A new track for government digital governance: How power grids generate user "health codes"

Text | eo reporter Zhou Huizhi

Editor | Jiang Li

In the super epidemic moment, the health code, as a government application that everyone has, is regarded as a government digital governance experiment of the whole people's collaboration in the digital age. In fact, the mechanism of the health code operation is similar to the participation of high-frequency basic data such as electricity in the government's "scattered, messy and polluted" governance scenario.

The basic idea of ​​conducting a "large-scale investigation" of the epidemic through the online health code is to collect dynamic information such as health status, travel history, and travel status reported by citizens, and make preliminary judgments and screening according to local regulations. Citizens will then receive different results of green, yellow, or red codes, and either pass or be isolated, as a health certificate for inter-provincial mobility.

In the "scattered, disorderly and polluted" governance scenario, according to the official definition, "scattered" means failing to enter industrial parks as required, "disorderly" means operating illegally in residential areas, and "polluted" means failing to achieve stable and standard emissions. These illegal operations cannot be tracked, and it is even more difficult to track the actual production status, which is almost in a "regulatory vacuum".

In a vast number of places, searching and screening out disguised and hidden "scattered and disorderly pollution" has become a key link in improving the efficiency of government governance.

High-frequency basic data represented by electricity load data can outline the attributes of different users and conduct preliminary identity checks based on information such as energy load characteristics and electricity consumption curves, which is equivalent to automatically generating a "health code" for each energy-consuming user.

The judgment conditions constructed by the local managers for "scattered and disorderly pollution" places are used as the "diagnostic basis". Abnormal users who fall into the preliminary identification scope of "scattered and disorderly pollution" will receive a "red code" warning, and the staff can lock on the suspected targets. After on-site verification and reporting and approval confirmation, they can implement rectification measures and realize dynamic monitoring.

This is one of the attempts to use electricity data to participate in government services (to G) or urban governance.

The case of power data assisting in the governance of "scattered and disorderly pollution" also reflects the change in the governance thinking of local governments in China - from the traditional social governance method of "big rivers and lakes" to the "capillary" digital governance model.

This not only relies on digital transformation to achieve high-frequency data collection, but also depends on multi-party interaction and cross-border collaboration.

By analyzing the ideas and difficulties of the "scattered, disorderly and dirty" data governance cases, we can observe a series of common problems to be resolved in big data applications, such as data collection standardization, cross-border data fusion, algorithm fairness, and data governance limitations.

Physical enterprises, including power companies, are standing at a new starting point for improving mechanisms to adapt to the government's digital government governance track.

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Government governance switches to data thinking

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From the "Ten Measures for Air Pollution Control" (2013-2017) to the "Blue Sky Defense Campaign" (2018-2020), the two rounds of air pollution prevention and control and environmental governance cycles are seamlessly connected in time, but they are at different stages of rapid changes in social governance. In the latter stage, digital transformation and business reconstruction have gradually begun to be brewed from the government to physical enterprises.

There has been a response at the local government level. In October 2017, the Guangdong Provincial Government promoted the establishment of Digital Guangdong Network Construction Co., Ltd. (co-funded by Tencent, China Unicom, China Telecom, and China Mobile) to undertake the platform operation of the provincial "digital government". Many other provinces have also established similar provincial "digital government" operating entities.

In 2018, the "Notice on Issuing the Three-Year Action Plan to Win the Blue Sky Defense Battle" was released. One of the key tasks was to carry out comprehensive remediation of "scattered, disorderly and polluting" enterprises and clusters, and required the country to basically complete it by the end of 2020.

It can be said that the "Blue Sky Defense Campaign" is an advancement and further deepening of the "Ten Measures for Air Pollution Control". At this stage, the upgrade and addition of data thinking are also reflected in the specific governance of "scattered and disorderly pollution" in some areas.

Take Guangzhou as an example. In October 2018, the Guangzhou Municipal People's Government issued the "Guangzhou City Strengthening Cleanup and Rectification of "Scattered, Disordered and Polluted" Sites Action Plan" (hereinafter referred to as the "Scattered, Disordered and Polluted" Rectification Action Plan). At that time, the Guangzhou Municipal Government decided to add the Industry and Information Technology System to the ecological and environmental protection system as the leading department, intending to introduce digital tools in terms of basic data and process control.

"The traditional model (for environmental protection) is that the province sends people to cities, cities to counties, and counties to towns and streets, and the grassroots staff in towns and streets act as both referees and athletes." A relevant person said, "The data is reported layer by layer, and the data is very good every year, but the environmental governance results have not been ideal, and there are many complaints from citizens."

It is understood that "scattered, messy and polluted" places usually cannot obtain legal business licenses, and most of them are illegal operations with incomplete procedures. The government cannot track the actual production status of the above places based on the existing information registration. In the past governance style, only grassroots staff conducted on-site inspections, which was not efficient enough, and there was a dilemma of concealing and false reporting and difficulty in holding people accountable.

The advantages of using high-frequency basic data to assist governance are: first, it can provide a basis for quantitative analysis and improve governance efficiency by screening quantitative indicators; second, it can achieve real-time monitoring of the data system to avoid repeated governance without results; third, the transparency presented by the data is supervisory and can break the information asymmetry in grassroots governance to a certain extent.

As a field of universal service attributes, data in the power industry has become one of the most important data dimensions in the conception of data modeling in the initial stage of "scattered, disorderly and polluted" due to its advantages in coverage, authenticity and the degree of transformation of intelligent collection.

In February this year, the Ministry of Industry and Information Technology announced the 2020 big data industry development pilot demonstration projects, and the "scattered, disorderly and polluted" big data intelligent supervision and governance project in megacities (referred to as "scattered, disorderly and polluted" data governance) submitted by the Guangzhou Power Supply Bureau was among them. This project has been put into trial use in Guangzhou since October 2018, and the modeling is based on power data and supplemented by water data.

Sui Neng Tong Company, a subsidiary of Guangdong Power Grid, is the contractor of the project. Yuan Chao, the head of the system development team and director of the innovation incubation center of Sui Neng Tong Company, said that at the end of 2018, the number of electricity users registered in Guangzhou was about 5.8 million, and data modeling screened out 260,000 suspected places. Based on more than 10,000 grassroots investigators, each person only needs to investigate 26 places on average.

At present, the right to use the data collected by various industries and enterprises is still generally exclusive. For data security reasons, Guangzhou Power Supply Bureau currently only authorizes the use of power data to Sui Neng Tong Company.

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Experience + data dual-wheel drive

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During the epidemic, personal identity, medical history, and address information collected in some local areas were leaked during the circulation process, and the video conferencing software Zoom also had serious security vulnerabilities, which once again triggered the industry's reaffirmation and strong concern about data security.

In order to avoid data leakage, many companies currently adopt the delivery method of "data products", that is, only providing analysis results to the demand side without involving the original data.

In the case of "scattered and disorderly pollution" governance, the data analysis team of the power sector provided the government department with "data products" after cleaning, analyzing and mining the data, that is, "result lists rather than raw data" based on the judgment criteria. The recirculation of information after desensitization has reduced the risk of data leakage to a certain extent.

It is generally believed that the important transformation of government or enterprise digital transformation is the change from "experience-driven" to "data-driven". However, in algorithm design, model construction, and rule setting, human experience (familiar with the business side) judgment and comprehensive trade-offs are still injected. It can be said that in the current stage of digital transformation, "experience-driven" and "data-driven" complement and support each other.