during upgrades are becoming increasingly prominent. Traditional fixed switch stations suffer from long deployment cycles and poor flexibility, resulting in long operation cycles and low efficiency in localized power supply maintenance. Mobile switch stations, on the other hand, are convenient for on-site deployment due to their mobility, reducing maintenance cycle time and facilitating temporary operations. However, current maintenance operations using mobile switch stations rely heavily on the experience and intervention of operators, and they cannot autonomously diagnose and carry out operations in complex temporary work environments. Therefore, they cannot meet the current demand for intelligent on-site operations. Summary of the Invention

[0003] The purpose of this invention is to provide a technical solution for a mobile intelligent switch station and its diagnostic control system to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A diagnostic control system: The system includes a network model construction module, an energy consumption balance analysis module, a regional energy consumption analysis module, and a risk instruction control module; The network point model construction module is used to determine the target area, obtain the building location data and historical energy consumption data within the target area, and construct a network point model to obtain a building network point distribution map within the target area; The energy consumption balance analysis module retrieves historical energy consumption data for each building site based on the building site distribution map, analyzes the periodic load energy consumption rate of the building site, and performs energy consumption balance index analysis on the building site based on the periodic load energy consumption rate. The regional energy consumption analysis module divides the building network into windows to obtain building network window areas, and performs energy consumption balance index classification analysis on the building network points within the window area to determine the energy consumption classification index of the window area. The risk instruction control module compares the energy consumption classification index of each building network window area within the target area and generates a maintenance route; it then performs maintenance risk index analysis on the road segments between adjacent building network window areas on the maintenance route and outputs maintenance instructions based on the analysis data.

[0005] Furthermore, the network model construction module includes a building positioning acquisition unit and a network model construction unit; The building positioning and acquisition unit locates and acquires the buildings within the target area, and collects the historical energy consumption data of the corresponding buildings based on the positioning data; The network model construction unit constructs a network model and imports the location data and historical energy consumption data of each building in the target area into the network model, and outputs a building network distribution map of each building in the target area. The building network distribution map is a planar distribution map constructed by mapping the coordinates of building location data collected in the real scene and connecting them according to the adjacent relationships between corresponding buildings in the real scene; therefore, in the building network distribution map, the corresponding location network point is the building network point.

[0006] Furthermore, the energy consumption balance analysis module includes a building network energy consumption analysis unit and a building network energy consumption balance analysis unit; The building network energy consumption analysis unit locates building network points based on the building network distribution map, sets a monitoring period T, and retrieves historical energy consumption data for the corresponding building network points within the monitoring period. It then analyzes the load energy consumption rate LEC(n,t) at each time point within the monitoring period for the corresponding building network point, as calculated below: ; Where LEC(n,t) represents the load energy consumption rate at time t within the monitoring period T of building site n; E(n,t) represents the load consumption at time t within the monitoring period T of building site n; E(n) max The maximum historical single-moment load consumption of building site number n is the building site number; t represents each time point within the monitoring period. It should be noted that the monitoring period T is selected from the time interval closest to the current time that meets the length of the monitoring period T interval. By coordinating the load energy consumption rate of building sites at various time points within the monitoring period, determining the energy consumption screening coefficient k, and classifying the load energy consumption rate of building sites within the monitoring period; When LEC(n,t) > k×LEC(n,t) max At time t, the active load energy consumption rate of the building network is determined. When LEC(n,t)≤k×LEC(n,t) max When t is the current time, the building network point is determined as the passive load energy consumption rate; where LEC(n,t) max The maximum load energy consumption rate of building site number n within the period; Based on the classification results of load energy consumption rate at each time point within the building network monitoring cycle, the cycle duration h occupied by the active load energy consumption rate of the building network is obtained. ftThe periodic load energy consumption rate (LEC(n,T)) of the building network is analyzed and calculated as follows: ; Where N(T) is the number of time points within the monitoring period T; It should be noted that the periodic load energy consumption rate of building outlets is analyzed by introducing a time coefficient. This is done by determining the actual load energy consumption status of building outlets and limiting the analysis of active load energy consumption scenarios, which can better reflect the actual load energy consumption of building outlets. The building network energy consumption balance analysis unit performs convergence analysis on the load energy consumption rate at each time point within the monitoring period based on the periodic load energy consumption rate of the building network, and obtains the corresponding building network's energy consumption balance index Ecb(n,T) within the monitoring period, which is calculated as follows: ; Where Ecb(n,T) is the energy balance index of monitoring point n within the monitoring period T; LEC(n,t) max and LEC(n,t) min These represent the maximum and minimum load consumption rates of monitoring point n within the monitoring period T, respectively. Furthermore, the regional energy consumption analysis module includes a building network window area division unit and a regional energy consumption classification index analysis unit; The building network window area division unit obtains the number of objects m that the mobile intelligent switch station faces in a single operation, and performs a number of non-repeating coverage operations on the building network in the building network distribution map with m as the window until the building network is fully covered. The coverage result of each building network is recorded. The coverage area of ​​a single building network is determined based on the building network coverage record and is recorded as the building network window area. The regional energy consumption classification index analysis unit extracts the energy consumption balance index of each building point within the building point window area, and performs energy consumption balance index classification analysis to obtain the energy consumption classification index of the window area. The energy consumption balance index classification analysis steps are as follows: S1. Calculate the average energy consumption balance index of each building site within the building site window area to obtain the average energy consumption balance index Ecb(A) of each building site. S2. By using the average energy balance index Ecb(A) as the comparison value, the energy balance index Ecb(n,T) of each building network point within the building network point window area is classified, and the high-energy-consuming building network points and low-energy-consuming building network points are determined according to the classification results. Among them, the building network points corresponding to Ecb(n,T)>Ecb(A) are classified as high-energy-consuming building network points; The building sites corresponding to Ecb(n,T)≤Ecb(A) are classified as low-energy-consumption building sites; S3. Based on the classification results of each building network point within the window area, the energy consumption classification index ECC(k) of the window area is analyzed, and its calculation is as follows: ; Where ECC(k) is the energy consumption classification index for the window area of ​​building network point k; Ecb(n,T) q,k and Ecb(n,T) r,k These are the energy balance indices for high-energy-consuming and low-energy-consuming building sites within the window area labeled k, respectively; Ecb(n,T) max,k and Ecb(n,T) min,k These represent the maximum and minimum energy balance index values ​​for building outlets within the window area labeled k; m represents the total number of building outlets within the window area; u represents the number of high-energy-consuming building outlets within the loading area; mu represents the number of low-energy-consuming building outlets within the loading area; and k represents the label of the building outlet window area. Furthermore, the risk instruction control module includes a road segment risk assessment unit and a risk instruction generation unit; The road segment risk assessment unit locates the window areas of each building network within the target area, compares the energy consumption classification index of each window area, points the window areas sequentially from largest to smallest according to the comparison results, and generates maintenance routes based on the pointing direction. Based on the maintenance route, maintenance sections between adjacent building network window areas are extracted, and historical maintenance event data of the corresponding building network window areas are obtained to analyze the historical maintenance defect index D(k) of the corresponding window areas. Among them, historical maintenance time data refers to the maintenance data recorded by the mobile intelligent switch station for maintenance events of the building network in the current building network window area; Simultaneously, real-time environmental data of the corresponding maintenance road sections are collected, and the maintenance risk index MAT(k,k+1) is analyzed for maintenance road sections between adjacent building network window areas. The specific calculation is as follows: ; Wherein, MAT(k,k+1) is the maintenance risk index of the maintenance section between adjacent building network window areas k and k+1; ECC(k) and ECC(k+1) are the energy consumption classification indices of adjacent building network window areas k and k+1, respectively; D(k) and D(k+1) are the historical maintenance defect indicators of adjacent building network window areas k and k+1, respectively; X iReal-time data of the i-th type of environmental parameters for the maintenance road section between adjacent building network window areas k and k+1; X i (B) is the reference data for the operation of the mobile intelligent switch station under the i-th type of environmental parameters; α i η(k) represents the influence weight of the i-th type of environmental parameter on the operation of the mobile intelligent switch station; η(k) represents the historical fault handling rate of the building network window area k; wt max and wt act These represent the maximum permissible maintenance time and the average historical maintenance time for the mobile intelligent switch station corresponding to window area k of the building network outlet; The risk instruction generation unit uses the maintenance risk index MAT(k,k+1) of the maintenance section between adjacent building network window areas and introduces the safety risk index MAT(SF) for risk comparison. When MAT(k,k+1)≥MAT(SF), it is determined that there is a risk, and the maintenance section between the corresponding adjacent building network window areas is marked as a risk, generating a mobile switch station auxiliary maintenance operation instruction to prompt the operator to perform manual intervention; If MAT(k,k+1)<MAT(SF), then the risk is considered normal, and the maintenance section between the corresponding adjacent building network window areas is marked as normal operation.

[0007] A mobile intelligent switch station includes a processor and a memory; When the processor executes the computer program stored in the memory, it implements a mobile intelligent switch station diagnostic and control system.

[0008] Compared with the prior art, the beneficial effects of the present invention are: This invention combines mobile intelligent switch stations to collect building location information and analyze energy consumption in a regional setting. It constructs a network model for intelligent mapping and analyzes the historical energy consumption status of corresponding building network points before failure. It also determines the energy consumption distribution of local areas by dividing window regions and generates maintenance routes for adjacent areas. By analyzing the risk rate in real time, it determines the operation risk of road sections. This invention realizes autonomous operation of mobile switch stations and improves the intelligent operation of mobile switch stations. On the other hand, it reduces the demand for human resources and improves the decision-making efficiency of on-site operations. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the structure of a mobile intelligent switch station diagnostic control system according to the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Example 1: As Figure 1 As shown, the present invention provides a technical solution: A diagnostic control system: The system includes a network model construction module, an energy consumption balance analysis module, a regional energy consumption analysis module, and a risk instruction control module. The site model building module is used to determine the target area, obtain the building location data and historical energy consumption data within the target area, and build a site model to obtain a building site distribution map within the target area; The energy balance analysis module retrieves historical energy consumption data for each building network point based on the building network distribution map, analyzes the periodic load energy consumption rate of the building network point, and performs energy balance index analysis on the building network point based on the periodic load energy consumption rate. The regional energy consumption analysis module divides the building network into windows to obtain the building network window area, and performs energy consumption balance index classification analysis on the building network within the window area to determine the energy consumption classification index of the window area. The risk instruction control module compares the energy consumption classification index of each building network window area within the target area and generates a maintenance route; it then performs maintenance risk index analysis on the road segments between adjacent building network window areas on the maintenance route and outputs maintenance instructions based on the analysis data.

[0012] Furthermore, the site model construction module includes a building location acquisition unit and a site model construction unit; The building location acquisition unit locates and acquires the buildings within the target area, and collects the historical energy consumption data of the corresponding buildings based on the location data; The network model building unit constructs a network model and imports the location data and historical energy consumption data of each building in the target area into the network model, and outputs a building network distribution map of each building in the target area. It should be noted that when determining the location data of buildings within a region, latitude and longitude or coordinates can be used for representation; in this embodiment, in order to facilitate the analysis and calculation in the subsequent process of building a network model to output a building network distribution map, coordinate positioning is generally used. A building network distribution map is a planar distribution map constructed by mapping the coordinates of building location data collected in the real scene and connecting the adjacent relationships between corresponding buildings in the real scene; therefore, in the building network distribution map, the corresponding location network points are building network points.

[0013] Furthermore, the energy consumption balance analysis module includes a building network energy consumption analysis unit and a building network energy consumption balance analysis unit; The building network energy consumption analysis unit locates building network points based on the building network distribution map, sets a monitoring period T, and retrieves historical energy consumption data for the corresponding building network points within the monitoring period. It then analyzes the load energy consumption rate LEC(n,t) for each time point within the monitoring period for the corresponding building network point, as calculated below: ; Where LEC(n,t) represents the load energy consumption rate at time t within the monitoring period T of building site n; E(n,t) represents the load consumption at time t within the monitoring period T of building site n; E(n) max The maximum historical single-moment load consumption of building site number n is the building site number; t represents each time point within the monitoring period. It should be noted that the monitoring period T is selected from the time interval closest to the current time that meets the length of the monitoring period T interval. By coordinating the load energy consumption rate of building sites at various time points within the monitoring period, determining the energy consumption screening coefficient k, and classifying the load energy consumption rate of building sites within the monitoring period; When LEC(n,t) > k×LEC(n,t) max At time t, the active load energy consumption rate of the building network is determined. When LEC(n,t)≤k×LEC(n,t) max When t is the current time, the passive load energy consumption rate of the building network is determined; where LEC(n,t) is the value of LEC(n,t). max The maximum load energy consumption rate of building site number n within the period; Based on the classification results of load energy consumption rate at each time point within the building network monitoring cycle, the cycle duration h occupied by the active load energy consumption rate of the building network is obtained. ft The periodic load energy consumption rate (LEC(n,T)) of the building network is analyzed and calculated as follows: ; Where N(T) is the number of time points within the monitoring period T; It should be noted that the periodic load energy consumption rate of building outlets is analyzed by introducing a time coefficient. This is done by determining the actual load energy consumption status of building outlets and limiting the analysis of active load energy consumption scenarios, which can better reflect the actual load energy consumption of building outlets. The building network energy balance analysis unit performs convergence analysis on the load energy consumption rate at each time point within the monitoring period based on the periodic load energy consumption rate of the building network, and obtains the corresponding building network's energy balance index Ecb(n,T) within the monitoring period, which is calculated as follows: ; Where Ecb(n,T) is the energy balance index of monitoring point n within the monitoring period T; LEC(n,t) max and LEC(n,t) min These represent the maximum and minimum load consumption rates of monitoring point n within the monitoring period T, respectively. Furthermore, the regional energy consumption analysis module includes a building network window area division unit and a regional energy consumption classification index analysis unit; The building network window area division unit obtains the number of objects m that the mobile intelligent switch station faces in a single operation, and performs a number of non-repeating coverage operations on the building network points in the building network distribution map with m as the window until the building network points are fully covered. The coverage result of each building network point is recorded. Based on the coverage record of the building network points, the coverage area of ​​the building network points in a single operation is determined and recorded as the building network point window area. The regional energy consumption classification index analysis unit extracts the energy consumption balance index of each building point within the building point window area, and performs energy consumption balance index classification analysis to obtain the energy consumption classification index of the window area. The energy consumption balance index classification analysis steps are as follows: S1. Calculate the average energy consumption balance index of each building site within the building site window area to obtain the average energy consumption balance index Ecb(A) of each building site. S2. By using the average energy balance index Ecb(A) as the comparison value, the energy balance index Ecb(n,T) of each building network point within the building network point window area is classified, and the high-energy-consuming building network points and low-energy-consuming building network points are determined according to the classification results. Among them, the building network points corresponding to Ecb(n,T)>Ecb(A) are classified as high-energy-consuming building network points; The building sites corresponding to Ecb(n,T)≤Ecb(A) are classified as low-energy-consumption building sites; S3. Based on the classification results of each building network point within the window area, the energy consumption classification index ECC(k) of the window area is analyzed, and its calculation is as follows: ; Where ECC(k) is the energy consumption classification index for the window area of ​​building network point k; Ecb(n,T) q,k and Ecb(n,T) r,kThese are the energy balance indices for high-energy-consuming and low-energy-consuming building sites within the window area labeled k, respectively; Ecb(n,T) max,k and Ecb(n,T) min,k These represent the maximum and minimum energy balance index values ​​for building outlets within the window area labeled k; m represents the total number of building outlets within the window area; u represents the number of high-energy-consuming building outlets within the loading area; mu represents the number of low-energy-consuming building outlets within the loading area; and k represents the label of the building outlet window area. Where m is the number of objects that the mobile intelligent switch station faces in a single operation, when dividing the target area into building network windows, the building network coverage is divided with m as the window size. Therefore, the number of building network points in the building network window area after the division is completed is m. It should be noted that the energy consumption classification index analysis for the building network window area adopts a classification approach, dividing building networks into high energy consumption and low energy consumption. By conducting cross-analysis of the energy consumption balance index between high-energy-consuming and low-energy-consuming building networks, the deviation of the energy consumption balance index between high-energy-consuming building networks and the lowest-energy-consuming building networks, and the deviation of the energy consumption balance index between low-energy-consuming building networks and the highest-energy-consuming building networks, are determined. Combined with the proportion of the corresponding building networks, the energy consumption classification index of the building network window area is calculated, reflecting the offset distribution of each building network within the window area. Furthermore, the risk instruction control module includes a road segment risk assessment unit and a risk instruction generation unit; The road section risk assessment unit locates the window areas of each building network within the target area, compares the energy consumption classification index of each window area, points the window areas sequentially from largest to smallest based on the comparison results, and generates maintenance routes based on the pointing direction. The sequential pointing of window areas refers to pointing from the window area with the larger energy consumption classification index to the window area with the smaller energy consumption classification index, based on the comparison results of the energy consumption classification index of the window areas; while the generation of maintenance routes is based on the pointing direction between the window areas generated by the comparison to plan the route. Based on the maintenance route, maintenance sections between adjacent building network window areas are extracted, and historical maintenance event data of the corresponding building network window areas are obtained to analyze the historical maintenance defect index D(k) of the corresponding window areas. Among them, historical maintenance time data refers to the maintenance data recorded by the mobile intelligent switch station for maintenance events of the building network in the current building network window area; Simultaneously, real-time environmental data of the corresponding maintenance road sections are collected, and the maintenance risk index MAT(k,k+1) is analyzed for maintenance road sections between adjacent building network window areas. The specific calculation is as follows: ; Wherein, MAT(k,k+1) is the maintenance risk index of the maintenance section between adjacent building network window areas k and k+1; ECC(k) and ECC(k+1) are the energy consumption classification indices of adjacent building network window areas k and k+1, respectively; D(k) and D(k+1) are the historical maintenance defect indicators of adjacent building network window areas k and k+1, respectively; X i Real-time data of the i-th type of environmental parameters for the maintenance road section between adjacent building network window areas k and k+1; X i (B) is the reference data for the operation of the mobile intelligent switch station under the i-th type of environmental parameters; α i η(k) represents the influence weight of the i-th type of environmental parameter on the operation of the mobile intelligent switch station; η(k) represents the historical fault handling rate of the building network window area k; wt max and wt act These represent the maximum permissible maintenance time and the average historical maintenance time for the mobile intelligent switch station corresponding to window area k of the building network outlet; In this embodiment, the calculation of the historical fault handling rate η(k) for the window area k of the building network is to retrieve the maintenance data of several smart switch stations for the building network recorded in the historical maintenance event data, calculate the average number of successful maintenance of the building network in the window area, and analyze the proportion of the average data in the number of building network points in the window area. The proportion result is recorded as the historical fault handling rate η(k). The risk instruction generation unit uses the maintenance risk index MAT(k,k+1) of the maintenance section between adjacent building network window areas and introduces the safety risk index MAT(SF) for risk comparison. When MAT(k,k+1)≥MAT(SF), it is determined that there is a risk, and the maintenance section between the corresponding adjacent building network window areas is marked as a risk, generating a mobile switch station auxiliary maintenance operation instruction to prompt the operator to perform manual intervention; If MAT(k,k+1)<MAT(SF), then the risk is considered normal, and the maintenance section between the corresponding adjacent building network window areas is marked as normal operation.

[0014] Example 2: This invention provides a mobile intelligent switch station: Including processor and memory; The processor executes the computer program stored in the memory to implement the mobile intelligent switch station diagnostic control system.

[0015] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A diagnostic control system, characterized in that: The system includes a network model construction module, an energy consumption balance analysis module, a regional energy consumption analysis module, and a risk instruction control module; The network point model construction module is used to determine the target area, obtain the building location data and historical energy consumption data within the target area, and construct a network point model to obtain a building network point distribution map within the target area; The energy consumption balance analysis module retrieves historical energy consumption data for each building site based on the building site distribution map, analyzes the periodic load energy consumption rate of the building site, and performs energy consumption balance index analysis on the building site based on the periodic load energy consumption rate. The energy balance index analysis involves performing a convergence analysis on the load energy consumption rate at each time point within the monitoring period based on the periodic load energy consumption rate of the building network, thereby obtaining the corresponding energy balance index of the building network within the monitoring period. The regional energy consumption analysis module divides the building network into windows to obtain building network window areas, and performs energy consumption balance index classification analysis on the building network points within the window area to determine the energy consumption classification index of the window area. The energy consumption balance index classification analysis steps are as follows: S1. Calculate the average energy consumption balance index of each building site within the building site window area to obtain the average energy consumption balance index Ecb(A) of each building site. S2. By using the average energy balance index Ecb(A) as the comparison value, the energy balance index Ecb(n,T) of each building network point within the building network point window area is classified, and the high-energy-consuming building network points and low-energy-consuming building network points are determined according to the classification results. Among them, the building network points corresponding to Ecb(n,T)>Ecb(A) are classified as high-energy-consuming building network points; The building sites corresponding to Ecb(n,T)≤Ecb(A) are classified as low-energy-consumption building sites; S3. Based on the classification results of each building network point within the window area, the energy consumption classification index ECC(k) of the window area is analyzed, and its calculation is as follows: ； Where ECC(k) is the energy consumption classification index for the window area of ​​building network point k; Ecb(n,T) q,k and Ecb(n,T) r,k These are the energy balance indices for high-energy-consuming and low-energy-consuming building sites within the window area labeled k, respectively; Ecb(n,T) max,k and Ecb(n,T) min,k These represent the maximum and minimum energy balance index values ​​for building sites within the window area labeled k; m represents the total number of building sites within the window area; u represents the number of high-energy-consuming building sites within the window area; mu represents the number of low-energy-consuming building sites within the window area; and k represents the label of the building site window area. The risk instruction control module compares the energy consumption classification index of each building network window area within the target area to generate a maintenance route; it then performs maintenance risk index analysis on the road segments between adjacent building network window areas on the maintenance route and outputs maintenance instructions based on the analysis data.

2. The diagnostic control system according to claim 1, characterized in that: The site model construction module includes a building location acquisition unit and a site model construction unit; The building positioning and acquisition unit locates and acquires the buildings within the target area, and collects the historical energy consumption data of the corresponding buildings based on the positioning data; The network model construction unit constructs a network model and imports the location data and historical energy consumption data of each building in the target area into the network model, and outputs a building network distribution map of each building in the target area.

3. A diagnostic control system according to claim 1, characterized in that: The energy consumption balance analysis module includes a building network energy consumption analysis unit and a building network energy consumption balance analysis unit; The building network energy consumption analysis unit locates building network points based on the building network distribution map, sets a monitoring period T, and retrieves historical energy consumption data for the corresponding building network points within the monitoring period. It then analyzes the load energy consumption rate LEC(n,t) at each time point within the monitoring period for the corresponding building network point, as calculated below: ; Where LEC(n,t) represents the load energy consumption rate at time t within the monitoring period T of building site n; E(n,t) represents the load consumption at time t within the monitoring period T of building site n; E(n) max The maximum historical single-moment load consumption of building site number n is defined as follows: n is the number of building sites; t represents each time point within the monitoring period.

4. A diagnostic control system according to claim 3, characterized in that: By coordinating the load energy consumption rate of building sites at various time points within the monitoring period, determining the energy consumption screening coefficient k, and classifying the load energy consumption rate of building sites within the monitoring period; When LEC(n,t) > k×LEC(n,t) max At time t, the active load energy consumption rate of the building network is determined. When LEC(n,t)≤k×LEC(n,t) max When t is the current time, the building network point is determined as the passive load energy consumption rate; where LEC(n,t) max The maximum load energy consumption rate of building site number n within the period; Based on the classification results of load energy consumption rate at each time point within the building network monitoring cycle, the cycle duration h occupied by the active load energy consumption rate of the building network is obtained. ft The periodic load energy consumption rate (LEC(n,T)) of the building network is analyzed and calculated as follows: Where N(T) is the number of time points within the monitoring period T.

5. A diagnostic control system according to claim 3, characterized in that: The building network energy consumption balance analysis unit obtains the energy consumption balance index Ecb(n,T) of the corresponding building network during the monitoring period, which is calculated as follows: ； Where Ecb(n,T) is the energy balance index of monitoring point n within the monitoring period T; LEC(n,t) max and LEC(n,t) min These represent the maximum and minimum load consumption rates of monitoring point n within the monitoring period T, respectively.

6. A diagnostic control system according to claim 1, characterized in that: The regional energy consumption analysis module includes a building network window area division unit and a regional energy consumption classification index analysis unit. The building network window area division unit obtains the number of objects m that the mobile intelligent switch station faces in a single operation, and performs a number of non-repeating coverage operations on the building network in the building network distribution map with m as the window until the building network is fully covered. The coverage result of each building network is recorded. The coverage area of ​​a single building network is determined based on the building network coverage record and is recorded as the building network window area. The regional energy consumption classification index analysis unit extracts the energy consumption balance index of each building point within the building point window area, and performs energy consumption balance index classification analysis to obtain the energy consumption classification index of the window area.

7. A diagnostic control system according to claim 1, characterized in that: The risk instruction control module includes a road segment risk assessment unit and a risk instruction generation unit; The road segment risk assessment unit locates the window areas of each building network within the target area, compares the energy consumption classification index of each window area, points the window areas sequentially from largest to smallest according to the comparison results, and generates maintenance routes based on the pointing direction. Based on the maintenance route, maintenance sections between adjacent building network window areas are extracted, and historical maintenance event data of the corresponding building network window areas are obtained to analyze the historical maintenance defect index D(k) of the corresponding window areas. Simultaneously, real-time environmental data of the corresponding maintenance road sections are collected, and the maintenance risk index MAT(k,k+1) is analyzed for maintenance road sections between adjacent building network window areas. The specific calculation is as follows: ； Wherein, MAT(k,k+1) is the maintenance risk index of the maintenance section between adjacent building network window areas k and k+1; ECC(k) and ECC(k+1) are the energy consumption classification indices of adjacent building network window areas k and k+1, respectively; D(k) and D(k+1) are the historical maintenance defect indicators of adjacent building network window areas k and k+1, respectively; X i Real-time data of the i-th type of environmental parameters for the maintenance road section between adjacent building network window areas k and k+1; X i (B) is the reference data for the operation of the mobile intelligent switch station under the i-th type of environmental parameters; α i The influence weight of the i-th type of environmental parameter on the operation of the mobile intelligent switch station; η(k) represents the historical fault handling rate of window area k at the building network point; wt max and wt act These represent the maximum permissible maintenance time and the average historical maintenance time for the mobile intelligent switch station corresponding to window area k of the building network.

8. A diagnostic control system according to claim 7, characterized in that: The risk instruction generation unit uses the maintenance risk index MAT(k,k+1) of the maintenance section between adjacent building network window areas and introduces the safety risk index MAT(SF) for risk comparison. When MAT(k,k+1)≥MAT(SF), it is determined that there is a potential risk, and the maintenance section between the corresponding adjacent building network window areas is marked as a risk, generating a mobile switch station auxiliary maintenance operation instruction to prompt the operator to perform manual intervention. If MAT(k,k+1)<MAT(SF), then the risk is considered normal, and the maintenance section between the corresponding adjacent building network window areas is marked as normal operation.

9. A mobile intelligent switch station, characterized in that: The diagnostic control system as described in any one of claims 1-8 is applied.

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